Authors,Author(s) ID,Title,Year,Source title,Volume,Issue,Art. No.,Page start,Page end,Page count,Cited by,DOI,Link,Affiliations,Authors with affiliations,Abstract,Author Keywords,Index Keywords,Molecular Sequence Numbers,Chemicals/CAS,Tradenames,Manufacturers,Funding Details,Funding Text 1,Funding Text 2,Funding Text 3,Funding Text 4,Funding Text 5,Funding Text 6,Funding Text 7,Funding Text 8,Funding Text 9,Funding Text 10,References,Correspondence Address,Editors,Sponsors,Publisher,Conference name,Conference date,Conference location,Conference code,ISSN,ISBN,CODEN,PubMed ID,Language of Original Document,Abbreviated Source Title,Document Type,Publication Stage,Open Access,Source,EID
"Neves A.C., González I., Leander J., Karoumi R.","57208017647;57192368654;30467830300;6505962168;","Structural health monitoring of bridges: a model-free ANN-based approach to damage detection",2017,"Journal of Civil Structural Health Monitoring","7","5",,"689","702",,124,"10.1007/s13349-017-0252-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034638701&doi=10.1007%2fs13349-017-0252-5&partnerID=40&md5=7728386586246214c1b3879b15087b2c","Structural Engineering and Bridges, KTH-Royal Institute of Technology, Brinellvägen 23, Stockholm, 10044, Sweden; Sweco AB, Stockholm, 112 60, Sweden","Neves, A.C., Structural Engineering and Bridges, KTH-Royal Institute of Technology, Brinellvägen 23, Stockholm, 10044, Sweden; González, I., Sweco AB, Stockholm, 112 60, Sweden; Leander, J., Structural Engineering and Bridges, KTH-Royal Institute of Technology, Brinellvägen 23, Stockholm, 10044, Sweden; Karoumi, R., Structural Engineering and Bridges, KTH-Royal Institute of Technology, Brinellvägen 23, Stockholm, 10044, Sweden","As civil engineering structures are growing in dimension and longevity, there is an associated increase in concern regarding the maintenance of such structures. Bridges, in particular, are critical links in today’s transportation networks and hence fundamental for the development of society. In this context, the demand for novel damage detection techniques and reliable structural health monitoring systems is currently high. This paper presents a model-free damage detection approach based on machine learning techniques. The method is applied to data on the structural condition of a fictitious railway bridge gathered in a numerical experiment using a three-dimensional finite element model. Data are collected from the dynamic response of the structure, which is simulated in the course of the passage of a train, considering the bridge in healthy and two different damaged scenarios. In the first stage of the proposed method, artificial neural networks are trained with an unsupervised learning approach with input data composed of accelerations gathered on the healthy bridge. Based on the acceleration values at previous instants in time, the networks are able to predict future accelerations. In the second stage, the prediction errors of each network are statistically characterized by a Gaussian process that supports the choice of a damage detection threshold. Subsequent to this, by comparing damage indices with said threshold, it is possible to discriminate between different structural conditions, namely between healthy and damaged. From here and for each damage case scenario, receiver operating characteristic curves that illustrate the trade-off between true and false positives can be obtained. Lastly, based on the Bayes’ Theorem, a simplified method for the calculation of the expected total cost of the proposed strategy, as a function of the chosen threshold, is suggested. © 2017, The Author(s).","Artificial neural networks; Bayes’ theorem; Damage detection; Model-free-based method; Probability-based expected cost; Receiver operating characteristic curve; Statistical model development; Structural health monitoring","Chemical sensors; Economic and social effects; Finite element method; Learning algorithms; Learning systems; Neural networks; Numerical methods; Structural health monitoring; Civil engineering structures; Damage detection technique; Expected costs; Model free; Receiver operating characteristic curves; Statistical modeling; Structural health monitoring systems; Three dimensional finite element model; Damage detection",,,,,,,,,,,,,,,,"Huang, Y., Ludwig, S.A., Deng, F., Sensor optimization using a genetic algorithm for structural health monitoring in harsh environments (2016) J Civ Struct Health Monit, 6 (3), pp. 509-519; Li, J., Zhang, X., Xing, J., Wang, P., Yang, Q., He, C., Optimal sensor placement for long-span cable-stayed bridge using a novel particle swarm optimization algorithm (2015) J Civ Struct Health Monit, 5 (5), pp. 677-685; Yi, T.-H., Li, H.-N., Wang, C.-W., Multiaxial sensor placement optimization in structural health monitoring using distributed wolf algorithm (2016) Struct Control Health Monit, 23 (4), pp. 719-734; Jin, C., Jang, S., Sun, X., Li, J., Christenson, R., Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network (2016) J Civ Struct Health Monit, 6 (3), pp. 545-560; Farrar, C.R., Worden, K., (2013) Structural health monitoring. A machine learning perspective, , Wiley, Hoboken; Rao, A.R.M., Lakshmi, K., Damage diagnostic technique combining POD with time-frequency analysis and dynamic quantum PSO (2015) Meccanica, 50 (6), pp. 1551-1578; Diez, A., Khoa, N.L.D., Alamdari, M.M., Wang, Y., Chen, F., A clustering approach for structural health monitoring on bridges. J Civ (2016) Struct Health Monit, pp. 1-17; Zhou, Q., Zhou, H., Zhou, Q., Yang, F., Luo, L., Li, T., Structural damage detection based on posteriori probability support vector machine and Dempster-Shafer evidence theory (2015) Appl Soft Comput, 36, pp. 368-374; Gonzalez, I., Karoumi, R., BWIM aided damage detection in bridges using machine learning (2015) J Civ Struct Health Monit, 5 (5), pp. 715-725; Das, S., Saha, P., Patro, S., Vibration-based damage detection techniques used for health monitoring of structures: a review (2016) J Civ Struct Health Monit, 6 (3), pp. 477-507; Figueiredo, E., Figueiras, J., Park, G., Farrar, C.R., Worden, K., Influence of the autoregressive model order on damage detection (2011) Comput Aid Civ Infrastruct Eng, 26 (3), pp. 225-238; Neves, A., Simões, F., Pinto da Costa, A., Vibrations of cracked beams: discrete mass and stiffness models (2016) Comput Struct, 168, pp. 68-77; Bandara, R.P., Chan, T.H., Thambiratnam, D.P., Structural damage detection method using frequency response functions (2014) Struct Health Monit, 13 (4), pp. 418-429; Moradipour, P., Chan, T.H., Gallage, C., An improved modal strain energy method for structural damage (2015) Struct Eng Mech, 54 (1), pp. 105-119; Xu, Y.L., Xia, Y., (2012) Structural health monitoring of long-span suspension bridges, , CRC Press, Boca Raton; Department of defense handbook (2009) Nondestructive evaluation system reliability assessment, , MIL-HDBK-1823A; Fawcett, T., An introduction to ROC analysis (2006) Pattern Recognit Lett, 27 (8), pp. 861-874; Abaqus, F.E.A., (2017) ABAQUS Inc., [Online], , http://www.3ds.com/products-services/simulia/products/abaqus/, Accessed May 2017; (1991) 1991-2, Eurocode 1: Actions on structures—part 2: traffic loads on bridges, , European Committee for Standardization, C.-E; White, K., (1992) Bridge maintenance inspection and evaluation; (2017) The MathWorks, Inc., [Online]. h, , http://www.mathworks.com/products/matlab, Accessed May 2017; Press, W., Teukolsky, S., Vetterling, W., Flannery, B., (1992) Numerical recipes in C, , Cambridge University Press, Cambridge; Berry, M., Linoff, G., (1997) Data mining techniques, , Wiley, Hoboken; Blum, A., (1992) Neural networks in C++, , Willey, Hoboken; Swingler, K., (1996) Applying Neural networks: a practical guide, , Academic Press, London; Rasmussen, C., Williams, C., (2006) Gaussian processes for machine learning, , The MIT Press, Cambridge; Worden, K., Manson, G., Fieller, N., Damage detection using outlier analysis (2000) J Sound Vib, 229 (3), pp. 647-667; Hejll, A., (2007) Civil structural health monitoring—strategies, methods and applications, , Doctoral Thesis, Luleå University of Technology; Farrar, C.R., Worden, K., (2013) Structural health monitoring. A machine learning perspective, p. 368. , Wiley, Hoboken","Neves, A.C.; Structural Engineering and Bridges, Brinellvägen 23, Sweden; email: acneves@kth.se",,,"Springer Verlag",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85034638701
"Ebrahimian H., Astroza R., Conte J.P., de Callafon R.A.","57112070500;55619989200;7101953827;6701828966;","Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation",2017,"Mechanical Systems and Signal Processing","84",,,"194","222",,80,"10.1016/j.ymssp.2016.02.002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961151873&doi=10.1016%2fj.ymssp.2016.02.002&partnerID=40&md5=9e3e10cd6d467382c5452ea2605907a6","Department of Structural Engineering, University of California, San Diego, United States; Department of Structural Engineering, University of California, San Diego / Assistant Professor, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile; Department of Mechanical and Aerospace Engineering, University of California, San Diego, United States","Ebrahimian, H., Department of Structural Engineering, University of California, San Diego, United States; Astroza, R., Department of Structural Engineering, University of California, San Diego / Assistant Professor, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile; Conte, J.P., Department of Structural Engineering, University of California, San Diego, United States; de Callafon, R.A., Department of Mechanical and Aerospace Engineering, University of California, San Diego, United States","This paper presents a framework for structural health monitoring (SHM) and damage identification of civil structures. This framework integrates advanced mechanics-based nonlinear finite element (FE) modeling and analysis techniques with a batch Bayesian estimation approach to estimate time-invariant model parameters used in the FE model of the structure of interest. The framework uses input excitation and dynamic response of the structure and updates a nonlinear FE model of the structure to minimize the discrepancies between predicted and measured response time histories. The updated FE model can then be interrogated to detect, localize, classify, and quantify the state of damage and predict the remaining useful life of the structure. As opposed to recursive estimation methods, in the batch Bayesian estimation approach, the entire time history of the input excitation and output response of the structure are used as a batch of data to estimate the FE model parameters through a number of iterations. In the case of non-informative prior, the batch Bayesian method leads to an extended maximum likelihood (ML) estimation method to estimate jointly time-invariant model parameters and the measurement noise amplitude. The extended ML estimation problem is solved efficiently using a gradient-based interior-point optimization algorithm. Gradient-based optimization algorithms require the FE response sensitivities with respect to the model parameters to be identified. The FE response sensitivities are computed accurately and efficiently using the direct differentiation method (DDM). The estimation uncertainties are evaluated based on the Cramer–Rao lower bound (CRLB) theorem by computing the exact Fisher Information matrix using the FE response sensitivities with respect to the model parameters. The accuracy of the proposed uncertainty quantification approach is verified using a sampling approach based on the unscented transformation. Two validation studies, based on realistic structural FE models of a bridge pier and a moment resisting steel frame, are performed to validate the performance and accuracy of the presented nonlinear FE model updating approach and demonstrate its application to SHM. These validation studies show the excellent performance of the proposed framework for SHM and damage identification even in the presence of high measurement noise and/or way-out initial estimates of the model parameters. Furthermore, the detrimental effects of the input measurement noise on the performance of the proposed framework are illustrated and quantified through one of the validation studies. © 2016 Elsevier Ltd","Bayesian inference; Gradient-based optimization; Model updating; Nonlinear finite element model; Nonlinear system identification; Uncertainty quantification","Algorithms; Bayesian networks; Computation theory; Concrete bridges; Cramer-Rao bounds; Damage detection; Fisher information matrix; Inference engines; Maximum likelihood; Maximum likelihood estimation; Nonlinear analysis; Nonlinear systems; Optimization; Parameter estimation; Spurious signal noise; Structural analysis; Structural health monitoring; Time varying control systems; Uncertainty analysis; Bayesian inference; Gradient-based optimization; Model updating; Non-linear finite element model; Uncertainty quantifications; Finite element method",,,,,,,,,,,,,,,,"Ebrahimian, H., Astroza, R., Conte, J.P., Parametric Identification of Hysteretic Material Constitutive Laws in Nonlinear Finite Element Models using Extended Kalman Filter (2014), SSRP 14/06 Department of Structural Engineering, University of California San Diego, La Jolla, CA; Ebrahimian, H., Astroza, R., Conte, J.P., Extended kalman filter for material parameter estimation in nonlinear structural finite element models using direct differentiation method (2015) Earthquake Eng. 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"Vagnoli M., Remenyte-Prescott R., Andrews J.","56798645300;24175194400;7403360345;","Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges",2018,"Structural Health Monitoring","17","4",,"971","1007",,71,"10.1177/1475921717721137","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042101920&doi=10.1177%2f1475921717721137&partnerID=40&md5=abede7b7d19ae5534c55523638e7c52e","Resilience Engineering Research Group, University of Nottingham, Nottingham, United Kingdom","Vagnoli, M., Resilience Engineering Research Group, University of Nottingham, Nottingham, United Kingdom; Remenyte-Prescott, R., Resilience Engineering Research Group, University of Nottingham, Nottingham, United Kingdom; Andrews, J., Resilience Engineering Research Group, University of Nottingham, Nottingham, United Kingdom","Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted. © 2017, The Author(s).","artificial neural network; fault detection and diagnosis; finite element model updating; future challenges; railway bridges; Structural health monitoring","Bayesian networks; Cost effectiveness; Fault detection; Finite element method; Life cycle; Monitoring; Neural networks; Railroad bridges; Railroads; Reliability; Steel bridges; Bridge structural health monitoring; Fault detection and diagnosis; Finite element updating; Finite-element model updating; Future challenges; Railway bridges; State-of-the-art methods; Transportation industry; Structural health monitoring",,,,,"Horizon 2020 Framework Programme, H2020: 642453","This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sk1odowska-Curie grant agreement no. 642453.",,,,,,,,,,"(2015) Passenger rail usage 2015-16 Q1 statistical release, , London, Office of Rail and Road; Lee, J.J., Lee, J.W., Yi, J.H., Neural networks-based fault detection for bridges considering errors in baseline finite element models (2005) J Sound Vib, 280 (3-5), pp. 555-578; Chattopadhyay, G., Reddy, V., Development of integrated model for assessment of operational risks in rail track, pp. 612-616. , Proceedings of the IEEE international conference on industrial engineering and engineering management, Singapore, New York, IEEE, In; Sharma, K., Kumawat, J., Maheshwari, S., Railway security system based on wireless sensor networks: state of the art (2014) Int J Comput Appl, 96 (25), pp. 32-35; Tantele, E.A., Onoufriou, T., Optimization of life-cycle preventative maintenance strategies using genetic algorithm and Bayesian updating, pp. 1603-1610. , Frangopol D.M., Richard S., Kusko C.S., (eds), Proceedings of the 5th international conference on bridge maintenance, safety and management, Philadelphia, PA, CRC Press, In:, (eds; (2012) EU transport in figures, , Brussels, European Commission, (statistical pocketbook; Elfgren, L., Olofsson, J., Bell, B., Sustainable bridges: assessment for future traffic demands and longer lives (2008) Priority sixth sustainable development global change & ecosystems integrated project, publishable final activity report, , http://uic.org/cdrom/2008/11_wcrr2008/pdf/I.2.3.1.1.pdf; Reyer, M., Hurlebaus, S., Mander, J., Design of a wireless sensor network for structural health monitoring of bridges, pp. 515-520. , Proceedings of the international conference on sensing technology (ICST), Palmerston North, New Zealand, IEEE, In:, article no. 6137033; Le, B., Andrews, J., Modelling railway bridge asset management (2013) Proc IMechE, Part F: J Rail and Rapid Transit, 227 (6), pp. 644-656; Pipinato, A., Patton, R., Pipinato, A., Chapter 19-railway bridges (2016) Innovative bridge design handbook, pp. 509-527. , Boston, MA, Butterworth-Heinemann, In:, (ed; Phares, B.M., Graybeal, B.A., Rolander, D.D., Reliability and accuracy of routine inspection of highway bridges (2001) Transp Res Record, 1749, pp. 82-92; Gangone, M.V., Whelan, M.J., Janoyan, K.D., Wireless monitoring of a multispan bridge superstructure for diagnostic load testing and system identification (2011) Comput-Aided Civ Inf, 26 (7), pp. 560-579; Rafiq, M.I., Chryssanthopoulos, M.K., Sathananthan, S., Bridge condition modelling and prediction using dynamic Bayesian belief networks (2015) Struct Infrastruct E, 11 (1), pp. 38-50; Doebling, S.W., Farrar, C.R., Prime, M.B., A summary review of vibration-based fault identification methods, the shock and vibration (1998) Digest, 30 (2), pp. 91-105; Chase, S.B., Aktan, A.E., (2001) Health monitoring and management of civil infrastructure systems, 4337. , Bellingham, WA, SPIE; Yeung, W.T., Smith, J.W., Fault detection in bridges using neural networks for pattern recognition of vibration signatures (2005) Eng Struct, 27 (5), pp. 685-698; Ignat-Coman, A., Isoc, D., Joldiş, A., A case-based reasoning approach for fault detection state in bridges assessment, pp. 178-183. , Proceedings of the IEEE international conference on automation, quality and testing, robotics, Cluj-Napoca, New York, IEEE, In:, article no. 4588730; Office of Rail and H.W. 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Brownjohn, J.M.W., De Stafano, A., Xu, Y.-L., Vibration-based monitoring of civil infrastructure: challenges and successes (2011) J Civil Struct Health Monit, 1 (3-4), pp. 79-95; Alves, V., Meixedo, A., Ribeiro, D., Evaluation of the performance of different damage indicators in railway bridges (2015) Proc Eng, 114, pp. 746-753; Fernando, D., Adey, B.T., Walbridge, S., A methodology for the prediction of structure level costs based on element condition states (2013) Struct Infrastruct E, 9 (8), pp. 735-748; Ntotsios, E., Papadimitriou, C., Panetsos, P., Bridge health monitoring system based on vibration measurements (2009) B Earthq Eng, 7 (2), pp. 469-483; Vardanega, P.J., Webb, G.T., Fidler, P.R.A., Assessing the potential value of bridge monitoring systems (2016) P I Civil Eng Brid Eng, 169 (2), pp. 126-138; Wellalage, N.K.W., Zhang, T., Dwight, R., Calibrating Markov chain-based deterioration models for predicting future conditions of railway bridge elements (2015) J Bridge Eng, 20 (2), p. 04014060; Jardine, A.K.S., Lin, D., Banjevic, D., A review on machinery diagnostics and prognostics implementing condition-based maintenance (2006) Mech Syst Signal Pr, 20 (7), pp. 1483-1510; Hu, X., Wang, B., Ji, H., A wireless sensor network-based structural health monitoring system for highway bridges (2013) Comput-Aided Civ Inf, 28 (3), pp. 193-209; Flouri, K., Saukh, O., Sauter, R., A versatile software architecture for civil structure monitoring with wireless sensor networks (2012) Smart Struct Syst, 10 (3), pp. 209-228; Zio, E., Challenges in the vulnerability and risk analysis of critical infrastructures (2016) Reliab Eng Syst Safe, 152, pp. 137-150; Xia, Y., Hao, H., Zanardo, G., Long term vibration monitoring of an RC slab: temperature and humidity effect (2006) Eng Struct, 28 (3), pp. 441-452; Khelifa, A., Garrow, L.A., Higgins, M.J., Impacts of climate change on scour-vulnerable bridges: assessment based on HYRISK (2013) J Infrastruct Syst, 19 (2), pp. 138-146; Ikpong, A., Bagchi, A., New method for climate change resilience rating of highway bridges (2015) J Cold Reg Eng, 29 (3), p. 04014013; Sahlin, U., Di Maio, F., Vagnoli, M., Evaluating the impact of climate change on the risk assessment of nuclear power plants, pp. 2613-2621. , Podofillini L., Sudret B., Stojadinovic B., (eds), Proceedings of the 25th European safety and reliability conference (ESREL 2015), Zrich, CRC Press, In:, (eds; Hooper, E., Chapman, L., The impacts of climate change on national road and rail networks (2012) Transp Sustain, 2, pp. 105-136; Santillán, D., Salete, E., Toledo, M.Á., A methodology for the assessment of the effect of climate change on the thermal-strain-stress behaviour of structures (2015) Eng Struct, 92, pp. 123-141","Vagnoli, M.; Resilience Engineering Research Group, University Park, United Kingdom; email: Matteo.vagnoli@nottingham.ac.uk",,,"SAGE Publications Ltd",,,,,14759217,,,,"English","Struct. Health Monit.",Review,"Final","All Open Access, Green",Scopus,2-s2.0-85042101920
"Li S., Wei S., Bao Y., Li H.","57218879558;57195927855;22950245000;57202721115;","Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio",2018,"Engineering Structures","155",,,"1","15",,69,"10.1016/j.engstruct.2017.09.063","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033593210&doi=10.1016%2fj.engstruct.2017.09.063&partnerID=40&md5=d5267683e2e153335d8fb0d59b67e8af","Key Laboratory of Intelligent Disaster Prevention for Civil Infrastructure, Ministry of Industry and Information Technology, China; School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, 150090, China; School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China","Li, S., Key Laboratory of Intelligent Disaster Prevention for Civil Infrastructure, Ministry of Industry and Information Technology, China, School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, 150090, China; Wei, S., Key Laboratory of Intelligent Disaster Prevention for Civil Infrastructure, Ministry of Industry and Information Technology, China, School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China; Bao, Y., Key Laboratory of Intelligent Disaster Prevention for Civil Infrastructure, Ministry of Industry and Information Technology, China, School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China; Li, H., Key Laboratory of Intelligent Disaster Prevention for Civil Infrastructure, Ministry of Industry and Information Technology, China, School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China","The stay cables are one of most critical elements for cable-stayed bridges. This paper proposes a machine-learning based condition assessment method for stay cables by using the monitored cable tension force. First, based on the correlation of cable tension response between cable pairs (defined as the two cables at the upriver side and the opposite downriver side in the double cable planes), cable tension ratio is extracted as the feature variable, and the cable tension ratio is defined as the ratio of vehicle-induced cable tension between a cable pair. It is found that cable tension ratio is only related with cable properties and the transverse position of a vehicle over the deck. Vehicles on the bridge naturally cluster themselves into a few clusters that correspond to the traffic lanes, i.e. the vehicles in one lane form a cluster. Consequently, the vehicle-induced cable tension ratio forms the corresponding clusters or patterns. Gaussian Mixture Model (GMM) is employed for modelling the patterns of cable tension ratio, and each pattern (corresponds to a certain traffic lane) is modelled by a mono-Gaussian distribution. The Gaussian distribution parameters of tension ratio are used as condition indicator of stay cables because they are only related to cable conditions (the information of vehicle transverse location is presented in the number of tension ration patterns). The number of patterns which represents the model complexity are determined by Bayesian Information Criteria (BIC), while other parameters of GMM are estimated by using Expectation-Maximization algorithm under the Maximum Likelihood criteria, based on the monitored cable tension force. The cable condition is then evaluated according to the variation in estimated parameters of GMM. It is noted that pre-process of source separation is conducted to make the cable tension ratio independent from vehicle weight, environmental variant, and possible sensor errors. An FE model analysis is carried out to qualitatively illustrate the principle of the proposed method and physical sense of the cable tension ratio. © 2017 Elsevier Ltd","Cable tension force; Cable-stayed bridges; Condition assessment; Gaussian mixture model; Pattern recognition; Structural health monitoring","Bridge cables; Cable stayed bridges; Gaussian distribution; Image segmentation; Learning systems; Maximum likelihood; Maximum principle; Parameter estimation; Pattern recognition; Source separation; Structural health monitoring; Vehicles; Bayesian information criterion; Cable tension; Condition assessments; Condition indicators; Distribution parameters; Expectation-maximization algorithms; Gaussian Mixture Model; Maximum likelihood criteria; Cables; bridge; cable; cable laying; finite element method; Gaussian method; maximum likelihood analysis; pattern recognition; qualitative analysis; tension",,,,,"National Natural Science Foundation of China, NSFC: 2013CB036305, 51478149, 51638007, 51678204; Ministry of Science and Technology of the People's Republic of China, MOST: 2015DFG82080; Science and Technology Project of Nantong City: 2015C110020","This study was financially supported by the NSFC (Grant No. 51638007 , 51478149 , 51678204 ), the 973-Program project (No. 2013CB036305 ), Ministry of Science and Technology of China (Grant No: 2015DFG82080 ) and by Ningbo science and technology project (Grant No: 2015C110020 ).",,,,,,,,,,"Li, H., Ou, J., The state of the art in structural health monitoring of cable-stayed bridges (2015) J Civil Struct Health Monit, 6 (1), pp. 43-67; Cross, E.J., Koo, K.Y., Brownjohn, J.M.W., Worden, K., Long-term monitoring and data analysis of the Tamar Bridge (2013) Mech Syst Signal Process, 35 (1-2), pp. 16-34; Brownjohn, J.M.W., De Stefano, A., Xu, Y., Wenzel, H., Aktan, A.E., Vibration-based monitoring of civil infrastructure: challenges and successes (2011) J Civil Struct Health Monit, 1 (3-4), pp. 79-95; Ko, J., Ni, Y., Technology developments in structural health monitoring of large-scale bridges (2005) Eng Struct, 27 (12), pp. 1715-1725; Svensson, H., Cable-stayed bridges: 40 years of experience worldwide (2013), John Wiley & Sons; Li, S., Xu, Y., Zhu, S., Guan, X., Bao, Y., Probabilistic deterioration model of high-strength steel wires and its application to bridge cables (2014) Struct Infrastruct Eng, 11 (9), pp. 1-10; Li, H., Zhang, F., Jin, Y., Real-time identification of time-varying tension in stay cables by monitoring cable transversal acceleration (2014) Struct Contr Health Monit, 21 (7), pp. 1100-1117; Yang, Y., Li, S., Nagarajaiah, S., Li, H., Zhou, P., Real-time output-only identification of time-varying cable tension from accelerations via complexity pursuit (2015) J Struct Eng, 4015083; Li, H., Ou, J., Zhou, Z., Applications of optical fibre Bragg gratings sensing technology-based smart stay cables (2009) Opt Lasers Eng, 47 (10), pp. 1077-1084; Xu, Z., Wu, Z., Simulation of the effect of temperature variation on damage detection in a long-span cable-stayed bridge (2007) Struct Health Monit, 6 (3), pp. 177-189; Lepidi, M., Gattulli, V., Static and dynamic response of elastic suspended cables with thermal effects (2012) Int J Solids Struct, 49 (9), pp. 1103-1116; Montassar, S., Mekki, O.B., Vairo, G., On the effects of uniform temperature variations on stay cables (2015) J Civil Struct Health Monit, 15 (5), pp. 1-8; Bishop, C.M., Pattern recognition and machine learning (2006), Springer New York; Han, J., Kamber, M., Pei, J., Data mining: concepts and techniques: concepts and techniques (2011), Elsevier Amsterdam; Farrar, C.R., Duffey, T.A., Doebling, S.W., Nix, D.A., (1999), A Statistical Pattern Recognition Paradigm for Vibration-Based Structural Health Monitoring. In: Proceedings of the Proceedings of the 2nd International Workshop on Structural Health Monitoring, Palo Alto, CA, USA, 8–10 September 1999;; Farrar, C.R., Worden, K., Structural health monitoring: a machine learning perspective (2012), John Wiley & Sons, Ltd; Worden, K., Manson, G., The application of machine learning to structural health monitoring (1851) Philos Trans Royal Soc Math Phys Eng Sci, 2007 (365), pp. 515-537; Gul, M., Catbas, F.N., Statistical pattern recognition for structural health monitoring using time series modeling: theory and experimental verifications (2009) Mech Syst Signal Process, 23 (7), pp. 2192-2204; Figueiredo, E., Park, G., Farrar, C.R., Worden, K., Figueiras, J., Machine learning algorithms for damage detection under operational and environmental variability (2010) Struct Health Monit, 10 (6), pp. 559-572; Yang, Y., Nagarajaiah, S., Output-only modal identification with limited sensors using sparse component analysis (2013) J Sound Vib, 332 (19), pp. 4741-4765; Yang, Y., Nagarajaiah, S., Structural damage identification via a combination of blind feature extraction and sparse representation classification (2014) Mech Syst Signal Process, 45 (1), pp. 1-23; Yang, Y., Nagarajaiah, S., Harnessing data structure for recovery of randomly missing structural vibration responses time history: sparse representation versus low-rank structure (2016) Mech Syst Signal Process, 74, pp. 165-182; Catbas, F.N., Gokce, H.B., Gul, M., Nonparametric analysis of structural health monitoring data for identification and localization of changes: concept, lab, and real-life studies (2012) Struct Health Monit, 11 (5), pp. 613-626; Figueiredo, E., Radu, L., Worden, K., Farrar, C.R., A Bayesian approach based on a Markov-chain Monte Carlo method for damage detection under unknown sources of variability (2014) Eng Struct, 80, pp. 1-10; Ou, J., (2005), The state-of-the-art and the state-of-the-practice of Structural Health Monitoring for civil infrastructure in the Mainland of China. In: Proceedings of the 2nd International Conference on Structural Health Monitoring of Intelligent Infrasture, Shenzhen, China, Nov. 16–18; 2005; Omenzetter, P., Brownjohn, J.M.W., Application of time series analysis for bridge monitoring (2006) Smart Mater Struct, 15 (1), pp. 129-138; Guo, W., Xu, Y., Fully computerized approach to study cable-stayed bridge–vehicle interaction (2001) J Sound Vib, 248 (4), pp. 745-761; Eurocode, C.E.N., Eurocode 1. Actions on structures. Part 2: Traffic loads on bridges (2003), European Standard EN Brussels; Figueiredo, M.A., Jain, A.K., Unsupervised learning of finite mixture models (2002) IEEE Trans Pattern Anal Mach Intell, 24 (3), pp. 381-396; Schwarz, G., Estimating the dimension of a model (1978) Ann Stat, 6 (2), pp. 461-464; Vivó-Truyols, G., Torres-Lapasió, J.R., Van Nederkassel, A.M., Vander Heyden, Y., Massart, D.L., Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals: Part I: Peak detection (2005) J Chromatogr A, 1096 (1), pp. 133-145; Cui, B., Wu, C., Ding, W., Tong, Y., Influence of acting position of vehicle wheels on fatigue stress range of steel deck (2010) J Archit Civil Eng, 27 (3), pp. 19-23; Robert, C., Generalized inverse normal distributions (1991) Stat Probab Lett, 11 (1), pp. 37-41; Jorgensen, B., Statistical properties of the generalized inverse Gaussian distribution (1982), Springer-Verlag; Brownjohn, J.M.W., Xia, P., Hao, H., Xia, Y., Civil structure condition assessment by FE model updating (2001) Finite Elem Anal Des, 37 (10), pp. 761-775; Li, H., Lan, C., Ju, Y., Li, D., Experimental and numerical study of the fatigue properties of corroded parallel wire cables (2011) J Bridge Eng, 17 (2), pp. 211-220","Li, H.; School of Transportation Science and Engineering, China; email: lihui@hit.edu.cn",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","",Scopus,2-s2.0-85033593210
"Xu X., Augello R., Yang H.","56427179900;57195365257;56427164800;","The generation and validation of a CUF-based FEA model with laser-based experiments",2021,"Mechanics of Advanced Materials and Structures","28","16",,"1648","1655",,66,"10.1080/15376494.2019.1697473","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076905257&doi=10.1080%2f15376494.2019.1697473&partnerID=40&md5=68a23e837ef029ca4912a92b4116e61a","Department of Aeronautics and Aerospace Engineering, Politecnico di Torino, Torino, Italy","Xu, X., Department of Aeronautics and Aerospace Engineering, Politecnico di Torino, Torino, Italy; Augello, R., Department of Aeronautics and Aerospace Engineering, Politecnico di Torino, Torino, Italy; Yang, H., Department of Aeronautics and Aerospace Engineering, Politecnico di Torino, Torino, Italy","Architectural structures today are increasingly complex and structural health monitoring plays an important role in guaranteeing their safety. How to improve the reliability of deformation analysis is, thus, one of the key problems. This article combines laser-based measurement technology and the Carrera unified formulation (CUF) method to investigate the deformation of engineering structures. Within this article, we simulate architectural structures using the CUF geometric model, which is consistent with the results of the laser tracker experiment. We aimed at constructing an intelligent and efficient CUF model which can be applied extensively in the monitoring of various constructs, such as tunnels and bridges. The innovation of this article is that high-accuracy laser tracker technology is integrated with an effective CUF model to investigate the load-displacement relationship considering lateral displacement. © 2019 Taylor & Francis Group, LLC.","FEA; laser tracker; multi-sensor; SHM; terrestrial laser scanning","Deformation; Finite element method; Reliability analysis; Architectural structure; Carrera unified formulations; Engineering structures; Laser tracker; Laser-based measurement; Lateral displacements; Multi sensor; Terrestrial laser scanning; Structural health monitoring",,,,,"Gottfried Wilhelm Leibniz Universität Hannover, LUH; Natural Science Foundation of Jiangsu Province: BK20160558","The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The publication of this article was supported by Natural Science Foundation of Jiangsu Province (no. BK20160558). The authors would like to acknowledge the support of all the colleagues in Geodetic Institute of Leibniz University Hannover, especially for Ingo Neumann and Johannes Bureick.","The authors would like to acknowledge the support of all the colleagues in Geodetic Institute of Leibniz University Hannover, especially for Ingo Neumann and Johannes Bureick.",,,,,,,,,"Park, H.S., Lee, H.M., Adeli, H., Lee, I., A new approach for health monitoring of structures: Terrestrial laser scanning (2007) Comput. Aided Civ. Infrastruct. Eng, 22 (1), pp. 19-30; Xu, X., Kargoll, B., Bureick, J., Yang, H., Alkhatib, H., Neumann, I., TLS-based profile model analysis of major composite structures with robust B-spline method (2018) Compos. Struct, 184, pp. 814-820; Yang, H., Xu, X., Neumann, I., Deformation behavior analysis of composite structures under monotonic loads based on terrestrial laser scanning technology (2018) Compos. Struct, 183, pp. 594-599; Xu, X., Yang, H., Augello, R., Carrera, E., Optimized free-form surface modeling of point clouds from laser-based measurement (2019) Mech. Adv. Mater. Struct, pp. 1-9; Yang, H., Xu, X., Neumann, I., Laser scanning-based updating of a finite-element model for structural health monitoring (2016) IEEE Sens. J, 16 (7), pp. 2100-2104; Yang, H., Xu, X., Neumann, I., Optimal finite element model with response surface methodology for concrete structures based on terrestrial laser scanning technology (2018) Compos. Struct., 183, pp. 2-6; Xu, X., Zhao, X., Yang, H., Neumann, I., TLS-based feature extraction and 3D modeling for arch structures (2017) J. Sens, 2017, p. 1,. , Article ID 9124254; Xu, X., Yang, H., Zhang, Y., Neumann, I., Intelligent 3D data extraction method for deformation analysis of composite structures (2018) Compos. Struct, 203, pp. 254-258; Xu, X., Yang, H., Network method for deformation analysis of three-dimensional point cloud with terrestrial laser scanning sensor (2018) Int. J. Distrib. Sens. Netw, 14 (11). , 155014771881413; Yang, H., Xu, X., Kargoll, B., Neumann, I., An automatic and intelligent optimal surface modeling method for composite tunnel structures (2019) Compos. Struct, 208, pp. 702-710; Yang, H., Xu, X., Xu, W., Neumann, I., Terrestrial laser scanning-based deformation analysis for arch and beam structures (2017) IEEE Sens. J, 17, pp. 4605-4611; Xu, X., Yang, H., Neumann, I., Time-efficient filtering method for three-dimensional point clouds data of tunnel structures (2018) Adv. Mech. Eng, 10 (5). , 168781401877315, 168781401877316, and; Yang, H., Omidalizarandi, M., Xu, X., Neumann, I., Terrestrial laser scanning technology for deformation monitoring and surface modeling of arch structures (2017) Compos. Struct, 169, pp. 173-179; Xu, X., Bureick, J., Yang, H., Neumann, I., TLS-based composite structure deformation analysis validated with laser tracker (2018) Compos. Struct, 202, pp. 60-65; Carrera, E., Petrolo, M., Maiarù, M., Giunta, G., 1D higher-order finite element models with only displacement variables for the analysis of fiber-reinforced composite structures (2011) ECCOMAS 3rd Thematic Conference on Mechanical Response of Composites, , September 21–23, Hannover, Germany; Mantari, J.L., Ramos, I.A., Carrera, E., Petrolo, M., Static analysis of functionally graded plates using new non-polynomial displacement fields via Carrera Unified Formulation (2016) Compos. B, 89, pp. 127-142; Carrera, E., Filippi, M., Mahato, P.K., Pagani, A., Accurate static response of single- and multi-cell laminated box beams (2016) Compos. Struct, 136, pp. 372-383; Carrera, E., Petrolo, M., Refined beam elements with only displacement variables and plate/shell capabilities (2012) Meccanica, 47 (3), pp. 537-556; Varello, A., Lamberti, A., Carrera, E., Static aeroelastic response of wing-structures accounting for in-plane cross-section deformation (2013) Int. J. Aeronaut. Space Sci, 14 (4), pp. 310-323; Pagani, A., Carrera, E., Large-deflection and post-buckling analyses of laminated composite beams by Carrera Unified Formulation (2017) Compos. Struct, 170, pp. 40-52; Carrera, E., Giunta, G., Refined beam theories based on a unified formulation (2010) Int. J. Appl. Mech, 2 (1), pp. 117-143; Carrera, E., Demasi, L., Classical and advanced multilayered plate elements based upon PVD and MVT. Part 1: Derivation of finite element matrices (2002) Int. J. Numer. Methods Eng, 55 (2), pp. 191-231; Carrera, E., Petrolo, M., On the effectiveness of higher-order terms in refined beam theories (2011) Trans. ASME, J. Appl. Mech, 78 (2), p. 021013,; Maiarú, M., Petrolo, M., Carrera, E., Evaluation of energy and failure parameters in composite structures via a component-wise approach (2017) Composites Part B: Engineering, 108, pp. 53-64; Carrera, E., de Miguel, A.G., Pagani, A., Hierarchical theories of structures based on Legendre polynomial expansions with finite element applications (2017) Int. J. Mech. Sci, 120, pp. 286-300; Schmitt, C., Neuner, H., Neumann, I., Piehler, J., Hansen, M., Marx, S., Erstellung von Ist-Geometrien für strukturmechanische (2014) Berechnungen. Ingenieurvermessung, 14, pp. 37-48; Reu, P.L., Rohe, D.P., Jacobs, L.D., Comparison of DIC and LDV for practical vibration and modal measurements (2017) Mech. Syst. Signal Process, 86, pp. 2-16; Carrera, E., Cinefra, M., Petrolo, M., Zappino, E., (2014) Finite Element Analysis of Structures through Unified Formulation, , Wiley, and, John Wiley & Sons; Pagani, A., Augello, R., Carrera, E., Frequency and mode change in the large deflection and post-buckling of compact and thin-walled beams (2018) J. Sound Vib, 432, pp. 88-104; Bathe, K.J., (1996) Finite Element Procedure, , Prentice hall, Upper Saddle River, NJ; Carrera, E., Cinefra, M., Petrolo, M., Zappino, E., (2014) Finite Element Analysis of Structures through Unified Formulation, , Wiley, New York; Young, W.C., Budynas, R.G., Sadegh, A.M., (2002) Roark’s Formulas for Stress and Strain, 7. , McGraw-Hill, New York","Xu, X.; Department of Aeronautics and Aerospace Engineering, Italy",,,"Bellwether Publishing, Ltd.",,,,,15376494,,,,"English","Mech. Adv. Mater. Struct.",Article,"Final","",Scopus,2-s2.0-85076905257
"Moreu F., Kim R.E., Spencer B.F., Jr.","16024768400;36135544500;7201938602;","Railroad bridge monitoring using wireless smart sensors",2017,"Structural Control and Health Monitoring","24","2","e1863","","",,62,"10.1002/stc.1863","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963961489&doi=10.1002%2fstc.1863&partnerID=40&md5=7888a0ba1b8cab7f321dca58614b77dc","Department of Civil Engineering, University of New Mexico, Centennial Engineering Center 3056, MSC01 1070, Albuquerque, NM  87131, United States; Center for Integrated Smart Sensors, KAIST, Daejeon, 34141, South Korea; Newmark Structural Engineering Laboratory, University of Illinois at Urbana-Champaign, 2113 Newmark Civil Engineering Laboratory, Department of Civil and Environmental Engineering, 205 N. Matthews Ave., Urbana, IL  61801, United States","Moreu, F., Department of Civil Engineering, University of New Mexico, Centennial Engineering Center 3056, MSC01 1070, Albuquerque, NM  87131, United States; Kim, R.E., Center for Integrated Smart Sensors, KAIST, Daejeon, 34141, South Korea; Spencer, B.F., Jr., Newmark Structural Engineering Laboratory, University of Illinois at Urbana-Champaign, 2113 Newmark Civil Engineering Laboratory, Department of Civil and Environmental Engineering, 205 N. Matthews Ave., Urbana, IL  61801, United States","Railroads carry more than 40% of the freight, in terms of tons per mile transported in North America. A critical portion of the railroad infrastructure is the more than 100,000 bridges, which occur, on the average, every 1.4 miles of track. Railroads have a limited budget for capital investment. Therefore, decisions on which bridges to repair/replace become critical for both safety and economy. North American railroads regularly inspected bridges to ensure safe operation that can meet transport demands, using inspection reports to decide which bridges may need maintenance, replacement, or further investigation. Current bridge inspection practices recommend observing bridge responses under live load to help assess bridge condition. However, measuring bridge responses under train loads in the field is a challenging, expensive, and complex task. This research explores the potential of using wireless smart sensors (WSS) to measure bridge responses under revenue service traffic that can be used to inform bridge management decisions. Wireless strain gages installed on the rail measure real-time train loads. Wireless accelerometers and magnetic strain gages installed in the bridge measure associated bridge responses. The system is deployed and validated on a double-track steel truss bridge on the south side of Chicago, Illinois, owned by the Canadian National Railway. A calibrated finite element model of the bridge with known train input load estimated the responses of the bridge at arbitrary, unmeasured locations, showing the possibility of applying the system for decision making process. These results demonstrate the potential of WSS technology to assist with railroad bridge inspection and management practice. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.","autonomous monitoring; live load estimation; railroad bridge management; strain; structural health monitoring; wireless smart sensors","Budget control; Decision making; Economics; Finite element method; Inspection; Investments; Magnetic levitation vehicles; Railroad bridges; Railroad transportation; Railroads; Smart sensors; Steel bridges; Strain; Strain gages; Structural health monitoring; Taxation; Trusses; Autonomous monitoring; Bridge management; Decision making process; Live loads; Management practices; Railroad infrastructure; Wireless accelerometers; Wireless smart sensors; Bridges",,,,,"Federal Railroad Administration, FRA: DTFR53-13-C-00047; University of Tokyo","This research was funded by the Federal Railroad Administration under the BAA 2010-1 project entitled “Campaign Monitoring of Railroad Bridges in High-Speed Rail Shared Corridors using Wireless Smart Sensors”, Contract No. DTFR53-13-C-00047 (Project Manager: Cameron Stuart). The authors thank Canadian National Railway (CN), Norfolk Southern Corporation (NS), and The Bridge & Structure Laboratory of the Department of Civil Engineering at the University of Tokyo at Japan for different support during the different campaign monitoring tests.",,,,,,,,,,"(2013) High speed rail: a vision for the future, , www.geometrx.com, (December 22, 2013); (2014) A short history of U.S. freight railroads, , https://www.aar.org/BackgroundPapers/A%20Short%20History%20of%20US%20Freight%20Railroads.pdf#search=a%20short%20history%20of%20u%2Es%2E%20freight%20railroads, (Jun. 18, 2014); Berman, J., Class I railroads are on track to spend $13 billion in 2012 capital expenditures, says AAR (2012) Logistics Management, , www.logisticsmgmt.com/article/class_i_railroads_are_on_track_to_spend_13_billion_in_2012_capital_expendit/, January 30; (2013), http://freightrailworks.org/>, (December 22, 2013); Weatherford, B.A., Willis, H.H., Ortiz, D., (2008) The State of U.S. Railroads, a Review of Capacity and Performance Data, , RAND Corporation, Pittsburgh, Pensylvannia; Thompson, L., A vision for railways in 2050 (2010) A vision for railways in 2050; (2013) Report card for America's infrastructure, , http://www.infrastructurereportcard.org/, (November 20, 2014); (2007) National Rail Freight Infrastructure Capacity and Investment Study; (2003) Practical guide to railway engineering, , Lanham, MD; (1999) FRA's Interim Statement of Policy on the Safety of Railroad Bridges, TR-1999-077, , Office of Inspector General, Audit Report. 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International Society for Optics and Photonics; (2014) Strain Gauge Users' Guide. Home website, , https://www.tml.jp/e/product/strain_gauge/option/fgmh-1.html, (November 20, 2014); Kouroussis, G., Caucheteur, C., Kinet, D., Alexandrou, G., Verlinden, O., Moeyaert, V., Review of trackside monitoring solutions: from Strain Gages to Optical Fibre Sensors (2015) Sensors, 15 (8), pp. 20115-20139; Jo, H., Park, J.W., Spencer, B.F., Jung, H.J., Develoment of high-sensitivity wireless strain sensor for structural health monitoring (2013) Smart Structure and Systems, 11, pp. 477-496; Ghafoori, E., Motavalli, M., Nussbaumer, A., Herwig, A., Prinz, G.S., Fontana, M., Design criterion for fatigue strengthening of riveted beams in a 120-year-old railway metallic bridge using pre-stressed CFRP plates (2015) Composites Part B: Engineering, 68, pp. 1-13; Nagayama, T., Spencer, B.F., Jr., (2007) Structural health monitoring using smart sensors. Newmark Structural Engineering Laboratory, , University of Illinois at Urbana-Champaign; Steel structures (2014) Manual for railway engineering, Vol. 2, , Chapter 15,. In, Lanham, MD; (2014) Main Website, , http://www.createprogram.org/about.htm>, (October 18, 2014)","Moreu, F.; Department of Civil Engineering, Centennial Engineering Center 3056, MSC01 1070, United States; email: fmoreu@unm.edu",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-84963961489
"Ebrahimian H., Astroza R., Conte J.P., Papadimitriou C.","57112070500;55619989200;7101953827;7103065916;","Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures",2018,"Structural Control and Health Monitoring","25","4","e2128","","",,50,"10.1002/stc.2128","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041010998&doi=10.1002%2fstc.2128&partnerID=40&md5=147081cc81322c3991fe055ca79c3728","Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA, United States; Faculty of Engineering and Applied Sciences, University of the Andes, Santiago, Chile; Department of Structural Engineering, University of California San Diego, San Diego, CA, United States; Department of Mechanical Engineering, University of Thessaly, Thessaly, Greece","Ebrahimian, H., Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA, United States; Astroza, R., Faculty of Engineering and Applied Sciences, University of the Andes, Santiago, Chile; Conte, J.P., Department of Structural Engineering, University of California San Diego, San Diego, CA, United States; Papadimitriou, C., Department of Mechanical Engineering, University of Thessaly, Thessaly, Greece","This paper presents a new framework for output-only nonlinear system and damage identification of civil structures. This framework is based on nonlinear finite element (FE) model updating in the time-domain, using only the sparsely measured structural response to unmeasured or partially measured earthquake excitation. The proposed framework provides a computationally feasible approach for structural health monitoring and damage identification of civil structures when accurate measurement of the input seismic excitations is challenging (e.g., buildings with significant foundation rocking and bridges with piers in deep water) or the measured seismic excitations are erroneous and/or distorted by significant measurement error (e.g., malfunctioning sensors). Grounded on Bayesian inference, the proposed framework estimates the unknown FE model parameters and the ground acceleration time histories simultaneously, using the sparse measured dynamic response of the structure. Two approaches are presented in this study to solve the joint structural system parameter and input identification problem: (a) a sequential maximum likelihood estimation approach, which reduces to a sequential nonlinear constrained optimization method, and (b) a sequential maximum a posteriori estimation approach, which reduces to a sequential iterative extended Kalman filtering method. Both approaches require the computation of FE response sensitivities with respect to the unknown FE model parameters and the values of base acceleration at each time step. The FE response sensitivities are computed efficiently using the direct differentiation method. The two proposed approaches are validated using the seismic response of a 5-story reinforced concrete building structure, numerically simulated using a state-of-the-art mechanics-based nonlinear structural FE modeling technique. The simulated absolute acceleration response time histories of 3 floors and the relative (to the base) roof displacement response time histories of the building to a bidirectional horizontal seismic excitation are polluted with artificial measurement noise. The noisy responses of the structure are then used to estimate the unknown FE model parameters characterizing the nonlinear material constitutive laws of the concrete and reinforcing steel and the (assumed) unknown time history of the ground acceleration in the longitudinal direction of the building. The same nonlinear FE model of the structure is used to simulate the structural response and to estimate the dynamic input and system parameters. Thus, modeling uncertainty is not considered in this paper. Although the validation study demonstrates the estimation accuracy of both approaches, the sequential maximum a posteriori estimation approach is shown to be significantly more efficient computationally than the sequential maximum likelihood estimation approach. Copyright © 2018 John Wiley & Sons, Ltd.","Bayesian method; direct differentiation method; joint parameter and input estimation; nonlinear finite element model; output-only system identification; structural health monitoring","Bayesian networks; Concrete bridges; Concrete buildings; Concretes; Constrained optimization; Damage detection; Finite element method; Inference engines; Iterative methods; Kalman filters; Maximum likelihood; Maximum likelihood estimation; Nonlinear analysis; Nonlinear control systems; Nonlinear systems; Reinforced concrete; Response time (computer systems); Seismic response; Seismology; Structural health monitoring; Structures (built objects); Time domain analysis; Uncertainty analysis; Bayesian methods; Direct differentiation methods; Input estimation; Non-linear finite element model; Output only; Parameter estimation",,,,,"Comisión Nacional de Investigación Científica y Tecnológica, CONICYT: 11160009; Universidad de los Andes, Uniandes","The authors wish to thank Dr. Quan Gu at Xiamen University, China, and Dr. Frank McKenna at the Pacific Earthquake Engineering Research Center at UC Berkeley for their help with the implementation in OpenSees of the DDM for earthquake ground acceleration input as sensitivity parameters. Their assistance was most valuable and is highly appreciated. Rodrigo Astroza acknowledges the financial support from the Universidad de los Andes, Chile, through the research grant Fondo de Ayuda a la Investigación (FAI) and from the Chilean National Commission for Scientific and Technological Research (CONICYT), FONDECYT‐Iniciación research project 11160009.",,,,,,,,,,"Doebling, S.W., Farrar, C.R., Prime, M.B., Shevitz, D.W., (1996) Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review. Report LA-13070-MS, , Los Alamos, New Mexico; Carden, E.P., Fanning, P., (2004) Struct. Health Monit., 3 (4), p. 355; Astroza, R., Ebrahimian, H., Conte, J.P., Restrepo, J.I., Hutchinson, T.C., (2016) Struct. Control Health Monit., 23 (3), p. 535; Ntotsios, E., Karakostas, C., Lekidis, V., Panetsos, P., Nikolaou, I., Papadimitriou, C., Salonikos, T., (2009) Bull. Earthq. Eng., 7, p. 485; Moaveni, B., He, X., Conte, J.P., Restrepo, J.I., Panagiotou, M., (2011) ASCE J. of Struct. Eng., 137 (6), p. 705; Kerschen, G., Worden, K., Vakakis, A.F., Golinval, J., (2006) Mech. Syst. Signal Process., 20, p. 505; Ebrahimian, H., Astroza, R., Conte, J.P., (2015) Earthq. Eng. Struct. Dyn., 44 (10), p. 1495; Ebrahimian, H., Astroza, R., Conte, J.P., de Callafon, R.A., (2016) Mech. Syst. Signal Process., 84, p. 194. , https://doi.org/10.1016/j.ymssp.2016.02.002; Astroza, R., Ebrahimian, H., Conte, J.P., (2015) ASCE J. Eng. Mech., 141 (5), pp. 1-17. , 04014149; Huang, C.-H., (2001) J. Sound Vib., 242 (5), p. 749; Huang, C.-H., (2001) J. Sound Vib., 248 (5), p. 789; Huang, C.-H., Shih, C.-C., Kim, S., (2009) App. Math. Model., 33 (6), p. 2683; Lee, M.-H., Liu, Y.-W., (2014) Procedia Eng., 79, p. 540; Ma, C.-K., Ho, C.-C., (2004) J. Sound Vib., 275 (3-5), p. 953; Ma, C.-K., Chang, J.-M., Lin, D.-C., (2003) J. 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Control Health Monit., 17 (7), p. 825; Ebrahimian, H., Astroza, R., Conte, J.P., (2015) Output-only identification of civil structures using nonlinear finite element model updating, , in SPIE 9438, Health Monitoring of Structural and Biological Systems, San Diego, CA; Vidal, C.A., Lee, H.-S., Haber, R.B., (1991) Comput. Syst. Eng., 2 (5-6), p. 509; Kleiber, M., Antunez, H., Hien, T.D., Kowalczyk, P., (1997) Parameter Sensitivity in Nonlinear Mechanics: Theory and Finite Element Computations, , John Wiley & Sons, New York, NY; Zhang, Y., Der Kiureghian, A., (1993) Comput. Methods Appl. Mech. Eng., 108 (1-2), p. 23; Conte, J.P., Vijalapura, P.K., Meghella, M., (2003) ASCE J. Eng. Mech., 129 (12), p. 1380; Moon, T.K., Stirling, W.C., (2000) Mathematical Methods and Algorithms for Signal Processing, , Prentice-Hall, Upper Saddle River, NJ; Goodwin, G.C., Payne, R.L., (1977) Dynamic System Identification: Experiment Design and Data Analysis, , Academic Press, New York, NY; Kay, S.M., (1993) Fundamentals of Statistical Signal Processing. Volume 1: Estimation Theory, , Upper Saddle River, NJ, Prentice Hall; Tichavsky, P., Muravchik, C.H., Nehorai, A., (1998) IEEE Trans. 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Mech., 123 (2), p. 143; Zona, A., Barbato, M., Conte, J.P., (2006) Steel Compos. Struct., 6 (3), p. 183; Filippou, F.C., Popov, E.P., Bertero, V.V., (1983) Effects of bond deterioration on hysteretic behavior of reinforced concrete joints, , EERC Report 83-19, Earthquake Engineering Research Center, College of Engineering, UC Berkeley, Berkeley, CA; A cooperative effort, , http://strongmotioncenter.org, [Online]. Available, / [Accessed 08 2015]; Panagiotou, M., (2008) Seismic design, testing and analysis of reinforced concrete wall buildings, , PhD Thesis, Department of Structural Engineering, University of California San Diego, La Jolla, CA; (2010) Modeling and acceptance criteria for seismic design and analysis of tall buildings, , PEER/ATC 72-1, Pacific Earthquake Engineering Research Center, Richmond, CA; Ebrahimian, H., (2015) Nonlinear finite element model updating for nonlinear system and damage identification of civil structures, , PhD Thesis, Department of Structural Engineering, University of California San Diego, La Jolla, CA; Byrd, R.H., Hribar, M.E., Nocedal, J., (1999) SIAM J. Optim., 9 (4), p. 877; Byrd, R.H., Gilbert, J.C., Nocedal, J., (2000) Math. Program., 89 (1), p. 149; (2014) MATLAB Optimization Toolbox, User's Guide, R2014a, , The MathWorks Inc., Natick, MA","Conte, J.P.; Department of Structural Engineering, United States; email: jpconte@ucsd.edu",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-85041010998
"Alavi A.H., Hasni H., Jiao P., Borchani W., Lajnef N.","33867483600;56964369900;55604705500;56008051600;14047090600;","Fatigue cracking detection in steel bridge girders through a self-powered sensing concept",2017,"Journal of Constructional Steel Research","128",,,"19","38",,48,"10.1016/j.jcsr.2016.08.002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982299376&doi=10.1016%2fj.jcsr.2016.08.002&partnerID=40&md5=87c466a640191f40e980c2d7ba8dba73","Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI  48823, United States","Alavi, A.H., Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI  48823, United States; Hasni, H., Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI  48823, United States; Jiao, P., Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI  48823, United States; Borchani, W., Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI  48823, United States; Lajnef, N., Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI  48823, United States","Development of fatigue cracking is affecting the structural performance of many of welded steel bridges in the United States. One of the main sources of fatigue cracking is out-of-plane distortion occurring at connections of transverse structural members and longitudinal girders. Distortion-induced fatigue cracks mostly occur in older bridges with members prone to fatigue. Prediction of secondary stresses in these members is difficult using conventional design specifications. This limitation suggests the necessity of utilizing new strategies to analyze the damage caused by distortion-related cracking. This study presents a new approach for detection of distortion-induced fatigue cracking of steel bridges based on the interpretation of the data provided by a newly developed self-powered piezo-floating-gate (PFG) sensor. The PFG sensors are empowered using piezoelectric transducers through harvesting energy from the mechanical loading experienced by the structure. In order to assess the performance of the proposed sensing system, three-dimensional finite element models were developed and the structural response of the girder was subsequently obtained. The fatigue life of the girder was determined based on J-integral concept and Paris Law. Several damage states were defined by extending the fatigue crack lengths. Thereafter, features representing the PFG sensor output were extracted from the strain data for different sensing nodes to detect the damage scenarios. Furthermore, a new data fusion concept based on the effect of group of sensors was proposed to improve the damage detection performance. The results indicate that the proposed method is capable of detecting different damage progression states. This is specifically evident for the sensors that are located close to the damage location. The acceptable performance of the proposed sensing system implies its applicability for other modalities of infrastructure/structural health monitoring (I/SHM). © 2016","Damage detection; Data fusion; Distortion-induced fatigue cracking; Probability density function; Self-powered sensor; Welded steel bridges","Beams and girders; Cracks; Damage detection; Data fusion; Fatigue crack propagation; Fatigue damage; Fatigue of materials; Finite element method; Probability density function; Sensor data fusion; Structural analysis; Welding; Acceptable performance; Detection performance; Distortion-induced fatigue; Out-of-plane distortions; Self-powered; Steel bridge girders; Structural performance; Three dimensional finite element model; Steel bridges",,,,,"Federal Highway Administration, FHWA: DTFH61-13-C-00015","The presented work is supported by a research grant from the Federal Highway Administration (FHWA) ( DTFH61-13-C-00015 ).",,,,,,,,,,"Fisher, J.W., Fatigue and Fracture in Steel Bridges: Case Studies (1984), John Wiley & Sons. 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"Lin S.-W., Yi T.-H., Li H.-N., Ren L.","57194334605;8726425800;36065853500;8931198600;","Damage Detection in the Cable Structures of a Bridge Using the Virtual Distortion Method",2017,"Journal of Bridge Engineering","22","8","4017039","","",,47,"10.1061/(ASCE)BE.1943-5592.0001072","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019654254&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001072&partnerID=40&md5=fb2ea54027bb332669637b5152bdd7d6","School of Civil Engineering, Putian Univ., Putian, 351100, China; School of Civil Engineering, Dalian Univ. of Technology, Dalian, 116023, China","Lin, S.-W., School of Civil Engineering, Putian Univ., Putian, 351100, China; Yi, T.-H., School of Civil Engineering, Dalian Univ. of Technology, Dalian, 116023, China; Li, H.-N., School of Civil Engineering, Dalian Univ. of Technology, Dalian, 116023, China; Ren, L., School of Civil Engineering, Dalian Univ. of Technology, Dalian, 116023, China","This study investigated damage detection of cable structures in bridge engineering using the virtual distortion method (VDM). The main theory of damage detection based on the VDM, including the influence matrix, the stiffness reduction, and an optimized algorithm, was applied to damage detection in hangers in a real three-dimensional finite-element model (FEM) of an existing arch bridge. Two damage-detection methods for hangers were used. One method is based on the static VDM, which has high precision but requires more sensors. The other method is based on the dynamic VDM, which does not require many sensors but has a lower precision. The effectiveness of damage detection in hangers was investigated through a numerical simulation. The steps of damage detection that can be combined with practical engineering were determined. The anti-interference ability of the wind load, the optimal placement of sensors, and the antinoise capability were considered in the case study. It was verified that the proposed methods can rapidly and accurately identify the damage location and degree of damage. © 2017 American Society of Civil Engineers.","Cable structure; Damage detection; Structural health monitoring; Virtual distortion method","Arch bridges; Bridges; Cable stayed bridges; Cables; Finite element method; Stiffness matrix; Structural health monitoring; Three dimensional computer graphics; Antinoise capability; Cable structure; Optimal placement of sensors; Optimized algorithms; Practical engineering; Stiffness reduction; Three dimensional finite element model; Virtual distortion method; Damage detection",,,,,"National Natural Science Foundation of China, NSFC: 51478081, 51625802; National Basic Research Program of China (973 Program): 2015CB060000; National Science Fund for Distinguished Young Scholars: 2015J12JH209","This research work was jointly supported by the National Natural Science Foundation of China (Grants 51625802 and 51478081), the 973 Program (Grant 2015CB060000), and the Science Fund for Distinguished Young Scholars of Dalian (Grant 2015J12JH209).",,,,,,,,,,"Abdullah, A.B.M., Rice, J.A., Hamilton, H.R., Consolazio, G.R., Experimental and numerical evaluation of unbonded posttensioning tendons subjected to wire breaks (2016) J. 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Bridge Eng., pp. 921-927; Zhang, Q., Jankowski, L., Duan, Z., Simultaneous identification of excitation time histories and parametrized structural damages (2012) Mech. Syst. Sig. Process., 33, pp. 56-68","Yi, T.-H.; School of Civil Engineering, China; email: yth@dlut.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85019654254
"Weinstein J.C., Sanayei M., Brenner B.R.","57203723952;6701343798;7203074253;","Bridge Damage Identification Using Artificial Neural Networks",2018,"Journal of Bridge Engineering","23","11","04018084","","",,45,"10.1061/(ASCE)BE.1943-5592.0001302","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052737830&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001302&partnerID=40&md5=93b00e80108b18cc7380b025e6cc54ac","Dept. of Civil and Environmental Engineering, Tufts Univ., Medford, MA  02155, United States","Weinstein, J.C., Dept. of Civil and Environmental Engineering, Tufts Univ., Medford, MA  02155, United States; Sanayei, M., Dept. of Civil and Environmental Engineering, Tufts Univ., Medford, MA  02155, United States; Brenner, B.R., Dept. of Civil and Environmental Engineering, Tufts Univ., Medford, MA  02155, United States","An objective, data-driven approach to evaluate the performance of bridges for developing a structural health monitoring system is introduced as bridge behavior. A method of identifying structural damage through the evaluation of response data from an instrumented bridge is proposed. Strains during operational traffic events at the Powder Mill Bridge in Barre, Massachusetts, are recorded at many locations on the bridge. Bridge behavior is defined as each sensor location's range of expected peak strain during a traffic event based on all other sensor locations' strains measured at that instance in time. Artificial neural networks (ANNs) are trained with operational bridge response data in a bootstrapping scheme to generate a probabilistic model of bridge behavior. When tested against new data, the ANN-learned model of predicted bridge behavior is proven effective and applicable to varying traffic events with unknown loading conditions. A method for long-term performance assessment using the expected bridge behavior is proposed. Structural damage can impact bridge behavior and thus bridge performance. The effects of structural damage are extracted from simulated HS20 design truck runs on a calibrated finite-element model (FEM) and are applied to operational strain data to assess the damage identification method. When assessed, the damage identification method is effective at detecting the presence of damage, with no Type I or Type II errors when using a Wilcoxon rank-sum test of an appropriate significance level. Damage is effectively localized for most types of simulated damage. © 2018 American Society of Civil Engineers.","Artificial neural networks (ANNs); Bridge behavior; Damage identification; Hypothesis test; Operational strain measurements; Response only; Structural health monitoring","Location; Neural networks; Strain; Structural analysis; Structural health monitoring; Damage Identification; Data-driven approach; Hypothesis tests; Long term performance; Probabilistic modeling; Response only; Structural health monitoring systems; Wilcoxon rank sum test; Damage detection",,,,,,,,,,,,,,,,"Alampalli, S., Effects of testing, analysis, damage, and environment on modal parameters (2000) Mech. Syst. Sig. Process., 14 (1), pp. 63-74. , https://doi.org/10.1006/mssp.1999.1271; Baker, W.E., Bell, T.M., Farrar, C.R., Migliori, A., Duffey, T.A., Darling, T.W., Eklund, A., Cone, K.M., (1994) Dynamic Characterization and Damage Detection in the I-40 Bridge over the Rio Grande., , and. 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Chase, Bellingham, WA: SPIE; Weinstein, J., (2018) Objective Performance Assessment Using Artificial Neural Networks, , M.S. thesis, Tufts Univ; Wilcoxon, F., Individual comparisons by ranking methods (1945) Biom. Bull., 1 (6), pp. 80-83. , https://doi.org/10.2307/3001968; Zhang, G., Harichandran, R., Ramuhalli, P., Automatic delamination detection of concrete bridge decks using impact signals (2012) J. Bridge Eng., 17 (6), pp. 951-954. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000326; Zhao, J., Ivan, J., Dewolf, J., Structural damage detection using artificial neural networks (1998) J. Infrastruct. Syst., 4 (3), pp. 93-101. , https://doi.org/10.1061/(ASCE)1076-0342(1998)4:3(93)","Sanayei, M.; Dept. of Civil and Environmental Engineering, United States; email: masoud.sanayei@tufts.edu",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85052737830
"Roselli I., Malena M., Mongelli M., Cavalagli N., Gioffrè M., De Canio G., de Felice G.","6507903563;23501781800;7005882343;24075415000;7004031279;36100105000;57213360105;","Health assessment and ambient vibration testing of the “Ponte delle Torri” of Spoleto during the 2016–2017 Central Italy seismic sequence",2018,"Journal of Civil Structural Health Monitoring","8","2",,"199","216",,45,"10.1007/s13349-018-0268-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044996552&doi=10.1007%2fs13349-018-0268-5&partnerID=40&md5=10494663eee22845e4ef4a54dad3793d","ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia R. C, Via Anguillarese 301, S. Maria Di Galeria, Rome  00123, Italy; Department of Engineering, Roma Tre University, Via Vito Volterra 62, Rome, 00146, Italy; Department of Civil and Environmental Engineering, University of Perugia, via G. Duranti 93, Perugia, 06125, Italy","Roselli, I., ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia R. C, Via Anguillarese 301, S. Maria Di Galeria, Rome  00123, Italy; Malena, M., Department of Engineering, Roma Tre University, Via Vito Volterra 62, Rome, 00146, Italy; Mongelli, M., ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia R. C, Via Anguillarese 301, S. Maria Di Galeria, Rome  00123, Italy; Cavalagli, N., Department of Civil and Environmental Engineering, University of Perugia, via G. Duranti 93, Perugia, 06125, Italy; Gioffrè, M., Department of Civil and Environmental Engineering, University of Perugia, via G. Duranti 93, Perugia, 06125, Italy; De Canio, G., ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia R. C, Via Anguillarese 301, S. Maria Di Galeria, Rome  00123, Italy; de Felice, G., Department of Engineering, Roma Tre University, Via Vito Volterra 62, Rome, 00146, Italy","The Ponte delle Torri is a large medieval masonry bridge, one of the main architectural heritage of Spoleto, Italy. The location of the bridge is less than 50 km from the main epicenters of the recent Central Italy earthquakes (Mw > 5.0) that occurred between August 2016 and February 2017. In addition, some minor quakes of the sequence (Mw between 3.0 and 4.0) occurred within 10 km from the bridge, causing some damages and fear among the population around Spoleto. In this context, the present paper aims at contributing to understand the effects on the structural health of the bridge by analyzing the ambient vibration data acquired before, during and after the seismic sequence, as changes in the dynamic behavior of the structure might indicate the evolution of the state of damage of the monument. In particular, vibration data were processed by modal analysis techniques for mutual validation of the extracted modal parameters. Environmental and vibration data were simultaneously acquired to take into account the seasonal effects on the dynamic behavior. Through a preliminary finite-element model (FEM) the modal shapes were obtained to choose the positions where to locate the sensors for the vibration spot acquisition session of June 2015. The same positions were acquired in October 2016 and at the end of May 2017. Subsequently, a more detailed FEM was produced based on a 3D reconstruction by structure-from-motion stereo-photogrammetry technique with high-resolution photos from unmanned aerial vehicle of the bridge. The model was validated through comparison with the damage pattern experienced by the bridge and then used for assessing the seismic safety by means of both, nonlinear dynamic and static push-over analyses. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.","Ambient vibration testing; Central Italy earthquakes; Earthquake engineering; FEM; Historic structures; Structural health monitoring","Antennas; Earthquake engineering; Earthquakes; Finite element method; Modal analysis; Photogrammetry; Structural health monitoring; Ambient Vibration Testing; Ambient vibrations; Analysis techniques; Architectural heritage; Central Italy; High-resolution photos; Historic structures; Structure from motion; Vibration analysis",,,,,,,,,,,,,,,,"(2008) Direttiva Del Presidente Del Consiglio Dei Ministri per La Valutazione E La Riduzione Del Rischio Sismico Del Patrimonio Culturale Con Riferimento Alle Norme Tecniche per Le Costruzioni, , Suppl. Ord. alla Gazzetta ufficiale, n. 24, 29 gennaio 2008 [in Italian]. Rome, Italy: Istituto Poligrafico della Zecca di Stato; Balageas, D., Fritzen, C.P., Güemes, A., (2006) Structural health monitoring, , ISTE Ltd., London; De Stefano, A., Matta, E., Clemente, P., Structural health monitoring of historical heritage in Italy: some relevant experiences (2016) J Civil Struct Health Monit, 6 (1), pp. 83-106; Saisi, A., Gentile, C., Guidobaldi, M., Post-earthquake continuous dynamic monitoring of the Gabbia Tower in Mantua, Italy (2015) Constr Build Mater, 81, pp. 101-112; Gentile, C., Guidobaldi, M., Saisi, A., One-year dynamic monitoring of a historic tower: damage detection under changing environment (2016) Meccanica, 51 (11), pp. 2873-2889; Cavalagli, N., Comanducci, G., Ubertini, F., Earthquake-induced damage detection in a monumental masonry bell-tower using long-term dynamic monitoring data (2017) J Earthq Eng; Orlowitz, E., Andersen, P., Brandt, A., Comparison of simultaneous and multi-setup measurement strategies in operational modal analysis (2015) 6Th International Operational Modal Analysis Conference (IOMAC’15), , Gijon, Spain 12–14 May 2015; Fajfar, P., A nonlinear analysis method for performance-based seismic design (2000) Earthq Spectra, 16 (3), pp. 573-592; Gentili, L., Giacché, L., Ragni, B., Toscano, B., (1978) L’Umbria, pp. 432-434. , Manuali per il Territorio. Spoleto, Roma; Sansi, A., (1984) Storia del comune di Spoleto, 9. , Accademia Spoletina, Spoleto; Gioffrè, M., Gusella, V., Cluni, F., Performance evaluation of monumental bridges: testing and monitoring ‘Ponte delle Torri’ in Spoleto (2008) Struct Infrastruct E, 4 (2), pp. 95-106; Coccetta, M., Marchetti, M., Marziani, M., Scatolini, G., Il ponte delle Torri progetto preliminare e lotto fun-zionale per il consolidamento ed il restauro (2013) Comune Di Spoleto; Raineri, C., Fabbrocino, G., Cosenza, E., Automatic operational modal analysis as structural health monitoring tool: theoretical and applications aspects (2007) Key Eng Mater, 347, pp. 479-484; Ruzzo, C., Failla, G., Collu, M., Nava, V., Fiamma, V., Arena, F., Operational modal analysis of a spar-type floating platform using frequency domain decomposition method (2016) Energies, 9 (11), p. 870; de Canio, G., Mongelli, M., Roselli, I., Tatì, A., Addessi, D., Nocera, M., Liberatore, D., Numerical and operational modal analyses of the “Ponte delle Torri”, Spoleto, Italy (2016) 10Th International Conference on Structural Analysis of Historical Constructions, , Leuven, Belgium, 13–15 September 2016; Brincker, R., de Stefano, A., Piombo, B., Ambient data to analyse the dynamic behaviour of bridges: A first comparison between different techniques (1996) 14Th International Modal Analysis Conference (IMAC), , Dearborn, USA, 12–15 February 1996; Araiza Garaygordobil, J.C., Dynamic identification and model updating of historical buildings. State-of-the-art review (2004) 4Th International Seminar on Structural Analysis of Historical Constructions, , Padua, Italy, 10–13 November 2004; (2013) Artemis Modal Pro Software 2013, , http://www.svibs; Brincker, R., Zhang, L., Andersen, P., Modal identification of output-only systems using frequency domain decomposition (2001) Smart Mater Struct, 10, pp. 441-445; Jacobsen, N.J., Andersen, P., Brincker, R., Using enhanced frequency domain decomposition as a robust technique to harmonic excitation in operational modal analysis (2006) Proceedings of ISMA2006: International Conference on Noise & Vibration Engineering, , Leuven, Belgium; Peeters, B., De Roeck, G., Stochastic system identification for operational modal analysis: a review (2001) J Dyn Syst Meas Control, 123, pp. 659-667; Maia, N., Silva, J., (1998) Theoretical and experimental modal analysis, , Research Studies Press, Baldock; Gallipoli, M.R., Mucciarelli, M., Vona, M., Empirical estimate of fundamental frequencies and damping for Italian buildings (2009) Earthq Eng Struct D, 38 (8), pp. 973-988; Ewins, D.J., (2000) Modal testing: theory, practice and application, , Research Studies Press, Philadelphia; Elmenshawi, A., Sorour, M., Mufti, A., Jaeger, L.G., Shrive, N., Damping mechanisms and damping ratios in vibrating unreinforced stone masonry (2010) Eng Struct, 32 (10), pp. 3269-3278; Ubertini, F., Comanducci, G., Cavalagli, N., Pisello, L., Materazzi, L., Cotana, F., Environmental effects on natural frequencies of the San Pietro bell tower in Perugia, Italy, and their removal for structural performance assessment (2017) Mech Syst Signal Process, 82 (1), pp. 307-322; Verhoeven, G., Taking computer vision aloft—archaeological threedimensional reconstructions from aerial photographs with Photoscan, Archaeol (2011) Prospection, 18, pp. 67-73; Mongelli, M., de Canio, G., Roselli, I., Malena, M., Nacuzi, A., de Felice, G., 3D Photogrammetric reconstruction by drone scanning for FE analysis and crack pattern mapping of the “Bridge of the Towers”, Spoleto (2017) In: Mechanics of Masonry Structures Strengthened with Composite Materials (Murico5), , Bologna, Italy, 28–30 June 2017; Ponti, G., Palombi, F., Abate, D., Ambrosino, F., Aprea, G., Bastianelli, T., Beone, F., Vita, A., The role of medium size facilities in the HPC ecosystem: The case of the new CRESCO4 cluster integrated in the ENEAGRID infrastructure (2014) International Conference on High Performance Computing and Simulation (HPCS), , Bologna, Italy, 21–25 July 2014; Pastor, M., Binda, M., Harčarik, T., Modal assurance criterion (2012) Procedia Eng, 48, pp. 543-548; Lourenco, P.B., Rots, J.G., Multisurface interface model for analysis of masonry structures (1997) J Eng Mech, 123 (7), pp. 660-668; Berto, L., Saetta, A., Scotta, R., Vitaliani, R., An orthotropic damage model for masonry structures (2002) Int J Numer Methods Eng, 55 (2), pp. 127-157; de Felice, G., Amorosi, A., Malena, M., Elasto-plastic analysis of block structures through a homogenization method (2010) Int J Numer Anal Methods Geomech, 34 (3), pp. 221-247; Pelà, L., Cervera, M., Roca, P., Continuum damage model for orthotropic materials: application to masonry (2011) Comput Methods Appl Mech Eng, 200 (9), pp. 917-930; Valente, M., Milani, G., Seismic assessment of historical masonry towers by means of simplified approaches and standard FEM (2016) Constr Build Mater, 108, pp. 74-104; Castellazzi, G., D’Altri, A.M., de Miranda, S., Ubertini, F., An innovative numerical modeling strategy for the structural analysis of historical monumental buildings (2017) Eng Struct, 132, pp. 229-248; Page, A.W., The biaxial compressive strength of brick masonry (1981) Proceeding of the Institution of Civil Engineers, 71, pp. 93-906. , part 2; Chiaraluce, L., Di Stefano, R., Tinti, E., Scognamiglio, L., Michele, M., Casarotti, E., Cattaneo, M., Marzorati, S., The 2016 Central Italy Seismic sequence: a first look at the mainshocks, aftershocks, and source models (2017) Seismol Res Lett, 88 (3), pp. 757-771; Peeters, B., Maeck, J., De Roeck, G., Vibration-based damage detection in civil engineering: excitation sources and temperature effects (2001) Smart Mater Struct, 10 (3), pp. 518-527; Ni, Y.Q., Hua, X.G., Fan, K.Q., Ko, J.M., Correlating modal properties with temperature using long-term monitoring data and support vector machine technique (2005) Eng Struct, 27 (12), pp. 1762-1773; Lenaerts, V., Kerschen, G., Golinval, J.C., Identification of a continuous structures with a geometrical non-linearity, part II: proper orthogonal decomposition (2003) J Sound Vib, 262, pp. 907-919; Hair, J., Anderson, R., Tatham, R., Black, W., (1998) Multivariate data analysis, , Prentice Hall, New Jersey","Roselli, I.; ENEA, Via Anguillarese 301, Italy; email: ivan.roselli@enea.it",,,"Springer Verlag",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85044996552
"Locke W., Sybrandt J., Redmond L., Safro I., Atamturktur S.","57209638868;57195601501;57212024018;13605531100;36476988300;","Using drive-by health monitoring to detect bridge damage considering environmental and operational effects",2020,"Journal of Sound and Vibration","468",,"115088","","",,42,"10.1016/j.jsv.2019.115088","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075759755&doi=10.1016%2fj.jsv.2019.115088&partnerID=40&md5=de1c94cd4816c06fc2493ad351277253","Glenn Department of Civil Engineering, Clemson University, United States; School of Computing, Clemson University, United States; Department of Architectural Engineering, Pennsylvania State University, United States","Locke, W., Glenn Department of Civil Engineering, Clemson University, United States; Sybrandt, J., School of Computing, Clemson University, United States; Redmond, L., Glenn Department of Civil Engineering, Clemson University, United States; Safro, I., School of Computing, Clemson University, United States; Atamturktur, S., Department of Architectural Engineering, Pennsylvania State University, United States","Drive-by Health Monitoring utilizes accelerometers mounted on vehicles to gather dynamic response data that can be used to continuously evaluate the health of bridges faster and with less equipment than traditional structural health monitoring practices. Because vehicles and bridges create a coupled system, vehicle acceleration data contains information about bridge frequencies that can be used as health indicators. However, for drive-by health monitoring to be viable, variabilities in dynamic measurements caused by environmental and operational parameters, such as temperature, vehicle speed, traffic, and surface roughness need to be considered. In this paper, a finite element model of a simply supported bridge is developed considering the aforementioned variabilities and various levels of structural damage. Vehicle acceleration data obtained from the model is analyzed in the frequency domain and processed using a neural network architecture. This method is used to determine the relationships between noise inducing variables and changes in vehicle dynamic response spectrum; these relationships are leveraged to predict the overall health of the subject bridge. The results from this study indicate that the proposed approach can serve as a viable health monitoring strategy and should be further tested on physical bridge systems. Reproducibility: our code and data are available at [https://github.com/JSybrandt/HighPerformanceBridgeSim]. © 2019 Elsevier Ltd","Drive-by health monitoring; Finite element models; Highway bridge; Neural net; Structural health monitoring; Transportation infrastructure","Digital storage; Dynamic response; Finite element method; Frequency domain analysis; Highway bridges; Monitoring; Network architecture; Neural networks; Surface roughness; Vehicles; Bridge frequencies; Dynamic measurement; Health monitoring; Operational effects; Operational parameters; Simply supported bridge; Transportation infrastructures; Vehicle acceleration; Structural health monitoring",,,,,"National Science Foundation, NSF: 1633608, 1725573","The authors gratefully acknowledge the support of the National Science Foundation Research Traineeship (NRT) Program under grant # 1633608 .","The authors gratefully acknowledge the support of the National Science Foundation Research Traineeship (NRT) Program under grant #1633608.",,,,,,,,,"ASCE, Report card of America's bridge infrastructure 2017 https://www.infrastructurereportcard.org/cat-item/bridges/; Young, R., Transportation Infrastructure: an Overview of Highway Systems and south carolina's Position and Status, Institute for Public Service and Policy Research, University of South Carolina; Willsher, K., Tondo, L., Henley, J., Bridges across Europe are in a dangerous state, warn experts, the Guardian https://www.theguardian.com/world/2018/aug/16/bridges-across-europe-are-in-a-dangerous-state-warn-experts; FHWA, F.H.A., National bridge inspection standards (nbis) (2009) Fed. 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Signal Process., 14 (1), pp. 63-74; Tompson, J., Schlachter, K., Sprechmann, P., Perlin, K., Accelerating Eulerian Fluid Simulation with Convolutional Networks, arXiv preprint arXiv:1607.03597; Grzeszczuk, R., Terzopoulos, D., Hinton, G., Neuroanimator: fast neural network emulation and control of physics-based models (1998) Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, pp. 9-20. , ACM; Xu, Y., Du, J., Dai, L.-R., Lee, C.-H., A regression approach to speech enhancement based on deep neural networks (2015) IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 23 (1), pp. 7-19; Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks (2012) Advances in Neural Information Processing Systems, pp. 1097-1105; Simonyan, K., Zisserman, A., Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv preprint arXiv:1409.1556; Nasrabadi, N.M., Pattern recognition and machine learning (2007) J. Electron. Imaging, 16 (4); Heywood, R., Roberts, W., Boully, G., Dynamic loading of bridges, transportation research record (2001) Journal of the Transportation Research Board, pp. 58-66. , 1770","Locke, W.; Glenn Department of Civil Engineering, United States; email: wrlocke@g.clemson.edu",,,"Academic Press",,,,,0022460X,,JSVIA,,"English","J Sound Vib",Article,"Final","",Scopus,2-s2.0-85075759755
"Kostic B., Gül M.","56928166100;22940711700;","Vibration-Based Damage Detection of Bridges under Varying Temperature Effects Using Time-Series Analysis and Artificial Neural Networks",2017,"Journal of Bridge Engineering","22","10","04017065","","",,41,"10.1061/(ASCE)BE.1943-5592.0001085","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025163229&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001085&partnerID=40&md5=f01e63f81bc1f1aff1f9c7ff780da47f","Dept. of Civil and Environmental Engineering, Univ. of Alberta, Edmonton, AB  T6G 1H9, Canada","Kostic, B., Dept. of Civil and Environmental Engineering, Univ. of Alberta, Edmonton, AB  T6G 1H9, Canada; Gül, M., Dept. of Civil and Environmental Engineering, Univ. of Alberta, Edmonton, AB  T6G 1H9, Canada","Structural health monitoring (SHM) has become a very important research area for evaluating the performances of bridges. An important issue with continuous SHM and damage detection of bridges is the effects of temperature variations on the measurement data, which can produce bigger effects in the response than the damage itself. In this study, a sensor-clustering-based time-series analysis method integrated with artificial neural networks (ANNs) was employed for damage detection under temperature variations. The damage features obtained solely using the time-series-based damage-detection algorithm are very effective for damage assessment; however, they yield false positives and negatives when temperature variations are present. Therefore, ANNs were used to compensate the temperature effects on the damage features obtained from time-series analysis. This methodology is applied to a footbridge finite-element model in which 2,000 simulations with temperature effects and damage cases were conducted. Results reveal that the proposed method can successfully determine the existence, location, and relative severity of damage using output-only vibration and temperature data even when temperature variations are present. © 2017 American Society of Civil Engineers.","Damage detection under environmental variables; Environmental effects on bridges; Neural networks; Structural health monitoring; Time-series analysis","Computer networks; Finite element method; Harmonic analysis; Neural networks; Structural health monitoring; Temperature; Temperature distribution; Time series analysis; Vibration analysis; Damage assessments; Detection algorithm; Effects of temperature; Environmental variables; Structural health monitoring (SHM); Temperature variation; Varying temperature; Vibration-based damage detection; Damage detection",,,,,,,,,,,,,,,,"Bridge design specifications, customary U.S. Units (2010) AASHTO LRFD, , AASHTO. 5th Ed; Alampalli, S., Influence of in-service environment on modal parameters (1998) Proc. 16th Annual International Modal Analysis Conference, International Society for Optics and Photonics, pp. 111-116. , Bellingham, WA; Baptista, F.G., Budoya, D.E., De Almeida, V.A.D., Ulson, J.A.C., An experimental study on the effect of temperature on piezoelectric sensors for impedance-based structural health monitoring (2014) Sensors, 14 (1), pp. 1208-1227; Barr, P.J., Stanton, J.F., Eberhard, M.O., Effects of temperature variations on precast, prestressed concrete bridge girders (2005) J. 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A, 365 (1851), pp. 539-560; Sohn, H., Park, G., Wait, J.R., Limback, N.P., Farrar, C.R., Wavelet-based active sensing for delamination detection in composite structures (2004) Smart Mater. Struct., 13 (1), pp. 153-160; Sohn, H., Worden, K., Farrar, C.R., Statistical damage classification under changing environmental and operational conditions (2002) J. Intell. Mater. Syst. Struct., 13 (9), pp. 561-574; Torres-Arredondo, M.-A., Sierra-Pérez, J., Tibaduiza, D.-A., McGugan, M., Rodellar, J., Fritzen, C.-P., Signal-based nonlinear modelling for damage assessment under variable temperature conditions by means of acousto-ultrasonics (2015) Struct. Control Health Monit., 22 (8), pp. 1103-1118; Xu, Z.D., Wu, Z., Simulation of the effect of temperature variation on damage detection in a long-span cable-stayed bridge (2007) Struct. Health Monit., 6 (3), pp. 177-189; Yan, A.M., Kerschen, G., De Boe, P., Golinval, J.C., Structural damage diagnosis under changing environmental conditions - Part II: Local PCA for non-linear cases (2005) J. Mech. Syst. Sig. Process., 19 (4), pp. 865-880; Yarnold, M.T., Moon, F.L., Temperature-based structural health monitoring baseline for long-span bridges (2015) Eng. Struct., 86, pp. 157-167; Zhou, H.F., Ni, Y.Q., Ko, J.M., Eliminating temperature effect in vibration-based structural damage detection (2012) J. Eng. Mech., pp. 785-796; Zhou, L., Xia, Y., Brownjohn, J.M.W., Koo, K.Y., Temperature analysis of a long-span suspension bridge based on field monitoring and numerical simulation (2016) J. Bridge Eng., p. 04015027","Gül, M.; Dept. of Civil and Environmental Engineering, Canada; email: mustafa.gul@ualberta.ca",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85025163229
"Zhou Y., Sun L.","57191652362;7403956279;","Insights into temperature effects on structural deformation of a cable-stayed bridge based on structural health monitoring",2019,"Structural Health Monitoring","18","3",,"778","791",,39,"10.1177/1475921718773954","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048079904&doi=10.1177%2f1475921718773954&partnerID=40&md5=9dd023f6b32c2f46dcbfeda5ecec30a6","Department of Civil Engineering, University of Science & Technology Beijing, Beijing, China; State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China","Zhou, Y., Department of Civil Engineering, University of Science & Technology Beijing, Beijing, China; Sun, L., State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China","Structural deformation is an important consideration in the health monitoring of bridges, and its dependence on temperature variations is quite complex. Based on field measurements performed for an operational cable-stayed bridge, the proposed study investigates mechanisms of thermally induced variations in girder length and mid-span deflection through plane geometric and finite element analyses. The objective of this study is to understand the behaviour of such bridges over annual and diurnal cycles. It has been observed that the girder length and mid-span deflection of a cable-stayed bridge exhibit different modes of the temperature–response correlation. Thermally induced changes in girder length are solely governed by the average girder temperature, and its annual variation in amplitude is significantly larger compared to the diurnal variation. However, thermally induced mid-span deflections are simultaneously influenced by the cable temperature and average girder temperature, and these do not vary monotonously with temperature, thereby resulting in nearly equal variation amplitudes over both annual and diurnal cycles. Temperature-induced deformations of a cable-stayed bridge could well be approximated through multiple linear superposition of thermal-expansion effects of individual components. Besides thermal-expansion coefficients of structural materials, the temperature dependency of mid-span deflection of a symmetrical twin-tower cable-stayed bridge is closely related to the ratio of tower height above the deck to central span of the girder as well as span ratio of the side span to central span. The proposed simplified formulae to estimate the sensitivities of temperature effects could be readily extended to other cable-stayed bridges with different geometric arrangements, thereby providing valuable insights into thermally induced deformation of such bridges. © The Author(s) 2018.","Cable-stayed bridge; data interpretation; deformation; mechanism; structural health monitoring; temperature effect","Cables; Deflection (structures); Deformation; Mechanisms; Steel beams and girders; Structural health monitoring; Temperature; Thermal effects; Thermal expansion; Data interpretation; Linear superpositions; Structural deformation; Temperature dependencies; Temperature variation; Thermal expansion coefficients; Thermal expansion effect; Thermally induced deformations; Cable stayed bridges",,,,,"National Natural Science Foundation of China, NSFC: 51608034; China Postdoctoral Science Foundation: 2016M600925; Fundamental Research Funds for the Central Universities: FRF-TP-16-012A1","The authors are thankful for the significant assistance received from the Shanghai Yangtze River Bridge Management Co., Ltd. and the Shanghai Just One Technology Development Co., Ltd. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (grant No. 51608034), the China Postdoctoral Science Foundation (grant No. 2016M600925) and the Fundamental Research Funds for the Central Universities (grant No. FRF-TP-16-012A1).","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (grant No. 51608034), the China Postdoctoral Science Foundation (grant No. 2016M600925) and the Fundamental Research Funds for the Central Universities (grant No. FRF-TP-16-012A1).",,,,,,,,,"Brownjohn, J.M.W., Koo, K.Y., Scullion, A., Operational deformations in long-span bridges (2015) Struct Infrastruct Eng, 11, pp. 556-574; Yarnold, M.T., Moon, F.L., Temperature-based structural health monitoring baseline for long-span bridges (2015) Eng Struct, 86, pp. 157-167; Kromanis, R., Kripakaran, P., Harvey, B., Long-term structural health monitoring of the Cleddau bridge: evaluation of quasi-static temperature effects on bearing movements (2016) Struct Infrastruct Eng, 12, pp. 1342-1355; Sun, Z., Zou, Z., Zhang, Y., Utilization of structural health monitoring in long-span bridges: case studies (2017) Struct Control Health Monit, 24, p. e1979; Zhu, J., Meng, Q., Effective and fine analysis for temperature effect of bridges in natural environments (2017) J Bridge Eng, 22, pp. 1-19; Xia, Y., Chen, B., Zhou, X., Field monitoring and numerical analysis of Tsing Ma suspension bridge temperature behavior (2013) Struct Control Health Monit, 20, pp. 560-575; Desjardins, S.L., Londoño, N.A., Lau, D.T., Real-time data processing, analysis and visualization for structural monitoring of the confederation bridge (2006) Adv Struct Eng, 9, pp. 141-157; Catbas, F.N., Susoy, M., Frangopol, D.M., Structural health monitoring and reliability estimation: long span truss bridge application with environmental monitoring data (2008) Eng Struct, 30, pp. 2347-2359; Westgate, R., Koo, K.Y., Brownjohn, J.M.W., Effect of solar radiation on suspension bridge performance (2015) J Bridge Eng, 20. , Article 040140775; Xu, Y.L., Chen, B., Ng, C.L., Monitoring temperature effect on a long suspension bridge (2010) Struct Control Health Monit, 17, pp. 632-653; Cao, Y., Yim, J., Zhao, Y., Temperature effects on cable stayed bridge using health monitoring system: a case study (2011) Struct Health Monit, 10, pp. 523-537; Guo, T., Liu, J., Zhang, Y., Displacement monitoring and analysis of expansion joints of long-span steel bridges with viscous dampers (2015) J Bridge Eng, 20. , Article 04014099; Lee, J., Chang, S.P., Kim, H., Statistical time series analysis of long-term monitoring results of a cable-stayed bridge, , Proceedings of the third international conference on bridge maintenance, safety and management (IABMAS’06), Porto, 16–19 July 2006, Porto, Taylor & Francis, In; Zhu, Y., Fu, Y., Chen, W., Online deflection monitoring system for Dafosi cable-stayed bridge (2006) J Intell Mat Syst Struct, 17, pp. 701-707; Li, X., (2012) Static behavior analysis of cable-stayed bridge based on long-term monitoring data, , Tongji University, Shanghai, China, Master’s Thesis., (In Chinese; Zhou, Y., Sun, L., Peng, Z., Mechanisms of thermally induced deflection of a long-span cable-stayed bridge (2015) Smart Struct Syst, 15, pp. 505-522; (2003) Eurocode 1: actions on structures, part 1–5: general actions – thermal actions, , Brussels, European Committee for Standardization CEN; (2007) Guidelines for design of highway cable-stayed bridge, , Beijing, Ministry of Transport of the People’s Republic of China, :, (In Chinese; Ni, Y.Q., Hua, X.G., Wong, K.Y., Assessment of bridge expansion joints using long-term displacement and temperature measurement (2007) J Perform Constr Fac, 21, pp. 143-151; (2009) ANSYS 12.0, , Canonsburg, PA, ANSYS Inc","Sun, L.; State Key Laboratory of Disaster Reduction in Civil Engineering, China; email: lmsun@tongji.edu.cn",,,"SAGE Publications Ltd",,,,,14759217,,,,"English","Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85048079904
"Soman R., Kyriakides M., Onoufriou T., Ostachowicz W.","56624222000;35976121000;6603582261;24756515200;","Numerical evaluation of multi-metric data fusion based structural health monitoring of long span bridge structures",2018,"Structure and Infrastructure Engineering","14","6",,"673","684",,37,"10.1080/15732479.2017.1350984","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025166814&doi=10.1080%2f15732479.2017.1350984&partnerID=40&md5=338eae0f827d00add6187235589482a1","Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Gdansk, Poland; Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus; Faculty of Automotive and Construction Machinery, Warsaw University of Technology, Warsaw, Poland","Soman, R., Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Gdansk, Poland, Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus; Kyriakides, M., Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus; Onoufriou, T., Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus; Ostachowicz, W., Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Gdansk, Poland, Faculty of Automotive and Construction Machinery, Warsaw University of Technology, Warsaw, Poland","This work focuses on structural health monitoring of long span bridges for damage detection. A feature extraction level data fusion based damage isolation strategy is presented using multi-metric sensing. The multi-metric sensing uses two types of sensors, namely strain sensors and accelerometers. The methodology combines the advantages offered by each type of sensors, while at the same time overcomes their limitations. The flexibility index method is applied and the flexibility matrices based on the strain and displacement data are combined after performing co-ordinate transformation. A study has been carried out on a simulated finite element model of the Great Belt East Bridge where realistic damage scenarios like damage in the girder, breaking of hanger cables, pier settlement, and loss of cable pretension were introduced on the structure. The study indicates that multi-metric sensing is indeed necessary as it reduces the possibility of false detections and increases the sensitivity and robustness of the methodology. © 2017 Informa UK Limited, trading as Taylor & Francis Group.","data fusion; displacement flexibility index; flexibility index; long span bridge; multi-metric measurements; strain flexbility index; Structural health monitoring","Bridge cables; Cables; Damage detection; Data fusion; Finite element method; Linear transformations; Metadata; Co-ordinate transformation; False detections; Feature extraction levels; Flexibility index; Flexibility matrices; Long-span bridge; Long-span bridge structures; Metric measurement; Structural health monitoring",,,,,"238726, 309395","The authors would like to acknowledge the European Commission for the Marie Curie FP7 ITN grants SmartEN [grant number 238726]; MAREWINT [grant number 309395] as the research work presented here is supported by these programs.",,,,,,,,,,"Annamdas, V.G.M., Bhalla, S., Soh, C.K., Applications of structural health monitoring technology in Asia (2017) Structural Health Monitoring, 16, pp. 324-346; Abaqus, (2013) Abaqus analysis user’s manual v. 6.13. 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Infrastructure Eng.",Article,"Final","",Scopus,2-s2.0-85025166814
"Gatti M.","7102802298;","Structural health monitoring of an operational bridge: A case study",2019,"Engineering Structures","195",,,"200","209",,35,"10.1016/j.engstruct.2019.05.102","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066863453&doi=10.1016%2fj.engstruct.2019.05.102&partnerID=40&md5=93690ab364f95b52cdf694638dd57647","Department of Engineering, Via Saragat 1, Ferrara, Italy","Gatti, M., Department of Engineering, Via Saragat 1, Ferrara, Italy","During testing of the structural reliability of a prestressed reinforced concrete bridge built in the late 1960s, the author compared the structural responses, performances and costs of jointly conducted static and dynamic load tests. In the static load test, the precision spirit leveling technique was used to measure the deflections of the deck induced by four trucks weighing about 36 tonnes each. In the dynamic load test, accelerometers placed on the main beam were used to measure the vibration frequencies following an impulse produced by a 2-tonne truck. The dynamic load test resulted in a refined finite element model of the bridge. The comparison showed that the dynamic load test can supplement the static load test for the structural testing of new bridges or be an alternative to it for the monitoring of operational bridges. © 2019 Elsevier Ltd","Bridge; Dynamic testing; FEM; Finite element model; Load testing; Reinforced concrete; SHM; Structural health monitoring","Automobile testing; Bridges; Dynamic analysis; Dynamic loads; Finite element method; Load testing; Pile driving; Reinforced concrete; Trucks; Dynamic testing; Prestressed reinforced; Static and dynamic load tests; Static load tests; Structural reliability; Structural response; Structural testing; Vibration frequency; Structural health monitoring; bridge; finite element method; health monitoring; loading test; reinforced concrete; testing method; vibration",,,,,,,,,,,,,,,,"(2012), AASHTO (American Association of State and Highway Transportation Officials). LRFD bridge design specifications, 6th ed. 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"Bharadwaj K., Sheidaei A., Afshar A., Baqersad J.","57195733509;36133472800;46660891100;55236538600;","Full-field strain prediction using mode shapes measured with digital image correlation",2019,"Measurement: Journal of the International Measurement Confederation","139",,,"326","333",,35,"10.1016/j.measurement.2019.03.024","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062923783&doi=10.1016%2fj.measurement.2019.03.024&partnerID=40&md5=7eb1925f5367d21b2c7501463520637d","NVH & Experimental Mechanics Laboratory, Kettering University, 1700 University Avenue, Flint, MI  48504, United States; Iowa State University, Ames, IA  50011, United States; Mercer University, Macon, GA  31207, United States","Bharadwaj, K., NVH & Experimental Mechanics Laboratory, Kettering University, 1700 University Avenue, Flint, MI  48504, United States; Sheidaei, A., Iowa State University, Ames, IA  50011, United States; Afshar, A., Mercer University, Macon, GA  31207, United States; Baqersad, J., NVH & Experimental Mechanics Laboratory, Kettering University, 1700 University Avenue, Flint, MI  48504, United States","Health and condition monitoring of composite structures are critical in engineering especially in the wind, civil, aviation, and auto industries. However, considering the geometry and size of the structures, analyzing critical locations can become challenging. Traditional sensors such as strain-gauges are widely used to collect operating data, but these conventional methods cannot present full-field data and only show the measurement data at a few discrete locations. Baqersad and Bharadwaj have recently developed a Strain Expansion-Reduction Approach (SERA) to bridge this gap and to expand a limited set of measurements and obtain full-field strain data. This approach uses the strain mode shapes from Finite Element Analysis (FEA) to develop a transformation matrix that expands the limited strain data measured using strain-gauges and predicts full-field strain over the entire structure. However, for many structures, it is challenging to accurately model the geometry or material properties for finite element analysis. Many of these structures are made of composite materials and material modes for these structures might not be readily available. In this paper, we use the strain mode shapes extracted using Digital Image Correlation (DIC) in the expansion process. These mode shapes represent actual properties of the structures. The strain mode shapes for a sample structure of a product can be extracted in a test facility using this approach (e.g., a wind turbine blade or a suspension A-arm). An in situ limited set of measurement can be performed using strain-gauges or fiber optic sensors on the structure. Then, the limited data can be expanded using the strain mode shapes to extract full-field strain results. To demonstrate the merit of the approach, we applied the proposed technique to expand real-time operating data measured using a few strain-gauges mounted to a composite spoiler. Using a transformation matrix generated using the DIC operating deflection shapes, the expansion technique predicted the full field strain on the spoiler. It was shown that the proposed methodology could effectively expand the strain data at limited locations to accurately predict the strain at locations where no sensors were placed. © 2019","Composite materials; Condensation techniques; Digital image correlation; Modal expansion; Strain mode shapes; Structural health monitoring","Automotive industry; Composite materials; Condition monitoring; Fiber optic sensors; Finite element method; Image analysis; Linear transformations; Location; Matrix algebra; Metadata; Strain gages; Strain measurement; Turbomachine blades; Wind turbines; Condensation techniques; Conventional methods; D. digital image correlation (DIC); Digital image correlations; Modal expansion; Operating deflection shapes; Strain mode shapes; Transformation matrices; Structural health monitoring",,,,,"National Science Foundation, NSF: 1625987; Kettering University","This research presented in this paper is partly supported by the National Science Foundation under Grant Number 1625987 (Acquisition of a 3D Digital Image Correlation System to Enhance Research and Teaching at Kettering University). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsoring organizations.",,,,,,,,,,"Siringoringo, D.M., Fujino, Y., Experimental study of laser Doppler vibrometer and ambient vibration for vibration-based damage detection (2006) Eng. Struct., 28, pp. 1803-1815; Baghalian, A., Tashakori, S., Senyurek, V.Y., McDaniel, D., Fekrmandi, H., Tansel, I.N., Non-contact quantification of longitudinal and circumferential defects in pipes using the surface response to excitation (SuRE) method (2017) J. Progn. Health Manage., 8, pp. 1-8; Tashakori, S., Baghalian, A., Unal, M., Fekrmandi, H., McDaniel, D., Tansel, I.N., Contact and non-contact approaches in load monitoring applications using surface response to excitation method (2016) Measurement, 89, pp. 197-203; Baqersad, J., Poozesh, P., Niezrecki, C., Avitabile, P., Photogrammetry and optical methods in structural dynamics – a review (2018) Mech. Syst. Sig. Process.; Sarrafi, A., Poozesh, P., Mao, Z., A comparison of computer-vision-based structural dynamics characterizations (2017) Model Validation and Uncertainty Quantification, pp. 295-301. , R. Barthorpe R. Platz I. Lopez B. Moaveni C. Papadimitriou Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics 2017 Springer International Publishing Cham; Niezrecki, C., Baqersad, J., Sabato, A., Digital Image Correlation Techniques for NDE and SHM (2018) Handbook of Advanced Non-Destructive Evaluation, pp. 1-46; Javh, J., Slavič, J., Boltežar, M., High frequency modal identification on noisy high-speed camera data (2018) Mech. Syst. Sig. Process., 98, pp. 344-351; Carr, J., Baqersad, J., Niezrecki, C., Avitabile, P., Slattery, M., Dynamic stress–strain on turbine blades using digital image correlation techniques Part 2: Dynamic measurements (2012) Topics in Experimental Dynamics Substructuring and Wind Turbine Dynamics, pp. 221-226. , Springer; Baqersad, J., Carr, J., Lundstrom, T., Niezrecki, C., Avitabile, P., Slattery, M., Dynamic characteristics of a wind turbine blade using 3D digital image correlation (2012) Int. Soc. Opt. Photonics, , pp. 83482I-83482I-83489; Busca, G., Cigada, A., Mazzoleni, P., Tarabini, M., Zappa, E., Static and dynamic monitoring of bridges by means of vision-based measuring system (2013) Topics in Dynamics of Bridges, pp. 83-92. , A. Cunha Springer New York; Busca, G., Cigada, A., Mazzoleni, P., Zappa, E., Vibration monitoring of multiple bridge points by means of a unique vision-based measuring system (2014) Exp. Mech., 54, pp. 255-271; Sarrafi, A., Poozesh, P., Niezrecki, C., Mao, Z., Mode extraction on wind turbine blades via phase-based video motion estimation (2017) Int. Soc. Opt. Photonics, , pp. 101710E-101710E-101712; Poozesh, P., Sarrafi, A., Mao, Z., Niezrecki, C., Modal parameter estimation from optically-measured data using a hybrid output-only system identification method (2017) Measurement, 110, pp. 134-145; Poozesh, P., Baqersad, J., Niezrecki, C., Avitabile, P., Harvey, E., Yarala, R., Large-area photogrammetry based testing of wind turbine blades Mech. Syst. Signal Process., , doi; Patil, K., Srivastava, V., Baqersad, J., A multi-view optical technique to obtain mode shapes of structures (2018) Measurement, 122, pp. 358-367; Yang, Y., Dorn, C., Mancini, T., Talken, Z., Kenyon, G., Farrar, C., Mascareñas, D., Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification (2017) Mech. Syst. Sig. Process., 85, pp. 567-590; Sarrafi, A., Mao, Z., Wind turbine blade damage detection via 3-dimensional phase-based motion estimation (2017) Struct. Health Monit., 2017; Rizo-Patron, S., Sirohi, J., Operational modal analysis of a helicopter rotor blade using digital image correlation (2017) Exp. Mech., 57, pp. 367-375; Lundstrom, T., Baqersad, J., Niezrecki, C., Monitoring the dynamics of a helicopter main rotor with high-speed stereophotogrammetry (2015) Exp. Tech.; Tessler, A., Structural analysis methods for structural health management of future aerospace vehicles (2007) Key Eng. Mater., 347, pp. 57-66; Baqersad, J., Niezrecki, C., Avitabile, P., Full-field dynamic strain prediction on a wind turbine using displacements of optical targets measured by stereophotogrammetry (2015) Mech. Syst. Sig. Process., 62, pp. 284-295; Guyan, R.J., Reduction of stiffness and mass matrices (1965) AIAA J., 3. , 380-380; Kidder, R.L., Reduction of structural frequency equations (1973) AIAA J., 11. , 892-892; Oallahan, J., Avitabile, P., Riemer, R., System equivalent reduction expansion process (SEREP) (1989) Proceedings of the 7th International Modal Analysis Conference, pp. 29-37. , Union College Schenectady, NY; O'Callahan, J.C., A procedure for an improved reduced system (IRS) model (1989) Proceedings of the 7th International Modal Analysis Conference, Las Vegas, pp. 17-21; Noppe, N., Iliopoulos, A., Weijtjens, W., Devriendt, C., Full load estimation of an offshore wind turbine based on SCADA and accelerometer data (2016) J. Phys.: Conf. Ser., IOP Publ.; Maes, K., Iliopoulos, A., Weijtjens, W., Devriendt, C., Lombaert, G., Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms (2016) Mech. Syst. Sig. Process., 76-77, pp. 592-611; Iliopoulos, A., Shirzadeh, R., Weijtjens, W., Guillaume, P., Hemelrijck, D.V., Devriendt, C., A modal decomposition and expansion approach for prediction of dynamic responses on a monopile offshore wind turbine using a limited number of vibration sensors (2016) Mech. Syst. Sig. Process., 68-69, pp. 84-104; Rahneshin, V., Chierichetti, M., An integrated approach for non-periodic dynamic response prediction of complex structures: Numerical and experimental analysis (2016) J. Sound Vib., 378, pp. 38-55; Skafte, A., Kristoffersen, J., Vestermark, J., Tygesen, U.T., Brincker, R., Experimental study of strain prediction on wave induced structures using modal decomposition and quasi static Ritz vectors (2017) Eng. Struct., 136, pp. 261-276; Baqersad, J., Niezrecki, C., Avitabile, P., Extracting full-field dynamic strain on a wind turbine rotor subjected to arbitrary excitations using 3D point tracking and a modal expansion technique (2015) J. Sound Vib., 352, pp. 16-29; Baqersad, J., Poozesh, P., Niezrecki, C., Avitabile, P., A noncontacting approach for full-field strain monitoring of rotating structures (2016) J. Vib. Acoust., 138. , 031008-031008; Chen, Y., Joffre, D., Avitabile, P., Underwater dynamic response at limited points expanded to full-field strain response (2018) J. Vib. Acoust., 140; Baqersad, J., Bharadwaj, K., Poozesh, P., Modal Expansion using Strain Mode Shapes, Shock & Vibration, Aircraft/Aerospace, Energy Harvesting, Acoustics & Optics (2017), pp. 219-226. , Springer; Baqersad, J., Bharadwaj, K., Strain expansion-reduction approach (2018) Mech. Syst. Sig. Process., 101, pp. 156-167; dos Santos, F., Peeters, B., Lau, J., Desmet, W., Góes, L., An overview of experimental strain-based modal analysis methods (2014), Proceedings of the International Conference on Noise and Vibration Engineering (ISMA) Leuven, Belgium","Baqersad, J.; NVH & Experimental Mechanics Laboratory, 1700 University Avenue, United States; email: jbaqersad@kettering.edu",,,"Elsevier B.V.",,,,,02632241,,MSRMD,,"English","Meas J Int Meas Confed",Article,"Final","",Scopus,2-s2.0-85062923783
"Lydon M., Robinson D., Taylor S.E., Amato G., Brien E.J.O., Uddin N.","55973288100;7404644259;55462782700;52663099200;57218648462;7003593965;","Improved axle detection for bridge weigh-in-motion systems using fiber optic sensors",2017,"Journal of Civil Structural Health Monitoring","7","3",,"325","332",,35,"10.1007/s13349-017-0229-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024849542&doi=10.1007%2fs13349-017-0229-4&partnerID=40&md5=216f9e41986fffd3594f1ca04f6f97d3","School of Natural and Built Environment, David Keir Building, Queens University Belfast, Belfast, BT95AG, United Kingdom; School of Civil, Structural and Environmental Engineering, University College Dublin, Dublin 4, Ireland; Department of Civil, Construction and Environmental Engineering, University of Alabama at Birmingham, Birmingham, United States","Lydon, M., School of Natural and Built Environment, David Keir Building, Queens University Belfast, Belfast, BT95AG, United Kingdom; Robinson, D., School of Natural and Built Environment, David Keir Building, Queens University Belfast, Belfast, BT95AG, United Kingdom; Taylor, S.E., School of Natural and Built Environment, David Keir Building, Queens University Belfast, Belfast, BT95AG, United Kingdom; Amato, G., School of Natural and Built Environment, David Keir Building, Queens University Belfast, Belfast, BT95AG, United Kingdom; Brien, E.J.O., School of Civil, Structural and Environmental Engineering, University College Dublin, Dublin 4, Ireland; Uddin, N., Department of Civil, Construction and Environmental Engineering, University of Alabama at Birmingham, Birmingham, United States","Bridge weigh-in-motion (B-WIM) systems provide a non-destructive means of gathering traffic loading information by using an existing bridge as a weighing scale to determine the weights of vehicles passing over. In this research critical locations for sensors for the next-generation B-WIM were determined from a full 3D explicit finite element analysis (FEA) model. Although fiber optic sensors (FOS) have become increasingly popular in SHM systems there are currently no commercially available fiber optic WIM systems available. The FEA in this research facilitated the development of the first ever full fiber optic B-WIM and its potential has been demonstrated with the site installation of this system. The system combined nothing-on-the-road axle detection and alternative methods of measuring strain at the supports. The system was installed on a 20-m span beam and slab RC bridge in Northern Ireland and the results presented in this paper confirm the suitability of FOS in providing the clear defined peaks required for accurate axle detection in B-WIM. © 2017, The Author(s).","Bridge weigh-in-motion; Fiber optic sensors; Finite element analysis; Structural health monitoring","Axles; Fiber optics; Fibers; Finite element method; Scales (weighing instruments); Structural health monitoring; Traffic surveys; Weigh-in-motion (WIM); Critical location; Existing bridge; Explicit finite element analysis; Non destructive; Northern Ireland; Span beams; Traffic loading; Weigh-in-motion systems; Fiber optic sensors",,,,,"USI023; National Science Foundation, NSF; Invest Northern Ireland; Science Foundation Ireland, SFI","The authors acknowledge the financial support of DEL, Invest Northern Ireland, Science Foundation Ireland and the United States National Science Foundation for this research. The assistance of the Technical Staff at Queens University Belfast and the staff at Cestel and ZAG (SiWIM) is sincerely appreciated. The authors would also like to thank the Northern Ireland Roads Service and Transport NI for their cooperation throughout this research. Funding was provided by US-Ireland (Grant No. USI023).",,,,,,,,,,"Report card for America’s infrastructure (2013) Am Soc Civ Eng, 2013, pp. 1-74; Helmi, K., Bakht, B., Mufti, A., Accurate measurements of gross vehicle weight through bridge weigh-in-motion: a case study (2014) J Civ Struct Health Monit, 4, pp. 195-208; Jacob, B., Assessment of the accuracy and classification of weigh-in-motion systems. Part 1: statistical background (2000) Int J Veh Des Heavy Veh Syst, 7, pp. 136-152; Jacob, B., (2002) Weigh-in-motion of axles and vehicles for Europe (WAVE), , General Report, Paris; Rowley, C.W., Obrien, E.J., Gonzalez, A., Žnidarič, A., Experimental testing of a moving force identification bridge weigh-in-motion algorithm (2008) Exp Mech, 49, pp. 743-746; Obrien, E., Rowley, C.W., González, A., Green, M., A regularised solution to the bridge weigh in motion equations (2009) Int J Heavy Veh Syst, 16, pp. 310-327; Corbaly, R., Žnidarič, A., Leahy, C., Kalin, J., Hajializadeh, D., Zupan, E., Algorithms for improved accuracy of static bridge-WIM system. D3.1 (2014) Report of Bridgemon project, , Trzin, Cestal; Gonzalez, I., Karoumi, R., BWIM aided damage detection in bridges using machine learning (2015) J Civ Struct Health Monit, 5, pp. 715-725; Dowling, J., Obrien, E.J., González, A., Adaptation of cross entropy optimisation to a dynamic bridge WIM calibration problem (2012) Eng Struct, 44, pp. 13-22; Law, S.S., Fang, Y., Moving force identification: optimal state estimation approach (2001) J Sound Vib, 239, pp. 233-254; Lydon, M., Taylor, S., Mufti, A., Obrien, E., Recnet developments in bridge weigh in motion (2016) J Civ Struct Health Monit, 6 (1), pp. 69-81; Bao, T., Babanajad, S.K., Taylor, T., Ansari, F., Generalized method and monitoring technique for shear-strain-based bridge weigh-in-motion (2016) J Bridge Eng, 21, pp. 1-13; Kalhori, H., Alamdari, M.M., Zhu, X., Samali, B., Musttapha, S., Non-intrusive schemes for speed and axle identification in bridge-weigh-in-motion systems (2017) Meas Sci Technol; Glisic, D.H.S.B., On-site validation of fiber-optic methods for structural health monitoring: Streicker bridge (2015) J Civ Struct Health Monit, 5, pp. 529-549; (1999) Weigh-in-motion of road vehicles: final report.; Jacob, B., O’Brien, E., Newton, W., Assessment of the accuracy and classification of weigh-in-motion systems. Part 2: European specification (2000) J Veh Des Heavy Veh Syst, 7, pp. 153-168; Schmidt, F., Jacob, B., Experimentation of a bridge WIM system in France and applications to bridge monitoring and overload screening. 6th International Conference Weigh-In-Motion (ICWIM 6) (2012) Dallas, pp. 33-42; Kalin, J., Žnidarič, A., Lavrič, I., Practical implementation of nothing-on-the-road bridge weigh-in-motion system (2006) International symposium on heavy vehicle weights and dimensions; Lydon, M., Taylor, S., Robinson, D., Development of a bridge weigh-in-motion sensor: performancecomparison using fibre optic and electric resistance strain sensor systems (2014) IEEE Sens J, 14, pp. 4284-4296; Lydon, M., Taylor, S., Robinson, D., Doherty, C., Callender, P., Assessment of various sensors for structural health monitoring for bridge weigh-in-motion (B-WIM). 6th International Conference Structure Health Monitoring Intelligent Infrastructure (2013) Hong Kong; Kim, S., Lee, J., Park, M., Jo, B., Vehicle signal analysis using artificial neural networks for a bridge weigh-in-motion system (2009) Sensors, 9, pp. 7943-7956; O’Connor, A., Brien, E.J.O., Traffic load modelling and factors influencing the accuracy of predicted extremes (2005) Can J Civ Eng, 32, pp. 270-278; Žnidarič, A., Lavrič, I., Kalin, J., Kulauzović, B., (2011) SiWIM Bridge Weigh-in-Motion Manual, , CestelTrzin, Slovenia","Lydon, M.; School of Natural and Built Environment, United Kingdom; email: m.lydon@qub.ac.uk",,,"Springer Verlag",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85024849542
"Zhang Z., Sun C.","56456966400;55921171700;","Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating",2021,"Structural Health Monitoring","20","4",,"1675","1688",,34,"10.1177/1475921720927488","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087060740&doi=10.1177%2f1475921720927488&partnerID=40&md5=5d7b940221375cf684a8cf1312841c78","Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA, United States","Zhang, Z., Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA, United States; Sun, C., Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA, United States","Structural health monitoring methods are broadly classified into two categories: data-driven methods via statistical pattern recognition and physics-based methods through finite elementmodel updating. Data-driven structural health monitoring faces the challenge of data insufficiency that renders the learned model limited in identifying damage scenarios that are not contained in the training data. Model-based methods are susceptible to modeling error due to model idealizations and simplifications that make the finite element model updating results deviate from the truth. This study attempts to combine the merits of data-driven and physics-based structural health monitoring methods via physics-guided machine learning, expecting that the damage identification performance can be improved. Physics-guided machine learning uses observed feature data with correct labels as well as the physical model output of unlabeled instances. In this study, physics-guided machine learning is realized with a physics-guided neural network. The original modal-property based features are extended with the damage identification result of finite element model updating. A physics-based loss function is designed to evaluate the discrepancy between the neural network model output and that of finite element model updating. With the guidance from the scientific knowledge contained in finite element model updating, the learned neural network model has the potential to improve the generality and scientific consistency of the damage detection results. The proposed methodology is validated by a numerical case study on a steel pedestrian bridge model and an experimental study on a three-story building model. © The Author(s) 2020.","damage detection; data insufficiency; model updating; modeling error; pattern recognition; physics-guided learning; Structural health monitoring","Damage detection; Footbridges; Machine learning; Monitoring; Neural networks; Pattern recognition; Statistical Physics; Structural analysis; Structural health monitoring; Damage Identification; Finite-element model updating; Model idealizations; Neural network model; Physics-based methods; Scientific knowledge; Statistical pattern recognition; Structural damage identification; Finite element method",,,,,"AWD-001515; Louisiana Transportation Research Center, LTRC: 20-3TIRE","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Industrial Ties Research Subprogram of Louisiana State Board of Regents (Project No. AWD-001515) and the support of Louisiana Transportation Research Center (Project No. 20-3TIRE).",,,,,,,,,,"Farrar, C.R., Worden, K., (2012) Structural health monitoring: a machine learning perspective, , Hoboken, NJ, John Wiley & Sons; Barthorpe, R.J., (2010) On model-and data-based approaches to structural health monitoring, , University of Sheffield, UK, PhD Thesis; Zhang, Z., Sun, C., Multi-site structural damage identification using a multi-label classification scheme of machine learning (2020) Measurement; Zhang, Z., Sun, C., A numerical study on multi-site damage identification: a data-driven method via constrained independent component analysis (2020) Structural Control and Health Monitoring; Ying, Y., Garrett, J.H., Jr., Oppenheim, I.J., Toward data-driven structural health monitoring: application of machine learning and signal processing to damage detection (2012) J Comput Civil Eng, 27 (6), pp. 667-680; Barthorpe, R., Manson, G., Worden, K., On multi-site damage identification using single-site training data (2017) J Sound Vib, 409, pp. 43-64; Worden, K., Manson, G., The application of machine learning to structural health monitoring (2007) Philos Trans R Soc, 365 (1851), pp. 515-537; Sanayei, M., Arya, B., Santini, E.M., Significance of modeling error in structural parameter estimation (2001) Comput Aided Civ Infrastruct Eng, 16 (1), pp. 12-27; Friswell, M.I., Damage identification using inverse methods (2006) Philos T R Soc, 365 (1851), pp. 393-410; Behmanesh, I., Moaveni, B., Lombaert, G., Hierarchical bayesian model updating for structural identification (2015) Mech Syst Signal Process, 64, pp. 360-376; Karpatne, A., Atluri, G., Faghmous, J.H., Theory-guided data science: a new paradigm for scientific discovery from data (2017) IEEE Trans Knowl Data Eng, 29 (10), pp. 2318-2331; Kawale, J., Liess, S., Kumar, A., A graph-based approach to find teleconnections in climate data (2013) Stat Anal Data Min, 6 (3), pp. 158-179; Wang, J.X., Wu, J.L., Xiao, H., Physics-informed machine learning for predictive turbulence modeling: using data to improve RANS modeled Reynolds stresses arXiv preprint arXiv:1606.07987, 2016, pp. 1041-4347; Hautier, G., Fischer, C.C., Jain, A., Finding natures missing ternary oxide compounds using machine learning and density functional theory (2010) Chem Mater, 22 (12), pp. 3762-3767; Li, L., Snyder, J.C., Pelaschier, I.M., Understanding machine-learned density functionals (2016) Int J Quantum Chem, 116 (11), pp. 819-833; Karpatne, A., Watkins, W., Read, J., Physics-guided neural networks (PGNN): an application in lake temperature modeling (2017) arXiv preprint arXiv:1710.11431; Wang, Y., Dong, X., Li, D., (2019) SMU: MATLAB package for structural model updating (version 1.1), , https://github.com/ywang-structures/Structural-Model-Updating; Figueiredo, E., Park, G., Figueiras, J., (2009) Structural health monitoring algorithm comparisons using standard data sets. Report No, , Los Alamos, NM, Los Alamos National Laboratory, LA-14393-NM; Dong, X., Wang, Y., (2018) Formulation and optimization algorithm comparison for the FE model updating of large-scale models, , https://github.com/ywang-structures/Structural-Model-Updating; Kaminski, P., The approximate location of damage through the analysis of natural frequencies with artificial neural networks (1995) Proc IMechE, Part E J Process Mechanical Engineering, 209 (2), pp. 117-123; Wilby, R.L., Wigley, T., Conway, D., Statistical downscaling of general circulation model output: a comparison of methods (1998) Water Resour Res, 34 (11), pp. 2995-3008; Bishop, C.M., (2006) Pattern recognition and machine learning, , New York, Springer Science + Business Media; LeCun, Y., Bengio, Y., Hinton, G., Deep learning (2015) Nature, 521 (7553), pp. 436-444; Ketkar, N., (2017) Springer, pp. 195-208. , Berkeley, CA; Ramachandran, P., Zoph, B., Le, Q.V., Searching for activation functions (2017) arXiv preprint Arxiv:1710.05941; Kingma, D.P., Ba, J., Adam: a method for stochastic optimization (2014) arXiv preprint arXiv:1412.6980; Yao, Y., Rosasco, L., Caponnetto, A., On early stopping in gradient descent learning (2007) Constr Approx, 26 (2), pp. 289-315; Patro, S., Sahu, K.K., Normalization: a preprocessing stage (2015) arXiv preprint arXiv:1503.06462; Bergstra, J., Bengio, Y., Random search for hyper-parameter optimization (2012) J Mach Learn Res, 13, pp. 281-305; Sun, H., Betti, R., A hybrid optimization algorithm with bayesian inference for probabilistic model updating (2015) Comput Aided Civ Infrastruct Eng, 30 (8), pp. 602-619; Brincker, R., Zhang, L., Andersen, P., Modal identification of output-only systems using frequency domain decomposition (2001) Smart Mater Struct, 10 (3), p. 441","Sun, C.; Department of Civil and Environmental Engineering, United States; email: csun@lsu.edu",,,"SAGE Publications Ltd",,,,,14759217,,,,"English","Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85087060740
"Figueiredo E., Moldovan I., Santos A., Campos P., Costa J.C.W.A.","35619844900;26321771600;57196030277;57196536503;35567030700;","Finite Element-Based Machine-Learning Approach to Detect Damage in Bridges under Operational and Environmental Variations",2019,"Journal of Bridge Engineering","24","7","04019061","","",,34,"10.1061/(ASCE)BE.1943-5592.0001432","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065156040&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001432&partnerID=40&md5=bb7a50fbbe7ea0c155c54e6d07f55285","Faculty of Engineering, Univ. Lusófona de Humanidades e Tecnologias, Campo Grande 376, Lisbon, Portugal; CERIS, Instituto Superior Técnico, Univ. de Lisboa, Av Rovisco Pais, Lisbon, 1049-001, Portugal; Faculty of Computing and Electrical Engineering, Univ. Federal Do sul e Sudeste Do Pará, F. 17, Q. 4, L. E., Marabá, Pará, 68505-080, Brazil; Applied Electromagnetism Laboratory, Univ. Federal Do Pará, R. Augusto Corrêa, Guamá 1, Belém, Pará, 66075-110, Brazil; CONSTRUCT, Institute of RandD in Structures and Construction, R. Dr. Roberto Frias s/n, Porto, 4200-465, Portugal","Figueiredo, E., Faculty of Engineering, Univ. Lusófona de Humanidades e Tecnologias, Campo Grande 376, Lisbon, Portugal, CONSTRUCT, Institute of RandD in Structures and Construction, R. Dr. Roberto Frias s/n, Porto, 4200-465, Portugal; Moldovan, I., Faculty of Engineering, Univ. Lusófona de Humanidades e Tecnologias, Campo Grande 376, Lisbon, Portugal, CERIS, Instituto Superior Técnico, Univ. de Lisboa, Av Rovisco Pais, Lisbon, 1049-001, Portugal; Santos, A., Faculty of Computing and Electrical Engineering, Univ. Federal Do sul e Sudeste Do Pará, F. 17, Q. 4, L. E., Marabá, Pará, 68505-080, Brazil; Campos, P., Faculty of Engineering, Univ. Lusófona de Humanidades e Tecnologias, Campo Grande 376, Lisbon, Portugal; Costa, J.C.W.A., Applied Electromagnetism Laboratory, Univ. Federal Do Pará, R. Augusto Corrêa, Guamá 1, Belém, Pará, 66075-110, Brazil","In the last decades, the long-term structural health monitoring of civil structures has been mainly performed using two approaches: model based and data based. The former approach tries to identify damage by relating the monitoring data to the prediction of numerical (e.g., finite-element) models of the structure. The latter approach is data driven, where measured data from a given state condition are compared to the baseline or reference condition. A challenge in both approaches is to make the distinction between the changes of the structural response caused by damage and environmental or operational variability. This issue was tackled here using a hybrid technique that integrates model- and data-based approaches into structural health monitoring. Data recorded in situ under normal conditions were combined with data obtained from finite-element simulations of more extreme environmental and operational scenarios and input into the training process of machine-learning algorithms for damage detection. The addition of simulated data enabled a sharper classification of damage by avoiding false positives induced by wide environmental and operational variability. The procedure was applied to the Z-24 Bridge, for which 1 year of continuous monitoring data were available. © 2019 American Society of Civil Engineers.","Damage detection; Damage identification; Finite-element modeling; Machine learning; Structural health monitoring","Damage detection; Finite element method; Learning algorithms; Learning systems; Machine learning; Monitoring; Continuous monitoring; Damage Identification; Environmental variations; Finite element simulations; Machine learning approaches; Operational scenario; Reference condition; Structural response; Structural health monitoring",,,,,,,,,,,,,,,,"Barthorpe, R.J., (2010) On Model- And Data-based Approaches to Structural Health Monitoring, , Ph. D. thesis, Univ. of Sheffield; Box, G.E.P., Jenkins, G.M., Reinsel, G.C., (2008) Time Series Analysis: Forecasting and Control., , 4th ed. Hoboken, NJ: John Wiley & Sons, Inc; Catbas, F.N., Gokce, H.B., Frangopol, D.M., Predictive analysis by incorporating uncertainty through a family of models calibrated with structural health-monitoring data (2013) J. Eng. Mech., 139 (6), pp. 712-723. , https://doi.org/10.1061/(ASCE)EM.1943-7889.0000342; Charles, R.F., Doebling, S.W., Nix, D.A., Vibration-based structural damage identification (2001) Philos. Trans. R. Soc. London, Ser. A, 359 (1778), pp. 131-149. , https://doi.org/10.1098/rsta.2000.0717; Dempster, A.P., Laird, N.M., Rubin, D.B., Maximum likelihood from incomplete data via the em algorithm (1977) J. R. Stat. Soc. Ser. B Stat. Method., 39 (1), pp. 1-38. , https://doi.org/10.1111/j.2517-6161.1977.tb01600.x; (1999) Long Term Monitoring and Bridge Tests, , EMPA. Rep. No. 168349/20e. Dübendorf, Switzerland: Project SIMCES; Figueiredo, E., Cross, E., Linear approaches to modeling nonlinearities in long-term monitoring of bridges (2013) J. Civ. Struct. Health Monit., 3 (3), pp. 187-194. , https://doi.org/10.1007/s13349-013-0038-3; Figueiredo, E., Moldovan, I., Marques, M.B., (2013) Condition Assessment of Bridges: Past, Present, and Future - A Complementary Approach., , Lisboa, Portugal: University Católica Editora; Figueiredo, E., Park, G., Farrar, C.R., Worden, K., Figueiras, J., Machine learning algorithms for damage detection under operational and environmental variability (2011) Struct. Health Monit., 10 (6), pp. 559-572. , https://doi.org/10.1177/1475921710388971; Grätsch, T., Bathe, K., A posteriori error estimation techniques in practical finite element analysis (2005) Comput. Struct., 83 (45), pp. 235-265. , https://doi.org/10.1016/j.compstruc.2004.08.011; Gresil, M., Lin, B., Shen, Y., Giurgiutiu, V., (2011) Predictive Modelling of Space Structures for SHM with Multiple PWAS Transducers, , In Proc. ASME 2011 Conf. on Smart Materials, Adaptive Structures and Intelligent Systems. Scottsdale, AZ: ASME; Liu, Y., Zhang, S., Probabilistic baseline of finite element model of bridges under environmental temperature changes (2017) Comput.-Aided Civ. Infrastruct. Eng., 32 (7), pp. 581-598. , https://doi.org/10.1111/mice.12268; Malekzadeh, M., Atia, G., Catbas, F.N., Performance-based structural health monitoring through an innovative hybrid data interpretation framework (2015) J. Civ. Struct. Health Monit., 5 (3), pp. 287-305. , https://doi.org/10.1007/s13349-015-0118-7; Masciotta, M.-G., Ramos, L.F., Lourenço, P.B., Vasta, M., De Roeck, G., A spectrum-driven damage identification technique: Application and validation through the numerical simulation of the Z24 Bridge (2016) Mech. Syst. 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Eng., 134 (4), pp. 553-561. , https://doi.org/10.1061/(ASCE)0733-9445(2008)134:4(553); Sohn, H., Effects of environmental and operational variability on structural health monitoring (2007) Philos. Trans. R. Soc. A, 365 (1851), pp. 539-560. , https://doi.org/10.1098/rsta.2006.1935; Watson, D.K., Rajapakse, R.K.N.D., Seasonal variation in material properties of a flexible pavement (2000) Can. J. Civ. Eng., 27 (1), pp. 44-54. , https://doi.org/10.1139/l99-049; Wursten, E.R.G., Roeck, G.D., Output-only structural health monitoring in changing environmental conditions by means of nonlinear system identification (2014) Struct. Health Monit., 13 (1), pp. 82-93. , https://doi.org/10.1177/1475921713502836; Zienkiewicz, O.C., Taylor, R.L., Zhu, J., (2013) The Finite Element Method: Its Basis and Fundamentals., , Amsterdam, Netherlands: Elsevier","Figueiredo, E.; Faculty of Engineering, Campo Grande 376, Portugal; email: eloi.figueiredo@ulusofona.pt",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85065156040
"Cancelli A., Laflamme S., Alipour A., Sritharan S., Ubertini F.","57191258577;34168028300;56414498100;8638811200;55891659200;","Vibration-based damage localization and quantification in a pretensioned concrete girder using stochastic subspace identification and particle swarm model updating",2020,"Structural Health Monitoring","19","2",,"587","605",,33,"10.1177/1475921718820015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062454538&doi=10.1177%2f1475921718820015&partnerID=40&md5=44a2a01c602c824c80d102b4e3173ac9","Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA, United States; Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, United States; Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy","Cancelli, A., Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA, United States; Laflamme, S., Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA, United States, Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, United States; Alipour, A., Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA, United States; Sritharan, S., Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA, United States; Ubertini, F., Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy","A popular method to conduct structural health monitoring is the spatio-temporal study of vibration signatures, where vibration properties are extracted from collected vibration responses. In this article, a novel methodology for extracting and analyzing distributed acceleration data for condition assessment of bridge girders is proposed. Three different techniques are fused, enabling robust damage detection, localization, and quantification. First, stochastic subspace identification is used as an output-only method to extract modal properties of the monitored structure. Second, a reduced-order stiffness matrix is reconstructed from the stochastic subspace identification data using the system equivalent reduction expansion process. Third, a particle swarm optimization algorithm is used to update a finite element model of the bridge girder to match the extracted reduced-order stiffness matrix and modal properties. The proposed approach is first verified through numerically simulated data of the girder and then validated using experimental data obtained from a full-scale pretensioned concrete beam that experienced two distinct states of damage. Results show that the method is capable of localizing and quantifying damages along the girder with good accuracy, and that results can be used to create a high-fidelity finite element model of the girder that could be leveraged for condition prognosis and forecasting. © The Author(s) 2019.","output only; particle swarm; Vibration-based","Damage detection; Data reduction; Finite element method; Highway bridges; Particle swarm optimization (PSO); Plate girder bridges; Stiffness; Stiffness matrix; Stochastic models; Stochastic systems; Structural health monitoring; Output only; Particle swarm; Pre-tensioned concrete; Pretensioned concrete beams; Stochastic subspace identification; Three different techniques; Vibration-based; Vibration-based damage; Concrete beams and girders",,,,,"National Science Foundation, NSF: 1537626; California Department of Transportation, CT: 65A0586","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is partly supported by the California Department of Transportation (grant no.: 65A0586), and the National Science Foundation (grant no.: 1537626). 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September 2018, Iowa State University, Ames, Iowa","Cancelli, A.; Department of Civil, United States; email: acancell@iastate.edu",,,"SAGE Publications Ltd",,,,,14759217,,,,"English","Struct. Health Monit.",Review,"Final","All Open Access, Bronze",Scopus,2-s2.0-85062454538
"Nguyen D.H., Bui T.T., De Roeck G., Abdel Wahab M.","57210340778;57218703821;7007019763;7102582536;","Damage detection in Ca-Non Bridge using transmissibility and artificial neural networks",2019,"Structural Engineering and Mechanics","71","2",,"175","183",,33,"10.12989/sem.2019.71.2.175","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070397925&doi=10.12989%2fsem.2019.71.2.175&partnerID=40&md5=147929db4f012b41cd6fb4f4fa65ffce","Department of Electrical Energy, Metals, Mechanical Constructions and Systems, Faculty of Engineering and Architecture, Ghent University, Belgium; National University of Civil Engineering, Hanoi, Viet Nam; University of Transport and Communications, Hanoi, Viet Nam; KU Leuven, Department of Civil Engineering, Structural Mechanics, Leuven, B-3001, Belgium; Division of Computational Mechanics, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam","Nguyen, D.H., Department of Electrical Energy, Metals, Mechanical Constructions and Systems, Faculty of Engineering and Architecture, Ghent University, Belgium, National University of Civil Engineering, Hanoi, Viet Nam; Bui, T.T., University of Transport and Communications, Hanoi, Viet Nam; De Roeck, G., KU Leuven, Department of Civil Engineering, Structural Mechanics, Leuven, B-3001, Belgium; Abdel Wahab, M., Division of Computational Mechanics, Ton Duc Thang University, Ho Chi Minh City, Viet Nam, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam","This paper deals with damage detection in a girder bridge using transmissibility functions as input data to Artificial Neural Networks (ANNs). The original contribution in this work is that these two novel methods are combined to detect damage in a bridge. The damage was simulated in a real bridge in Vietnam, i.e. Ca-Non Bridge. Finite Element Method (FEM) of this bridge was used to show the reliability of the proposed technique. The vibration responses at some points of the bridge under a moving truck are simulated and used to calculate the transmissibility functions. These functions are then used as input data to train the ANNs, in which the target is the location and the severity of the damage in the bridge. After training successfully, the network can be used to assess the damage. Although simulated responses data are used in this paper, the practical application of the technique to real bridge data is potentially high. Copyright © 2019 Techno-Press, Ltd.","Artificial Neural Networks (ANNs); Bridge monitoring; Finite Element Method (FEM); Structural Health Monitoring (SHM); Transmissibility","Damage detection; Finite element method; Input output programs; Structural health monitoring; Bridge monitoring; Girder bridges; Novel methods; Simulated response; Structural health monitoring (SHM); Transmissibility; Transmissibility functions; Vibration response; Neural networks",,,,,"VN2018TEA479A103; Vlaamse regering","The authors acknowledge the financial support of VLIR-UOS TEAM Project, VN2018TEA479A103, ‘Damage assessment tools for Structural Health Monitoring of Vietnamese infrastructures’, funded by the Flemish Government",,,,,,,,,,"Beale, M.H., Hagan, M.T., Demuth, H.B., (1992) Neural Network Toolbox™ User's Guide, , The Mathworks Inc., MA, USA; Cao, H., Zhou, Y.L., Chen, Z., Abdel Wahab, M., Form-finding analysis of suspension bridges using an explicit iterative approach (2017) Struct. Eng. 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Mech., 68 (6), pp. 735-745. , http://dx.doi.org/10.12989/sem.2018.68.6.735; Zang, C., Imregun, M., Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection (2001) J. Sound Vib., 242 (5), pp. 813-827. , https://doi.org/10.1006/jsvi.2000.3390; Zapico, J.L., GonzÁLez, M.P., Worden, K., Damage assessment using neural networks (2003) Mech. Syst. Signal Process., 17, pp. 119-125. , https://doi.org/10.1006/mssp.2002.1547; Zheng, T., Liu, J., Luo, W., Lu, Z., Structural damage identification using cloud model based fruit fly optimization algorithm (2018) Struct. Eng. Mech., 67, pp. 245-254. , http://dx.doi.org/10.12989/sem.2018.67.3.245; Zhou, Y.-L., Abdel Wahab, M., Cosine based and extended transmissibility damage indicators for structural damage detection (2017) Eng. Struct., 141, pp. 175-183. , https://doi.org/10.1016/j.engstruct.2017.03.030; Zhou, Y.L., Cao, H., Liu, Q., Wahab, M.A., Output-based structural damage detection by using correlation analysis together with transmissibility (2017) Materials, 10 (8), p. 866. , https://doi.org/10.3390/ma10080866; Zhou, Y.L., Maia, N.M.M., Sampaio, R., Wahab, M.A., Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure (2016) Struct. Health Monitor., 16 (6), pp. 711-731. , https://doi.org/10.1177/1475921716680849; Zhou, Y.L., Wahab, M.A., Damage detection using vibration data and dynamic transmissibility ensemble with auto-associative neural network (2017) Mechanika, 23 (5), pp. 688-695. , http://dx.doi.org/10.5755/j01.mech.23.5.15339","Abdel Wahab, M.; Division of Computational Mechanics, Viet Nam; email: magd.abdelwahab@tdt.edu.vn",,,"Techno-Press",,,,,12254568,,SEGME,,"English","Struct Eng Mech",Article,"Final","",Scopus,2-s2.0-85070397925
"Zhang W., Sun L.M., Sun S.W.","56646249600;7403956279;39262671400;","Bridge-Deflection Estimation through Inclinometer Data Considering Structural Damages",2017,"Journal of Bridge Engineering","22","2","04016117","","",,33,"10.1061/(ASCE)BE.1943-5592.0000979","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009811749&doi=10.1061%2f%28ASCE%29BE.1943-5592.0000979&partnerID=40&md5=4664e1bf0aeed08cf2cdbed470ad39b0","Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China","Zhang, W., Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China; Sun, L.M., Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China; Sun, S.W., Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China","A new framework for bridge-deflection estimation through inclinometer data is presented in this paper. Whether the structure is damaged or not, deflection could be precisely estimated via the finite-element model (FEM) combined with the partial least-square regression (PLSR) method. In addition, damage localization is achieved at the same time. First, the deflection of a damaged structure is equivalent to that of an undamaged structure but subjected to special virtual loads. Second, the basis functions used in regression are calculated by the FEM of the undamaged structure rather than pure mathematical derivations. Third, the PLSR model is built on the basis of these basis functions and measured inclination data. By solving the PLSR equation, all the nodal inclinations and corresponding nodal loads of the structure could be obtained despite a limited number of measured nodes. Finally, the rotation-displacement transfer matrix is derived to reconstruct deflection from estimated inclinations, and the changes in nodal loads are served as damage indicators. To verify its effectiveness, numerical simulations considering various damage scenarios and different noise levels are performed. The results indicate that the proposed method is quite accurate and reliable in deflection estimation, and it could provide rough damage localization as well, which reveals a great potential in the field of structural health monitoring. © 2016 American Society of Civil Engineers.","Bridge deflection; Damage localization; Equivalent damage loads; Finite-element model (FEM); Inclinometer; Partial least-square regression (PLSR)","Damage detection; Deflection (structures); Estimation; Functions; Least squares approximations; Regression analysis; Structural health monitoring; Transfer matrix method; Bridge deflection; Damage localization; Equivalent damage; Inclinometer; Partial least square regression; Finite element method",,,,,,,,,,,,,,,,"ANSYS [Computer software]. ANSYS, Canonsburg, PA; Barth, K., Steel bridge design handbook design example 2a: Two-span continuous straight composite steel I-girder bridge (2012) Federal Highway Administration Rep. FHWA-IF-12-052, , U.S. DOT, Washington, DC; Bartoli, G., Facchini, L., Pieraccini, M., Fratini, M., Atzeni, C., Experimental utilization of interferometric radar techniques for structural monitoring (2008) Struct. Control Health Monit., 15 (3), pp. 283-298; Burdet, O., Automatic deflection and temperature monitoring of a balanced cantilever concrete bridge (1998) Proc. 5th Int. Conf. of Short and Medium Span Bridges, pp. 13-16; Burdet, O., Zanella, J.-L., Automatic monitoring of bridges using electronic inclinometers (2000) Proc. IABSE Congress, International Association for Bridge and Structural Engineering, , Zurich, Switzerland; Fanning, P., Sobczak, L., Boothby, T., Salomoni, V., Load testing and model simulations for a stone arch bridge (2005) Bridge Struct., 1 (4), pp. 367-378; Geladi, P., Kowalski, B.R., Partial least-squares regression: A tutorial (1986) Anal. Chim. Acta, 185, pp. 1-17; Guoping, L., (2007) Research about Low Frequency Dynamic Characteristics of Deflection Testing System for LianTongGuan Type Bridge, , M.S. dissertation, Chongqing Univ. Chongqing, China; Hou, X., Yang, X., Huang, Q., Using inclinometers to measure bridge deflection (2005) J. Bridge Eng., pp. 564-569; Jáuregui, D., White, K., Woodward, C., Leitch, K., Noncontact photogrammetric measurement of vertical bridge deflection (2003) J. Bridge Eng., pp. 212-222; Ko, J., Ni, Y., Technology developments in structural health monitoring of large-scale bridges (2005) Eng. Struct., 27 (12), pp. 1715-1725; Leary, P.O., Harker, M., A framework for the evaluation of inclinometer data in the measurement of structures (2012) IEEE Trans. Instrum. Meas., 61 (5), pp. 1237-1251; McCarthy, D.M., Chandler, J.H., Palmeri, A., (2014) 3-D Case Studies of Monitoring Dynamic Structural Tests Using Long Exposure Imagery, pp. 407-411. , ISPRS Technical Commission V Symp. Riva del Garda, Italy, Copernicus Publications, Gottingen, Germany; Nassif, H.H., Gindy, M., Davis, J., Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration (2005) NDT and e Int., 38 (3), pp. 213-218; Ni, Y., Xia, Y., Liao, W., Ko, J., Technology innovation in developing the structural health monitoring system for Guangzhou New TV tower (2009) Struct. Control Health Monit, 16 (1), pp. 73-98; Rosipal, R.N., Krämer, N., (2006) Overview and Recent Advances in Partial Least Squares. 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Conf., pp. 2-5. , Orlando, FL; Wold, H., Estimation of principal components and related models by iterative least squares (1966) Multivariate Analysis, pp. 391-420. , P. R. Krishnaiah, ed. Academic Press, New York; Wold, H., Path models with latent variables: The NIPALS approach (1975) Quantitative Sociology: Intentional Perspective on Mathematical and Statistical Modeling, pp. 307-357. , H. M. Blalock, A. Aganbegian, F. M. Borodkin, R. Boudon, and V. Capecchi, eds. Academic Press, New York; Wong, K.Y., Design of a structural health monitoring system for long-span bridges (2007) Struct. Infrastruct. Eng., 3 (2), pp. 169-185; Yu, Y., Liu, H., Li, D., Mao, X., Ou, J., Bridge deflection measurement using wireless mems inclination sensor systems (2013) Int. J. Smart Sens. Intell. Syst., 6 (1)","Sun, L.M.; Dept. of Bridge Engineering, China; email: lmsun@tongji.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85009811749
"Mohseni H., Ng C.-T.","57199057042;25823104100;","Rayleigh wave propagation and scattering characteristics at debondings in fibre-reinforced polymer-retrofitted concrete structures",2019,"Structural Health Monitoring","18","1",,"303","317",,32,"10.1177/1475921718754371","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042203271&doi=10.1177%2f1475921718754371&partnerID=40&md5=dc22a193f53e9ed9bb4d37a5d96cf199","School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, SA, Australia","Mohseni, H., School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, SA, Australia; Ng, C.-T., School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, SA, Australia","Structural health monitoring is of paramount importance to ensure safety and serviceability of structures. Among different damage detection techniques, guided wave–based approach has been the subject of intensive research activities. This article investigates the capability of Rayleigh wave for debonding detection in fibre-reinforced polymer-retrofitted concrete structures through studying wave scattering phenomenon at debonding between fibre-reinforced polymer and concrete. A three-dimensional finite element model is presented to simulate Rayleigh wave propagation and scattering at the debonding. Numerical simulations of Rayleigh wave propagation are validated with analytical solutions. Absorbing layers by increasing damping is employed in the fibre-reinforced polymer-retrofitted concrete numerical model to maximise computational efficiency in the scattering study. Experimental measurements are also carried out using a three-dimensional laser Doppler vibrometer to validate the three-dimensional finite element model. Very good agreement is observed between the numerical and experimental results. The experimentally and analytically validated finite element model is then used in numerical case studies to investigate the wave scattering characteristic at the debonding. The study investigates the directivity patterns of scattered Rayleigh waves, in both backward and forward directions, with respect to different debonding size-to-wavelength ratios. This study also investigates the suitability of using bonded mass to simulate debonding in the fibre-reinforced polymer-retrofitted concrete structures. By enhancing physical understanding of Rayleigh wave scattering at the debonding between fibre-reinforced polymer/concrete interfaces, this study can lead to further advance of Rayleigh wave–based damage detection techniques. © The Author(s) 2018.","debonding; experiment; fibre-reinforced polymer-retrofitted concrete; finite element; guided wave; Rayleigh wave; scattering; three-dimensional scanning laser vibrometer","Bridge decks; Chemical detection; Composite bridges; Computational efficiency; Concrete buildings; Concrete construction; Concretes; Damage detection; Experiments; Fiber reinforced plastics; Fibers; Finite element method; Forward scattering; Guided electromagnetic wave propagation; Numerical models; Polymers; Rayleigh waves; Reinforced plastics; Reinforcement; Scattering; Structural health monitoring; Vibration measurement; Damage detection technique; Directivity pattern; Fibre reinforced polymers; Laser Doppler vibrometers; Rayleigh wave scattering; Scattering char-acteristics; Three dimensional finite element model; Three-dimensional scanning; Debonding",,,,,"Australian Research Council, ARC: DE130100261","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was support by the Australian Research Council (ARC) under Grant Number DE130100261. The support is greatly appreciated.",,,,,,,,,,"Xu, Y.L., Xia, Y., (2012) Structural health monitoring of long-span suspension bridges, , London, Spon Press; Ng, C.T., Chan, T.H.T., Special issue on structural health monitoring of civil structures (2014) Struct Health Monit, 13, pp. 345-346; Hellier, C., (2013) Handbook of nondestructive evaluation, , 2nd ed., New York, McGraw-Hill; Adams, D.E., (2007) Health monitoring of structural materials and components, , Chichester, John Wiley & Sons; Ng, C.T., Bayesian model updating approach for experimental identification of damage in beams using guided waves (2014) Struct Health Monit, 13, pp. 359-373; Rose, J.L., (2014) Ultrasonic guided waves in solid media, , New York, Cambridge University Press; He, S., Ng, C.T., A probabilistic approach for quantitative identification of multiple delaminations in laminated composite beams using guided waves (2016) Eng Struct, 127, pp. 602-614; Haynes, C., Todd, M., Nadabe, T., Monitoring of bearing failure in composite bolted connections using ultrasonic guided waves: a parametric study (2014) Struct Health Monit, 13, pp. 94-105; 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Health Monit.",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85042203271
"Gianneo A., Carboni M., Giglio M.","55850882800;8243850800;7004876674;","Feasibility study of a multi-parameter probability of detection formulation for a Lamb waves–based structural health monitoring approach to light alloy aeronautical plates",2017,"Structural Health Monitoring","16","2",,"225","249",,32,"10.1177/1475921716670841","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014725847&doi=10.1177%2f1475921716670841&partnerID=40&md5=266163058cbace5a8e10b650becdf8ac","Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy","Gianneo, A., Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy; Carboni, M., Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy; Giglio, M., Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy","In view of an extensive literature about guided waves–based structural health monitoring of plate-like structures made of metallic and composite materials, a lack of information is pointed out regarding an effective and universally accepted approach for characterizing capability and reliability in detecting, localizing and sizing in-service damages. On the other hand, in the frame of traditional non-destructive testing systems, capability is typically expressed by means of suitable ‘probability of detection’ curves based on Berens’ model, where a linear relationship is established between probability of detection and flaw size. Although the uncertain factors are usually different between a non-destructive inspection technique and a structural health monitoring approach, it seems that a similar mathematical framework could be assumed. From this point of view, this research investigates the feasibility of application of the very recent ‘multi-parameter’ probability of detection approach, developed within the traditional non-destructive testing field, to guided waves–based structural health monitoring. In particular, numerical simulations as well as experimental responses from flawed aluminium alloy plates were combined to bring about a ‘master’ probability of detection curve. Once established, this curve can be used to study the intrinsic capability of the system in terms of probability of detection curves, overcoming the intrinsic limitation of a single predictor (like the crack size) and a statistical model typically based upon a linear behaviour between the predictor and the response. © 2016, © The Author(s) 2016.","aluminium alloy; finite element method; Lamb waves; probability of detection; structural health monitoring","Aluminum; Aluminum alloys; Bridge decks; Damage detection; Finite element method; Guided electromagnetic wave propagation; Nondestructive examination; Plates (structural components); Probability; Surface waves; Ultrasonic waves; Linear relationships; Mathematical frameworks; Metallic and composite materials; Non destructive inspection; Non destructive testing; Plate-like structure; Probability of detection; Statistical modeling; Structural health monitoring",,,,,,,,,,,,,,,,"Boller, C., Ways and options for aircraft structural health management (2001) Smart Mater Struct, 10, pp. 432-440; Staszewski, W., Boller, C., Tomlinson, G.R., (2004) Health monitoring of aerospace structures: smart sensor technologies and signal processing, , https://books.google.it/books/about/Health_Monitoring_of_Aerospace_Structure.html?id=WHjoo6VdBHMC&pgis=1, accessed 9 January 2016; Su, Z., Ye, L., (2009) Identification of damage using Lamb waves: from fundamentals to applications, , https://books.google.com/books?id=jvzkoGBwBl0C&pgis=1, Springer Science & Business Media, accessed 9 January 2016; Rolek, P., Bruni, S., Carboni, M., Condition monitoring of railway axles based on low frequency vibrations (2016) Int J Fatigue, 86, pp. 88-97; Roth, W., Giurgiutiu, V., Adhesive disbond detection using piezoelectric wafer active sensors, p. 94370S. , Shull, (ed), Proceedings of the structural health monitoring and inspection of advanced materials, aerospace, and civil infrastructure, Saiego, CA, Bellingham, WA, International Society for Optics and Photonics,. In:, (ed, p; Cawley, P., The sensitivity of the mechanical impedance method of nondestructive testing (1987) NDT&E Int, 20, pp. 209-215; Gutkin, R., Green, C.J., Vangrattanachai, S., On acoustic emission for failure investigation in CFRP: pattern recognition and peak frequency analyses (2011) Mech Syst Signal Pr, 25, pp. 1393-1407; Bernasconi, A., Carboni, M., Comolli, L., Fatigue crack growth monitoring in composite bonded lap joints by a distributed fiber optic sensing system and comparison with ultrasonic testing (2016) J Adhes, 92, pp. 739-757; Sbarufatti, C., Manes, A., Giglio, M., Application of sensor technologies for local and distributed structural health monitoring (2014) Struct Control Hlth, 21, pp. 1057-1083; Bernasconi, A., Comolli, L., An investigation of the crack propagation in a carbon fiber bonded joint using backface strain measurements with FBG sensors, pp. 1-4. , Liao, Jin, Sampson, (eds), Proceedings of the 22nd international conference on optical fiber sensor (OFS2012), Beijing, China, Bellingham, WA, International Society for Optics and Photonics,. In:, (ed; Lamb, H., On waves in an elastic plate (1917) P Roy Soc A: Math Phy, 93, pp. 114-128; Su, Z., Ye, L., Lamb wave-based quantitative identification of delamination in CF/EP composite structures using artificial neural algorithm (2004) Compos Struct, 66, pp. 627-637; Seth, S., Kessler, S.M.S.C.S., Damage detection in composite materials using Lamb wave methods, , http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.493.708, accessed 9 January 2016; Biemans, C., Staszewski, W.J., Boller, C., Crack detection in metallic structures using piezoceramic sensors (1999) Key Eng Mat, 167-168, pp. 112-121; Valle, C., Littles, J.W., Flaw localization using the reassigned spectrogram on laser-generated and detected Lamb modes (2002) Ultrasonics, 39, pp. 535-542; Silva, M.Z., Gouyon, R., Lepoutre, F., Hidden corrosion detection in aircraft aluminum structures using laser ultrasonics and wavelet transform signal analysis (2003) Ultrasonics, 41, pp. 301-305; Su, Z., Ye, L., Lu, Y., Guided Lamb waves for identification of damage in composite structures: a review (2006) J Sound Vib, 295, pp. 753-780; Rose, J.L., (2014) Ultrasonic guided waves in solid media, , https://books.google.com/books?id=UWfwAwAAQBAJ&pgis=1, Cambridge University Press, accessed 9 January 2016; Giurgiutiu, V., Tuned Lamb wave excitation and detection with piezoelectric wafer active sensors for structural health monitoring (2005) J Intel Mat Syst Str, 16, pp. 291-305; Kim, S.B., Sohn, H., Instantaneous reference-free crack detection based on polarization characteristics of piezoelectric materials (2007) Smart Mater Struct, 16, pp. 2375-2387; Calomfirescu, M., (2008) Lamb waves for structural health monitoring in viscoelastic composite materials, , https://books.google.it/books?id=_p6muhkbVB0C, Logos Verlag Berlin GmbH; Lanza di Scalea, F., Rizzo, P., Marzani, A., Propagation of ultrasonic guided waves in lap-shear adhesive joints: case of incident a0 Lamb wave (2004) J Acoust Soc Am, 115, p. 146; Clarke, T., Simonetti, F., Cawley, P., Guided wave health monitoring of complex structures by sparse array systems: influence of temperature changes on performance (2010) J Sound Vib, 329, pp. 2306-2322; Croxford, A.J., Moll, J., Wilcox, P.D., Efficient temperature compensation strategies for guided wave structural health monitoring (2010) Ultrasonics, 50, pp. 517-528; Roy, S., Lonkar, K., Janapati, V., A novel physics-based temperature compensation model for structural health monitoring using ultrasonic guided waves (2014) Struct Health Monit, 13, pp. 321-342; Willberg, C., Koch, S., Mook, G., Continuous mode conversion of Lamb waves in CFRP plates (2012) Smart Mater Struct, 21, p. 075022; Berens, A.P., NDE reliability data analysis (1989) ASM handbook volume 17: nondestructive evaluation and quality control, pp. 689-701. , Materials Park, OH, ASM International,. 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In:, (eds); Stepinski, T., Uhl, T., Staszewski, W., (2013) Advanced structural damage detection, , Chichester, John Wiley & Sons, Ltd; Chang, F.K., (2014) The need of SHM Quantification for Implementation, , http://memsic.ccsd.cnrs.fr/EWSHM2014/hal-01010064, Proceedings of the 7th European workshop on structural health monitoring (EWSHM), accessed 20 May 2016,. 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In:; Peng, H., Ye, L., Meng, G., Concise analysis of wave propagation using the spectral element method and identification of delamination in CF/EP composite beams (2010) Smart Mater Struct, 19, p. 11; Krautkrämer, J., Krautkrämer, H., (1990) Ultrasonic testing of materials, , Berlin, Heidelberg, Springer-Verlag; Annis, C., Aldrin, J.C., Sabbagh, H.A., NDT capability (2015) Mater Eval, 73, pp. 44-54; Memmolo, V., Maio, L., Boffa, N.D., Damage detection tomography based on guided waves in composite structures using a distributed sensor network (2015) Opt Eng, 55, p. 011007","Carboni, M.; Department of Mechanical Engineering, Via La Masa 1, Italy; email: michele.carboni@polimi.it",,,"SAGE Publications Ltd",,,,,14759217,,,,"English","Struct. Health Monit.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85014725847
"Pachón P., Castro R., García-Macías E., Compan V., Puertas E.","55932249500;55775365000;56939045400;7005434464;56432860600;","E. Torroja's bridge: Tailored experimental setup for SHM of a historical bridge with a reduced number of sensors",2018,"Engineering Structures","162",,,"11","21",,30,"10.1016/j.engstruct.2018.02.035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044625868&doi=10.1016%2fj.engstruct.2018.02.035&partnerID=40&md5=88b01ce30a6b45e6450e2f31966f4600","Dept. of Continuum Mechanics, Universidad de Sevilla, Avenida Reina Mercedes, Sevilla, 41012, Spain; Dept. of Mechanics, Universidad de Córdoba, Campus de Rabanales, Cordoba, 14071, Spain; Department of Continuum Mechanics and Structural Analysis, Universidad de Sevilla, Camino de los Descubrimientos s/n, Seville, 41092, Spain; Dept. of Mechanical Structures and Hydraulic Engineering, Universidad de Granada, Avenida Fuentenueva, Granada, 18001, Spain","Pachón, P., Dept. of Continuum Mechanics, Universidad de Sevilla, Avenida Reina Mercedes, Sevilla, 41012, Spain; Castro, R., Dept. of Mechanics, Universidad de Córdoba, Campus de Rabanales, Cordoba, 14071, Spain; García-Macías, E., Department of Continuum Mechanics and Structural Analysis, Universidad de Sevilla, Camino de los Descubrimientos s/n, Seville, 41092, Spain; Compan, V., Dept. of Continuum Mechanics, Universidad de Sevilla, Avenida Reina Mercedes, Sevilla, 41012, Spain; Puertas, E., Dept. of Mechanical Structures and Hydraulic Engineering, Universidad de Granada, Avenida Fuentenueva, Granada, 18001, Spain","This paper presents the design of an experimental setup with a reduced number of sensors for the structural health monitoring of the historical bridge of Posadas (Córdoba, Spain), designed by the eminent engineer Eduardo Torroja in 1957. The motivation of this study stems from the need for safeguarding this piece of cultural heritage. In particular, the singularity of this historical construction, a steel–concrete composite typology consisting of a concrete deck slab and inverted bowstring steel trusses, makes continuous in-service condition assessment essential for its maintenance. Nevertheless, the application of existing continuous monitoring systems to such large-scale structures entails considerable investments as well as complex signal processing algorithms. Whereby the optimization of the number of sensors and their location is of the utmost interest. In this line, this work presents the application of an Optimal Sensor Placement (OSP) methodology to tailor an experimental setup for a cost-efficient continuous monitoring of the E. Torroja's bridge. Due to the fact that most OSP approaches are model-based, it is essential to count on a sufficiently accurate numerical model. To this aim, an extensive vibration-based operational modal analysis is first conducted with a large number of accelerometers. Afterward, a three-dimensional finite element model of the E. Torroja's bridge is updated on the basis of the experimentally identified dynamic properties with a genetic optimization algorithm. Finally, an optimal sensor placement methodology is utilized to design an experimental setup with a limited number of sensors for long-term monitoring purposes. The results demonstrate that few sensors are needed to accurately assess the main resonant frequencies and mode shapes. © 2018 Elsevier Ltd","Ambient vibration; Cultural heritage; Genetic algorithm; Historical constructions; Operational modal analysis; Optimal sensor placement; Structural Health Monitoring","Concretes; Finite element method; Genetic algorithms; Modal analysis; Monitoring; Natural frequencies; Signal processing; Structural health monitoring; Three dimensional computer graphics; Vibration analysis; Ambient vibrations; Cultural heritages; Historical construction; Operational modal analysis; Optimal sensor placement; Bridges; bridge; bridge construction; cultural heritage; genetic algorithm; health monitoring; sensor; vibration; Andalucia; Cordoba [Andalucia]; Spain",,,,,"Ministerio de Educación, Cultura y Deporte, MECD: FPU13/04892","The authors are pleased to acknowledge the Regional Government of Andalusia, for the support and the availability supplied during the experimental campaign. On the other hand, we cannot forget the help given by the researches of the investigation groups TEP-167 and TEP-245 of the Universities of Granada and Seville, respectively. E. G-M was also supported by a FPU contract-fellowship from the Spanish Ministry of Education Ref: FPU13/04892 .",,,,,,,,,,"Türker, T., Bayraktar, A., Structural safety assessment of bowstring type RC arch bridges using ambient vibration testing and finite element model calibration (2014) Measurement, 58, pp. 33-45; Torres, W., Almazán, J.L., Sandoval, C., Boroschek, R., Operational modal analysis and FE model updating of the metropolitan cathedral of santiago, chile (2017) Eng Struct, 143, pp. 169-188; Pepi, C., Gioffrè, M., Comanducci, G., Cavalagli, N., Bonaca, A., Ubertini, F., Dynamic characterization of a severely damaged historic masonry bridge (2017) Proc Eng, 199, pp. 3398-3403; Conde, B., Ramos, L.F., Oliveira, D.V., Riveiro, B., Solla, M., Structural assessment of masonry arch bridges by combination of non-destructive testing techniques and three-dimensional numerical modelling: application to Vilanova bridge (2017) Eng Struct, 148, pp. 621-638; Gentile, C., Saisi, A., Operational modal testing of historic structures at different levels of excitation (2013) Construct Build Mater, 48, pp. 1273-1285; Altunişik, A.C., Bayraktar, A., Sevim, B., Birinci, F., Vibration-based operational modal analysis of the Mikron historic arch bridge after restoration (2011) Civil Eng Environ Syst, 28 (3), pp. 247-259; Torroja, E., (2008), Razón y ser de los tipos estructurales. Consejo Superior de Investigaciones Científicas;; García-Macías, E., Castro-Triguero, R., Gallego, R., Carretero, J., (2015) Proceedings of the society for experimental mechanics series, pp. 147-55. , Ambient vibration testing of historic steel-composite bridge, the E. Torroja bridge, for structural identification and finite element model updating. In: Conference Springer International Publishing;; (2015), Dassault Systemes S. Abaqus/CAE 6.13 User's Guide;; Ramos, J.L.S., (2007), Damage identification on masonry structures based on vibration signatures, Ph.D. thesis. Universidade do Minho;; Rodrigues, J., (2004), Identificação modal estocastica, metodos de analise e aplicações em estruturas de engenharia civil, Ph.D. thesis. 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In: International modal analysis conference;; Brewick, P., Smyth, A., An investigation of the effects of traffic induced local dynamics on global damping estimates using operational modal analysis (2013) Mech Syst Sig Process, 41 (1-2), pp. 433-453; Pachón, P., Compán, V., Rodríguez-Mayorga, E., Sáez, A., Control of structural intervention in the area of the Roman Theatre of Cadiz (Spain) by using non-destructive techniques (2015) Construct Build Mater, 101, pp. 572-583; Teughels, A., (2003), Inverse modelling of civil engineering structures based on operational modal data, Ph.D. thesis. University of Leuven;; (2015), MathWorks, MATLAB R2015a. User's Guide;; Triguero, R.C., Murugan, S., Gallego, R., Friswell, M.I., Robustness of optimal sensor placement under parametric uncertainty (2013) Mech Syst Sig Process, 41 (1-2), pp. 268-287; Meo, M., Zumpano, G., On the optimal sensor placement techniques for a bridge structure (2005) Eng Struct, 27 (10), pp. 1488-1497; Kammer, D., Yao, L., Enhancement of on orbit modal identification of large space structures through sensor placement (1994) J Sound Vib, 171 (1), pp. 119-139","Pachón, P.; Dept. of Continuum Mechanics, Avenida Reina Mercedes, Spain; email: ppachon@us.es",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","",Scopus,2-s2.0-85044625868
"Wang F.-Y., Xu Y.-L., Sun B., Zhu Q.","57191494085;55695003100;55238855000;42562154200;","Updating Multiscale Model of a Long-Span Cable-Stayed Bridge",2018,"Journal of Bridge Engineering","23","3","04017148","","",,30,"10.1061/(ASCE)BE.1943-5592.0001195","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039855876&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001195&partnerID=40&md5=2886935d20099f329c78bab7f503feeb","Dept. of Civil and Environmental Engineering, Hong Kong Polytechnic Univ, Hong Kong, Hong Kong; Dept. of Engineering Mechanics, Southeast Univ., Nanjing, 210096, China; Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China","Wang, F.-Y., Dept. of Civil and Environmental Engineering, Hong Kong Polytechnic Univ, Hong Kong, Hong Kong; Xu, Y.-L., Dept. of Civil and Environmental Engineering, Hong Kong Polytechnic Univ, Hong Kong, Hong Kong; Sun, B., Dept. of Engineering Mechanics, Southeast Univ., Nanjing, 210096, China; Zhu, Q., Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China","To accurately calculate stress responses and evaluate fatigue damage of the critical structural members/joints of a long-span cable-stayed bridge, a multiscale finite-element (FE) model of the bridge has been developed by using shell/plate elements to simulate the critical structural components (local models) and by using beam/truss elements to simulate the rest part of the bridge (global model). Nevertheless, the multiscale FE model will be updated to best represent the real bridge, and accordingly the multiscale model updating method is required. This paper presents a novel multiscale model updating method for long-span cable-stayed bridges. The many-objective optimization problem with four or more conflicting objective functions is first formulated because global model and local models will be updated simultaneously. The metamodel-assisted multiobjective optimization evolutionary algorithm (MOEA) is then developed by a combination of R2 indicator-based MOEA, kriging metamodel, and evolution control strategy for the multiscale model of the bridge, which is large in size and complex in system. The R2 indicator-based MOEA is used because of its high performance in solving the many-objective optimization problem. The kriging metamodel is used to improve the computational efficiency of the optimization. The evolutionary control strategy is developed to prevent the R2-MOEA from finding false optimal solutions or losing some of the optimal solutions. Finally, the developed method is applied to a long-span cable-stayed bridge in Hong Kong to demonstrate its feasibility and accuracy. The modal frequencies, displacement, and stress influence lines measured by the structural health monitoring (SHM) system installed in the bridge are used to define the multiple objective functions. The updated results show that the proposed updating method is feasible and can improve the accuracy of the multiscale model in global and local structural responses. © 2017 American Society of Civil Engineers.","Evolution control; Kriging metamodels; Long-span cable-stayed bridge; Many-objective optimization; Multiscale model updating","Buffeting; Cables; Computational efficiency; Evolutionary algorithms; Fatigue damage; Finite element method; Interpolation; Multiobjective optimization; Optimal systems; Optimization; Shape optimization; Structural health monitoring; Surface morphology; Evolution control; Kriging metamodels; Long span cable stayed bridges; Many-objective optimizations; Multi-scale Modeling; Cable stayed bridges",,,,,"Research Grants Council, University Grants Committee, RGC, UGC: GRF 15218414","The works described in this paper are financially supported by the Hong Kong Research Grants Council through its competitive grants (GRF 15218414), to which the authors are most grateful. Any opinions and conclusions presented in this paper are entirely those of the authors.",,,,,,,,,,"ANSYS [ Computer Software ], , ANSYS, Canonsburg, PA; Bader, J., Zitzler, E., HypE: An algorithm for fast hypervolume-based many-objective optimization (2011) Evol. Comput., 19 (1), pp. 45-76; Beume, N., Naujoks, B., Emmerich, M., SMS-EMOA: Multiobjective selection based on dominated hypervolume (2007) Eur. J. Oper. Res., 181 (3), pp. 1653-1669; Brockhoff, D., Bader, J., Thiele, L., Zitzler, E., Directed multiobjective optimization based on the weighted hypervolume indicator (2013) J. Multi-Criteria Decis. Anal., 20 (56), pp. 291-317; Brockhoff, D., Wagner, T., Trautmann, H., On the properties of the R2 indicator (2012) Proc. 14th Annual Conf. on Genetic and Evolutionary Computation, ACM, pp. 465-472. , New York; Brockhoff, D., Wagner, T., Trautmann, H., 2 indicator-based multiobjective search (2015) Evol. 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Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems (EUROGEN 2001), International Center for Numerical Methods in Engineering, pp. 95-100. , Barcelona, Spain","Xu, Y.-L.; Dept. of Civil and Environmental Engineering, Hong Kong; email: ceylxu@polyu.edu.hk",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85039855876
"Chen G., Zhang W., Zhang Z., Jin X., Pang W.","55976499500;57206987031;14053189500;57199736334;57189328153;","A new rosette-like eddy current array sensor with high sensitivity for fatigue defect around bolt hole in SHM",2018,"NDT and E International","94",,,"70","78",,30,"10.1016/j.ndteint.2017.12.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037691405&doi=10.1016%2fj.ndteint.2017.12.001&partnerID=40&md5=193dea41b3102fff5029ae49ef378a8a","School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China; Department of Aircraft Health Management, Beijing Aeronautical Science &Technology Research Institute, Beijing, China","Chen, G., School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China; Zhang, W., School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China; Zhang, Z., School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China; Jin, X., School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China; Pang, W., Department of Aircraft Health Management, Beijing Aeronautical Science &Technology Research Institute, Beijing, China","Boosting the sensitivity of eddy current sensors is one of the challenges of non-destructive testing to improve structure health monitoring. Rosette-like eddy current sensors have the best application prospect in structure health monitoring to detect fatigue-induced defects around a bolt hole. To boost the sensitivity of this kind of sensor, a new rosette-like eddy current array sensor is proposed. Same-direction exciting currents are used to make the eddy current distribution more sensitive to detect defect propagation. Moreover, a new layout of pickup coils and a graph algorithm are proposed to improve the sensor's angular resolution. Through finite element simulation and experimental tests, the eddy current distribution and the output signal during defect propagation of a traditional sensor and a new one are compared. Results show that the proposed sensor has higher sensitivity in detecting defect propagation than the traditional one. © 2017 Elsevier Ltd","Eddy current testing; Non-destructive testing; Rosette-like eddy current array sensor; Structure health monitoring","Bridge decks; Defects; Electric current distribution measurement; Finite element method; Health; Nondestructive examination; Structural health monitoring; Application prospect; Defect propagation; Eddy current array sensors; Eddy current distribution; Eddy current sensors; Finite element simulations; Non destructive testing; Structure health monitoring; Eddy current testing",,,,,"National Natural Science Foundation of China, NSFC: 51275048","This work was financially supported by the National Nature Science Foundation of China (51275048).","This work was financially supported by the National Nature Science Foundation of China ( 51275048 ).",,,,,,,,,"Speckmann, H., Henrich, R., Structural health monitoring (SHM)-overview on airbus activities (2004) WCNDT; Leung, C.K.Y., Wan, K.T., Inaudi, D., Bao, X., Habel, W., Zhou, Z., Review: optical fiber sensors for civil engineering applications (2015) Mater Struct, 48, pp. 871-906; Almeida, V., Baptista, F.G., Aguiar, P., Piezoelectric transducers assessed by the pencil lead break for impedance-based structural health monitoring (2015) IEEE Sens J, 15, pp. 693-702; Park, G., Sohn, H., Farrar, C.R., Inman, D.J., Overview of piezoelectric impedance-based health monitoring and path forward (2003) Shock Vib Digest, 35, pp. 451-463; Roach, D., Real time crack detection using mountable comparative vacuum monitoring sensors (2008) Smart Struct Syst, 5, pp. 317-328; Rakow, A., Chang, F.K., A structural health monitoring fastener for tracking fatigue crack growth in bolted metallic joints (2012) Struct Health Monit, 11, pp. 253-267; Sodano, H.A., Development of an automated eddy current structural health monitoring technique with an extended sensing region for corrosion detection (2007) Struct Health Monit, 6, pp. 111-119; Zilberstein, V., Walrath, K., Grundy, D., Schlicker, D., Goldfine, N., Abramovici, E., MWM eddy-current arrays for crack initiation and growth monitoring (2003) Int J Fatig, 25, pp. 1147-1155; Jiao, S., Cheng, L., Li, X., Li, P., Gao, J., Construction of crack perturbation model and forward semi-analytical model of attached eddy current sensor (2015) Trans Nan Jing Univ Aeronaut Astronaut, 32, pp. 279-288; Li, P., Cheng, L., He, Y., Jiao, S., Du, J., Ding, H., Sensitivity boost of rosette eddy current array sensor for quantitative monitoring crack (2016) Sens Actuators A Phys, 246, pp. 129-139; Barbato, L., Poulakis, N., Tamburrino, A., Theodoulidis, T., Ventre, S., Solution and extension of a new benchmark problem for eddy-current nondestructive testing (2015) IEEE Trans Magn, 51; Martinos, J., Theodoulidis, T., Poulakis, N., Tamburrino, A., A benchmark problem for eddy current nondestructive evaluation (2014) Magn IEEE Trans, 50, pp. 1053-1056; Hamia, R., Cordier, C., Dolabdjian, C., Eddy-current non-destructive testing system for the determination of crack orientation (2014) NDT E Int, 61, pp. 24-28; Zilberstein, V., Schlicker, D., Walrath, K., Weiss, V., Goldfine, N., MWM eddy current sensors for monitoring of crack initiation and growth during fatigue tests and in service (2001) Int J Fatig, 23, pp. 477-485; West, D.B., Introduction to graph theory (2001), second ed. China Machine Press","Zhang, W.; School of Mechanical Engineering, China; email: zhangwm@bit.edu.cn",,,"Elsevier Ltd",,,,,09638695,,NDTIE,,"English","NDT E Int",Article,"Final","",Scopus,2-s2.0-85037691405
"Zhao H.W., Ding Y.L., An Y.H., Li A.Q.","57191694306;55768944900;36149156400;7403291516;","Transverse Dynamic Mechanical Behavior of Hangers in the Rigid Tied-Arch Bridge under Train Loads",2017,"Journal of Performance of Constructed Facilities","31","1","04016072","","",,28,"10.1061/(ASCE)CF.1943-5509.0000932","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010391233&doi=10.1061%2f%28ASCE%29CF.1943-5509.0000932&partnerID=40&md5=9c89bf83fd6ff3cd23e0cccfcc164c4c","School of Civil Engineering, Key Laboratory of CandPC Structures, Ministry of Education, Southeast Univ, Nanjing, 210096, China; Key Laboratory of CandPC Structures, Ministry of Education, Southeast Univ, Nanjing, 210096, China; Dept. of Civil Engineering, State Key Laboratory of Coastal and Offshore Engineering, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian Univ. of Technology, Dalian, 116023, China; School of Civil Engineering, Southeast Univ, Nanjing, 210096, China; Beijing Univ. of Civil Engineering and Architecture, Beijing, 100044, China","Zhao, H.W., School of Civil Engineering, Key Laboratory of CandPC Structures, Ministry of Education, Southeast Univ, Nanjing, 210096, China; Ding, Y.L., Key Laboratory of CandPC Structures, Ministry of Education, Southeast Univ, Nanjing, 210096, China; An, Y.H., Dept. of Civil Engineering, State Key Laboratory of Coastal and Offshore Engineering, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian Univ. of Technology, Dalian, 116023, China; Li, A.Q., School of Civil Engineering, Southeast Univ, Nanjing, 210096, China, Beijing Univ. of Civil Engineering and Architecture, Beijing, 100044, China","The rigid hanger is one of the main load-bearing components in a rigid tied-arch bridge that is common used in high-speed railway lines, and the mechanism for influences of train loads on the hangers' transverse vibration -still requires clarification. This paper investigates the transverse vibration of rigid hangers in the rigid tied-arch bridge under train loads from three foci. Firstly, the accurate finite-element model of a rigid tied-arch bridge and the sub-model of each hanger of this bridge are established. The dynamic characteristics analysis of the whole bridge and each hanger are presented. Secondly, because the resonance theory used for stay cables and main girder cannot be used for rigid hangers in arch bridges, the hanger's transverse vibration formula with consideration of the interaction of rigid hangers and main girder is proposed based on the classic structural dynamics. Thirdly, a simplified load model that can reflect the mechanical characteristics of high-speed rail Electric Multiple Units is established. The transverse dynamic displacements of hangers and their dynamic amplification factors in 6 field load cases are presented by nonlinear dynamic analysis; and also they have been validated based on the data from the structural health monitoring system. The conclusions show that generally the resonance between hangers and main girder is unlikely to happen in a rigid tied-arch bridge and the transverse dynamic displacement at long rigid hangers and the dynamic amplification factors of transverse dynamic displacements at short rigid hangers are required to be paid more attention; moreover, the parameters that affect the transverse vibration of hangers have been determined; finally, the geometry, cross sectional form, the spatial location of hangers and train speed can affect the transverse dynamic mechanical behavior of hangers. This work also gives a suggestion which lays a foundation for the better design, maintenance and long-term monitoring of hangers in a long-span rigid tied-arch bridge. © 2016 American Society of Civil Engineers.","High-speed trains; Rigid hangers; Tied-arch bridge; Transverse dynamic and static mechanical behavior","Arches; Beams and girders; Bridge cables; Bridge decks; Bridges; Dynamic loads; Dynamics; Finite element method; Railroad cars; Railroad engineering; Railroad plant and structures; Railroad transportation; Railroads; Structural dynamics; Structural health monitoring; Vibrations (mechanical); Dynamic amplification factors; Dynamic characteristics analysis; High speed train (HST); Mechanical characteristics; Rigid hangers; Static mechanical behavior; Structural health monitoring systems; Tied arch bridges; Arch bridges",,,,,,,,,,,,,,,,"Afonso Costa, B.J., Figueiras, J.A., Evaluation of a strain monitoring system for existing steel railway bridges (2012) J. Constr. Steel Res., 72, pp. 179-191; An, Y.H., Spencer, B.F., Ou, J.P., A test method for damage diagnosis of suspension bridge suspender cables (2015) Comput. -Aided Civ. Infrastruct. Eng., 30 (10), pp. 771-784; Andersson, A., Karoumi, R., Attenuating resonant behavior of a tied-arch railway bridge using increased hanger damping (2012) 6th Int. Conf. on Bridge Maintenance, Safety and Management (IABMAS): Stresa, pp. 2572-2577. , Italy, Bridge Maintenance, Safety, Management, Resilience and Sustainability, International Association for Bridge Maintenance and Safety; Polytechnic Univ. of Milan, Milan, Italy; Clough, R., Penzien, J., (2003) Dynamics of Structures, pp. 377-378. , 2nd Ed. Computers and Structures, Berkeley, CA; De Backer, H., Outtier, A., Van Bogaert, P., Determining geometric out-of-plane imperfections in steel tied-arch bridges using strain measurements (2014) J. Perform. Constr. Facil., pp. 549-558; De Freitas, M.S.T., Viana, R.L., Grebogi, C., Basins of attraction of periodic oscillations in suspension bridges (2004) Nonlinear Dyn., 37 (3), pp. 207-226; Deng, L., Wang, W., Yu, Y., State-of-The-art review on the causes and mechanisms of bridge collapse (2015) J. Perform. Constr. Facil., , 04015005; Faridani, H.M., Barghian, M., Improvement of dynamic performances of suspension footbridges by modifying the hanger systems (2012) Eng. Struct., 34, pp. 52-68; Feng, D.M., Feng, M.Q., Model updating of railway bridge using in situ dynamic displacement measurement under trainloads (2015) J. Bridge Eng., , 04015019; Huang, D.Z., Vehicle-induced vibration of steel deck arch bridges and analytical methodology (2012) J. Bridge Eng., pp. 241-248; Ju, S.H., Lin, H.T., Numerical investigation of a steel arch bridge and interaction with high-speed trains (2003) Eng Struct., 25 (2), pp. 241-250; Kang, H.J., Zhao, Y.Y., Zhu, H.P., Out-of-plane free vibration analysis of a cable-arch structure (2013) J. Sound Vib., 332 (4), pp. 907-921; Kim, H.K., Kim, N.S., Jang, J.H., Kim, Y.H., Analysis model verification of a suspension bridge exploiting configuration survey and field-measured data (2012) J. Bridge Eng., pp. 794-803; Kong, M.S., Yhim, S.S., Son, S.H., Kim, D.Y., Dynamic analysis of the multiple-arch bowstring bridge and conventional arch subjected to moving loads (2006) Steel Struct., 6 (3), pp. 227-236; Lepidi, M., Gattulli, V., A parametric multi-body section model for modal interactions of cable-supported bridges (2014) J. Sound Vib., 333 (19), pp. 4579-4596; Li, A.Q., Ding, Y.L., Wang, H., Guo, T., Analysis and assessment of bridge health monitoring mass data - Progress in research/development of 'structural health monitoring (2012) Sci. China Technol. Sci., 55 (8), pp. 2212-2224; Li, D.S., Zhi, Z., Ou, J.P., Dynamic behavior monitoring and damage evaluation for arch bridge suspender using GFRP optical fiber Bragg grating sensors (2012) Opt. Laser Technol., 44 (4), pp. 1031-1038; Li, L.Y., Cheng, Z.Q., Ge, Y.J., Effects of arch rib crossbars on dynamic and stabilization characteristics of concrete filled steel tubular arch bridge (2008) J. Highway Transp. Res. Dev. (English Ed.), 3 (2), pp. 98-103; Li, Y.B., Zhang, Q.W., Vibration effect on gloss sectional stress distribution of short suspenders in arch bridges (2009) J. Tongji Univ. (Nat. Sci.), 37 (2), pp. 159-163. , (in Chinese); Lin, K., Zou, D.J., Wei, M.H., Nonlinear analysis of cable vibration of a multispan cable-stayed bridge under transverse excitation (2014) Math. Prob. Eng., pp. 1-13; Lu, W., He, Z., Vulnerability and robustness of corroded large-span cable-stayed bridges under marine environment (2014) J. Perform. Constr. Facil., , 04014204; Malm, R., Andersson, A., Field testing and simulation of dynamic properties of a tied-arch railway bridge (2006) Eng Struct., 28 (1), pp. 143-152; Marsico, M.R., Tzanov, V., Wagg, D.J., Neild, S.A., Krauskopf, B., Bifurcation analysis of a parametrically excited inclined cable close to two-to-one internal resonance (2011) J. Sound Vib., 330 (24), pp. 6023-6035; Meng, X., Yao, J.C., Liu, P.H., Wang, W., Yang, Y.Q., Ke, Z.T., Field test and analysis on dynamic performance of Dashengguan Yangtze River Bridge (2015) China Railway Sci., 36 (3), pp. 30-36. , (in Chinese); Temporary specification for 200 km / h speed level above railway vehicle design and test the strength of identification (2001) Acad. Railway Sci., , People's Republic of China Ministry of Railways. in press (in Chinese); Shao, Y., Sun, Z.G., Chen, Y.F., Li, H.L., Impact effect analysis for hangers of half-through arch bridge by vehicle-bridge coupling (2015) Struct. Monit. Maintenance, 2 (1), pp. 65-75; Sun, Z.X., Zhang, Y.Y., Guo, D.L., Yang, G.W., Liu, Y.B., Research on running stability of CRH3 high speed trains passing by each other (2014) Eng. Appl. Comput. Fluid Mech., 8 (1), pp. 140-157; Turmo, J., Luco, J.E., Effect of hanger flexibility on dynamic response of suspension bridges (2010) J. Eng. Mech., pp. 1444-1459; Wang, W., Yan, W.C., Deng, L., Kang, H.J., Dynamic analysis of a cable-stayed concrete-filled steel tube arch bridge under vehicle loading (2015) J. Bridge Eng., , 04014082; Wang, Z.W., Li, T.J., Nonlinear dynamic analysis of parametrically excited space cable-beam structures due to thermal loads (2015) Eng. Struct., 83, pp. 50-61; Yang, Y.B., Lin, C.W., Vehicle-bridge interaction dynamics and potential applications (2005) J. Sound Vib., 284 (12), pp. 205-226; Yoshimura, M., Wu, Q.X., Takahashi, K., Nakamura, S., Furukawa, K., Vibration analysis of the Second Saikai bridge - A concrete filled tubular (CFT) arch bridge (2006) J. Sound Vib., 290 (12), pp. 388-409","Ding, Y.L.; Key Laboratory of CandPC Structures, China; email: civilchina@hotmail.com",,,"American Society of Civil Engineers (ASCE)",,,,,08873828,,JPCFE,,"English","J. Perform. Constr. Facil.",Article,"Final","",Scopus,2-s2.0-85010391233
"Yin T., Zhu H.-P.","55277579100;7404664698;","An efficient algorithm for architecture design of Bayesian neural network in structural model updating",2020,"Computer-Aided Civil and Infrastructure Engineering","35","4",,"354","372",,27,"10.1111/mice.12492","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070997878&doi=10.1111%2fmice.12492&partnerID=40&md5=1e840b4dd16d36bf102c131f431f7607","School of Civil Engineering, Wuhan University, Wuhan, China; School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, China","Yin, T., School of Civil Engineering, Wuhan University, Wuhan, China; Zhu, H.-P., School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, China","There has been growing interest in applying the artificial neural network (ANN) approach in structural system identification and health monitoring. The learning process of neural network can be more robust when presented in the Bayesian framework, and rational architecture of the Bayesian neural network is critical to its performance. Apart from number of hidden neurons, the specific forms of the transfer functions in both hidden and output layers are also crucially important. To the best of our knowledge, however, the simultaneous design of proper number of hidden neurons, and specific forms of hidden- and output-layer transfer functions has not yet been reported in terms of the Bayesian neural network. It is even more challenging when the transfer functions of both layers are parameterized instead of using fixed shape forms. This paper proposes a tailor-made algorithm for efficiently designing the appropriate architecture of Bayesian neural network with simultaneously optimized hidden neuron number and custom transfer functions in both hidden and output layers. To cooperate with the proposed algorithm, both the Jacobian of the network function and Hessian of the negative logarithm of weight posterior are derived analytically by matrix calculus. This is much more accurate and efficient than the finite difference approximation, and also vital for properly designing the Bayesian neural network architecture as well as further quantifying the confidence interval of network prediction. The validity and efficiency of the proposed methodology is verified through probabilistic finite element (FE) model updating of a pedestrian bridge by using the field measurement data. © 2019 Computer-Aided Civil and Infrastructure Engineering",,"Calculations; Closed loop control systems; Finite difference method; Footbridges; Multilayer neural networks; Neurons; Structural health monitoring; Transfer functions; Architecture designs; Bayesian neural networks; Field measurement data; Finite difference approximations; Number of hidden neurons; Probabilistic finite elements; Structural model updating; Structural system identification; Network architecture; algorithm; architectural design; artificial neural network; Bayesian analysis; finite element method",,,,,"University of Liverpool; National Natural Science Foundation of China, NSFC: 51778506, 51838006; China Scholarship Council, CSC: 201806275091","The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (Grant Nos. 51778506, 51838006), and a scholarship from the China Scholarship Council (Grant No. 201806275091) while the first author was visiting the Center for Engineering Dynamics and Institute for Risk and Uncertainty in the University of Liverpool. Special thanks are due to Prof. S. K. Au, University of Liverpool, for many useful discussions. Finally, the authors would like to thank the Editor and the anonymous reviewers for their constructive comments and valuable suggestions to improve the quality of the article.",,,,,,,,,,"Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., Inmand, D.J., Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks (2017) Journal of Sound and Vibration, 388 (3), pp. 154-170; Adeli, H., Neural networks in civil engineering: 1989−2000 (2001) Computer-Aided Civil and Infrastructure Engineering, 16 (2), pp. 126-142; Adeli, H., Cheng, N.T., Integrated genetic algorithm for optimization of space structures (1993) Journal of Aerospace Engineering, 6 (4), pp. 315-328; Adeli, H., Jiang, X., Dynamic fuzzy wavelet neural network model for structural system identification (2006) Journal of Structural Engineering, 132 (1), pp. 102-111; Adeli, H., Jiang, X., (2009) Intelligent infrastructure: Neural networks, wavelets, and chaos theory for intelligent transportation systems and smart structures, , Boca Raton, Florida, CRC Press, Taylor & Francis; Arangio, S., Beck, J.L., Bayesian neural networks for bridge integrity assessment (2012) Structural Control and Health Monitoring, 19 (1), pp. 3-21; Astroza, R., Nguyen, L.T., Nestorovic, T., Finite element model updating using simulated annealing hybridized with unscented Kalman filter (2016) Computers & Structures, 177, pp. 176-191; Barber, D., Bayesian methods for supervised neural networks (2002) Handbook of brain theory and neural networks, , M. 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Weinberger, (Eds.),, Lake Tahoe, NV, Curran Associates, Inc; Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R., (2004) A review of structural health monitoring literature: 1996–2001, , (Report LA-13976-MS)., Los Alamos, NM, Los Alamos National Laboratory; Stochino, F., Cazzani, A., Poppi, S., Turco, E., Sardinia radio telescope finite element model updating by means of photogrammetric measurements (2017) Mathematics and Mechanics of Solids, 22 (4), pp. 885-901; Teughels, A., De Roeck, G., Suykens, J.A.K., Global optimization by coupled local minimizers and its application to FE model updating (2003) Computers and Structures, 81 (24-25), pp. 2337-2351; Torres, W., Almazán, J.L., Sandoval, C., Boroschek, R., Operational modal analysis and FE model updating of the Metropolitan Cathedral of Santiago, Chile (2017) Engineering Structures, 143, pp. 169-188; Wang, N.N., Zhao, Q.G., Li, S.Y., Zhao, X.F., Zhao, P., Damage classification for masonry historic structures using convolutional neural networks based on still images (2018) Computer-Aided Civil and Infrastructure Engineering, 33 (12), pp. 1073-1089; Yang, X.C., Li, H., Yu, Y.T., Luo, X.C., Huang, T., Yang, X., Automatic pixel-level crack detection and measurement using fully convolutional network (2018) Computer-Aided Civil and Infrastructure Engineering, 33 (12), pp. 1090-1109; Yin, T., Jiang, Q.H., Yuen, K.V., Vibration-based damage detection for structural connections using incomplete modal data by Bayesian approach and model reduction technique (2017) Engineering Structures, 132 (1), pp. 260-277; Yin, T., Lam, H.F., Chow, H.M., Zhu, H.P., Dynamic reduction-based structural damage detection of transmission tower utilizing ambient vibration data (2009) Engineering Structures, 31 (9), pp. 2009-2019; Yin, T., Yuen, K.V., Lam, H.F., Zhu, H.P., Entropy-based optimal sensor placement for model identification of periodic structures endowed with bolted joints (2017) Computer-Aided Civil and Infrastructure Engineering, 32 (12), pp. 1007-1024; Yin, T., Zhu, H.P., Probabilistic damage detection of a steel truss bridge model by optimally designed Bayesian neural network (2018) Sensors, 18 (10), p. 3371. , https://doi.org/10.3390/s18103371; Yin, T., Zhu, H.P., Fu, S.J., Damage identification of periodically-supported structures following the Bayesian probabilistic approach (2019) International Journal of Structural Stability and Dynamics, 19; Yu, L., Yin, T., Damage identification in frame structures based on FE model updating (2010) Journal of Vibration and Acoustics, 132 (5); Yuen, K.V., Efficient model correction method with modal measurement (2010) Journal of Engineering Mechanics, 136 (1), pp. 91-99; Yuen, K.V., Lam, H.F., On the complexity of artificial neural networks for smart structures monitoring (2006) Engineering Structures, 28 (7), pp. 977-984; Yuen, K.V., Mu, H.Q., Real-time system identification: An algorithm for simultaneous model class selection and parametric identification (2015) Computer-Aided Civil and Infrastructure Engineering, 30 (10), pp. 785-801; Zhu, X.Q., Hao, H., Peng, X.L., Dynamic assessment of underwater pipeline systems using statistical model updating (2008) International Journal of Structural Stability and Dynamics, 8 (2), pp. 271-297","Yin, T.; School of Civil Engineering, China; email: tyin@whu.edu.cn",,,"Blackwell Publishing Inc.",,,,,10939687,,CCIEF,,"English","Comput.-Aided Civ. Infrastruct. Eng.",Article,"Final","",Scopus,2-s2.0-85070997878
"Sitton J.D., Zeinali Y., Rajan D., Story B.A.","57192176500;57192175888;7005909374;35766942100;","Frequency Estimation on Two-Span Continuous Bridges Using Dynamic Responses of Passing Vehicles",2020,"Journal of Engineering Mechanics","146","1","04019115","","",,26,"10.1061/(ASCE)EM.1943-7889.0001698","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075028401&doi=10.1061%2f%28ASCE%29EM.1943-7889.0001698&partnerID=40&md5=b14719da2d67db8fd5ecbdf8560078b7","Dept. of Civil and Environmental Engineering, Southern Methodist Univ., Dallas, TX  75205, United States; Department Chair and Cecil and Ida Green Endowed Professor, Dept. of Electrical Engineering, Southern Methodist Univ., Dallas, TX  75205, United States","Sitton, J.D., Dept. of Civil and Environmental Engineering, Southern Methodist Univ., Dallas, TX  75205, United States; Zeinali, Y., Dept. of Civil and Environmental Engineering, Southern Methodist Univ., Dallas, TX  75205, United States; Rajan, D., Department Chair and Cecil and Ida Green Endowed Professor, Dept. of Electrical Engineering, Southern Methodist Univ., Dallas, TX  75205, United States; Story, B.A., Dept. of Civil and Environmental Engineering, Southern Methodist Univ., Dallas, TX  75205, United States","Researchers in the structural health monitoring field have recently focused on using instrumented vehicles, usually equipped with accelerometers, as mobile bridge inspection instruments. The vehicle plays two roles: that of the measurement device and that of the excitation source. Permanent changes to the bridge's stiffness due to damage or wear may manifest as changes in the bridge's fundamental frequencies. In vehicle-based inspection, bridge frequencies are extracted from vehicle responses. These bridge frequencies may be estimated periodically as a continuing bridge condition assessment. This paper establishes closed-form solutions for bridge and vehicle vibration as a vehicle traverses a two-span continuous bridge and provides a method of bridge frequency extraction from vehicle response. Results are validated using finite-element simulations and compared against the literature. Results show that bridge frequencies observed by the vehicle manifest as two peaks shifted below and above the fundamental bridge frequency. These shifts are linear functions and the average of these shifted bridge frequencies approximates the fundamental bridge frequency to within 7% error; this error decreases to 2% for equal spans. © 2019 American Society of Civil Engineers.","Dynamic response; Moving vehicle load; Multispan continuous bridge; Vehicle-based bridge inspection; Vehicle-bridge interaction","Bridges; Dynamic response; Frequency estimation; Inspection; Structural health monitoring; Vehicles; Bridge inspection; Closed form solutions; Condition assessments; Continuous bridges; Finite element simulations; Fundamental frequencies; Moving vehicle load; Vehicle-bridge interaction; Vibrations (mechanical); bridge; dynamic response; estimation method; finite element method; frequency analysis; structural response; vibration",,,,,,,,,,,,,,,,"Arangio, S., Bontempi, F., Soft computing based multilevel strategy for bridge integrity monitoring (2010) Comput.-Aided Civ. Infrastruct. 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Sound Vib., 272 (35), pp. 471-493. , https://doi.org/10.1016/S0022-460X(03)00378-X; Yang, Y.B., Yang, J.P., State-of-the-art review on modal identification and damage detection of bridges by moving test vehicles (2018) Int. J. Struct. Stab. Dyn., 18 (2), p. 1850025. , https://doi.org/10.1142/S0219455418500256; Zapico, J.L., González, M.P., Worden, K., Damage assessment using neural networks (2003) Mech. Syst. Sig. Process., 17 (1), pp. 119-125. , https://doi.org/10.1006/mssp.2002.1547; Zhu, X.Q., Law, S.S., Structural health monitoring based on vehicle-bridge interaction: Accomplishments and challenges (2015) Adv. Struct. Eng., 18 (12), pp. 1999-2015. , https://doi.org/10.1260/1369-4332.18.12.1999","Story, B.A.; Dept. of Civil and Environmental Engineering, United States; email: bstory@lyle.smu.edu",,,"American Society of Civil Engineers (ASCE)",,,,,07339399,,,,"English","J. Eng. Mech.",Article,"Final","",Scopus,2-s2.0-85075028401
"Yu S., Ou J.","57199305300;7202845830;","Structural health monitoring and model updating of Aizhai Suspension Bridge",2017,"Journal of Aerospace Engineering","30","2","B4016009","","",,25,"10.1061/(ASCE)AS.1943-5525.0000653","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014403176&doi=10.1061%2f%28ASCE%29AS.1943-5525.0000653&partnerID=40&md5=5c7c68f76fa550cc33a5f3c59b6a8dec","School of Civil Engineering, Dalian Univ. of Technology, Dalian, Liaoning  116024, China; School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China","Yu, S., School of Civil Engineering, Dalian Univ. of Technology, Dalian, Liaoning  116024, China; Ou, J., School of Civil Engineering, Dalian Univ. of Technology, Dalian, Liaoning  116024, China, School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China","A complete structural health monitoring system has been implemented on Aizhai Suspension Bridge for monitoring its health status and assessing its safety for long-term services. This system consists of nearly 112 sensors of various types, including four subsystems: automatic data collection subsystem, artificial maintenance management subsystem, early safety warning and comprehensive assessment subsystem, and centralized database management subsystem. The deployments and functions of this structural health monitoring system are first introduced in this paper. Then, a finite-element model updating method, which combines the substructure method with the response surface model updating method, is proposed to reconstruct the actual working state of this suspension bridge in the early safety warning and comprehensive assessment subsystem. In the remaining part, the temperature fields, strain responses, tension forces of the hangers, longitudinal displacements of the stiffening girder, and the meteorological temperature are analyzed. Through the statistical analysis, the relationship between the temperature fields was found; the temperature fields induced strains of the stiffening girder satisfies the linear relationship. The cumulative probability distribution function of the cycle-index of the same daily stress amplitude follows a Weibull distribution. The monitored relative longitudinal displacements of the stiffening girder are linearly related with the meteorological temperatures. The monitored tension forces of the hangers verify the effects of the suspender-free zones of main cables on the normal hangers for this pylongirder detached suspension bridge. © 2016 American Society of Civil Engineers.","Aizhai Suspension Bridge; Linear; Model updating; Structural health monitoring (SHM); Temperature filed; Weibull distribution","Atmospheric temperature; Beams and girders; Bridge cables; Cable stayed bridges; Distribution functions; Finite element method; Information management; Monitoring; Probability distributions; Strain; Suspension bridges; Weibull distribution; Cumulative probability distribution function; Finite-element model updating; Linear; Longitudinal displacements; Model updating; Structural health monitoring (SHM); Structural health monitoring systems; Temperature filed; Structural health monitoring",,,,,"Ministry of Science and Technology of the People's Republic of China, MOST: 2011BAK02B01, 2013CB036305","The authors are grateful for the financial support from the Ministry of Science and Technology under the Grant Nos. 2011BAK02B01 and 2013CB036305.",,,,,,,,,,"Andersen, E.Y., Pedersen, L., Structural monitoring of the great belt east bridge (1994) Proc., 3th Symp. on Stait Crossing, 94, pp. 189-195. , J. 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"Xiao F., Chen G.S., Leroy Hulsey J.","56070134700;55615798900;6602858255;","Monitoring bridge dynamic responses using fiber bragg grating tiltmeters",2017,"Sensors (Switzerland)","17","10","2390","","",,24,"10.3390/s17102390","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032010602&doi=10.3390%2fs17102390&partnerID=40&md5=f6197e751f73283dc777232585351369","Department of Civil and Environmental Engineering, University of Alaska Fairbanks, Fairbanks, AK  99775, United States; College of Information Technology and Engineering, Marshall University, Huntington, WV  25755, United States","Xiao, F., Department of Civil and Environmental Engineering, University of Alaska Fairbanks, Fairbanks, AK  99775, United States; Chen, G.S., College of Information Technology and Engineering, Marshall University, Huntington, WV  25755, United States; Leroy Hulsey, J., Department of Civil and Environmental Engineering, University of Alaska Fairbanks, Fairbanks, AK  99775, United States","In bridge health monitoring, tiltmeters have been used for measuring rotation and curvature; however, their application in dynamic parameter identification has been lacking. This study installed fiber Bragg grating (FBG) tiltmeters on the bearings of a bridge and monitored the dynamic rotational angle. The dynamic features, including natural frequencies and mode shapes, have been identified successfully. The innovation presented in this paper is the first-time use of FBG tiltmeter readings to identify the natural frequencies of a long-span steel girder bridge. The identified results have been verified using a bridge finite element model. This paper introduces a new method for the dynamic monitoring of a bridge using FBG tiltmeters. Limitations and future research directions are also discussed in the conclusion. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.","Bridge health monitoring; Dynamic identification; Expansion bearing; Fiber Bragg grating (FBG) tiltmeter; Vibration monitoring","Bragg gratings; Finite element method; Natural frequencies; Bridge health monitoring; Dynamic identification; Dynamic parameters; Expansion bearings; Future research directions; Natural frequencies and modes; Steel girder bridge; Vibration monitoring; Fiber Bragg gratings",,,,,,,,,,,,,,,,"Fan, W., Qiao, P., Vibration-based damage identification methods: A review and comparative study (2011) Struct. Health Monit., 10, pp. 83-111; Li, Y., Cai, C.S., Liu, Y., Chen, Y., Liu, J., Dynamic analysis of a large span specially shaped hybrid girder bridge with concrete-filled steel tube arches (2016) Eng. Struct., 106, pp. 243-260; Bedon, C., Morassi, A., Dynamic testing and parameter identification of a base-isolated bridge (2014) Eng. 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Sound Vib, 305, pp. 534-542; Wu, B., Lu, H., Chen, B., Gao, Z., Study on Finite Element Model Updating in Highway Bridge Static Loading Test Using Spatially-Distributed Optical Fiber Sensors (2017) Sensors, 7, 1657p; Moschas, F., Stiros, S., Measurement of the dynamic displacements and of the modal frequencies of a short-span pedestrian bridge using GPS and an accelerometer (2011) Eng. Struct., 33, pp. 10-17; Lee, J.J., Ho, H.N., Lee, J.H., A vision-based dynamic rotational angle measurement system for large civil structures (2012) Sensors, 12, pp. 7326-7336; Fukuda, Y., Feng, M.Q., Narita, Y., Kaneko, S.I., Tanaka, T., Vision-based displacement sensor for monitoring dynamic response using robust object search algorithm (2013) IEEE Sens. J, 13, pp. 4725-4732; Erol, B., Evaluation of high-precision sensors in structural monitoring (2010) Sensors, 10, pp. 10803-10827; Park, Y.S., Agbayani, J.A., Lee, J.H., Lee, J.J., Rotational angle measurement of bridge support using image processing techniques (2016) J. Sens, 2016; Dong, Y., Song, R., Liu, H., (2010) Bridges Structural Health Monitoring and Deterioration Detection-Synthesis of Knowledge and Technology, , Final Report; Alaska University Transportation Center: Fairbanks, AK, USA; Zhou, J., Li, X., Xia, R., Yang, J., Zhang, H., Health Monitoring and Evaluation of Long-Span Bridges Based on Sensing and Data Analysis: A Survey (2017) Sensors, 17, 603p; Helmi, K., Taylor, T., Zarafshan, A., Ansari, F., Reference free method for real time monitoring of bridge deflections (2015) Eng. 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Syst., 4, pp. 103-110; Ye, X.W., Su, Y.H., Han, J.P., Structural health monitoring of civil infrastructure using optical fiber sensing technology: A comprehensive review (2014) Sci. World J., 2014; Alan, V.O., Ronald, W.S., John, R., (1999) Discrete-Time Signal Processing, , 2nd ed.; Prentice Hall: Upper Saddle River, NJ, USA; Rato, R.T., Ortigueira, M.D., Batista, A.G., On the HHT, its problems, and some solutions (2008) Mech. Syst. Signal Process., 22, pp. 1374-1394; Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Liu, H.H., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis (1998) Proc. R. Soc. Lond. a Math. Phys. Eng. Sci., 454, pp. 903-995; Shi, W.X., Shang, J.Z., Lu, X.L., Modal identification of Shanghai World Financial Center both from free and ambient vibration response (2012) Eng. Struct., 36, pp. 14-26; Wang, Z.C., Chen, G., Analytical mode decomposition with Hilbert transform for modal parameter identification of buildings under ambient vibration (2014) Eng. Struct., 59, pp. 173-184; Chen, J., Xu, Y.L., Zhang, R.C., Modal parameter identification of Tsing Ma suspension bridge under Typhoon Victor: EMD-HT method (2004) J. Wind Eng. Ind. Aerodyn, 92, pp. 805-827","Chen, G.S.; College of Information Technology and Engineering, United States; email: chenga@marshall.edu",,,"MDPI AG",,,,,14248220,,,"29053572","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85032010602
"Abedin M., Mehrabi A.B.","57211253861;7005771645;","Novel approaches for fracture detection in steel girder bridges",2019,"Infrastructures","4","3","infrastructures4030042","","",,23,"10.3390/infrastructures4030042","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073153439&doi=10.3390%2finfrastructures4030042&partnerID=40&md5=b7f23ec5413b09fc8a1e000022c9cf2e","Department of Civil and Environmental Engineering, Florida International University, Miami, FL  33174, United States","Abedin, M., Department of Civil and Environmental Engineering, Florida International University, Miami, FL  33174, United States; Mehrabi, A.B., Department of Civil and Environmental Engineering, Florida International University, Miami, FL  33174, United States","The bottom flanges of steel plate girder bridges can be considered fracture-critical elements depending on the number of girders and bridge configuration. For such cases, it is required that inspection of these bridges be carried out using costly ""arms-length"" approach. New techniques in structural health monitoring (SHM) that use non-contact sensors and self-powered wireless sensors present alternative approach for inspection. Application of such techniques would allow timely detection and application of repair and strengthening, in other word, providing for more resilient bridges. This paper investigates the feasibility of using a handful of self-powered wireless or non-contact sensors for continuous or periodic monitoring and detection of fracture in steel plate girder bridges. To validate this concept, vibration measurements were performed on an actual bridge in the field, and detailed finite element analyses were carried out on a multi-girder bridge. The records obtained show that vibration amplitude was significantly increased for fractured girder, and a distinct pattern of strain variation was registered in the vicinity of fracture, all of which can be detected effectively with relevant sensors. Moreover, the amplitude and frequency of the vibration was shown to be significant enough for providing the required power for typical sensor(s). © 2019 by the authors.","Damage detection; Fracture critical; Health monitoring; Laser vibrometer; Non-contact sensor; Self-powered sensor; Steel bridges; Wireless sensors",,,,,,,"Acknowledgments: The authors greatly acknowledge the internal support by the Department of Civil and Environmental Engineering at Florida International University. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein.",,,,,,,,,,"(2012) AASHTO LRFD Bridge Design Specifications, 7th Ed., , The American Association of State Highway Transportation Officials (AASHTO). American Association of State Highway and Transportation Officials: Washington, DC, USA; Yu, J., Ziehl, P., Zrate, B., Caicedo, J., Prediction of fatigue crack growth in steel bridge components using acoustic emission (2011) J. Constr. Steel Res., 67, pp. 1254-1260. , [CrossRef]; Fisher, J.W., Menzemer, C.C., Fatigue cracking in welded steel bridges (1990) Transp. Res. 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Final Report, , Transportation Research Board: Washington, DC, USA; (2016) ABAQUS/CAE Doc., , Dassault ABAQUS Documentation. ; Simulia: Providence, RI, USA; Lubliner, J., Oliver, J., Oller, S., Onate, E., A plastic-damage model for concrete (1989) Int. J. Solids Struct., 25, pp. 299-326. , [CrossRef]; (2014) Building Code Requirements for Structural Concrete (ACI 318-14) and Commentary (ACI 318R-14), , American Concrete Institute. ; American Concrete Institute: Farmington Hills, MI, USA; Sazonov, E., Li, H., Curry, D., Pillay, P., Self-powered sensors for monitoring of highway bridges (2009) IEEE Sens. J., 9, pp. 1422-1429. , [CrossRef]","Abedin, M.; Department of Civil and Environmental Engineering, United States; email: mabed005@fiu.edu",,,"MDPI Multidisciplinary Digital Publishing Institute",,,,,24123811,,,,"English","Infrastructures",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85073153439
"Jiao P., Borchani W., Hasni H., Lajnef N.","55604705500;56008051600;56964369900;14047090600;","A new solution of measuring thermal response of prestressed concrete bridge girders for structural health monitoring",2017,"Measurement Science and Technology","28","8","085005","","",,23,"10.1088/1361-6501/aa6c8e","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021798643&doi=10.1088%2f1361-6501%2faa6c8e&partnerID=40&md5=e4d7d12845616cabd6463a9f3caf2e84","Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48823, United States","Jiao, P., Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48823, United States; Borchani, W., Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48823, United States; Hasni, H., Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48823, United States; Lajnef, N., Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48823, United States","This study develops a novel buckling-based mechanism to measure the thermal response of prestressed concrete bridge girders under continuous temperature changes for structural health monitoring. The measuring device consists of a bilaterally constrained beam and a piezoelectric polyvinylidene fluoride transducer that is attached to the beam. Under thermally induced displacement, the slender beam is buckled. The post-buckling events are deployed to convert the low-rate and low-frequency excitations into localized high-rate motions and, therefore, the attached piezoelectric transducer is triggered to generate electrical signals. Attaching the measuring device to concrete bridge girders, the electrical signals are used to detect the thermal response of concrete bridges. Finite element simulations are conducted to obtain the displacement of prestressed concrete girders under thermal loads. Using the thermal-induced displacement as input, experiments are carried out on a 3D printed measuring device to investigate the buckling response and corresponding electrical signals. A theoretical model is developed based on the nonlinear Euler-Bernoulli beam theory and large deformation assumptions to predict the buckling mode transitions of the beam. Based on the presented theoretical model, the geometry properties of the measuring device can be designed such that its buckling response is effectively controlled. Consequently, the thermally induced displacement can be designed as limit states to detect excessive thermal loads on concrete bridge girders. The proposed solution sufficiently measures the thermal response of concrete bridges. © 2017 IOP Publishing Ltd.","experiment; finite element (FE) modeling; piezoelectricity; prestressed concrete bridge girder; structural health monitoring (SHM); theoretical model; thermal response","3D printers; Beams and girders; Bridges; Buckling; Concrete beams and girders; Concrete bridges; Concretes; Continuum mechanics; Crystallography; Electric measuring bridges; Experiments; Finite element method; Highway bridges; Piezoelectric transducers; Piezoelectricity; Plate girder bridges; Prestressed concrete; Signal detection; Thermal load; Transducers; Concrete bridge girders; Continuous temperature; Euler Bernoulli beam theory; Finite element simulations; Polyvinylidene fluoride transducers; Structural health monitoring (SHM); Theoretical modeling; Thermal response; Structural health monitoring",,,,,,,,,,,,,,,,"Roy, M., Ray, I., Davalos, J.F., High-performance fiber-reinforced concrete: Development and evaluation as a repairing material (2013) J. 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"Baqersad J., Bharadwaj K.","55236538600;57195733509;","Strain expansion-reduction approach",2018,"Mechanical Systems and Signal Processing","101",,,"156","167",,22,"10.1016/j.ymssp.2017.08.023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029722615&doi=10.1016%2fj.ymssp.2017.08.023&partnerID=40&md5=2b0d3d69b15619c1a3e69eec4e372e8a","NVH and Experimental Mechanics Laboratory, Kettering University, 1700 University Avenue, Flint, MI  48504, United States","Baqersad, J., NVH and Experimental Mechanics Laboratory, Kettering University, 1700 University Avenue, Flint, MI  48504, United States; Bharadwaj, K., NVH and Experimental Mechanics Laboratory, Kettering University, 1700 University Avenue, Flint, MI  48504, United States","Validating numerical models are one of the main aspects of engineering design. However, correlating million degrees of freedom of numerical models to the few degrees of freedom of test models is challenging. Reduction/expansion approaches have been traditionally used to match these degrees of freedom. However, the conventional reduction/expansion approaches are only limited to displacement, velocity or acceleration data. While in many cases only strain data are accessible (e.g. when a structure is monitored using strain-gages), the conventional approaches are not capable of expanding strain data. To bridge this gap, the current paper outlines a reduction/expansion technique to reduce/expand strain data. In the proposed approach, strain mode shapes of a structure are extracted using the finite element method or the digital image correlation technique. The strain mode shapes are used to generate a transformation matrix that can expand the limited set of measurement data. The proposed approach can be used to correlate experimental and analytical strain data. Furthermore, the proposed technique can be used to expand real-time operating data for structural health monitoring (SHM). In order to verify the accuracy of the approach, the proposed technique was used to expand the limited set of real-time operating data in a numerical model of a cantilever beam subjected to various types of excitations. The proposed technique was also applied to expand real-time operating data measured using a few strain gages mounted to an aluminum beam. It was shown that the proposed approach can effectively expand the strain data at limited locations to accurately predict the strain at locations where no sensors were placed. © 2017 Elsevier Ltd","Condensation techniques; Modal analysis; Modal expansion; Strain mode shapes; Structural health monitoring","Degrees of freedom (mechanics); Linear transformations; Metadata; Modal analysis; Numerical models; Strain gages; Structural health monitoring; Condensation techniques; Conventional approach; Digital image correlation technique; Engineering design; Modal expansion; Strain mode shapes; Structural health monitoring (SHM); Transformation matrices; Data reduction",,,,,,,,,,,,,,,,"Baghalian, A., Tahakori, S., Fekrmandi, H., Unal, M., Senyurek, V., McDaniel, D., Tansel, I., Implementation of the surface response to excitation method for pipes (2017) Mechanics of Composite and Multi-functional Materials, vol. 7, Springer, pp. 261-266; Tashakori, S., Baghalian, A., Unal, M., Fekrmandi, H., McDaniel, D., Tansel, I.N., Contact and non-contact approaches in load monitoring applications using surface response to excitation method (2016) Measurement, 89, pp. 197-203; Fekrmandi, H., Unal, M., Baghalian, A., Tashakori, S., Oyola, K., Alsenawi, A., Tansel, I.N., A non-contact method for part-based process performance monitoring in end milling operations (2016) Int. J. Adv. Manuf. Technol., 83, pp. 13-20; Gwashavanhu, B., Oberholster, A.J., Heyns, P.S., Rotating blade vibration analysis using photogrammetry and tracking laser Doppler vibrometry (2016) Mech. Syst. Signal Process., 76-77, pp. 174-186; Ehrhardt, D.A., Allen, M.S., Yang, S., Beberniss, T.J., Full-field linear and nonlinear measurements using continuous-scan laser doppler vibrometry and high speed three-dimensional digital image correlation, Mech. Syst. Signal Process. 86 (2017) 82–97; Baqersad, J., Poozesh, P., Niezrecki, C., Avitabile, P., Photogrammetry and optical methods in structural dynamics – a review, Mech. Syst. Signal Process. 86 (2017) 17–34; Poozesh, P., Baqersad, J., Niezrecki, C., Avitabile, P., Harvey, E., Yarala, R., Large-area photogrammetry based testing of wind turbine blades, Mech. Syst. Signal Process. 86 (2017) 98–115; Carr, J., Baqersad, J., Niezrecki, C., Avitabile, P., Full-field dynamic strain on wind turbine blade using digital image correlation techniques and limited sets of measured data from photogrammetric targets (2015) Exp. Tech.; LeBlanc, B., Niezrecki, C., Avitabile, P., Chen, J., Sherwood, J., Damage detection and full surface characterization of a wind turbine blade using three-dimensional digital image correlation (2013) Structural Health Monitoring, 12, pp. 430-439; Stasicki, B., Boden, F., In-flight measurements of aircraft propeller deformation by means of an autarkic fast rotating imaging system (2015), http://dx.doi.org/10.1117/12.2081393, International Conference on Experimental Mechanics 2014, ICEM 2014, November 15, 2014–November 17, 2014, SPIE, Singapore, Singapore; Stasicki, B., Boden, F., Application of high-speed videography for in-flight deformation measurements of aircraft propellers (2009), http://dx.doi.org/10.1117/12.822046, 28th International Congress on High-Speed Imaging and Photonics, November 9, 2008–November 14, 2008, SPIE, Canberra, Australia; Sicard, J., Sirohi, J., Modeling of the large torsional deformation of an extremely flexible rotor in hover (2014) AIAA J., 52, pp. 1604-1615; Sicard, J., Sirohi, J., Measurement of the deformation of an extremely flexible rotor blade using digital image correlation (2013) Meas. Sci. Technol., 24, p. 065203; Patil, K., Baqersad, J., Sheidaei, A., A multi-view digital image correlation for extracting mode shapes of a tire (2017) Shock & Vibration, Aircraft/Aerospace, Energy Harvesting, Acoustics & Optics, vol. 9, Springer, Cham, pp. 211-217; Cigada, A., Mazzoleni, P., Tarabini, M., Zappa, E., Static and dynamic monitoring of bridges by means of vision-based measuring system (2013) Topics in Dynamics of Bridges, vol. 3, Springer, pp. 83-92; Busca, G., Cigada, A., Mazzoleni, P., Zappa, E., Vibration monitoring of multiple bridge points by means of a unique vision-based measuring system (2014) Exp. Mech., 54, pp. 255-271; Rajaram, S., Vanniamparambil, P., Khan, F., Bolhassani, M., Koutras, A., Bartoli, I., Moon, F., Tyson, J., Full-field deformation measurements during seismic loading of masonry buildings (2016) Struct. Control Health Monit.; Lundstrom, T., Baqersad, J., Niezrecki, C., Monitoring the Dynamics of a Helicopter Main Rotor With High-Speed Stereophotogrammetry (2015) Exp. Tech.; Guyan, R.J., Reduction of stiffness and mass matrices (1965) AIAA J., 3. , 380-380; Kidder, R.L., Reduction of structural frequency equations (1973) AIAA J., 11. , 892-892; O'Callahan, J., Avitabile, P., Riemer, R., System equivalent reduction expansion process (SEREP) (1989) Proceedings of the 7th international modal analysis conference, Union College Schenectady, NY, pp. 29-37; O'Callahan, J.C., A procedure for an improved reduced system (IRS) model (1989) Proceedings of the 7th international modal analysis conference, Las Vegas, pp. 17-21; Yang, Y., Sun, P., Nagarajaiah, S., Bachilo, S.M., Weisman, R.B., Full-field, high-spatial-resolution detection of local structural damage from low-resolution random strain field measurements (2017) J. Sound Vib.; Chipman, C., Avitabile, P., Expansion of transient operating data (2012) Mech. Syst. Signal Process., 31, pp. 1-12; Avitabile, P., Pingle, P., Prediction of full field dynamic strain from limited sets of measured data (2012) Shock Vib., 19, pp. 765-785; Noppe, N., Iliopoulos, A., Weijtjens, W., Devriendt, C., Full load estimation of an offshore wind turbine based on SCADA and accelerometer data, Journal of Physics: Conference Series, IOP Publishing, 2016, pp. 072025; Maes, K., Iliopoulos, A., Weijtjens, W., Devriendt, C., Lombaert, G., Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms (2016) Mech. Syst. Signal Process., 76-77, pp. 592-611; Iliopoulos, A., Shirzadeh, R., Weijtjens, W., Guillaume, P., Hemelrijck, D.V., Devriendt, C., A modal decomposition and expansion approach for prediction of dynamic responses on a monopile offshore wind turbine using a limited number of vibration sensors (2016) Mech. Syst. Signal Process., 68-69, pp. 84-104; Baqersad, J., Poozesh, P., Niezrecki, C., Avitabile, P., A noncontacting approach for full-field strain monitoring of rotating structures (2016) J. Vib. Acoust., 138. , 031008-031008; Baqersad, J., Niezrecki, C., Avitabile, P., Full-field dynamic strain prediction on a wind turbine using displacements of optical targets measured by stereophotogrammetry (2015) Mech. Syst. Signal Process., 62, pp. 284-295; Baqersad, J., Niezrecki, C., Avitabile, P., Extracting full-field dynamic strain on a wind turbine rotor subjected to arbitrary excitations using 3D point tracking and a modal expansion technique (2015) J. Sound Vib., 352, pp. 16-29; Tessler, A., Structural analysis methods for structural health management of future aerospace vehicles (2007) Key Engineering Materials, Trans Tech Publ, pp. 57-66; Tessler, A., Spangler, J.L., A least-squares variational method for full-field reconstruction of elastic deformations in shear-deformable plates and shells (2005) Comput. Methods Appl. Mech. Eng., 194, pp. 327-339; Rahneshin, V., Chierichetti, M., An integrated approach for non-periodic dynamic response prediction of complex structures: numerical and experimental analysis (2016) J. Sound Vib., 378, pp. 38-55","Baqersad, J.; NVH and Experimental Mechanics Laboratory, 1700 University Avenue, United States; email: jbaqersad@kettering.edu",,,"Academic Press",,,,,08883270,,MSSPE,,"English","Mech Syst Signal Process",Article,"Final","",Scopus,2-s2.0-85029722615
"Kildashti K., Makki Alamdari M., Kim C.W., Gao W., Samali B.","32367724300;55814161700;54961963100;55286254200;7003397589;","Drive-by-bridge inspection for damage identification in a cable-stayed bridge: Numerical investigations",2020,"Engineering Structures","223",,"110891","","",,21,"10.1016/j.engstruct.2020.110891","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089100887&doi=10.1016%2fj.engstruct.2020.110891&partnerID=40&md5=20a2bc9189cda55219f10b5ef5a5789d","Centre for Infrastructure Engineering, Western Sydney UniversityNSW  2751, Australia; Centre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW  2052, Australia; Department of Civil & Earth Resource Engineering, Graduate School of Engineering, Kyoto University, Japan","Kildashti, K., Centre for Infrastructure Engineering, Western Sydney UniversityNSW  2751, Australia; Makki Alamdari, M., Centre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW  2052, Australia, Department of Civil & Earth Resource Engineering, Graduate School of Engineering, Kyoto University, Japan; Kim, C.W., Department of Civil & Earth Resource Engineering, Graduate School of Engineering, Kyoto University, Japan; Gao, W., Centre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW  2052, Australia; Samali, B., Centre for Infrastructure Engineering, Western Sydney UniversityNSW  2751, Australia","This paper presents one of the first attempts of indirect Bridge Health Monitoring (BHM) for cable damage identification in a cable-stayed bridge. The premise of the work is to identify the location and the severity of a sustained structural damage to the cables, measured solely by vibration response of a moving vehicle passing over the bridge. To this aim, new formulations of dynamic coupling between the vehicle and the bridge are developed, utilizing finite element (FE) approach and validated. Further, the proposed framework to obtain the Vehicle-Bridge Interaction (VBI) is extended to a large-scale cable-stayed bridge. Various damage cases, caused by a partial and incremental change in structural stiffness of cables, being representative of gradual sectional loss due to corrosion, are taken into account. A damage index based on the Empirical Mode Decomposition (EMD) scheme is presented, and through extensive numerical investigations, it is demonstrated that under certain vehicle parameters the vehicle vibration response not only is capable of identifying the suffered damage to the bridge, but also is able to identify the damage location, and further to assess its severity. The contributions of the work are fourfold: (1) Many of the existing studies only focus on the simplified models of the bridge based on a simply supported Euler–Bernoulli beam theory; however, this paper extends the VBI framework to a three-dimensional numerical model of a large-scale bridge structure, being rarely reported in the BHM context. (2) The validation of the technique is demonstrated through extensive numerical investigations on a statically indeterminate cable-stayed bridge. (3) Successful detection, localization and assessment of damage to the cables are obtained using realistic range of vehicle parameters without any bridge response measurements. (4) Through extensive parametric study, the significance of various parameters on the effectiveness of the proposed approach is carefully investigated and discussed. © 2020 Elsevier Ltd","Bridge Health Monitoring (BHM); Cable-stayed bridge; Damage identification; Vehicle-Bridge Interaction (VBI)","Cable stayed bridges; Corrosion; Damage detection; Seats; Signal processing; Structural analysis; Vehicles; Vibrations (mechanical); Bernoulli beam theory; Bridge health monitoring; Damage Identification; Empirical Mode Decomposition; Numerical investigations; Structural stiffness; Three-dimensional numerical modeling; Vehicle-bridge interaction; Bridge cables; bridge; computer simulation; damage mechanics; dynamic analysis; dynamic response; finite element method; identification method; numerical model; stiffness; structural analysis; structural response; vibration",,,,,"Commonwealth Scientific and Industrial Research Organisation, CSIRO; Japan Society for the Promotion of Science, JSPS; University of New South Wales, UNSW; University of Western Sydney, UWS","The authors wish to thank CSIRO's Digital Productivity business unit, Data61 for providing the research data. The instrumentation and the field tests of this bridge have been planned and conducted by researchers at Data61 in collaboration with academics at University of New South Wales (UNSW) and Western Sydney University (WSU). Thanks is also extended to Japan Society for Promotion of Science for providing support.",,,,,,,,,,"Farrar, C.R., Worden, K., An introduction to structural health monitoring (2006) Philosoph Trans Roy Soc A: Mathe Phys Eng Sci, 365 (1851), pp. 303-315; O'Brien, E.J., McGetrick, P., González, A., A drive-by inspection system via vehicle moving force identification (2014) Smart Struct Syst, 13 (5), pp. 821-848; Chen, Z., Zhou, X., Wang, X., Dong, L., Qian, Y., Deployment of a smart structural health monitoring system for long-span arch bridges: A review and a case study (2017) Sensors, 17 (9), p. 2151; https://www.theguardian.com/cities/2019/feb/26/what-caused-the-genoa-morandi-bridge-collapse-and-the-end-of-an-italian-national-myth; Kim, S., Pakzad, S., Culler, D., Demmel, J., Fenves, G., Glaser, S., Wireless sensor networks for structural health monitoring, in (2006) Proceedings of the 4th international conference on Embedded networked sensor systems, ACM, pp. 427-428; Alvandi, A., Cremona, C., Assessment of vibration-based damage identification techniques (2006) J Sound Vib, 292 (1-2), pp. 179-202; Elhattab, A., Uddin, N., O'Brien, E., Drive-by bridge damage monitoring using bridge displacement profile difference (2016) J Civil Struct Health Monitor, 6 (5), pp. 839-850; Lin, C., Yang, Y., Use of a passing vehicle to scan the fundamental bridge frequencies: An experimental verification (2005) Eng Struct, 27 (13), pp. 1865-1878; Cantero, D., McGetrick, P., Kim, C.-W., O'Brien, E., Experimental monitoring of bridge frequency evolution during the passage of vehicles with different suspension properties (2019) Eng Struct, 187, pp. 209-219; Quirke, P., Bowe, C., O'Brien, E.J., Cantero, D., Antolin, P., Goicolea, J.M., Railway bridge damage detection using vehicle-based inertial measurements and apparent profile (2017) Eng Struct, 153, pp. 421-442; Hester, D., González, A., A discussion on the merits and limitations of using drive-by monitoring to detect localised damage in a bridge (2017) Mech Syst Signal Process, 90, pp. 234-253; Yang, Y., Li, Y., Chang, K., Constructing the mode shapes of a bridge from a passing vehicle: a theoretical study (2014) Smart Struct Syst, 13 (5), pp. 797-819; Yang, Y.-B., Lin, C., Yau, J., Extracting bridge frequencies from the dynamic response of a passing vehicle (2004) J Sound Vib, 272 (3-5), pp. 471-493; González, A., O'Brien, E.J., McGetrick, P., Identification of damping in a bridge using a moving instrumented vehicle (2012) J Sound Vib, 331 (18), pp. 4115-4131; He, W.-Y., He, J., Ren, W.-X., Damage localization of beam structures using mode shape extracted from moving vehicle response (2018) Measurement, 121, pp. 276-285; Kong, X., Cai, C., Kong, B., Numerically extracting bridge modal properties from dynamic responses of moving vehicles (2016) J Eng Mech, 142 (6), p. 04016025; Malekjafarian, A., McGetrick, P.J., O'Brien, E.J., A review of indirect bridge monitoring using passing vehicles (2015) Shock Vib; Keenahan, J., O'Brien, E.J., McGetrick, P.J., Gonzalez, A., The use of a dynamic truck–trailer drive-by system to monitor bridge damping (2014) Struct Health Monitor, 13 (2), pp. 143-157; Malekjafarian, A., O'Brien, E.J., Identification of bridge mode shapes using short time frequency domain decomposition of the responses measured in a passing vehicle (2014) Eng Struct, 81, pp. 386-397; O'Brien, E.J., Malekjafarian, A., A mode shape-based damage detection approach using laser measurement from a vehicle crossing a simply supported bridge (2016) Struct Control Health Monitor, 23 (10), pp. 1273-1286; Tan, C., Uddin, N., O'Brien, E.J., McGetrick, P.J., Kim, C.-W., Extraction of bridge modal parameters using passing vehicle response (2019) J Bridge Eng, 24 (9), p. 04019087; McGetrick, P.J., Kim, C.W., (2013), 569, pp. 262-69. , A parametric study of a drive by bridge inspection system based on the morlet wavelet. 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In: Topics in Dynamics of Bridges, Springer; Bu, J., Law, S., Zhu, X., Innovative bridge condition assessment from dynamic response of a passing vehicle (2006) J Eng Mech, 132 (12), pp. 1372-1379; O'Brien, E.J., Malekjafarian, A., González, A., Application of empirical mode decomposition to drive-by bridge damage detection (2017) Eur J Mech-A/Solids, 61, pp. 151-163; Locke, W., Sybrandt, J., Redmond, L., Safro, I., Atamturktur, S., Using drive-by health monitoring to detect bridge damage considering environmental and operational effects (2020) J Sound Vib, 468. , 115088","Makki Alamdari, M.; Centre for Infrastructure Engineering and Safety, Australia; email: m.makkialamdari@unsw.edu.au",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","",Scopus,2-s2.0-85089100887
"Asadollahi P., Huang Y., Li J.","57190584457;57149144700;55892223900;","Bayesian finite element model updating and assessment of cable-stayed bridges using wireless sensor data",2018,"Sensors (Switzerland)","18","9","3057","","",,21,"10.3390/s18093057","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053662413&doi=10.3390%2fs18093057&partnerID=40&md5=c019f009c20d0b665bf5c5d8cb2edb62","Department of Civil, Environmental, and Architectural Engineering, The University of Kansas, Lawrence, KS  66049, United States; Key Lab of Structural Dynamic Behavior and Control of the Ministry of Education, School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China; Key Lab of Smart Prevention and Mitigation for Civil Engineering Disasters of the Ministry of Industry and Information, Harbin Institute of Technology, Harbin, 150090, China","Asadollahi, P., Department of Civil, Environmental, and Architectural Engineering, The University of Kansas, Lawrence, KS  66049, United States; Huang, Y., Key Lab of Structural Dynamic Behavior and Control of the Ministry of Education, School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China, Key Lab of Smart Prevention and Mitigation for Civil Engineering Disasters of the Ministry of Industry and Information, Harbin Institute of Technology, Harbin, 150090, China; Li, J., Department of Civil, Environmental, and Architectural Engineering, The University of Kansas, Lawrence, KS  66049, United States","We focus on a Bayesian inference framework for finite element (FE) model updating of a long-span cable-stayed bridge using long-term monitoring data collected from a wireless sensor network (WSN). A robust Bayesian inference method is proposed which marginalizes the prediction-error precisions and applies Transitional Markov Chain Monte Carlo (TMCMC) algorithm. The proposed marginalizing error precision is compared with other two treatments of prediction-error precisions, including the constant error precisions and updating error precisions through theoretical analysis and numerical investigation based on a bridge FE model. TMCMC is employed to draw samples from the posterior probability density function (PDF) of the structural model parameters and the uncertain prediction-error precision parameters if required. It is found that the proposed Bayesian inference method with prediction-error precisions marginalized as “nuisance” parameters produces an FE model with more accurate posterior uncertainty quantification and robust modal property prediction. When applying the identified modal parameters from acceleration data collected during a one-year period from the large-scale WSN on the bridge, we choose two candidate model classes using different parameter grouping based on the clustering results from a sensitivity analysis and apply Bayes’ Theorem at the model class level. By implementing the TMCMC sampler, both the posterior distributions of the structural model parameters and the plausibility of the two model classes are characterized given the real data. Computation of the posterior probabilities over the candidate model classes provides a procedure for Bayesian model class assessment, where the computation automatically implements Bayesian Ockham razor that trades off between data-fitting and model complexity, which penalizes model classes that “over-fit” the data. The results of FE model updating and assessment based on the real data using the proposed method show that the updated FE model can successfully predict modal properties of the structural system with high accuracy. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.","Bayesian model class assessment; Bayesian model updating; Cable-stayed bridge; Prediction-error precision; Structural health monitoring; Transitional Markov Chain Monte Carlo; Wireless sensor network","Bayesian networks; Buffeting; Cable stayed bridges; Cables; Chains; Clustering algorithms; Errors; Forecasting; Inference engines; Markov processes; Modal analysis; Monte Carlo methods; Probability density function; Sensitivity analysis; Structural health monitoring; Uncertainty analysis; Wireless sensor networks; Bayesian model; Bayesian model updating; Finite-element model updating; Long span cable stayed bridges; Long-term monitoring datum; Markov Chain Monte-Carlo; Prediction errors; Structural model parameters; Finite element method",,,,,"National Science Foundation, NSF: CMMI-0928886; Directorate for Engineering, ENG: 0928886; National Natural Science Foundation of China, NSFC: 51778192; National Research Foundation of Korea, NRF: NRF-2008-220-D00117; National Key Scientific Instrument and Equipment Development Projects of China: 2017YFF0108702","Acknowledgments: Real-world application of Bayesian FE model updating performed in this paper is based on the data collected under the support of the National Research Foundation of Korea Grant NRF-2008-220-D00117 (principal investigator, Hyung-Jo Jung) and the U.S. National Science Foundation Grant CMMI-0928886 (principal investigator, Billie F. Spencer, Jr.).","Funding: This research is supported by the new faculty startup fund of The University of Kansas, the National Key Scientific Instrument and Equipment Development Project of China (2017YFF0108702) and the National Natural Science Foundation of China (NSFC Grant No. 51778192).",,,,,,,,,"Perera, R., Fang, S., Huerta, C., Structural crack detection without updated baseline model by single and multiobjective optimization (2009) Mech. Syst. Signal Process., 23, pp. 752-768; Kabban, C., Uber, R., Lin, K., Lin, B., Bhuiyan, M.Y., Giurgiutiu, V., Uncertainty evaluation in the design of structural health monitoring systems for damage detection (2018) Aerospace, 5, p. 45; Zhang, Q., Chang, T.Y.P., Chang, C.C., Finite-element model updating for the Kap Shui Mun cable-stayed bridge (2001) J. Bridge Eng., 6, pp. 285-293; Zapico, J., Gonzalez, M., Friswell, M., Taylor, C., Crewe, A., Finite element model updating of a small scale bridge (2003) J. 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ASCE, 138, pp. 683-694; Asadollahi, P., Li, J., Statistical analysis of modal properties of a cable-stayed bridge through long-term structural health monitoring with wireless smart sensor networks (2016) Proceedings of the SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, , Las Vegas, NV, USA, 20–24 March; Asadollahi, P., Li, J., Statistical Analysis of Modal Properties of a Cable-Stayed Bridge through Long-Term Wireless Structural Health Monitoring (2017) J. Bridge Eng., 22; James, G.H., III, Carrie, T.G., Lauffer, J.P., The natural excitation technique (NExT) for modal parameter extraction from operating wind turbines (1995) Int. J. Anal. Exp. Modal Anal, 10, pp. 260-277; Juang, J.-N., Pappa, R.S., An eigensystem realization algorithm for modal parameter identification and model reduction (1985) J. Guid. Control Dyn., 8, pp. 620-627; Huang, Y., Beck, J., Full Gibbs Sampling Procedure for Bayesian System Identification incorporating Sparse Bayesian Learning with Automatic Relevance Determination (2018) Comput-Aided Civ. Inf., 33, pp. 712-730; Huang, Y., Beck, J., Li, H., Multi-task Sparse Bayesian Learning with Applications in Structural Health Monitoring (2018) Comput-Aided Civ. Inf.","Huang, Y.; Key Lab of Structural Dynamic Behavior and Control of the Ministry of Education, China; email: huangyong@hit.edu.cn",,,"MDPI AG",,,,,14248220,,,"30213096","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85053662413
"Sun L.M., Zhang W., Nagarajaiah S.","7403956279;56646249600;7003411593;","Bridge Real-Time Damage Identification Method Using Inclination and Strain Measurements in the Presence of Temperature Variation",2019,"Journal of Bridge Engineering","24","2",,"","",,20,"10.1061/(ASCE)BE.1943-5592.0001325","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057151869&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001325&partnerID=40&md5=9d3cb39502302937aa3433b6c8fc1966","Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China; Dept. of Civil and Environmental Engineering, Rice Univ., Houston, TX  77005, United States","Sun, L.M., Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China; Zhang, W., Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China; Nagarajaiah, S., Dept. of Civil and Environmental Engineering, Rice Univ., Houston, TX  77005, United States","In this paper, a new real-time damage identification method has been presented for bridge structural health monitoring (SHM) considering temperature variation. The method utilizes model-based damage identification that involves three major steps: (1) efficient basis functions - extracted from finite-element (FE) models prior to real-time identification; (2) partial least-squares regression (PLSR) analyses; and (3) the fusion of different types of structural responses into damage indicator. By treating local damages as equivalent vertical loads and then cross-referencing global (inclinations) and local (strain) data, the hidden damage information in bridge structures can be detected and localized in a timely fashion, even in the presence of unknown temperature variation as well as vehicle loads. Inclinations alone cannot reflect local damages, but by fusing inclinations and strains (that represent local damage) into the proposed damage indicator, local damages can be identified. Numerical simulations on a medium-span continuous bridge demonstrate that the proposed method is insensitive to measurement noise and some common modeling errors, revealing the potential of real-time damage identification in bridge SHM applications. © 2018 American Society of Civil Engineers.","Damage indicator; Finite-element model; Partial least-squares regression; Real-time damage identification; Structural health monitoring; Temperature variation","Finite element method; Least squares approximations; Numerical methods; Strain; Structural health monitoring; Temperature distribution; Bridge structural health monitoring; Damage Identification; Damage indicator; Partial least squares regression; Partial least squares regressions (PLSR); Real-time identification; Structural response; Temperature variation; Damage detection",,,,,"SLDRCE15-A-02; China Scholarship Council, CSC: 201506260124, SLDRCE13-MB-01","The authors acknowledge support for the work reported in this paper from State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ. (Grant SLDRCE15-A-02), China Scholarship Council (File 201506260124), and Tongji Grant (SLDRCE13-MB-01).",,,,,,,,,,"(2009) User's Manual, Version 12.0 Swanson Analysis System., , ANSYS. Canonsburg, PA: ANSYS; Barth, K., Steel bridge design handbook design example 2a: Two-span continuous straight composite steel I-girder bridge (2012) Federal Highway Administration Rep, , FHWA-IF-12-052. Pittsburgh, PA: HDR Engineering; Bernal, D., Load vectors for damage localization (2002) J. Eng. Mech., 128 (1), pp. 7-14. , https://doi.org/10.1061/(ASCE)0733-9399(2002)128:1(7); Bernal, D., Flexibility-based damage localization from stochastic realization results (2006) J. Eng. Mech., 132 (6), pp. 651-658. , https://doi.org/10.1061/(ASCE)0733-9399(2006)132:6(651); Carden, E.P., Fanning, P., Vibration based condition monitoring: A review (2004) Struct. Health Monit., 3 (4), pp. 355-377. , https://doi.org/10.1177/1475921704047500; Duan, Y.F., Xu, Y.L., Fei, Q.G., Wong, K.Y., Chan, K.W.Y., Ni, Y.Q., Ng, C.L., Advanced finite element model of Tsing Ma Bridge for structural health monitoring (2011) Int. J. Struct. Stab. Dyn., 11 (2), pp. 313-344. , https://doi.org/10.1142/S0219455411004117; Fan, W., Qiao, P., Vibration-based damage identification methods: A review and comparative study (2011) Struct. Health Monit., 10 (1), pp. 83-111. , https://doi.org/10.1177/1475921710365419; Follen, C.W., Brenner, R.B.S.M., Vogel, R.M., Statistical bridge signatures (2014) J. Bridge Eng., 19 (7), p. 04014022. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000596; Fritzen, C.-P., Jennewein, D., Kiefer, T., Damage detection based on model updating methods (1998) Mech. Syst. Sig. 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Bridge Eng., 22 (2), p. 04016117. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000979","Nagarajaiah, S.; Dept. of Civil and Environmental Engineering, United States; email: satish.nagarajaiah@rice.edu",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85057151869
"Fiore A., Marano G.C.","34972651600;57382102800;","Serviceability Performance Analysis of Concrete Box Girder Bridges Under Traffic-Induced Vibrations by Structural Health Monitoring: A Case Study",2018,"International Journal of Civil Engineering","16","5",,"553","565",,20,"10.1007/s40999-017-0161-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042483593&doi=10.1007%2fs40999-017-0161-3&partnerID=40&md5=56317e5cce0e1e9e31ac2573bc1a8ea4","Department of Science of Civil Engineering and Architecture, Technical University of Bari, via Orabona 4, Bari, 70125, Italy; College of Civil Engineering, Fuzhou University, Xue Yuan Road, Fuzhou, 350108, China","Fiore, A., Department of Science of Civil Engineering and Architecture, Technical University of Bari, via Orabona 4, Bari, 70125, Italy; Marano, G.C., Department of Science of Civil Engineering and Architecture, Technical University of Bari, via Orabona 4, Bari, 70125, Italy, College of Civil Engineering, Fuzhou University, Xue Yuan Road, Fuzhou, 350108, China","The perceptible vibration of concrete box girders under traffic loads is an important topic in existing bridges, on which vehicle movement often cause vibrations too strong from the viewpoints of travelers. In this paper, the results of an extensive program of full-scale ambient vibration tests involving a 380 m concrete box girder bridge, the Cannavino bridge in Italy, are presented. The human safety assessment procedure of the bridge includes ambient vibration testing, identification of modal parameters from ambient vibration data, comparison with a detailed finite element modeling as validation of experimental measurements, comparison of peak accelerations to reference values from technical standards/literature in order to estimate the vibration level, and evaluation of safety by the use of histograms. A total of nine modal frequencies are identified for the deck structure within the frequency range of 0–10 Hz. The results of the ambient vibration survey are compared to the modal frequencies computed by a detailed three-dimensional finite element model of the bridge, obtaining a very good agreement. It emerges that a linear finite element model appears to be capable of capturing the dynamic behavior of concrete box girder bridges with very good accuracy. For each direction, experimental peak accelerations are compared to acceptable human levels available in technical standards/literature, showing that traffic loads mainly induce a vertical component of vibration on the bridge deck. Finally, the elaboration of histograms allows to assess that the bridge is exposed to clearly perceptible vertical vibrations, requiring the adoption of suitable vibration reduction devices. © 2017, Iran University of Science and Technology.","Acceptable human levels; Ambient vibration testing; Concrete box girder bridge; Dynamic identification; Structural vibration; Traffic load","Box girder bridges; Bridge decks; Concrete beams and girders; Finite element method; Graphic methods; Modal analysis; Safety testing; Software testing; Standards; Steel bridges; Structural dynamics; Structural health monitoring; Structural panels; Ambient Vibration Testing; Concrete box girder bridge; Dynamic identification; Human levels; Structural vibrations; Traffic loads; Vibration analysis",,,,,,,,,,,,,,,,"Wilson, J.C., Liu, T., Ambient vibration measurements on a cable-stayed bridge (1991) Earthq Eng Struct Dyn, 20, pp. 723-747; Liu, M., Frangopol, D.M., Kim, S., Bridge system performance assessment from structural health monitoring: a case study (2009) J Struct Eng-ASCE, 135 (6), pp. 733-742; Ko, J.M., Ni, Y.Q., Technology developments in structural health monitoring of large-scale bridges (2005) Eng Struct, 27, pp. 1715-1725; Deraemaeker, A., Reyndersb, E., De Roeckb, G., Kullaac, J., Vibration-based structural health monitoring using output-only measurements under changing environment (2008) Mech Syst Signal Pr, 22, pp. 34-56; Brownjohn, J.M.W., Magalhaes, F., Caetano, E., Cunha, A., Ambient vibration re-testing and operational modal analysis of the Humber Bridge (2010) Eng Struct, 32, pp. 2003-2018; Moghimi, H., Ronagh, H.R., Development of a numerical model for bridge-vehicle interaction and human response to traffic-induced vibration (2008) Eng Struct, 30, pp. 3808-3819; Bosurgi, G., Bongiorno, N., Pellegrino, O., A nonlinear model to predict drivers’ track paths along a curve (2016) Int J Civil Eng, 14 (5), pp. 271-280; Fiore, A., Monaco, P., Raffaele, D., Viscoelastic behaviour of non-homogeneous variable-section beams with postponed restraints (2012) Comput Concrete, 9 (5), pp. 375-392; Fiore, A., Foti, D., Monaco, P., Raffaele, D., Uva, G., An approximate solution for the rheological behavior of non-homogeneous structures changing the structural system during the construction process (2013) Eng Struct, 46, pp. 631-642; Quaranta, G., Fiore, A., Marano, G.C., Optimum design of prestressed concrete beams using constrained differential evolution algorithm (2014) Struct Multidiscip Optim, 49 (3), pp. 441-453; Colapietro, D., Fiore, A., Netti, A., Fatiguso, F., Marano, G.C., de Fino, M., Cascella, D., Ancona, A., Dynamic identification and evaluation of the seismic safety of a masonry bell tower in the south of Italy (2013) COMPDYN 2013–4th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, , Kos Island, Greece, 12–14 June 2013; Magalhães, F., Cunha, Á., Explaining operational modal analysis with data from an arch bridge (2011) Mech Syst Signal Pr, 25, pp. 1431-1450; Altunişik, A.C., Bayraktar, A., Sevim, B., Operational modal analysis of a scaled bridge model using EFDD and SSI methods (2012) Indian J Eng Mater Sci, 19, pp. 320-330; Cury, A., Cremona, C., Dumoulin, J., Long-term monitoring of a PSC box girder bridge (2012) Mech Syst Signal Pr, 33, pp. 13-37; Bayraktar, A., Altunişik, A.C., Türker, T., Structural condition assessment of Birecik highway bridge using operational modal analysis (2016) Int J Civil Eng, 14 (1), pp. 35-46; Peeters, B., De Roeck, G., Stochastic system identification for operational modal analysis: a review (2001) J Dyn Syst Meas Control, 123 (4), pp. 659-667; Peeters, B., De Roeck, G., Reference-based stochastic subspace identification for output-only modal analysis (1999) Mech Syst Signal Pr, 13 (6), pp. 855-878; Van Overschee, P., Moor, B.D., (1996) Subspace Identification for Linear Systems, , Kluwer Academic Publishers, Dordrecht; Fiore, A., Monaco, P., POD-based representation of the alongwind Equivalent Static Force for long-span bridges (2009) Wind Struct, 12 (3), pp. 239-257; Mallock, H.R.A., (1902) Vibrations Produced by the Working of Traffic on the Central London Railway, , Board of Trade Report, Command Papers; Smith, J.W., (1988) Vibration of structures, application in civil engineering design, , Chapman and Hall, Boca Raton; Bachmann, H., Pretlove, A.J., Rainer, H., Human response to vibrations (1995) Vibration problems in structures: practical guidelines, , Birkhäuser Verlag, Basel; (2002) Eurocode 2002. Basis of Structural Design—prAnnex A2. EN1990, , European Committee for Standardization, Brussels; (1989) International organization for standards. Evaluation of human exposure to whole-body vibration—part 2: continuous and shock induced vibration in buildings (1–80Hz), , International Standards ISO 2631/2-1989 (E), Geneva; (1999) Recomendaciones Para La realización De Pruebas De Carga De recepción én Puentes De Carretera","Fiore, A.; Department of Science of Civil Engineering and Architecture, via Orabona 4, Italy; email: alessandra.fiore@poliba.it",,,"Springer International Publishing",,,,,17350522,,,,"English","Int. J. Civ. Eng.",Article,"Final","",Scopus,2-s2.0-85042483593
"Scarella A., Salamone G., Babanajad S.K., De Stefano A., Ansari F.","57193335799;57193330837;36187766600;55351762900;55407592700;","Dynamic brillouin scattering-based condition assessment of cables in cable-stayed bridges",2017,"Journal of Bridge Engineering","22","3","04016130","","",,20,"10.1061/(ASCE)BE.1943-5592.0001010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013052718&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001010&partnerID=40&md5=9c157ecc5b6d63edf49c5b5f2fc46317","Smart Sensors and NDT Laboratory, Dept. of Civil and Materials Engineering, Univ. of Illinois at Chicago, Chicago, IL  60607, United States; Politecnico di Torino, Torino, 10129, Italy","Scarella, A., Smart Sensors and NDT Laboratory, Dept. of Civil and Materials Engineering, Univ. of Illinois at Chicago, Chicago, IL  60607, United States; Salamone, G., Smart Sensors and NDT Laboratory, Dept. of Civil and Materials Engineering, Univ. of Illinois at Chicago, Chicago, IL  60607, United States; Babanajad, S.K., Smart Sensors and NDT Laboratory, Dept. of Civil and Materials Engineering, Univ. of Illinois at Chicago, Chicago, IL  60607, United States; De Stefano, A., Politecnico di Torino, Torino, 10129, Italy; Ansari, F., Smart Sensors and NDT Laboratory, Dept. of Civil and Materials Engineering, Univ. of Illinois at Chicago, Chicago, IL  60607, United States","A method for structural health monitoring of cable-stayed bridges based on the dynamic distributed sensing of bridge deck strains is introduced in this article. The objective is to detect the location and the magnitude of the cables that have totally or partially lost their tensile load-carrying capacities. Dynamic sensing provides a realistic approach for condition assessment of the bridge under operational conditions. A Brillouin scattering optical time domain analysis (BOTDA) fiber-optic sensor was used to monitor the distributed strain in the deck of a scaled model cable-stayed bridge in the laboratory. The formulations developed in the present study take advantage of the dynamic distributed sensing capability of the BOTDA in amplitude transfer (AT) mode for the establishment of a relationship between the redistribution of deck strains and the tension loss in the individual cables of cable-stayed bridges. The experimental program involved single cable and multicable damage scenarios. The applicability of the method was independently evaluated by the direct measurement of cable forces and nonlinear finite-element analysis of the bridge. © 2016 American Society of Civil Engineers.","Amplitude transfer Brillouin scattering optical time domain analysis (AT-BOTDA); Brillouin scattering; Cable damage; Cable-stayed bridges; Deck strain; Dynamic distributed strain; Fiber-optic sensors; Structural health monitoring","Bridge cables; Brillouin scattering; Cable stayed bridges; Cables; Fiber optic sensors; Fiber optics; Finite element method; Strain; Structural health monitoring; Amplitude transfer Brillouin scattering optical time domain analysis (AT-BOTDA); Cable damages; Condition assessments; Distributed sensing; Distributed strain; Experimental program; Non-linear finite-element analysis; Operational conditions; Time domain analysis",,,,,,,,,,,,,,,,"Bao, T., Babanajad, S.K., Taylor, T., Ansari, F., Shear force based real-time fiber optic bridge weigh-in-motion system (2015) J. Bridge Eng.; Bao, X., Optical fiber sensors based on Brillouin scattering (2009) Opt. Photonics News, 20 (9), pp. 40-45; Christen, R., Bergamini, A., Motavalli, M., Three-dimensional localization of defects in stay cables using magnetic flux leakage methods (2003) J. Nondestr. Eval., 22 (3), pp. 93-101; Fricker, S., Vogel, T., Site installation and testing of a continuous acoustic monitoring (2007) Constr. Build. Mater., 21 (3), pp. 501-510; Harris, G.H., Sabnis, G., (2011) Structural Modeling and Experimental Techniques, , 2nd Ed., CRC, Boca Raton, FL; Hegab, H.I.A., Energy analysis of cable-stayed bridges (1986) J. Struct. Eng., pp. 1182-1195; Horiguchi, T., Shimizu, K., Kurashima, T., Tateda, M., Koyamada, Y., Development of a distributed sensing technique using Brillouin scattering (1995) J. Lightwave Technol., 13 (7), pp. 1296-1302; Hotate, K., Tanaka, M., Correlation-based continuous-wave technique for optical fiber distributed strain measurement using Brillouin scattering with cm-order spatial resolution-applications to smart materials (2001) IEICE Trans. Electron., 84 (12), pp. 1823-1828; Kang, S.G., Kang, D.H., Kim, C.G., Real-time monitoring of transverse thermal strain of carbon fiber reinforced composites under long-term space environment using fiber optic sensors (2009) NDT&E Int., 42 (5), pp. 361-368; Kim, B.H., Park, T., Estimation of cable tension force using the frequency-based system identification method (2007) J. Sound Vib., 304 (3-5), pp. 660-676; Kishida, K., Che-Hien, L., Nishiguchi, K., Pulse prepump method for cm-order spatial resolution of BOTDA (2005) Proc., SPIE, 5855, pp. 559-562. , 17th Int. Conf. Optical Fiber Sensors, SPIE, Bellingham, WA; Kishida, K., Li, C.H., Mizutani, T., Takeda, N., 2 cm spatial resolution Brillouin distributed sensing system using PPP-BOTDA (2008) Materials Forum, 33. , S. Galea, W. Chiu, and A. Mita, eds., Institute of Materials Engineering Australasia Ltd., Melbourne, VC, Australia; Kishida, K., Zhang, H., Li, C.H., Guzik, A., Suzuki, H., Wu, Z., Diagnostic of corrosion based thinning in steam pipelines by means of Neubrescope high precision optical fiber sensing system (2005) Proc., 5th Int. Workshop on Struct. Health Monit., pp. 1363-1370. , Stanford Univ., Stanford, CA; Lanza Di Scalea, F., Rizzo, P., Seible, F., Stress measurement and defect detection in steel strands by guided stress waves (2003) J. Mater. Civil. Eng., pp. 219-227; Li, C.H., Guzik, A., Kishida, K., The high-performance BOTDA based systems for distributed strain sensing (2010) Proc., 3rd Int. Forum on Opto-Electronic Sensor-based Monit. in Geo-Eng., pp. 1-10. , Nanjing Univ., Nanjing, China; Li, D., Zhou, Z., Ou, J., Development and sensing properties study of FRP-FBG smart cable for bridge health monitoring applications (2011) Measurement, 44 (4), pp. 722-729; Li, H., Ou, J., Zhou, Z., Application of optical fibre Bragg grating sensing technology-based smart stay cables (2009) Opt. Lasers Eng., 47 (10), pp. 1077-1084; Liu, R.M., Babanajad, S.K., Taylor, T., Ansari, F., Experimental study on structural defect detection by monitoring distributed dynamic strain (2015) Smart Mater. Struct., 24 (11); Mehrabi, A., In-service evaluation of cable-stayed bridges, overview of available methods, and findings (2006) J. Bridge Eng., pp. 716-724; Nazarian, E., Ansari, F., Azari, H., Recursive optimization method for monitoring of tension loss in cables of cable-stayed bridges (2015) J. Intell. Mater. Syst. Struct., 27 (15), pp. 2091-2101; Nazarian, E., Ansari, F., Zhang, X., Taylor, T., Detection of tension loss in cables of cable-stayed bridges by distributed monitoring of bridge deck strains (2016) J. Struct. Eng.; Ohno, H., Naruse, H., Kihara, M., Shimada, A., Industrial applications of the BOTDR optical fiber strain sensor (2001) Opt. Fiber Technol., 7 (1), pp. 45-64; Ren, W.X., Chen, G., Hu, W., Empirical formulas to estimate cable tension by cable fundamental frequency (2005) Struct. Eng. Mech., 20 (3), pp. 363-380; Russell, J., Lardner, T., Experimental determination of frequencies and tension for elastic cables (1998) J. Eng. Mech., pp. 1067-1072; SAP2000, , [Computer software]. Computers and Structures, Inc., Walnut Creek, CA; Sumitro, S., Kurokawa, S., Shimano, K., Wang, M.L., Monitoring based maintenance utilizing actual stress sensory technology (2005) Smart Mater. Struct., 14 (3), pp. S68-S78; Voskoboinik, A., SBS-based fiber optical sensing using frequency-domain simultaneous tone interrogation (2011) J. Lightwave Technol., 29 (11), pp. 1729-1735; Yamauchi, Y., Guzik, A., Kishida, K., Li, C.H., A study of the stability, reliability, and accuracy of Neubrescope-based pipe thinning detection system (2007) Proc., 3rd Int. Conf. on Structural Health Monitoring of Intelligent Infrastructure, , International Society for Structural Health Monitoring of Intelligent Infrastructure, Winnipeg, Manitoba, Canada","Ansari, F.; Smart Sensors and NDT Laboratory, United States; email: fansari@uic.edu",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85013052718
"Schommer S., Nguyen V.H., Maas S., Zürbes A.","57112350900;57196466317;35311569000;25029398100;","Model updating for structural health monitoring using static and dynamic measurements",2017,"Procedia Engineering","199",,,"2146","2153",,20,"10.1016/j.proeng.2017.09.156","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029907515&doi=10.1016%2fj.proeng.2017.09.156&partnerID=40&md5=ef59694456b87cee04551eb7f425ab22","University of Luxembourg, Faculty of Science, Technology and Communication, Rue Coudenhove- Kalergi 6; L1359, Luxembourg; Technische Hochschule Bingen, Fachbereich 2 Technik, Informatik und Wirtschaft, Germany","Schommer, S., University of Luxembourg, Faculty of Science, Technology and Communication, Rue Coudenhove- Kalergi 6; L1359, Luxembourg; Nguyen, V.H., University of Luxembourg, Faculty of Science, Technology and Communication, Rue Coudenhove- Kalergi 6; L1359, Luxembourg; Maas, S., University of Luxembourg, Faculty of Science, Technology and Communication, Rue Coudenhove- Kalergi 6; L1359, Luxembourg; Zürbes, A., Technische Hochschule Bingen, Fachbereich 2 Technik, Informatik und Wirtschaft, Germany","Structural health monitoring is tracking static or dynamic characteristics of a structure to identify and localize stiffness reductions for damage detection. Different damage indicators are used and any indicator presents advantages and drawbacks. Hence the idea comes up to combine them in a model-updating procedure using a finite element model. In a first step, a model is fit to match the healthy reference state of the examined structure. Therefore it relies on minimizing a special objective function adding and weighting the differences between measured and calculated static and dynamic structural characteristics. For doing structural health monitoring the measurements are repeated in distinct time intervals and the finite element model is updated again, using the same objective function and minimization procedure. Damage can be identified and localized by highlighting reductions in the stiffness matrix of the model compared to the initial model. The efficiency of the method is illustrated by in-situ tests, where a single beam is examined that was part of a real prestressed concrete bridge. For static tests, 8 displacement transducers were disposed along the length of 40m, while the beam was mass-loaded and the deflection line is analyzed. Modal analysis was performed with swept sine excitation with constant force amplitude to identify eigenfrequencies and mode shapes. Stepwise artificial damage was provoked by cutting multiple prestressed tendons inside the concrete beam. A finite element model with a mapped mesh was created, allowing a variation of Young's modulus in grouped sections. On real bridges temperature is neither homogenous nor constant over time, which often has a considerable influence on measured static and dynamic characteristics as the stiffness of asphalt and/or bearings can be affected. The proposed methods show their efficiency when temperature effects were excluded or compensated after measurement, which is a topic on its own and not discussed here. © 2017 The Authors. Published by Elsevier Ltd.","compensation; damage; detection; model update; sagging; static; temperature","Compensation (personnel); Concrete beams and girders; Concretes; Damage detection; Efficiency; Elastic moduli; Error detection; Finite element method; Modal analysis; Prestressed concrete; Stiffness; Stiffness matrix; Structural dynamics; Temperature; Transducers; Wire; damage; Displacement transducer; Minimization procedures; Model updates; sagging; static; Static and dynamic characteristics; Structural characteristics; Structural health monitoring",,,,,,,,,,,,,,,,"Mordini, A., Savov, K., Wenzel, H., The Finite Element Model Updating: A Powerful Tool for Structural Health Monitoring (2007) Structural Engineering International, 17 (4), pp. 352-358. , · November; Zong, Zh., Lin, X., Niu, J., Finite element model validation of bridge based on structural health monitoring - Part I: Response surface-based finite element model updating (2015) Journal of Traffic and Transportation Engineering (English Edition), 2 (4), pp. 258-278; Xia, Y., Weng, S.H., Xu, Y.-L., A Substructuring Method for Model Updating and Damage Identification (2011) The Proceedings of the Twelfth East Asia-Pacific Conference on Structural Engineering and Construction - EASEC12, 14, pp. 3095-3103; Sheng, Yu., Asce, S.M., Ou, J., Structural Health Monitoring and Model Updating of Aizhai Suspension Bridge (2016) Journal of Aerospace Engineering, 7; Nguyen, V.H., Golinval, J.-C., Localization and quantification of damage in beam-like structures using sensitivities of principal component analysis results (2010) Mechanical Systems and Signal Processing, 24, pp. 1831-1843; Parlo, E., Guillaume, P., Van Overmeire, M., Damage assessment using mode shape sensitivities (2003) Mechanical Systems and Signal Processing, 17 (3), pp. 499-518. , May; Schlune, H., Plos, M., Gylltoft, K., Improved bridge evaluation through finite element model updating using static and dynamic measurements (2009) Engineering Structures (0141-0296), 31 (7), pp. 1477-1485; Nguyen, V.H., Schommer, S., Maas, S., Zürbes, A., Static load testing with temperature compensation for structural health monitoring of bridges (2016) Engineering Structures, 127 (2016), pp. 700-718; Maas, S., Schommer, S., Nguyen, V.H., Waldmann, D., Mahowald, J., Zürbes, A., Some remarks on the influence of temperature-variations, non-linearities, repeatability and ageing on modal-analysis for structural health monitoring of real bridges (2015) EVACES'15, 6th International Conference on Experimental Vibration Analysis for Civil Engineering Structures, , 19-21 October EMPA Zürich Switzerland; Nocedal, J., Wright, S.J., (2006) Numerical Optimization, , New York, NY: Springer Science+Business Media, LLC; Jaishi, B., Ren, W.-X., Damage detection by finite element model updating using modal flexibility residual (2006) Journal of Sound and Vibration, 290 (1-2), pp. 369-387; Teughels, A., Maeck, J., De Roeck, G., Damage assessment by FE model updating using damage functions (2002) Computers & Structures, 80 (25), pp. 1869-1879",,"Romeo F.Gattulli V.Vestroni F.","","Elsevier Ltd","10th International Conference on Structural Dynamics, EURODYN 2017","10 September 2017 through 13 September 2017",,130585,18777058,,,,"English","Procedia Eng.",Conference Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85029907515
"Ozer E., Feng M.Q.","56501583200;7201365644;","Structural reliability estimation with participatory sensing and mobile cyber-physical structural health monitoring systems",2019,"Applied Sciences (Switzerland)","9","14","2840","","",,19,"10.3390/app9142840","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073440301&doi=10.3390%2fapp9142840&partnerID=40&md5=c0a0a1422a2755b1b8ea40d1853393ce","Civil Engineering and Engineering Mechanics, Columbia University, 500 W 120th Street, 610 Mudd, New York, NY  10027, United States","Ozer, E., Civil Engineering and Engineering Mechanics, Columbia University, 500 W 120th Street, 610 Mudd, New York, NY  10027, United States; Feng, M.Q., Civil Engineering and Engineering Mechanics, Columbia University, 500 W 120th Street, 610 Mudd, New York, NY  10027, United States","With the help of community participants, smartphones can become useful wireless sensor network (WSN) components, forma self-governing structural healthmonitoring (SHM) system, andmerge structuralmechanicswith participatory sensing and server computing. This paper presents amethodology and framework of such a cyber-physical system (CPS) that generates a bridge finite element model (FEM) integrated with vibration measurements from smartphone WSNs and centralized/distributed computational facilities, then assesses structural reliability based on updated FEMs. Structural vibration data obtained from smartphones are processed on a server to identify modal frequencies of an existing bridge. Withoutdesigndrawings andsupportivedocumentation but fieldmeasurements andobservations, FEMof the bridge is drafted with uncertainties in the structural mass, stiffness, and boundary conditions (BCs). Then, 2700 FEMs are autonomously generated, and the baseline FEMis updated byminimizing the error between the crowdsourcing-based modal identification results and the FEManalysis. Furthermore, using 151 strong ground motion records from databases, the bridge response time history simulations are conducted to obtain displacement demand distribution. Finally, based on reference performance criteria, structural reliability of the bridge is estimated. Integrating the cyber (FEM analysis) and the physical (the bridge structure and measured vibration characteristics) worlds, this crowdsourcing-based CPS can provide a powerful tool for supporting rapid, remote, autonomous, and objective infrastructure-related decision-making. This study presents a new example of the emerging fourth industrial revolution from structural engineering and SHMperspective. © 2019 by the authors.","Crowdsourcing; Cyber-physical systems; Finite element model updating; Modal identification; Structural health monitoring; Structural reliability estimation",,,,,,"Columbia University","The authors would like to acknowledge Demosthenes Long from Public Safety and Daniel Held from Facilities, Columbia University, for their support throughout on-campus pedestrian bridge tests. This research received no external funding",,,,,,,,,,"(2014) Seismic Evaluation and Retrofit of Existing Buildings (ASCE/SEI 41-13), , American Society of Civil Engineers: Reston, VA, USA; (2015) LRFD Bridge Design Specifications, , American Association of State Highway and Transportation Officials: Washington, DC, USA; Doebling, S.W., Farrar, C.R., Prime, M.B., A summary review of vibration-based damage identification methods (1998) Shock Vib. Dig, 30, pp. 91-105; Carden, E.P., Fanning, P., Vibration based condition monitoring: A review (2004) Struct. Health Monit, 3, pp. 355-377; Brownjohn, J.M., Structural health monitoring of civil infrastructure (2007) Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci, 365, pp. 589-622; Farrar, C.R., Worden, K., An introduction to structural health monitoring (2007) Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci, 365, pp. 303-315; Wahbeh, A.M., Caffrey, J.P., Masri, S.F., A vision-based approach for the direct measurement of displacements in vibrating systems (2003) Smart Mater. Struct, 12, p. 785; Lee, J.J., Shinozuka, M., A vision-based system for remote sensing of bridge displacement (2006) Ndt E Int, 39, pp. 425-431; Feng, D., Feng, M.Q., Ozer, E., Fukuda, Y., A vision-based sensor for noncontact structural displacement measurement (2015) Sensors, 15, pp. 16557-16575; Farrar, C.R., Park, G., Allen, D.W., Todd, M.D., Sensor network paradigms for structural health monitoring (2006) Struct. Control Health Monit, 13, pp. 210-225; Lynch, J.P., Loh, K.J., A summary review of wireless sensors and sensor networks for structural health monitoring (2006) Shock Vib. Dig, 38, pp. 91-130; Gao, Y., Spencer, B.F., Ruiz-Sandoval, M., Distributed Computing Strategy for Structural Health Monitoring (2006) Struct. Control Health Monit, 13, pp. 488-507; Lynch, J.P., An overview of wireless structural health monitoring for civil structures (2007) Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci, 365, pp. 345-372; Aygün, B., Cagri Gungor, V., Wireless sensor networks for structure health monitoring: Recent advances and future research directions (2011) Sens. Rev, 31, pp. 261-276; Prasad, P., Recent trend in wireless sensor network and its applications: A survey (2015) Sens. Rev, 35, pp. 229-236; Farhey, D.N., Integrated virtual instrumentation and wireless monitoring for infrastructure diagnostics (2006) Struct. Health Monit, 5, pp. 29-43; Liu, S.C., Tomizuka, M., Ulsoy, G., Strategic issues in sensors and smart structures (2006) Struct. Control Health Monit, 13, pp. 946-957; Spencer, B.F., Ruiz-Sandoval, M.E., Kurata, N., Smart sensing technology: Opportunities and challenges (2004) Struct. Control Health Monit, 11, pp. 349-368; Jeong, M.J., Koh, B.H., A decentralized approach to damage localization through smart wireless sensors (2009) Smart Struct. Syst, 5, pp. 43-54; Taylor, S.G., Farinholt, K.M., Flynn, E.B., Figueiredo, E., Mascarenas, D.L., Moro, E.A., Park, G., Farrar, C.R., A mobile-agent-based wireless sensing network for structural monitoring applications (2009) Meas. Sci. Technol, 20; Chen, B., Liu, W., Mobile agent computing paradigm for building a flexible structural health monitoring sensor network (2010) Comput.-Aided Civ. Infrastruct. Eng, 25, pp. 504-516; Zhu, D., Yi, X., Wang, Y., Lee, K.M., Guo, J., A mobile sensing system for structural health monitoring: Design and validation (2010) Smart Mater. Struct, 19; OBrien, E.J., Keenahan, J., Drive-by damage detection in bridges using the apparent profile (2015) Struct. 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Eng, 139, pp. 72-83; Castellazzi, G., D'Altri, A., Bitelli, G., Selvaggi, I., Lambertini, A., From laser scanning to finite element analysis of complex buildings by using a semi-automatic procedure (2015) Sensors, 15, pp. 18360-18380; Yang, H., Xu, X., Neumann, I., Laser scanning-based updating of a finite-element model for structural health monitoring (2015) IEEE Sens. J, 16, pp. 2100-2104; Rakha, T., Gorodetsky, A., Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones (2018) Autom. Constr, 93, pp. 252-264","Ozer, E.; Civil Engineering and Engineering Mechanics, 500 W 120th Street, 610 Mudd, United States; email: eo2327@columbia.edu",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85073440301
"Li D., Liang Y., Feng Q., Song G.","57219460395;55359180500;55177077100;7402252860;","Load monitoring of the pin-connected structure based on wavelet packet analysis using piezoceramic transducers",2018,"Measurement: Journal of the International Measurement Confederation","122",,,"638","647",,19,"10.1016/j.measurement.2017.11.014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034043588&doi=10.1016%2fj.measurement.2017.11.014&partnerID=40&md5=15634cd42a0dde5192ffd493b7e90a41","School of Civil Engineering, Dalian University of Technology, Dalian, China; Hubei Key Laboratory of Earthquake Early Warning, Institute of Seismology, CEA, Wuhan, China; Wuhan Institute of Earthquake Engineering Co Ltd, Wuhan, China; Smart Materials and Structures Laboratory, Department of Mechanical Engineering, University of Houston, Houston, TX, United States","Li, D., School of Civil Engineering, Dalian University of Technology, Dalian, China; Liang, Y., Hubei Key Laboratory of Earthquake Early Warning, Institute of Seismology, CEA, Wuhan, China, Wuhan Institute of Earthquake Engineering Co Ltd, Wuhan, China; Feng, Q., Hubei Key Laboratory of Earthquake Early Warning, Institute of Seismology, CEA, Wuhan, China, Wuhan Institute of Earthquake Engineering Co Ltd, Wuhan, China; Song, G., School of Civil Engineering, Dalian University of Technology, Dalian, China, Smart Materials and Structures Laboratory, Department of Mechanical Engineering, University of Houston, Houston, TX, United States","Pin-connections have wide applications in civil structures, such as bridges. Health monitoring of the pin connection plays a significant role to ensure the safety and longevity of these structures. In this paper, a contact model for the pin connection and the normal applied force on the connection was first built based on the Hertzian contact theory, and the validity of the model was verified by the simulation results from a 3D finite element model of a pin connection. The contact model indicates that the contact area between the pin and the pin support increases with the applied force on the connection. Based on this contact model, the authors then present a feasibility study on the load monitoring of pin-connected structures using Lead Zirconate Titanate (PZT) transducers. A tension-controllable structure with a pin connection was fabricated and investigated to verify the effectiveness of the proposed method. Two PZT patches are mounted on the pin and the connected structural surface, respectively. One PZT patch, acting as an actuator, generates a swept sine wave that propagates through the contact area of the pin joint interface and the other one, acting as a sensor, detected the response signal. In the experiment, wavelet packet analysis was employed to quantitatively analyze the transmitted signal between two PZTs when different load levels were applied on the connection. Experimental results demonstrate that energy of the transmitted signal monotonously increases with the load on the pin connection, which is consistent with the simulation result of the contact model. The proposed method has the potential to be employed in real-time monitoring of the loading status of pin connections in practical applications. © 2017","Active sensing; Lead zirconate titanate (PZT); Load monitoring; Pin connection; Wavelet packet analysis","End effectors; Ferroelectric ceramics; Loads (forces); Piezoelectric ceramics; Semiconducting lead compounds; Structural health monitoring; Transducers; Wavelet analysis; Active Sensing; Lead zirconate titanate; Load monitoring; Pin connections; Wavelet Packet Analysis; Finite element method",,,,,"IS201626258; National Natural Science Foundation of China, NSFC: 51278084, 51478080, 51578107, 51708520; Major State Basic Research Development Program of China: 2015CB057704","This work was partially supported by the Major State Basic Research Development Program of China (973 Program, grant number 2015CB057704 ), National Natural Science Foundation of China (Grant number 51708520 , 51478080 , 51278084 and 51578107 ), and Director Foundation of Institute of seismology, China Earthquake Administration (Grant number IS201626258 ). The authors would like to thank for them for their financial support.",,,,,,,,,,"Luebkeman, C.H., (1998), http://web.mit.edu/4.441/1_lectures/1_lecture13/1_lecture13.html, Support and Connection Types, Available from: <; Camanho, P., Hallett, S.R., (2011), Composite joints and connections: Principles, Modelling and Testing, first ed., Woodhead Publishing; Yi, T.-H., Li, H.-N., Detection of shifts in GPS measurements for a long-span bridge using CUSUM chart (2016) Int. J. Struct. Stab. Dyn., 16 (4), p. 1640024; Yi, T.-H., Li, H.-N., Optimal sensor placement for health monitoring of high-rise structure using adaptive monkey algorithm (2015) Struct. Control Health Monitor., 22 (4), pp. 667-681; Yi, T.-H., Li, H.-N., Gu, M., Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge (2013) Measurement, 46 (1), pp. 420-432; Feng, Q., Zhang, Y., Zhu, N., Wang, H., Design of remote real-time monitoring system based on GPRS for railway roadbed subsidence (2013) J. 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Dyn., 14 (2), p. 1350061","Song, G.; Smart Materials and Structures Laboratory, United States; email: gsong@uh.edu",,,"Elsevier B.V.",,,,,02632241,,MSRMD,,"English","Meas J Int Meas Confed",Article,"Final","",Scopus,2-s2.0-85034043588
"Kafle B., Zhang L., Mendis P., Herath N., Maizuar M., Duffield C., Thompson R.G.","37037595300;37039013000;7003700296;55814003800;57193212931;7006447544;56754264400;","Monitoring the Dynamic Behavior of the Merlynston Creek Bridge Using Interferometric Radar Sensors and Finite Element Modeling",2017,"International Journal of Applied Mechanics","9","1","1750003","","",,19,"10.1142/S175882511750003X","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012050793&doi=10.1142%2fS175882511750003X&partnerID=40&md5=e8f9750b6c904a7b2d8509c5e22c0999","School of Engineering, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia; Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC, Australia","Kafle, B., School of Engineering, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia; Zhang, L., Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC, Australia; Mendis, P., Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC, Australia; Herath, N., Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC, Australia; Maizuar, M., Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC, Australia; Duffield, C., Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC, Australia; Thompson, R.G., Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC, Australia","Bridges play an important role in economic development and bring important social benefits. The development of innovative bridge monitoring techniques will enable road authorities to optimize operational and maintenance activities for bridges. However, monitoring the dynamic behavior of a bridge requires a comprehensive understanding of the interaction between the bridge and traffic loading which has not been fully achieved so far. In the present study, an integrated bridge health monitoring framework is developed using advanced 3D Finite Element modeling in conjunction with Weight-in-motion (WIM) technology and interferometric radar sensors (IBIS-S). The realistic traffic loads imposed on the bridge will be obtained through calibration and validation of traffic loading prediction model using real-time bridge dynamic behavior captured by IBIS-S and WIM data. Using the Merlynston Creek Bridge in Melbourne, Australia as a case study, it demonstrated that the proposed bridge monitoring framework can both efficiently and accurately capture the real-time dynamic behavior of the bridge under traffic loading as well as the dynamic characteristics of the bridge. The outcomes from this research could potentially enhance the durability of bridges which is an important component of the sustainability of transport infrastructure. © 2017 World Scientific Publishing Europe Ltd.","bridges; Dynamic performance; natural frequency; non-contact sensors; traffic loading","Bridges; Economic and social effects; Finite element method; Interferometry; Natural frequencies; Radar; Radar equipment; Traffic surveys; Weigh-in-motion (WIM); 3D finite element model; Calibration and validations; Dynamic characteristics; Dynamic performance; Non- contact sensors; Traffic loading; Transport infrastructure; Weight-in-motion technologies; Loading",,,,,,,,,,,,,,,,"(2002) American Association of State Highway and Transportation Officials, Standard Specifications for Highway Bridges, , AASHTO Washington D. 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F., Thompson, R. G. (CRC Press); Thompson, R.G., Maizuar, M., Zhang, L., Mendis, M., Lowell, K., Investigating the effects of high productivity vehicles on road infrastructure using weigh-in-motion technology (2016) Journal of Traffic and Logistics Engineering, 4 (2), pp. 93-97; Twayana, R.P., Mori, S., Changes of natural frequencies of a short-span concrete skew bridge during construction (2014) Journal of Structural Engineering (JSCE), 60 (1), pp. 501-512; Zhang, L., Maizuar, M., Mendis, P., Duffield, C., Thompson, R., Monitoring the dynamic behaviour of concrete bridges using non-contact sensors (IBIS-S) (2016) Applied Mechanics and Materials, 846, pp. 225-230; Zhang, L., Mendis, P., Hon, W.C., Fragomeni, S., Lam, N., Song, Y.L., Effects of cyclic loading on the long-term deflection of prestressed concrete beams (2013) Computers and Concrete, 12 (6), pp. 739-754","Kafle, B.; School of Engineering, Australia; email: bidur.kafle@deakin.edu.au",,,"World Scientific Publishing Co. Pte Ltd",,,,,17588251,,,,"English","Intl. J. Appl. Mech.",Article,"Final","",Scopus,2-s2.0-85012050793
"Azim M.R., Gül M.","57203927510;22940711700;","Damage detection of steel girder railway bridges utilizing operational vibration response",2019,"Structural Control and Health Monitoring","26","11","e2447","","",,18,"10.1002/stc.2447","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070958428&doi=10.1002%2fstc.2447&partnerID=40&md5=05d4707dcd15e41729f8eaeffcf6f7b3","Department of Civil and Environmental Engineering, Natural Resources Engineering Facility, University of Alberta, Edmonton, AB, Canada; Department of Civil and Environmental Engineering, Donadeo Innovation Centre for Engineering, University of Alberta, Edmonton, AB, Canada","Azim, M.R., Department of Civil and Environmental Engineering, Natural Resources Engineering Facility, University of Alberta, Edmonton, AB, Canada; Gül, M., Department of Civil and Environmental Engineering, Donadeo Innovation Centre for Engineering, University of Alberta, Edmonton, AB, Canada","In this paper, we develop a damage identification framework based on acceleration responses for railroad bridges. The methodology uses sensor-clustering-based time series analysis of bridge acceleration responses to the motion of the train. The results are expressed in terms of damage features, and damage to the bridge is investigated by observing the magnitude of these damage features. The investigation demonstrates the damage features by comparing the fit ratios of locations of interest so that damage can be identified and located and the relative severity of the damage assessed. The damage cases considered are stiffness loss, moment capacity reduction, and change in boundary conditions. In this study, a finite element analysis of a railway bridge model is used to verify our methodology. Our findings show that the proposed damage detection framework is very promising for continuously assessing the condition of railway bridges and thus will facilitate early detection of potential structural damage. This will be valuable for infrastructure owners seeking to develop more economical and effective maintenance strategies. © 2019 John Wiley & Sons, Ltd.","damage detection; operational vibration data; sensor clustering; steel girder railway bridges; structural health monitoring; time series analysis","Harmonic analysis; Railroad bridges; Railroads; Steel beams and girders; Steel bridges; Structural analysis; Structural health monitoring; Time series analysis; Vibration analysis; Acceleration response; Bridge accelerations; Damage Identification; Detection framework; Maintenance strategies; Operational vibration; Railway bridges; Sensor clustering; Damage detection",,,,,"Networks of Centres of Excellence of Canada, NCE","This study is funded by IC-IMPACTS (the India-Canada Centre for Innovative Multidisciplinary Partnerships to Accelerate Community Transformation and Sustainability), established through the Networks of Centres of Excellence of Canada.",,,,,,,,,,"(2011) Bridge Preservation Guide. US Department of Transportation, , Washington, DC, USA; (2015) Highway bridges by state and highway system, , http://www.fhwa.dot.gov/bridge/nbi/no10/defbr15.cfm, USA; (2009) Canada's National Highway System Condition Report, , www.comt.ca/english/NHS-Condition09.pdf; (2016) Canadian Infrastructure Report Card Key Messages, , www.canadianinfrastructure.ca/en; Mirza, S.M., Haider, M., (2003) The state of infrastructure in Canada: implications for infrastructure planning and policy, , Infrastructure Canada; Gaudreault, V., Lemire, P., (2006) The Age of Public Infrastructure in Canada, , Ottawa, Ontario, Canada, Statistics Canada; Otter, D., Joy, R., Jones, M.C., Maal, L., Need for bridge monitoring systems to counter railroad bridge service interruptions (2012) Transp Res Rec J Transp Res Board, 2313 (1), pp. 134-143; Choi, J.Y., Park, Y.G., Choi, E.S., Choi, J.H., Applying precast slab panel track to replace timber track in an existing steel plate girder railway bridge (2010) J Rail Rapid Transit, 224 (3), pp. 159-167; Wiberg, J., (2006) Bridge Monitoring to Allow for Reliable Dynamic FE Modelling, A Case Study of the New Årsta Railway Bridge, , Stockholm, Sweden, KTH Royal Institute of Technology; Arangio, S., Beck, J.L., Bayesian neural networks for bridge integrity assessment (2012) J Struct Control Health Monit, 19 (1), pp. 3-21; An, Y., Ou, J., Experimental and numerical studies on model updating method of damage severity identification utilizing four cost functions (2013) J Struct Control Health Monit, 20 (1), pp. 107-120; Banerji, P., Chikermane, S., Condition assessment of a heritage arch bridge using a novel model updation technique (2012) J Civ Struct Heal Monit, 2 (1), pp. 1-16; Catbas, F.N., Gokce, H.B., Gül, M., Nonparametric analysis of structural health monitoring data for identification and localization of changes: concept, lab, and real-life studies (2012) Struct Health Monit, 11 (5), pp. 613-626; Scott, R.H., Banerji, P., Chikermane, S., Commissioning and evaluation of a fiber-optic sensor system for bridge monitoring (2013) IEEE Sensors J, 13 (7), pp. 2555-2562; Tributsch, A., Adam, C., An enhanced energy vibration based approach for damage detection and localization (2018) J Struct Control Health Monit, 25 (1). , https://doi.org/10.1002/stc.2047; Shokrani, Y., Dertimanis, V.K., Chatzi, E.N., Savoia, M.N., On the use of mode shape curvatures for damage localization under varying environmental conditions (2018) J Struct Control Health Monit, 25 (4). , https://doi.org/10.1002/stc.2132; Doebling, S.W., Farrar, C.R., Prime, M.B., Shevitz, D.W., (1996) Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review, , A-13070-MS. Los Alamos National Laboratory, Los Alamos, N. Mex; Scianna, A.M., Christenson, R., Probabilistic structural health monitoring method applied to the bridge health monitoring benchmark problem (2009) Transp Res Rec J Transp Res Board, 2131 (1), pp. 92-97; Balsamo, L., Betti, R., Beigi, H., A structural health monitoring strategy using cepstral features (1994) J Sound Vib, 169 (1), pp. 4526-4542; Gül, M., Catbas, F.N., Statistical pattern recognition for structural health monitoring using time series modeling: theory and experimental verifications (2009) J Mech Syst Signal Process, 23 (7), pp. 2192-2204; Zhang, Q.W., Statistical damage identification for bridges using ambient vibration data (2007) Comput Struct, 85 (7-8), pp. 476-485; Kim, C.W., Kitauchi, S., Chang, K.C., McGetrick, P.J., Sugiura, K., Kawatani, M., (2014) Structural damage diagnosis of steel truss bridges by outlier detection, pp. 4631-4638. , In Proceedings of the 11, International Conference on Structural Safety and Reliability, ICOSSAR; Kopsaftopoulos, F.P., Fassois, S.D., Vibration based health monitoring for a lightweight truss structure: experimental assessment of several statistical time series methods (2010) Mech Syst Signal Process, 24 (7), pp. 1977-1997; Wang, L., Chan, T.H.T., Thambiratnam, D.P., Tan, A.C.C., Cowled, C.J.L., Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms (2012) Adv Struct Eng, 15 (5), pp. 693-706; Nuno, K., (2013) Damage detection of a steel truss bridge using frequency response function curvature method, Stockholm. ISRN KTH/BKN/R-148-SE; Siriwardane, S.C., Vibration measurement-based simple technique for damage detection of truss bridges: a case study (2015) J Case Stud Eng Fail Anal, 4, pp. 50-58; Beskhyroun, S., Oshima, T., Mikami, S., Wavelet-based technique for structural damage detection (2010) J Struct Control Health Monit, 17, pp. 473-494; Farahni, R.V., Penumadu, D., Damage identification of a full-scale five-girder bridge using time-series analysis of vibration data (2016) Eng Struct, 115, pp. 129-139; Bowe, C., Quirke, P., Cantero, D., O'Brien, E.J., (2015) Drive-by structural health monitoring of railway bridges using train mounted accelerometers, , 5, ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Greece; George, R.C., Posey, J., Gupta, A., Mukhopadhyay, S., Mishra, S.K., (2017) Damage detection in railway bridges under moving train load, 3, pp. 349-354. , In Proceedings of the Society for Experimental Mechanics Series. Model Validation and Uncertainty Quantification; Gonzalez, I., Karoumi, R., BWIM aided damage detection in bridges using machine learning (2015) J Civ Struct Heal Monit, 5 (5), pp. 715-725; Neves, A.C., Gonzalez, I., Leander, J., Karoumi, R., Structural health monitoring of bridges: a model-free ANN-based approach to damage detection (2017) J Civ Struct Heal Monit, 7 (5), pp. 689-702; Sohn, H., Czarnecki, J.A., Farrar, C.R., Structural health monitoring using statistical process control (2000) J Struct Eng, 126 (11), pp. 1356-1363; Sohn, H., Farrar, C.R., Hunter, N.F., Worden, K., Structural health monitoring using statistical pattern recognition techniques (2001) J Dyn Syst Meas Control, 123 (4), pp. 706-711; Nair, K.K., Kiremidjian, A.S., Law, K.H., Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure (2006) J Sound Vib, 291 (1-2), pp. 349-368; Roy, K., Bhattacharya, B., Ray-Chaudhuri, S., ARX model based damage sensitive features for structural damage localization using output-only measurements (2015) J Sound Vib, 349, pp. 99-122; Gül, M., Catbas, F.N., Damage assessment with ambient vibration data using a novel time series analysis methodology (2011) J Struct Eng, 137 (12), pp. 1518-1526. , https://doi.org/10.1061/(ASCE)ST.1943-541X.00010366; Gül, M., Catbas, F.N., Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering (2011) J Sound Vib, 330 (6), pp. 1196-1210; Mei, Q., Gül, M., A fixed-order time series model for damage detection and localization (2016) J Civ Struct Heal Monit, 6 (5), pp. 763-777; Otter, D., Joy, R., Jones, M.C., Maal, L., Need for bridge monitoring systems to 497 counter railroad bridge service interruptions (2012) Transp Res Rec, 2313 (1), pp. 134-143","Gül, M.; Department of Civil and Environmental Engineering, Canada; email: mustafa.gul@ualberta.ca",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-85070958428
"Badr J., Fargier Y., Palma-Lopes S., Deby F., Balayssac J.-P., Delepine-Lesoille S., Cottineau L.-M., Villain G.","57209652125;55800598700;10341272100;25924836600;6603396002;9739850400;6507417186;11139218100;","Design and validation of a multi-electrode embedded sensor to monitor resistivity profiles over depth in concrete",2019,"Construction and Building Materials","223",,,"310","321",,18,"10.1016/j.conbuildmat.2019.06.226","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068417017&doi=10.1016%2fj.conbuildmat.2019.06.226&partnerID=40&md5=b3d95e78137be15bc469e4d0f053cc12","LMDC, Université de Toulouse, INSA/UPS Génie Civil, Toulouse, 31077, France; IFSTTAR, Site de Nantes, Bouguenais 44344, France Site de Bron, Bron, 69675, France; CEREMA, Site de Blois, Blois, 41029, France; Andra, French National Radioactive Waste Management Agency, Chatenay-Malabry, 92298, France","Badr, J., LMDC, Université de Toulouse, INSA/UPS Génie Civil, Toulouse, 31077, France, IFSTTAR, Site de Nantes, Bouguenais 44344, France Site de Bron, Bron, 69675, France; Fargier, Y., IFSTTAR, Site de Nantes, Bouguenais 44344, France Site de Bron, Bron, 69675, France, CEREMA, Site de Blois, Blois, 41029, France; Palma-Lopes, S., IFSTTAR, Site de Nantes, Bouguenais 44344, France Site de Bron, Bron, 69675, France; Deby, F., LMDC, Université de Toulouse, INSA/UPS Génie Civil, Toulouse, 31077, France; Balayssac, J.-P., LMDC, Université de Toulouse, INSA/UPS Génie Civil, Toulouse, 31077, France; Delepine-Lesoille, S., Andra, French National Radioactive Waste Management Agency, Chatenay-Malabry, 92298, France; Cottineau, L.-M., IFSTTAR, Site de Nantes, Bouguenais 44344, France Site de Bron, Bron, 69675, France; Villain, G., IFSTTAR, Site de Nantes, Bouguenais 44344, France Site de Bron, Bron, 69675, France","Electrical resistivity is sensitive to various properties of concrete, such as water content. Usually used on the surface of old structures, devices for measuring such properties could also be adapted in order to be embedded inside the constitutive concrete of the linings of new tunnels or in new bridges, to contribute to structural health monitoring. This paper introduces a novel multi-electrode embedded sensor for monitoring the resistivity profile over depth in order to quantify concrete durability. The paper focuses on the design of the sensor as a printed circuit board (PCB), which brings several advantages, including geometric accuracy and mitigation of wiring issues, thus reducing invasiveness. The study also presents the numerical modeling of the sensor electrical response and its ability to assess an imposed resistivity profile, together with experimental validations using (i) saline solutions of known conductivity and (ii) concrete specimens subjected to drying. The results demonstrate the capability of the sensor to evaluate resistivity profiles in concrete with centimeter resolution. © 2019 Elsevier Ltd","(Multi-electrode) embedded sensor; Concrete structures; Electrical resistivity; Finite element modeling; Monitoring","Concrete construction; Concretes; Electric conductivity; Electrodes; Finite element method; Monitoring; Printed circuit boards; Printed circuit design; Concrete durability; Concrete specimens; Electrical response; Embedded sensors; Experimental validations; Printed circuit boards (PCB); Properties of concretes; Resistivity profile; Structural health monitoring",,,,,,,,,,,,,,,,"Bore, T., Wagner, N., Delepine Lesoille, S., Taillade, F., Six, G., Daout, F., Placko, D., Error analysis of clay-rock water content estimation with broadband high-frequency electromagnetic sensors—air gap effect (2016) Sensors, 16; Farhoud, R., Bertrand, J., Buschaert, S., Delepine-Lesoille, S., Hermand, G., Full scale in situ monitoring section test in the Andra's Underground Research Laboratory (2013) Proceedings of the 1st Conference on Technological Innovations in Nuclear Civil Engineering (TINCE), pp. 29-31. , Paris, France; Courtois, A., Clauzon, T., Taillade, F., Martin, G., (2015), Water Content Monitoring for Flamanville 3 EPR TM Prestressed Concrete Containment: an Application for TDR Techniques; Arsoy, S., Ozgur, M., Keskin, E., Yilmaz, C., Enhancing TDR based water content measurements by ANN in sandy soils (2013) Geoderma, 195, pp. 133-144; Dérobert, X., Iaquinta, J., Klysz, G., Balayssac, J.-P., Use of capacitive and GPR techniques for the non-destructive evaluation of cover concrete (2008) NDT & E Int., 41, pp. 44-52; Du Plooy, R., Lopes, S.P., Villain, G., Derobert, X., Development of a multi-ring resistivity cell and multi-electrode resistivity probe for investigation of cover concrete condition (2013) NDT & E Int., 54, pp. 27-36; Fares, M., Villain, G., Fargier, Y., Thiery, M., Derobert, X., Palma-Lopes, S., Estimation of water gradient and concrete durability indicators using capacitive and electrical probes (2015) NDT-CE 2015, International Symposium Non-Destructive Testing in Civil Engineering, p. 9; Sbartaï, Z.M., Laurens, S., Rhazi, J., Balayssac, J.P., Arliguie, G., Using radar direct wave for concrete condition assessment: correlation with electrical resistivity (2007) J. Appl. Geophys., 62, pp. 361-374; Ihamouten, A., Villain, G., Derobert, X., Complex permittivity frequency variations from multioffset GPR data: hydraulic concrete characterization (2012) IEEE Trans. Instrum. Meas., 61, pp. 1636-1648; Kaplanvural, İ., Pekşen, E., Özkap, K., Volumetric water content estimation of C-30 concrete using GPR (2018) Constr. Build. Mater., 166, pp. 141-146; Millard, S.G., Reinforced concrete resistivity measurement techniques (1991) Proceedings, , Institution of Civil Engineers; Balayssac, J.-P., Garnier, V., Non-Destructive Testing and Evaluation of Civil Engineering Structures (2017), Elsevier; Villain, G., Sbartaï, Z.M., Lataste, J.-F., Garnier, V., Dérobert, X., Abraham, O., Bonnet, S., Fares, M., Characterization of water gradients in concrete by complementary NDT methods (2015) International Symposium Non-Destructive Testing in Civil Engineering (NDT-CE 2015), p. 12; Balayssac, J.-P., Garnier, V., Villain, G., Sbartaï, Z.-M., Dérobert, X., Piwakowski, B., Breysse, D., Salin, J., An overview of 15 years of French collaborative projects for the characterization of concrete properties by combining NDT methods (2015) Proceedings of Int. Symp. on NDT-CE, pp. 15-17; Minagawa, H., Miyamoto, S., Hisada, M., Relationship of apparent electrical resistivity measured by four-probe method with water content distribution in concrete (2017) J. Adv. Concr. Technol., 15, pp. 278-289; Villain, G., Thiery, M., Gammadensimetry: a method to determine drying and carbonation profiles in concrete (2006) Ndt & E Int., 39, pp. 328-337; Villain, G., Thiery, M., Platret, G., Measurement methods of carbonation profiles in concrete: thermogravimetry, chemical analysis and gammadensimetry (2007) Cem. Concr. Res., 37, pp. 1182-1192; Albrand, M., Klysz, G., Ferrieres, X., Millot, P., Evaluation of the electromagnetic properties of non-homogeneous concrete by inversion of GPR measurements (2016) 2016 16th International Conference on Ground Penetrating Radar (GPR), pp. 1-4. , Ieee Hong-Kong; Xiao, X., Ihamouten, A., Villain, G., Dérobert, X., Use of electromagnetic two-layer wave-guided propagation in the GPR frequency range to characterize water transfer in concrete (2017) NDT & E Int., 86, pp. 164-174; Guan, B., Ihamouten, A., Dérobert, X., Guilbert, D., Lambot, S., Villain, G., Near-field full-waveform inversion of ground-penetrating radar data to monitor the water front in limestone (2017) IEEE J. Selected Topics Appl. Earth Observ. Remote Sens., 10, pp. 4328-4336; Chouteau, M., Vallières, S., Toe, E., A multi-dipole mobile array for the non-destructive evaluation of pavement and concrete infrastructures: a feasability study (2003) Proceedings of the BAM International Symposium NDT-CE, pp. 16-19. , Berlin, Germany; Polder, R.B., Critical chloride content for reinforced concrete and its relationship to concrete resistivity (2009) Mater. Corros., 60, pp. 623-630; Fares, M., Villain, G., Bonnet, S., Palma Lopes, S., Thauvin, B., Thiery, M., Determining chloride content profiles in concrete using an electrical resistivity tomography device (2018) Cem. Concr. Compos., 94, pp. 315-326; Hornbostel, K., Larsen, C.K., Geiker, M.R., Relationship between concrete resistivity and corrosion rate – a literature review (2013) Cem. Concr. Compos., 39, pp. 60-72; Nguyen, A.Q., Klysz, G., Deby, F., Balayssac, J.-P., Evaluation of water content gradient using a new configuration of linear array four-point probe for electrical resistivity measurement (2017) Cem. Concr. Compos., 83, pp. 308-322; Villain, G., Garnier, V., Sbartaï, Z.M., Derobert, X., Balayssac, J.-P., Development of a calibration methodology to improve the on-site non-destructive evaluation of concrete durability indicators (2018) Mater. Struct., 51, p. 40; Mendes, S.E.S., Oliveira, R.L.N., Cremonez, C., Pereira, E., Pereira, E., Medeiros-Junior, R.A., Electrical resistivity as a durability parameter for concrete design: experimental data versus estimation by mathematical model (2018) Constr. Build. Mater., 192, pp. 610-620; Rücker, C., Günther, T., The simulation of finite ERT electrodes using the complete electrode model (2011) Geophysics, 76, pp. F227-F238; Gowers, K., Millard, S., Measurement of concrete resistivity for assessment of corrosion (1999) ACI Mater. J., 96; Wenner, F., A method for measuring earth resistivity (1915) J. Washington Acad. Sci., 5, pp. 561-563; Polder, R.B., Test methods for on site measurement of resistivity of concrete — a RILEM TC-154 technical recommendation (2001) Constr. Build. Mater., 15, pp. 125-131; Andrade, C., Polder, R., Basheer, M., (2007), pp. 91-112. , Non-destructive methods to measure ion migration, RILEM TC; Bourreau, L., Bouteiller, V., Schoefs, F., Gaillet, L., Thauvin, B., Schneider, J., Naar, S., Uncertainty assessment of concrete electrical resistivity measurements on a coastal bridge (2019) Struct. Infrastruct. Eng., 15, pp. 443-453; Priou, J., Lecieux, Y., Chevreuil, M., Gaillard, V., Lupi, C., Leduc, D., Rozière, E., Schoefs, F., In situ DC electrical resistivity mapping performed in a reinforced concrete wharf using embedded sensors (2019) Constr. Build. Mater., 211, pp. 244-260; Marescot, L., Rigobert, S., Lopes, S.P., Lagabrielle, R., Chapellier, D., A general approach for DC apparent resistivity evaluation on arbitrarily shaped 3D structures (2006) J. Appl. Geophys., 60, pp. 55-67; Kunetz, G., (1966), Principles of direct current-Resistivity prospecting; Oldenborger, G.A., Routh, P.S., Knoll, M.D., Sensitivity of electrical resistivity tomography data to electrode position errors (2005) Geophys. J. Int., 163, pp. 1-9; Chang, C.-Y., Hung, S.-S., Implementing RFIC and sensor technology to measure temperature and humidity inside concrete structures (2012) Constr. Build. Mater., 26, pp. 628-637; LaBrecque, D., Daily, W., Assessment of measurement errors for galvanic-resistivity electrodes of different composition (2008) Geophysics, 73, pp. F55-F64; Liang, K., Zeng, X., Zhou, X., Ling, C., Wang, P., Li, K., Ya, S., Investigation of the capillary rise in cement-based materials by using electrical resistivity measurement (2018) Constr. Build. Mater., 173, pp. 811-819; Abbas, Y., Pargar, F., Olthuis, W., van den Berg, A., Activated carbon as a pseudo-reference electrode for potentiometric sensing inside concrete (2014) Proc. Eng., 87, pp. 1437-1440; Petrič, M., Kastelica, S., Mrvar, P., Selection of electrodes for the'in situ'electrical resistivity measurements of molten aluminium (2013) J. Min. Metall. B: Metall., 49, pp. 279-283; Kuras, O., Wilkinson, P.B., Meldrum, P.I., Swift, R.T., Uhlemann, S.S., Chambers, J.E., Walsh, F.C., Atherton, N., Performance assessment of novel electrode materials for long-term ERT monitoring (2015) Near Surface Geoscience 2015-21st European Meeting of Environmental and Engineering Geophysics; Song, J., Wang, L., Zibart, A., Koch, C., Corrosion protection of electrically conductive surfaces (2012) Metals, 2, pp. 450-477; Edward, L.S., A modified pseudo section for resistivity and induced-polarization (1977) Geophysics, 42, pp. 1020-1036; Loke, M., (2000), Electrical Imaging Surveys for Environmental and Engineering Studies; Park, S.K., Van, G.P., Inversion of pole-pole data for 3-D resistivity structure beneath arrays of electrodes (1991) Geophysics, 56, pp. 951-960; McCarter, W.J., Taha, H.M., Suryanto, B., Starrs, G., Two-point concrete resistivity measurements: interfacial phenomena at the electrode–concrete contact zone (2015) Meas. Sci. Technol., 26; Chapellier, D., Diagraphies appliquées à l'hydrologie, technique et documentation (Lavoisier) (1987) Diagraphies; Morris, W., Moreno, E.I., Sagüés, A.A., Practical evaluation of resistivity of concrete in test cylinders using a Wenner array probe (1996) Cem. Concr. Res., 26, pp. 1779-1787; Archie, G.E., The electrical resistivity log as an aid in determining some reservoir characteristics (1942) Trans. Am. Inst. Min. Metall. Eng., pp. 54-62","Badr, J.; Laboratoire Matériaux et Durabilité des Constructions de Toulouse LMDC, 135, Avenue de Rangueil, France; email: badr@insa-toulouse.fr",,,"Elsevier Ltd",,,,,09500618,,CBUME,,"English","Constr Build Mater",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85068417017
"Alamdari M.M., Ge L., Kildashti K., Zhou Y., Harvey B., Du Z.","55814161700;23392861200;32367724300;57208693034;7103106967;56733989700;","Non-contact structural health monitoring of a cable-stayed bridge: case study",2019,"Structure and Infrastructure Engineering","15","8",,"1119","1136",,18,"10.1080/15732479.2019.1609529","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065523698&doi=10.1080%2f15732479.2019.1609529&partnerID=40&md5=1549da9aa220abdbbd5b4055d4c6632e","School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia; Centre for Infrastructure Engineering, Western Sydney University, Sydney, NSW, Australia","Alamdari, M.M., School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia; Ge, L., School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia; Kildashti, K., Centre for Infrastructure Engineering, Western Sydney University, Sydney, NSW, Australia; Zhou, Y., School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia; Harvey, B., School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia; Du, Z., School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia","In this article, the condition assessment of a cable-stayed bridge using remote sensing is presented. The displacement influence line (DIL) of the bridge under the live load tests is measured for a discrete number of target points. Three different remote sensing techniques including, laser scanning, terrestrial robotic total station and digital levelling are adopted for this purpose. It is demonstrated that DIL obtained by non-contact system is capable of identifying an emulated damage in an actual operating system. The contribution of the work is fourfold. First, a damage index based on the displacement profile of the bridge under the weigh-in-motion is extracted from the non-contact sensing system. Second, our study compares three different remote sensing techniques, namely, digital levelling, robotic total station and laser scanning and uses the measurements to validate the finite element model. Third, the effectiveness of the proposed method for structural damage identification is validated in a real-world large-scale operating structure. Finally, it is validated that strain-based influence line is highly likely to misidentify damage especially when the location of damage is not in the close proximity of the sensor; however, DIL is a better damage indicator even if damage occurs far from the measurement point. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.","Bridge health monitoring; cable-stayed bridge; displacement; finite element; influence line; measurement; natural frequency; non-contact sensing","Cable stayed bridges; Cables; Damage detection; Electric measuring bridges; Finite element method; Laser applications; Load testing; Measurement; Natural frequencies; Remote sensing; Robotics; Structural analysis; Bridge health monitoring; Condition assessments; displacement; Influence lines; Non-contact sensing; Remote sensing techniques; Robotic total station; Structural damage identification; Structural health monitoring",,,,,,,,,,,,,,,,"Alamdari, M.M., Rakotoarivelo, T., Khoa, N.L.D., A spectral-based clustering for structural health monitoring of the Sydney Harbour bridge (2017) Mechanical Systems and Signal Processing, 87, pp. 384-400; Alamdari, M.M., Kildashti, K., Samali, B., Goudarzi, H.V., Damage diagnosis in bridge structures using rotation influence line: Validation on a cable-stayed bridge (2019) Engineering Structures, 185, pp. 1-14; Anaissi, A., Makki Alamdari, M., Rakotoarivelo, T., Khoa, N., A tensor-based structural damage identification and severity assessment (2018) Sensors, 18 (2), p. 111; Annamdas, V.G.M., Bhalla, S., Soh, C.K., Applications of structural health monitoring technology in Asia (2017) Structural Health Monitoring: An International Journal, 16 (3), pp. 324-346; Asadollahi, P., Li, J., (2016), Statistical analysis of modal properties of a cable-stayed bridge through long-term structural health monitoring with wireless smart sensor networks. 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Bridge Maint., Safety Manag., Lago di Como (346–353; Chen, Z.-W., Zhu, S., Xu, Y.-L., Li, Q., Cai, Q.-L., Damage detection in long suspension bridges using stress influence lines (2015) Journal of Bridge Engineering, 20 (3), p. 05014013; Cosser, E., Roberts, G.W., Meng, X., Dodson, A.H., (2003), pp. 605-612. , Measuring the dynamic deformation of bridges using a total station. 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II: Model updating using modal frequencies and influence lines (2015) Journal of Bridge Engineering, 20 (10), p. 04014113; Yang, Y.-B., Lin, C.W., Yau, J.D., Extracting bridge frequencies from the dynamic response of a passing vehicle (2004) Journal of Sound and Vibration, 272 (3-5), pp. 471-493; Zaurin, R., Catbas, F.N., Integration of computer imaging and sensor data for structural health monitoring of bridges (2010) Smart Materials and Structures, 19 (1), p. 015019; Zhu, Q., Xu, Y.L., Xiao, X., Multiscale modeling and model updating of a cable-stayed bridge. I: Modeling and influence line analysis (2015) Journal of Bridge Engineering, 20 (10), p. 04014112; Zogg, H.M., Ingensand, H., Terrestrial laser scanning for deformation monitoring–load tests on the Felsenau Viaduct (CH) (2008) The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37 (2008), pp. 555-562","Alamdari, M.M.; School of Civil and Environmental Engineering, Australia; email: m.makkialamdari@unsw.edu.au",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","",Scopus,2-s2.0-85065523698
"Wang D., Liu Y., Liu Y.","55910427100;36066339300;56295380700;","3D temperature gradient effect on a steel–concrete composite deck in a suspension bridge with field monitoring data",2018,"Structural Control and Health Monitoring","25","7","e2179","","",,18,"10.1002/stc.2179","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045749942&doi=10.1002%2fstc.2179&partnerID=40&md5=bb508aac4250e7357753cace2e160d00","School of Civil Engineering and Architecture, Changsha University of Science & Technology, Changsha, 410114, China; School for Engineering of Matter, Transport & Energy, Arizona State University, 501 E. Tyler Mall, Tempe, AZ  85281, United States","Wang, D., School of Civil Engineering and Architecture, Changsha University of Science & Technology, Changsha, 410114, China; Liu, Y., School for Engineering of Matter, Transport & Energy, Arizona State University, 501 E. Tyler Mall, Tempe, AZ  85281, United States; Liu, Y., School of Civil Engineering and Architecture, Changsha University of Science & Technology, Changsha, 410114, China","Spatial and temporal temperature variations are critical for the accurate stress analysis of a suspension bridge with a steel–concrete composite deck system. This issue has been widely investigated in recent years in the open literature. In current codes, only the vertical temperature gradient (VTG) is considered in the thermal stress calculation. A complete 3D temperature profile has rarely been investigated. In the proposed study, the Aizhai Suspension Bridge with a steel–concrete composite deck system in China was investigated to determine the realistic 3D temperature-gradient distributions and their effects on the structural performance using a finite element method. First, the distributions of the spatial-temperature gradient including the VTG, the transversal temperature gradient (TTG), and the longitudinal temperature gradient (LTG) were investigated based on a structural health monitoring system. The results showed that the values of these gradients were far greater than those suggested by the Chinese code. Next, a 3D finite element model was proposed to investigate the thermal stress variation in the steel–concrete composite bridge deck system. The thermal-induced stresses due to the VTG, TTG, and LTG were obtained using the monitored temperature data and the proposed 3D finite element model. The coupling effects of the 2D (coupling of the VTG and TTG) and 3D (coupling of the VTG, TTG, and LTG) temperature gradients were obtained and compared with those of the 1D approximation and Chinese code. Possible reasons for the 3D temperature-gradient effect were also discussed. Following this, conclusions and recommendations for future bridge analysis and design were provided based on the proposed study. Copyright © 2018 John Wiley & Sons, Ltd.","3D temperature gradient; FE method; field monitoring; steel–concrete composite deck; thermal-induced stress","Bridge decks; Codes (standards); Codes (symbols); Concretes; Monitoring; Stress analysis; Structural health monitoring; Suspension bridges; Suspensions (components); Thermal gradients; Thermal stress; FE method; Field monitoring; Steel-concrete composite bridges; Steel-concrete composite decks; Structural health monitoring systems; Temperature gradient distribution; Thermal induced stress; Vertical temperature gradients; Finite element method",,,,,"2015319825120; National Natural Science Foundation of China, NSFC: 51308071, 51378081; National Basic Research Program of China (973 Program): 2015CB057701","The research described in this paper was financially supported by the Traffic Department of Applied Basic Research Project (2015319825120), the National Basic Research Program of China (973 Program, 2015CB057701), and the Natural Science Foundation of China (51308071 and 51378081).",,,,,,,,,,"Kim, H.K., Kim, N.S., Jang, J.H., Kim, Y.H., (2012) J. 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Anal., 18 (1), p. 354; Wijesinghe, B.H.M.P., Zacharie, S.A., Mish, K.D., Baldwin, J.D., (2013) J. Bridg. Eng., 18 (4), p. 297; Miao, C.Q., Shi, C.H., (2013) Sci. Sin., 56 (8), p. 1929","Liu, Y.; School of Civil Engineering and Architecture, China; email: liuyangbridge2@163.com",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-85045749942
"Obrien E.J., Martinez D., Malekjafarian A., Sevillano E.","57218648462;57206271841;36019397700;56035272600;","Damage detection using curvatures obtained from vehicle measurements",2017,"Journal of Civil Structural Health Monitoring","7","3",,"333","341",,18,"10.1007/s13349-017-0233-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024886695&doi=10.1007%2fs13349-017-0233-8&partnerID=40&md5=ab1887c6633a9dd2f60e76d5a8f59f92","School of Civil Engineering, University College Dublin, Dublin 4, Ireland","Obrien, E.J., School of Civil Engineering, University College Dublin, Dublin 4, Ireland; Martinez, D., School of Civil Engineering, University College Dublin, Dublin 4, Ireland; Malekjafarian, A., School of Civil Engineering, University College Dublin, Dublin 4, Ireland; Sevillano, E., School of Civil Engineering, University College Dublin, Dublin 4, Ireland","This paper describes a new procedure for bridge damage identification through drive-by monitoring. Instantaneous curvature (IC) is presented as a means to determine a local loss of stiffness in a bridge through measurements collected from a passing instrumented vehicle. Moving reference curvature (MRC) is compared with IC as a damage detection tool. It is assumed that absolute displacements on the bridge can be measured by the vehicle. The bridge is represented by a finite element (FE) model. A Half-car model is used to represent the passing vehicle. Damage is represented as a local loss of stiffness in different parts of the bridge. 1% random noise and no noise environments are considered to evaluate the effectiveness of the method. A generic road surface profile is also assumed. Numerical simulations show that the local damage can be detected using IC if the deflection responses can be measured with sufficient accuracy. Damage quantification can be obtained from MRC. © 2017, Springer-Verlag GmbH Germany.","Bridge; Damage detection; Drive-by; Instantaneous curvature; Moving reference curvature; SHM; Structural health monitoring","Bridges; Finite element method; Integrated circuits; Model automobiles; Stiffness; Structural health monitoring; Vehicles; Absolute displacement; Damage quantification; Half-car model; Instantaneous curvature; Instrumented vehicle; Moving reference curvature; Noise environments; Road surface profiles; Damage detection",,,,,"Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA: 642453","The authors acknowledge the support for the work reported in this paper from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No. 642453.",,,,,,,,,,"Farrar, C.R., Worden, K., An introduction to structural health monitoring (2007) Philos Trans R S Lond A, 365 (1851), pp. 303-315; Chang, F.K., (1999) Structural health monitoring 2000, , CRC Press, Boca raton; Sohn, H., Czarnecki, J.A., Farrar, C.R., Structural health monitoring using statistical process control (2000) J Struct Eng, 126 (11), pp. 1356-1363; A review of structural health monitoring literature (2004) In 3rd World Conference on Structural control, , Sohn H, Farrar CR, Hemez FM, Shunk DD, Stinemates DW, Nadler BR, and Czarnecki JJ Como, Italy; PhD Thesis (1993) Department of Building Technology and Structural Engineering, , Aalborg University, Aalborg; Brownjohn, J.M., Structural health monitoring of civil infrastructure (2007) Philos Trans R Soc Lond A, 365 (1851), pp. 589-622; Li, H.-N., Li, D.-S., Song, G.-B., Recent applications of fiber optic sensors to health monitoring in civil engineering (2004) Eng Struct, 26 (11), pp. 1647-1657; Carden, E.P., Brownjohn, J.M., ARMA modelled time-series classification for structural health monitoring of civil infrastructure (2008) Mech Syst Signal Process, 22 (2), pp. 295-314; Lynch, J.P., An overview of wireless structural health monitoring for civil structures (2007) Philos Trans R Soc Lond A, 365 (1851), pp. 345-372; Ko, J., Ni, Y., Technology developments in structural health monitoring of large-scale bridges (2005) Eng Struct, 27 (12), pp. 1715-1725; Jang, S., Jo, H., Cho, S., Mechitov, K., Rice, J.A., Sim, S.-H., Jung, H.-J., Agha, G., Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation (2010) Smart Struct Syst, 6 (5-6), pp. 439-459; Zhou, Z., Graver, T.W., Hsu, L., Ou, J.-P., Techniques of advanced FBG sensors: fabrication, demodulation, encapsulation, and their application in the structural health monitoring of bridges (2003) Pac Sci Rev, 5 (1), pp. 116-121; Li, Z., Chan, T.H., Ko, J.M., Fatigue analysis and life prediction of bridges with structural health monitoring data—Part I: methodology and strategy (2001) Int J Fatigue, 23 (1), pp. 45-53; Machelski, C., Hildebrand, M., Efficiency of monitoring system of a cable-stayed bridge for investigation of live loads and pier settlements (2015) J Civ Struct Health Monit, 5 (1), pp. 1-9; Magalhães, F., Cunha, Á., Caetano, E., Dynamic monitoring of a long span arch bridge (2008) Eng Struct, 30 (11), pp. 3034-3044; Robertson, I.N., Prediction of vertical deflections for a long-span prestressed concrete bridge structure (2005) Eng Struct, 27 (12), pp. 1820-1827; Fan, W., Qiao, P., Vibration-based damage identification methods: a review and comparative study (2011) Struct Health Monitor, 10 (1), pp. 83-111; Obrien, E.J., Malekjafarian, A., A mode shape-based damage detection approach using laser measurement from a vehicle crossing a simply supported bridge (2016) Struct Control Health Monitor, 23 (10), pp. 1273-1286; Malekjafarian, A., McGetrick, P.J., Obrien, E.J., A review of indirect bridge monitoring using passing vehicles (2015) Shock and Vibration, 2015, p. 16; Yang, Y.-B., Lin, C., Yau, J., Extracting bridge frequencies from the dynamic response of a passing vehicle (2004) J Sound Vib, 272 (3), pp. 471-493; Yang, Y., Lin, C., Vehicle–bridge interaction dynamics and potential applications (2005) J Sound Vib, 284 (1), pp. 205-226; Obrien, E.J., Malekjafarian, A., González, A., Application of empirical mode decomposition to drive-by bridge damage detection (2017) Eur J Mech-A/Solids, 61, pp. 151-163; Bowe, C., Quirke, P., Cantero, D., O’Brien, E.J., Papadrakasis, M., Papadopoulos, V., Plevris, V., Drive-by structural health monitoring of railway bridges using train mounted accelerometers (2015) 5th ECCOMAS thematic conference on computational methods in structural dynamics and earthquake engineering. 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Civ. Struct. Health Monit.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85024886695
"Ding Y.-L., Wang G.-X., Hong Y., Song Y.-S., Wu L.-Y., Yue Q.","55768944900;55258947500;55782058800;55494118800;56703862600;56564809400;","Detection and Localization of Degraded Truss Members in a Steel Arch Bridge Based on Correlation between Strain and Temperature",2017,"Journal of Performance of Constructed Facilities","31","5","04017082","","",,17,"10.1061/(ASCE)CF.1943-5509.0001075","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020875764&doi=10.1061%2f%28ASCE%29CF.1943-5509.0001075&partnerID=40&md5=903c7591324538c6a8bad477e91acade","Key Laboratory of Concrete and Prestressed Concrete Structures, Ministry of Education, Southeast Univ., 2 Sipailou Rd., Xuanwu District, Nanjing, 210096, China; Southwest Jiaotong Univ., Xibuytuan Zone, Gaoxin District, Chengdu, 611756, China; China Railway Major Bridge (Nanjing), Bridge and Tunnel Inspect and Retrofit Co., Ltd, 8 Panneng Rd., Gaoxin District, Nanjing, 210096, China","Ding, Y.-L., Key Laboratory of Concrete and Prestressed Concrete Structures, Ministry of Education, Southeast Univ., 2 Sipailou Rd., Xuanwu District, Nanjing, 210096, China; Wang, G.-X., Key Laboratory of Concrete and Prestressed Concrete Structures, Ministry of Education, Southeast Univ., 2 Sipailou Rd., Xuanwu District, Nanjing, 210096, China; Hong, Y., Southwest Jiaotong Univ., Xibuytuan Zone, Gaoxin District, Chengdu, 611756, China; Song, Y.-S., Key Laboratory of Concrete and Prestressed Concrete Structures, Ministry of Education, Southeast Univ., 2 Sipailou Rd., Xuanwu District, Nanjing, 210096, China; Wu, L.-Y., China Railway Major Bridge (Nanjing), Bridge and Tunnel Inspect and Retrofit Co., Ltd, 8 Panneng Rd., Gaoxin District, Nanjing, 210096, China; Yue, Q., China Railway Major Bridge (Nanjing), Bridge and Tunnel Inspect and Retrofit Co., Ltd, 8 Panneng Rd., Gaoxin District, Nanjing, 210096, China","Taking advantage of the structural health monitoring system installed on the steel truss arch girder of the Dashengguan Yangtze Bridge, the monitoring correlation between strain and uniform temperature at night is obtained, and then the correlation analysis of wavelet packet decomposition is carried out, which can effectively decrease the discreteness of the monitoring correlation. After that, the whole finite-element model of the Dashengguan Yangtze Bridge is simplified into one top chord member, one diagonal web member, and one bottom chord member with imposed displacement movements on the supports. The simulated correlation between static strain and temperature is calculated using the flexibility equation of force method from the simplified finite-element model, which is close to the monitoring correlation, and the first-order derivative of flexibility equation is used to remove the influence of temperature action. Furthermore, the influence of degrading axial flexibility is analyzed, showing that the linear slopes kAA∼kH are primarily affected by the degradation of chord members {TCa,-2,TCa,-1,TCa,0,TCa,1,TCa,2,TCa,3}, {TCb,-2,TCb,-1,TCb,0,TCb,1,TCb,2,TCb,3}, {BCa,-2,BCa,-1,BCa,0,BCa,1,BCa,2,BCa,3}, and {CVa,1}, and using the monitoring data, the method of detecting and locating the aging truss members is finally put forward. © 2017 American Society of Civil Engineers.","Aging truss members; Bridge health monitoring; Static strain; Steel truss arch girder; Temperature field","Arches; Finite element method; Steel bridges; Structural health monitoring; Temperature distribution; Trusses; Wavelet analysis; Wavelet decomposition; Bridge health monitoring; Detection and localization; Equation of force methods; Static strain; Steel truss; Structural health monitoring systems; Truss members; Wavelet Packet Decomposition; Monitoring",,,,,,,,,,,,,,,,"Alamdari, M.M., Rakotoarivelo, T., Khoa, N.L.D., A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge (2017) Mech. Syst. Signal Process., 87, pp. 384-400; Anastasopoulos, D., Moretti, P., Geernaert, T., Identification of modal strains using sub-microstrain FBG data and a novel wavelength-shift detection algorithm (2017) Mech. Syst. Signal Process., 86, pp. 58-74; Bao, Y.Q., Li, H., Chen, Z.C., Zhang, F.J., Guo, A.X., Sparse l1 optimization-based identification approach for distribution of moving heavy vehicle loads on cable-stayed bridges (2016) Struct. Control Health Monit., 23 (1), pp. 144-155; Bueno, A., Torres, B.D., Calderon, P., Sales, S., Monitoring of a steel incrementally launched bridge construction with strain and temperature FBGs sensors (2010) Proc. SPIE-Int. Society for Optical Engineering, International Society for Optics and Photonics, p. 772620. , Bellingham, WA; Deng, L., Cai, C.S., Bridge model updating using response surface method and genetic algorithm (2010) J. 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Tools, 22 (5), p. 1360011; Wang, G.X., Ding, Y.L., Sun, P., Wu, L.Y., Yue, Q., Assessing static performance of the Dashengguan Yangtze Bridge by monitoring the correlation between temperature field and its static strains (2015) Math. Prob. Eng., 2015, pp. 1-12; Ye, X.W., Ni, Y.Q., Wong, K.Y., Ko, J.M., Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data (2012) Eng. Struct., 45, pp. 166-176; Yi, T.H., Li, H.N., Gu, M., Recent research and applications of GPS-based monitoring technology for high-rise structures (2013) Struct. Control Health Monit., 20 (5), pp. 649-670","Ding, Y.-L.; Key Laboratory of Concrete and Prestressed Concrete Structures, 2 Sipailou Rd., China; email: civilchina@hotmail.com",,,"American Society of Civil Engineers (ASCE)",,,,,08873828,,JPCFE,,"English","J. Perform. Constr. Facil.",Article,"Final","",Scopus,2-s2.0-85020875764
"Ye X.W., Yi T.-H., Su Y.H., Liu T., Chen B.","14829893000;8726425800;56069855200;56668989400;55723031600;","Strain-based structural condition assessment of an instrumented arch bridge using FBG monitoring data",2017,"Smart Structures and Systems","20","2",,"139","150",,17,"10.12989/sss.2017.20.2.139","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033481937&doi=10.12989%2fsss.2017.20.2.139&partnerID=40&md5=1a3ca3959259a176b8d3d8487421e289","Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China; School of Civil Engineering, Dalian University of Technology, Dalian, 116023, China","Ye, X.W., Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China; Yi, T.-H., School of Civil Engineering, Dalian University of Technology, Dalian, 116023, China; Su, Y.H., Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China; Liu, T., Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China; Chen, B., Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China","The structural strain plays a significant role in structural condition assessment of in-service bridges in terms of structural bearing capacity, structural reliability level and entire safety redundancy. Therefore, it has been one of the most important parameters concerned by researchers and engineers engaged in structural health monitoring (SHM) practices. In this paper, an SHM system instrumented on the Jiubao Bridge located in Hangzhou, China is firstly introduced. This system involves nine subsystems and has been continuously operated for five years since 2012. As part of the SHM system, a total of 166 fiber Bragg grating (FBG) strain sensors are installed on the bridge to measure the dynamic strain responses of key structural components. Based on the strain monitoring data acquired in recent two years, the strain-based structural condition assessment of the Jiubao Bridge is carried out. The wavelet multi-resolution algorithm is applied to separate the temperature effect from the raw strain data. The obtained strain data under the normal traffic and wind condition and under the typhoon condition are examined for structural safety evaluation. The structural condition rating of the bridge in accordance with the AASHTO specification for condition evaluation and load and resistance factor rating of highway bridges is performed by use of the processed strain data in combination with finite element analysis. The analysis framework presented in this study can be used as a reference for facilitating the assessment, inspection and maintenance activities of in-service bridges instrumented with longterm SHM system. © Copyright 2017 Techno-Press, Ltd.","Arch bridge; Finite element analysis; Strain-based structural condition assessment; Structural heath monitoring; Structural rating; Wavelet multi-resolution algorithm","Arch bridges; Arches; Condition monitoring; Fiber Bragg gratings; Finite element method; Redundancy; Structural analysis; Inspection and maintenance; Load and resistance factor ratings; Multi-resolution algorithms; Structural component; Structural condition; Structural health monitoring (SHM); Structural heath monitoring; Structural reliability; Structural health monitoring",,,,,"National Natural Science Foundation of China, NSFC: 51625802; Ministry of Education of the People's Republic of China, MOE; Harbin Institute of Technology, HIT; National Key Research and Development Program of China, NKRDPC: 2015CB060000; Fundamental Research Funds for the Central Universities: 2017QNA4024","The work described in this paper was jointly supported by the National Natural Science Foundation of China (Grant No. 51625802), the 973 Program (Grant No. 2015CB060000), the Fundamental Research Funds for the Central Universities of China (Grant No. 2017QNA4024), and the Key Lab of Structures Dynamic Behavior and Control (Harbin Institute of Technology), Ministry of Education of the PRC.",,,,,,,,,,"(2011) The Manual for Bridge Evaluation, , AASHTO the 2nd edition American Association of State Highway and Transportation Officials, Washington DC, USA; Barbosa, C., Costa, N., Ferreira, L.A., Araujo, F.M., Varum, H., Costa, A., Fernandes, C., Rodrigues, H., Weldable fibre bragg grating sensors for steel bridge monitoring (2008) Meas. 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Syst., 17 (6), pp. 1087-1105; Ye, X.W., Dong, C.Z., Liu, T., Force monitoring of steel cables using vision-based sensing technology: Methodology and experimental verification (2016) Smart Struct. Syst., 18 (3), pp. 585-599; Ye, X.W., Liu, T., Ni, Y.Q., Probabilistic corrosion fatigue life assessment of a suspension bridge instrumented with long-term SHM system (2017) Adv. Struct. Eng.; Zhang, W., Gao, J.Q., Shi, B., Cui, H.L., Zhu, H.H., Health monitoring of rehabilitated concrete bridges using distributed optical fiber sensing (2006) Comput.-Aided Civ. Inf., 21 (6), pp. 411-424; Zhou, G.D., Yi, T.H., Chen, B., Innovative design of a health monitoring system and its implementation in a complicated long-span arch bridge (2016) J. Aerospace Eng. - ASCE, B4016006, pp. 1-17","Yi, T.-H.; School of Civil Engineering, China; email: yth@dlut.edu.cn",,,"Techno-Press",,,,,17381584,,,,"English","Smart Struct. Syst.",Article,"Final","",Scopus,2-s2.0-85033481937
"Kulpa M., Howiacki T., Wiater A., Siwowski T., Sieńko R.","56646302400;56790057700;57200315302;25029342900;56866373700;","Strain and displacement measurement based on distributed fibre optic sensing (DFOS) system integrated with FRP composite sandwich panel",2021,"Measurement: Journal of the International Measurement Confederation","175",,"109099","","",,16,"10.1016/j.measurement.2021.109099","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100642267&doi=10.1016%2fj.measurement.2021.109099&partnerID=40&md5=99880f1cdd9683a798d11910fff64238","Faculty of Civil and Environmental Engineering and Architecture, Rzeszow University of Technology, al. Powstancow Warszawy 12, Rzeszow, 35-959, Poland; Faculty of Civil Engineering, Cracow University of Technology, ul. Warszawska 24, Krakow, 31-155, Poland; SHM System Company, Jana Pawla II 82A Libertów, Krakow, 30-444, Poland","Kulpa, M., Faculty of Civil and Environmental Engineering and Architecture, Rzeszow University of Technology, al. Powstancow Warszawy 12, Rzeszow, 35-959, Poland; Howiacki, T., Faculty of Civil Engineering, Cracow University of Technology, ul. Warszawska 24, Krakow, 31-155, Poland, SHM System Company, Jana Pawla II 82A Libertów, Krakow, 30-444, Poland; Wiater, A., Faculty of Civil and Environmental Engineering and Architecture, Rzeszow University of Technology, al. Powstancow Warszawy 12, Rzeszow, 35-959, Poland; Siwowski, T., Faculty of Civil and Environmental Engineering and Architecture, Rzeszow University of Technology, al. Powstancow Warszawy 12, Rzeszow, 35-959, Poland; Sieńko, R., Faculty of Civil Engineering, Cracow University of Technology, ul. Warszawska 24, Krakow, 31-155, Poland","An increase in application of advanced materials and high-tech monitoring systems is being observed in bridge engineering in recent years. The main goal is aimed at optimizing maintenance costs spent during entire lifecycle of a bridge. The paper describes the concept of the smart fibre reinforced polymer (FRP) sandwich deck panel, dedicated for newly-designed and renovated bridges. This panel is equipped with the distributed fibre optic sensing (DFOS) system, integrated with composite laminates. The DFOS system is provided to control strain and displacement measurement, further used in the structural health monitoring of a bridge. The DFOS system is characterized by the following features: accurate, reliable and distributed strain measurements, possibility of assessing shape and displacements, detection of local damages, reliable protection of the sensors, no need for surface installations, high durability, measurements from the real zero state of the structural element. Exemplary results of distributed fibre optic strain and displacement measurements performed under laboratory conditions on laminate specimens as well as the beam cut from the prototype panels are presented and compared to conventional measurements and FEM predictions. © 2021 Elsevier Ltd","Distributed fibre optic sensors; FRP bridge deck panel; Rayleigh scattering; Smart structures; Strain and displacement measurements; Testing validation","Bridge decks; Damage detection; Electric measuring bridges; Fiber optics; Fiber reinforced plastics; Fibers; Laminated composites; Life cycle; Strain measurement; Structural health monitoring; Advanced materials; Bridge engineering; Composite laminate; Conventional measurements; Distributed strain measurement; Fibre reinforced polymers; Laboratory conditions; Structural elements; Displacement measurement",,,,,"Narodowe Centrum Badań i Rozwoju, NCBR","The research described within this paper was performed within the R&D project: “OptiDeck - Intelligent FRP deck system for construction and rehabilitation of road bridge structures, equipped with fibre optic sensors for structural health monitoring and bridge load controlling” carried out by Rzeszow University of Technology, Poland. This project was funded by the grant of the National Centre for Research and Development (Poland) within the framework of the LIDER Program.","The research described within this paper was performed within the R&D project: ?OptiDeck - Intelligent FRP deck system for construction and rehabilitation of road bridge structures, equipped with fibre optic sensors for structural health monitoring and bridge load controlling? carried out by Rzeszow University of Technology, Poland. This project was funded by the grant of the National Centre for Research and Development (Poland) within the framework of the LIDER Program.",,,,,,,,,"Holloway, L.C., Head, P.R., (2001), Advanced Polymer Composites and Polymers in the Civil Infrastructure, Elsevier Science Ltd, London; Keller, T., (2003), Use of fibre reinforced polymers in bridge construction, Structural Engineering Documents SED 7, International Association for Bridge and Structural Engineering (IABSE), Zurich; Zoghi, M., (2014) The International Handbook of FRP Composites in Civil Engineering, , CRC Press, Taylor & Francis Group LLC Boca Raton; Siwowski, T., FRP composite bridges: Structural shaping, design, testing, Publisher: Wydawnictwo Naukowe PWN, Warszawa; Siwowski, T., Kulpa, M., Rajchel, M., Poneta, P., Design, manufacturing and structural testing of an all-composite FRP bridge girder (2018) Compos. 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"Deng Y., Zhang M., Feng D.-M., Li A.-Q.","55218285200;57214936116;55973702300;57204331975;","Predicting fatigue damage of highway suspension bridge hangers using weigh-in-motion data and machine learning",2021,"Structure and Infrastructure Engineering","17","2",,"233","248",,16,"10.1080/15732479.2020.1734632","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080883508&doi=10.1080%2f15732479.2020.1734632&partnerID=40&md5=a6c56d45d1eedeb0fc6e965f483d5c77","Beijing Advanced Innovation Center for Future Urban Design, Beijing Key Laboratory of Functional Materials for Building Structure and Environment Remediation, Beijing University of Civil Engineering and Architecture, Beijing, China; Weidlinger Transportation Practice, Thornton Tomasetti, Inc, New York, NY, United States","Deng, Y., Beijing Advanced Innovation Center for Future Urban Design, Beijing Key Laboratory of Functional Materials for Building Structure and Environment Remediation, Beijing University of Civil Engineering and Architecture, Beijing, China; Zhang, M., Beijing Advanced Innovation Center for Future Urban Design, Beijing Key Laboratory of Functional Materials for Building Structure and Environment Remediation, Beijing University of Civil Engineering and Architecture, Beijing, China; Feng, D.-M., Weidlinger Transportation Practice, Thornton Tomasetti, Inc, New York, NY, United States; Li, A.-Q., Beijing Advanced Innovation Center for Future Urban Design, Beijing Key Laboratory of Functional Materials for Building Structure and Environment Remediation, Beijing University of Civil Engineering and Architecture, Beijing, China","Continuous and real-time tension force monitoring is a key point in fatigue damage evaluation for bridge suspenders or hangers. Usually, effective sensors are not equipped in suspenders or hangers of in-service bridges to obtain tension force responses. Bridge-site-specified traffic loading information collected by Weigh-in-motion (WIM) system offers an opportunity to address this issue. The daily fatigue damage of hangers can be estimated by combination of the traffic loading data with finite element analysis. Support vector machine (SVM) is adopted to establish the regression models between daily fatigue damage and collected traffic loading parameters. Consequently, the future fatigue damage of cables or hangers can be predicted by feeding the subsequent WIM data into the regression models. This proposed method is validated in the fatigue life prediction of hangers on a suspension bridge. The SVM model configuration and generalisation ability are investigated in this study. This study presents a novel way to estimate the fatigue damage of the hanger without direct stress sensing equipment and provides new thoughts in interpreting the monitoring data to provide useful information for engineering decision makers. © 2020 Informa UK Limited, trading as Taylor & Francis Group.","Fatigue damage; hanger; structural health monitoring; support vector machine; suspension bridge; traffic load","Decision making; Fatigue damage; Structural health monitoring; Support vector machines; Support vector regression; Suspension bridges; Traffic surveys; Weigh-in-motion (WIM); Damage evaluation; Engineering decisions; Fatigue life prediction; hanger; Regression model; Traffic loads; Weigh-in-motion datum; Weigh-in-motion systems; Suspensions (components)",,,,,"National Natural Science Foundation of China, NSFC: 51878027; Beijing Municipal Education Commission: CIT&TCD201904060, KM201910016013; Beijing University of Civil Engineering and Architecture, BUCEA: X18004","This research was supported, in part, by Grant from the National Natural Science Foundation of China (Project 51878027), Beijing Municipal Education Commission (CIT&TCD201904060 and KM201910016013) and the Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (X18004). 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Zhang, Y., Li, H., Wang, Z.H., Zhang, W.B., Li, J., A preliminary study on time series forecast of fair-weather atmospheric electric field with WT-LSSVM method (2015) Journal of Electrostatics, 75, pp. 85-89; Zheng, R., Liu, L., Zhao, X., Chen, Z., Zhang, C., Hua, X., Investigation of measurability and reliability of adhesive-bonded built-in fiber Bragg grating sensors on steel wire for bridge cable force monitoring (2018) Measurement, 129, pp. 349-357","Deng, Y.; Beijing Advanced Innovation Center for Future Urban Design, China; email: dengyang@bucea.edu.cn",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","",Scopus,2-s2.0-85080883508
"Mashayekhi M., Santini-Bell E.","57204763685;9040150900;","Fatigue assessment of a complex welded steel bridge connection utilizing a three-dimensional multi-scale finite element model and hotspot stress method",2020,"Engineering Structures","214",,"110624","","",,16,"10.1016/j.engstruct.2020.110624","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083711970&doi=10.1016%2fj.engstruct.2020.110624&partnerID=40&md5=73efccf8cf0afd35d3063543dae2c8ba","Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, United States","Mashayekhi, M., Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, United States; Santini-Bell, E., Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, United States","Some novel complex structural components of steel bridges are not explicitly addressed in the existing fatigue design codes and require an alternative local fatigue assessment method. This paper proposes a fatigue assessment protocol for these complex critical components of steel bridges, using the hotspot stress method. A computationally efficient finite element model of a large-scale bridge is created to provide the local structural response of complex components, under simulated dynamic traffic loads. A multi-scale model is implemented to accommodate higher dimension elements, which are recommended for fatigue assessment via hotspot stress method. The multi-scale model is created for the case study, the Memorial Bridge in Portsmouth, NH, which is a vertical lift steel truss bridge with a novel gusset-less curve-welded connection. A truck load test is used to validate the multi-scale model by comparing numerical results to the field collected data through the structural health monitoring system of the bridge. The result shows that the multi-scale model can determine the critical hotspot stresses, to study the fatigue performance of the bridge's critical components. © 2020 Elsevier Ltd","Complex structural components; Fatigue assessment; Gusset-less connection; Hotspot stress; Multi-scale model; Traffic simulation","Automobile testing; Finite element method; Load testing; Steel bridges; Structural health monitoring; Trusses; Welding; Complex structural components; Computationally efficient; Dynamic traffic loads; Fatigue assessments; Fatigue design codes; Fatigue performance; Multi-scale Modeling; Structural health monitoring systems; Fatigue of materials; bridge; dynamic analysis; fatigue; finite element method; loading; steel structure; stress analysis; structural analysis; structural response; three-dimensional modeling; New Hampshire; Portsmouth [New Hampshire]; United States",,,,,"National Science Foundation, NSF; Directorate for Engineering, ENG: 1430260","This material is based upon work partially supported by the National Science Foundation under Grant No. 1430260 , FHWA AID: DEMO Program and funding from the NHDOT Research Advisory Council . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The research team is also grateful to HNTB Corporation, in particular Ted Zoli and Christopher Engel, for sharing design information on the Memorial Bridge.",,,,,,,,,,"Epison, B., The Vierendeel bridges over the Albert Canal, Belgium – their significance in the story of brittle failures (2012) Steel Construct Des Res, l5 (4), pp. 238-243; Dong, P., A structural stress definition and numerical implementation for fatigue analysis of welded joints (2001) Int J Fatigue, 23 (10), pp. 865-876; Niemi, E., Fricke, W., Maddox, S.J., Fatigue analysis of welded components: Designer's guide to the structural hot-spot stress approach (2006), Woodhead Publishing Cambridge, UK; Rageh, A., Azam, S.E., Linzell, D.G., Steel railway bridge fatigue damage detection using numerical models and machine learning: Mitigating influence of modeling uncertainty (2020) Int J Fatigue, 134, p. 105458; Li, Z.X., Zhoua, T.Q., Chan, T.H.T., Yu, Y., Multi-scale numerical analysis on dynamic response and local damage in long-span bridges (2007) Eng Struct, 29, pp. 1507-1524; Ni, Y.Q., Ye, X.W., Ko, J.M., Monitoring-based fatigue reliability assessment of steel bridges: analytical model and application (2012) J Struct Eng, 110 (12), pp. 1563-1573; Chen, Z.W., Xia, Y.L., Xia, Y., Li, Q., Wong, K.Y., Fatigue analysis of long-span suspension bridges under multiple loading: Case study (2011) Eng Struct, 33, pp. 3246-3256; Chiewanichakorn, M., Aref, A.J., Alampali, S., Dynamic and fatigue response of a truss bridge with fiber reinforced polymer deck (2007) Int J Fatigue, 29, pp. 1475-1489; Fu, Z., Wang, Y., Ji, B., Liu, T., Assessment approach for multiaxial fatigue damage of deck and U-rib weld in steel bridge decks (2018) Constr Build Mater, 20, pp. 276-285; Liu, Z., Hebdon, M.H., Correria, J.F.O., Carvalho, H., Vilela, P.M.L., de Jesus, A.M.P., Fatigue assessment of critical connections in a historic Eyebar suspension bridge (2019) J Perform Constr Facil, 33 (1), p. 04018091.“; Guo, T., Liu, Z., Pan, S., Pan, Z., Cracking of longitudinal diaphragms in long-span cable-stayed bridges (2015) J Bridge Eng, 20 (11), p. 04015011; Guo, T., Liu, Z.X., Zhu, J.S., Fatigue reliability assessment of orthotropic steel bridge decks based on probabilistic multi-scale finite element analysis (2015) Adv Steel Construct, 11 (3), pp. 334-346; Yan, F., Chen, W., Lin, Z., Prediction of fatigue life of welded details in cable-stayed orthotropic steel deck bridges (2016) Eng Struct, 127, pp. 344-358; Alencar, G.M.P., de Jesus, A.A.B., Calçada, R., Guilherme, S., daSilva, J., Fatigue life evaluation of a composite steel-concrete roadway bridge through the hot-spot stress method considering progressive pavement deterioration (2018) Eng Struct., 166 (1), pp. 46-61; Kużawa, M., Kamiński, T., Bień, J., Fatigue assessment procedure for old riveted road bridges (2018) Archives Civil Mech Eng, 18 (4), pp. 1259-1274; Chen, B., Li, X., Xie, X., Zhong, Z., Lu, P., Fatigue performance assessment of composite arch bridge suspenders based on actual vehicle loads (2015) Shock Vib, pp. 1-13; Liu, Z., Correia, J., Carvalho, H., Mourão, A., De Jesus, A., Calçada, R., Global-local fatigue assessment of an ancient riveted metallic bridge based on submodelling of the critical detail (2019) Fatigue Fract Eng Mater Struct, 42 (2), pp. 546-560; (2012), AASHTO. 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International Institute of Welding;; Mashayekhi, M., Santini-Bell, Fatigue assessment of the gusset-less connection using field data and numerical model (2019) Bridge Struct, 15 (1-2), pp. 75-86; Mashayekhizadeh, M., (2019), Fatigue assessment of complex structural components of steel bridges integrating finite element models and field-collected data Doctoral Dissertations. 249","Mashayekhi, M.; Department of civil and Environmental engineering, United States; email: mm1182@wildcats.unh.edu",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85083711970
"Yang H., Xu X., Neumann I.","56427164800;56427179900;26325889100;","An automatic finite element modelling for deformation analysis of composite structures",2019,"Composite Structures","212",,,"434","438",,16,"10.1016/j.compstruct.2019.01.047","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059945558&doi=10.1016%2fj.compstruct.2019.01.047&partnerID=40&md5=e7e1625ff5f36f41c9919152d0aff253","Jiangsu University of Science and Technology, Zhenjiang City, Jiangsu Province, China; Geodetic Institute, Faculty of Civil Engineering and Geodetic Science, Leibniz University Hanover, Germany","Yang, H., Jiangsu University of Science and Technology, Zhenjiang City, Jiangsu Province, China, Geodetic Institute, Faculty of Civil Engineering and Geodetic Science, Leibniz University Hanover, Germany; Xu, X., Geodetic Institute, Faculty of Civil Engineering and Geodetic Science, Leibniz University Hanover, Germany; Neumann, I., Geodetic Institute, Faculty of Civil Engineering and Geodetic Science, Leibniz University Hanover, Germany","Terrestrial laser scanning is extensively adopted in the area of high-precision monitoring and three-dimensional measurement. Architectural structures today are increasingly complex and health monitoring plays an important role in guaranteeing their safety. Therefore, how reliability deformation monitoring can be improved is one of the key problems in the field of engineering. This paper combines the three-dimensional laser scanning technology and finite element method (FEM) to investigate the deformation mechanism of arched structures. Within this paper, we simulated arched structures using the FEM, which is consistent with the result of terrestrial laser scanner (TLS) measurement. We aimed at constructing an intelligent and efficient FEM model which can be extensively applied in the monitoring of many constructs, such as bridges and ancient architecture. The focus in this research lies mainly on deformation analysis, which is based on FEM model simulation with the calibration of TLS measurement. © 2019 Elsevier Ltd","Deformation analysis; Displacement; Finite element method; Point cloud; Terrestrial laser scanning","Arches; Deformation; Laser applications; Scanning; Steel beams and girders; Structural health monitoring; Surveying instruments; Deformation analysis; Displacement; Finite element modelling; Point cloud; Terrestrial laser scanners; Terrestrial laser scanning; Three dimensional laser scanning technology; Three-dimensional measurements; Finite element method",,,,,"Natural Science Foundation of Jiangsu Province: BK20160558","The publication of this article was funded by the support of Natural Science Foundation of Jiangsu Province (No: BK20160558 ). The authors gratefully acknowledge the support of Massivbau Institute to this research work.",,,,,,,,,,"Park, H.S., Lee, H.M., Adeli, H., Lee, I., A new approach for health monitoring of structures: terrestrial laser scanning (2007) Comput-Aided Civ Infrastruct Eng, 22, pp. 19-30; Yang, H., Xu, X., Neumann, I., (2016) Laser Scanning-Based Updating of a Finite-Element Model for Structural Health Monitoring, pp. 2100-2104. , IEEE sensors; Xu, X., Yang, H., Zhang, Y., Neumann, I., Intelligent 3D data extraction method for deformation analysis of composite structures (2018) Compos Struct, 203, pp. 254-258; Yang, H., Omidalizarandi, M., Xu, X., Neumann, I., Terrestrial laser scanning technology for deformation monitoring and surface modeling of arch structures (2017) Compos Struct, 169, pp. 173-179; Xu, X., Yang, H., Network method for deformation analysis of three-dimensional point cloud with terrestrial laser scanning sensor (2018) Int J Distrib Sens Netw, 14; Yang, H., Xu, X., Neumann, I., Optimal finite element model with response surface methodology for concrete structures based on Terrestrial Laser Scanning technology (2016) Compos Struct; Xu, X., Bureick, J., Yang, H., Neumann, I., TLS-based composite structure deformation analysis validated with laser tracker (2018) Compos Struct, 202, pp. 60-65; Yang, H., Xu, X., Xu, W., Neumann, I., Terrestrial laser scanning-based deformation analysis for arch and beam structures (2017) IEEE Sens J, 17, pp. 4605-4611; Xu, X., Yang, H., Neumann, I., Monotonic loads experiment investigate of composite structure based on terrestrial laser scanner measurement (2018) Compos Struct, 183, pp. 563-567; Tuno, N., Mulahusic, A., Kogoj, D., Improving the positional accuracy of digital cadastral maps through optimal geometric transformation (2017) J Surv Eng, 143; Yang, H., Xu, X., Neumann, I., The benefit of 3d laser scanning technology in the generation and calibration of FEM models for health assessment of concrete structures (2014) Sensors, 14 (11), pp. 21889-21904; Xu, X., Zhao, X., Yang, H., Neumann, I., TLS-based feature extraction and 3D modeling for arch structures (2017) J Sensors, , Article ID 9124254; Xu, X., Yang, H., Neumann, I., Time-efficient filtering method for three-dimensional point clouds data of tunnel structures (2018) Adv Mech Eng, 10 (5), pp. 1-6; Xu, X., Kargoll, B., Bureick, J., Yang, H., Neumann, I., TLS-based profile model analysis of major composite structures with robust B-spline method (2017) Compos Struct, 184, pp. 814-820; Yang, H., Xu, X., Neumann, I., Deformation behavior analysis of composite structures under monotonic loads based on terrestrial laser scanning technology (2018) Compos Struct, 183, pp. 594-599; Xu, X., Yang, H., Neumann, I., A feature extraction method for deformation analysis of large-scale composite structures based on TLS measurement (2018) Compos Struct, 184, pp. 591-596; Xu, X., Yang, H., Neumann, I., Deformation monitoring of typical composite structures based on terrestrial laser scanning technology (2018) Composite Struct, Compos Struct, 202, pp. 77-81; Wilkinson, M.W., Jones, R.R., Woods, C.E., A comparison of terrestrial laser scanning and structure-from-motion photogrammetry as methods for digital outcrop acquisition (2016) Geosphere, 12, pp. 1865-1880; Wei, X., Xu, X., Yang, H., Neumann, I., Optimized finite element analysis model based on terrestrial laser scanning data (2019) Compos Struct, 207, pp. 62-71; Rodriguez-Gonzalvez, P., Jimenez Fernandez-Palacios, B., Luis Munoz-Nieto, A., Mobile LiDAR system: new possibilities for the documentation and dissemination of large cultural heritage sites (2017) Remote Sens, 9; Moldovan, N., Kulkarni, S., Ferrari, M., Use of laser scanning cytometry for analysis of endothelial cells attached to micropatterned silicon surfaces (2002) Sens Mater, 14, pp. 179-187; Oberlander, M., Bartsch, K., Schulte, C., Process monitoring of laser remote cutting of carbon fiber reinforced plastics by means of reflecting laser radiation (2017) J Laser Appl, 29 (22009); Xu, X., Kargoll, B., Bureick, J., Yang, H., Neumann, I., An automatic and intelligent optimal surface modeling method for composite tunnel structures (2019) Compos Struct, 208, pp. 702-710; Kitratporn, N., Takeuchi, W., Matsumoto, K., Structure deformation measurement with terrestrial laser scanner at pathein bridge in Myanmar (2018) J Disaster Res, 13, pp. 40-49; Hansen, M., Piehler, J., Kapphahn, G., (2015), Systemanalyse neugotischer Gewölbe, 8. Mauerwerkskalendertag, Dresden, March 24; Hansen, M., Schmidt, B., Kelma, S., Schmoor, K., Goretzka, J., Probabilistic assessment of the foundation of offshore wind turbines (2015) Proceedings of the IABSE Conference, Elegance in Structures; Banerjee, U., Osborn, J., Generalized finite element methods: main ideas, results, and perspective (2004) Int J Comput Methods, pp. 67-103; Schmalz, T., Buhl, V., Eichhorn, A., An adaptive kalman-filtering approach for the calibration of finite difference models of mass movements (2010) J Appl Geod, pp. 127-135; Wang, Y., Bo, Y., Sun, S., Fast prediction method for steady-state heat convection (2012) Chem Eng Technol, 35 (4), pp. 668-678; Ganapuram, S., Adams, M., Patnaik, A., Quantification of cracks in concrete bridge decks in Ohio district 3 (2012), The University of Akron Akron, American; Kang, D.S., Lee, H.M., Park, H.S., Lee, I., Computing method for estimating strain and stress of steel beams using terrestrial laser scanning and FEM (2007) Key Eng Mater, 347, pp. 517-522; Kumar, S., Bag, S., Baruah, M., Finite element model for femtosecond laser pulse heating using dual phase lag effect (2016) J Laser Appl, 28 (32008); Heunecke, O., Zur Identifikation und Verifikation von Deformations-prozessen mittels adaptiver KALMAN-Filterung (Hannoversches Filter) (1995), Leibniz Universität Hannover Hanover, Germany Thesis; Gülal, E., Geodätische Überwachung einer Talsperre: eine Anwendung der KALMAN-Filtertechnik (1997), Leibniz Universität Hannover, Hanover, Germany Thesis; Eichhorn, A., Ein Betrag zur Identifikation von dynamischen Strukturmodellen mit Methoden der adaptiven KALMAN-Filterung (2005), Universität Stuttgart Stuttgart, Germany Ph. D thesis; Lienhart, W., Analysis of inhomogeneous structural monitoring data (2007), Graz University of Technology Granz, Austria Ph. D thesis; Becker, T., Weisbrich, S., Euteneuer, F., Wu, C.C., Neitzel, F., (2014), Neue Möglichkeiten in der Bauwerksüberwachung durch integrierte Analyse von Sensormessungen und 3D-Bauwerksmodell. In: DGPF Proceedings of Sensors, Measurement and Testing Techniques, March 26–28, Hamburg; Wu, C.-C., (2016), 3 (4). , Sven Weisbrich and Frank Neitzel. Materials Today: Proceedings., Pages 1211-1215. 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Gemeinsame Tagung 2014 der DGfK, der DGPF, der GfGI und des GiN (DGPF Tagungsband 23/2014); Ribeiro, D., Calçada, R., Delgado, R., Brehm, M., Zabel, V., Finite element model updating of a bowstring-arch railway bridge based on experimental modal parameters (2012) Eng Struct, 40, pp. 413-435; Aras, F., Krstevska, L., Altay, G., Tashkov, L., Experimental and numerical modal analyses of a historical masonry palace (2011) Constr Build Mater, 25 (1), pp. 81-91; Foti, D., Diaferio, M., Giannoccaro, N.I., Michele Mongelli.Ambient vibration testing, dynamic identification and model updating of a historic tower (2012) NDT E Int, 47, pp. 88-95; Sevim, B., Finite element model calibration of berke arch dam using operationalmodal testing (2011) J Vibr Control, 17. , pp. 7 1065-1079; Zong, Z.H., Ren, W.X., Finite element model updating and model validation of bridge structures (2012), People's Commumication Press China; Beberniss, T.J., Ehrhardt, D.A., High-speed 3D digital image correlation vibration measurement: Recent advancements and noted limitations (2017) Mech Syst Signal Process, 86, pp. 35-48; Reu, P.L., Rohe, D.P., Jacobs, L.D., Comparison of DIC and LDV for practical vibration and modal measurements (2017) Mech Syst Signal Process, 86, pp. 2-16; Nakamura, S., GPS measurement of wind-induced suspension bridge girder displacements (2000) J Struct Eng, 126 (12), pp. 1413-1419","Xu, X.; Geodetic Institute, Germany; email: xu@gih.uni-hannover.de",,,"Elsevier Ltd",,,,,02638223,,COMSE,,"English","Compos. Struct.",Article,"Final","",Scopus,2-s2.0-85059945558
"Jamali S., Chan T.H.T., Nguyen A., Thambiratnam D.P.","57201483048;7402687570;57310688400;35583914600;","Reliability-based load-carrying capacity assessment of bridges using structural health monitoring and nonlinear analysis",2019,"Structural Health Monitoring","18","1",,"20","34",,16,"10.1177/1475921718808462","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059908447&doi=10.1177%2f1475921718808462&partnerID=40&md5=023a71d538f212b8ba2ac2abe8c0e8e2","School of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, QLD, Australia; School of Civil Engineering and Surveying, University of Southern Queensland, Springfield Central, QLD, Australia","Jamali, S., School of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, QLD, Australia; Chan, T.H.T., School of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, QLD, Australia; Nguyen, A., School of Civil Engineering and Surveying, University of Southern Queensland, Springfield Central, QLD, Australia; Thambiratnam, D.P., School of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, QLD, Australia","For assessment of existing bridges, load rating is usually performed to assess the capacity against vehicular loading. Codified load rating can be conservative if the rating is not coupled with the field data or if simplifications are incorporated into assessment. Recent changes made to the Australian Bridge assessment code (AS 5100.7) distinguish the difference between design and assessment requirements, and include addition of structural health monitoring for bridge assessment. However, very limited guidelines are provided regarding higher order assessment levels, where more refined approaches are required to optimize the accuracy of the assessment procedure. This article proposes a multi-tier assessment procedure for capacity estimation of existing bridges using a combination of structural health monitoring techniques, advanced nonlinear analysis, and probabilistic approaches to effectively address the safety issues on aging bridges. Assessment of a Box Girder bridge was carried out according to the proposed multi-tier assessment, using data obtained from modal and destructive testing. Results of analysis at different assessment tiers showed that both load-carrying capacity and safety index of the bridge vary significantly if current bridge information is used instead of as-designed bridge information. Findings emerged from this study demonstrated that accuracy of bridge assessment is significantly improved when structural health monitoring techniques along with reliability approaches and nonlinear finite element analysis are incorporated, which will have important implications that are relevant to both practitioners and asset managers. © The Author(s) 2018.","Box Girder; Load-carrying capacity; nonlinear analysis; reliability analysis; structural health monitoring","Box girder bridges; Load limits; Loads (forces); Monitoring; Nonlinear analysis; Reliability analysis; Steel bridges; Structural analysis; Assessment procedure; Box girder; Capacity assessment; Destructive testing; Non-linear finite-element analysis; Probabilistic approaches; Reliability approach; Reliability-based load; Structural health monitoring",,,,,"Australian Research Council, ARC: DP130104133; Queensland University of Technology, QUT","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research presented in this paper has been funded by Queensland University of Technology and Australian Research Council (ARC DP130104133), which are gratefully acknowledged.",,,,,,,,,,"Jamali, S., Chan, T.H.T., Thambiratnam, D.P., Pre-test finite element modelling of Box Girder overpass-application for bridge condition assessment, pp. 1-8. , Australasian structural engineering conference (ASEC), Brisbane, QLD, Australia, Engineers Australia, In; Bridge design—part 7: bridge assessment; Melchers, R.E., Beck, A.T., (2017) Structural reliability analysis and prediction, , 3rd ed., Hoboken, NJ, John Wiley & Sons; Chen, W.-F., Duan, L., (2014) Bridge engineering handbook: construction and maintenance, , 2nd ed., Boca Raton, FL, CRC Press; Lake, N., Ngo, H., Kotze, R., (2014) Review of AS 5100.7: Rating of Existing Bridges and the Bridge Assessment Group Guidelines, , Report for Austroads, Austroads Publication No. AP-R452-14, 20 February, Sydney, NSW, Australia: Austroads; Shmerling, R.Z., Catbas, F.N., Load rating and reliability analysis of an aerial guideway structure for condition assessment (2009) J Bridge Eng, 14, pp. 247-256; Taylor, P., Frauenfelder, P., Bridge Assessment for High Productivity Freight Vehicle Access: Guidelines on Processes and Procedures, , Report for Austroads, Austroads Publication No. AP-R532-16, 11 November 2016. Sydney, NSW, Australia: Austroads; Seskis, J., Lake, N., Ngo, H., Implementation of a Nationally Consistent Framework for the Assessment of Bridges in Australia, , Report for Austroads, Austroads Publication No. AP-R565-18, Sydney, NSW, Australia, 17 April 2018. Sydney, NSW, Australia: Austroads; Basis for design of structures—assessment of existing structures; General principles on reliability for structures; General principles on reliability for structures; Akgül, F., Frangopol, D.M., Bridge rating and reliability correlation: comprehensive study for different bridge types (2004) Struct Eng, 130, pp. 1063-1074; Nowak, A.S., Collins, K.R., (2012) Reliability of structures, , New York, McGraw-Hill; Rackwitz, R., Fiessler, B., Structural reliability under combined random load sequences (1978) Comput Struct, 9, pp. 489-494; Marek, P., Gustar, M., Anagnos, T., (1996) Simulation based reliability assessment for structural engineers, , Boca Raton, FL, CRC Press; Foster, S.J., Stewart, M.G., Loo, M., Calibration of Australian Standard AS3600 Concrete Structures: part I statistical analysis of material properties and model error (2016) Aust J Struct Eng, 17, pp. 242-253; Nowak, A.S., Calibration of LRFD Bridge Design Code, , NCHRP Report, No. 368, October 1999. Washington, DC: Transportation Research Board; Nowak, A.S., Szerszen, M.M., Reliability-Based Calibration for Structural Concrete, , Report UMCEE 01-04, 6 January 2005. Ann Arbor, MI: Department of Civil and Environmental Engineering, University of Michigan; Ellingwood, B.R., Reliability-based condition assessment and LRFD for existing structures (1996) Struct Saf, 18, pp. 67-80; Mirza, S.A., MacGregor, J.G., Variations in dimensions of reinforced concrete members (1979) J Struct Div: ASCE, 105, pp. 751-766; Taly, N., (2014) Highway bridge superstructure engineering: LRFD approaches to design and analysis, , Boca Raton, FL, CRC Press; Jamali, S., Chan, T.H.T., Nguyen, A., Modelling techniques for structural evaluation for bridge assessment (2018) Civ Struct Health Monit, 8, pp. 271-283; Jamali, S., Koo, K.Y., Chan, T.H.T., Assessment of flexural stiffness and load carrying capacity using substructural system, pp. 564-574. , International conference on structural health monitoring of intelligent infrastructure (SHMII-08), Brisbane, QLD, Australia, Curran Associates, In; Jamali, S., Chan, T.H.T., Koo, K.Y., Capacity estimation of beam-like structures using substructural method (2018) Int J Struct Stab Dy, 18. , 1850162; (2002) Bridge Management systems—the State of the Art, , Report for Austroads, Austroads Publication No. AP-R198-02, 1 February, Sydney, NSW, Australia: Austroads; Pathirage, T.S., (2017) Identification of prestress force in prestressed concrete Box Girder bridges using vibration-based techniques, , Queensland University of Technology, Brisbane, QLD, Australia, PhD Thesis; Jamali, S., Chan, T.H.T., Thambiratnam, D., Comparative study of grillage analogy and finite element method for bridge heavy load assessment, pp. 1-10. , Proceedings of Austroads bridge conference (ABC), Melbourne, VIC, Australia, Australia, Austroads, In; Alfarah, B., López-Almansa, F., Oller, S., New methodology for calculating damage variables evolution in plastic damage model for RC structures (2017) Eng Struct, 132, pp. 70-86; Kmiecik, P., Kamiński, M., Modelling of reinforced concrete structures and composite structures with concrete strength degradation taken into consideration (2011) Arch Civ Mech Eng, 11, pp. 623-636; Hanif, M.U., Ibrahim, Z., Jameel, M., A new approach to estimate damage in concrete beams using non-linearity (2016) Constr Build Mater, 124, pp. 1081-1089; Attard, M.M., Setunge, S., The stress-strain relationship of confined and unconfined normal and high strength concretes (1994) ACI Mater J, 93, pp. 432-442; Gopalaratnam, V., Shah, S.P., Softening response of plain concrete in direct tension (1985) ACI Mater J, 82, pp. 310-323; Benjeddou, O., Ouezdou, M.B., Bedday, A., Damaged RC beams repaired by bonding of CFRP laminates (2007) Constr Build Mater, 21, pp. 1301-1310; Sümer, Y., Aktaş, M., Defining parameters for concrete damage plasticity model (2015) Chall J Struct Mech, 1, pp. 149-155; Nguyen, A., Chan, T.H.T., Thambiratnam, D.P., (2017) Output-only modal testing and monitoring of civil engineering structures: instrumentation and test management, pp. 1134-1145. , International conference on structural health monitoring of intelligent infrastructure (SHMII-08), Brisbane, QLD, Australia, Curran Associates, In; Moravej, H., Jamali, S., Chan, T.H.T., (2017) Finite element model updating of civil engineering infrastructures: a review literature, pp. 1099-1110. , International conference on structural health monitoring of intelligent infrastructure (SHMII-08), Brisbane, QLD, Australia, Curran Associates, In; Law, S.S., Li, J., Updating the reliability of a concrete bridge structure based on condition assessment with uncertainties (2010) Eng Struct, 32, pp. 286-296; (2015) Femtools Model Updating Manual, , Dynamic Design Solutions, Belgium: Leuven; Li, J., Law, S.S., Hao, H., Improved damage identification in bridge structures subject to moving loads: numerical and experimental studies (2013) Int J Mech Sci, 74, pp. 99-111","Chan, T.H.T.; School of Civil Engineering and Built Environment, Australia; email: tommy.chan@qut.edu.au",,,"SAGE Publications Ltd",,,,,14759217,,,,"English","Struct. Health Monit.",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85059908447
"Sousa H., Rozsas A., Slobbe A., Courage W.","36603404200;57132846700;53864069000;6602865921;","A novel pro-active approach towards SHM-based bridge management supported by FE analysis and Bayesian methods",2020,"Structure and Infrastructure Engineering","16","2",,"233","246",,15,"10.1080/15732479.2019.1649287","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070463204&doi=10.1080%2f15732479.2019.1649287&partnerID=40&md5=55b532e8dc6ff9e5e91b95f3a44a728c","Research and Innovation, HS Consulting, Matosinhos, Portugal; BRISA Group, São Domingos de Rana, Lisbon, Portugal; The Netherlands Organization for Applied Scientific Research (TNO), Delft, Netherlands","Sousa, H., Research and Innovation, HS Consulting, Matosinhos, Portugal, BRISA Group, São Domingos de Rana, Lisbon, Portugal; Rozsas, A., The Netherlands Organization for Applied Scientific Research (TNO), Delft, Netherlands; Slobbe, A., The Netherlands Organization for Applied Scientific Research (TNO), Delft, Netherlands; Courage, W., The Netherlands Organization for Applied Scientific Research (TNO), Delft, Netherlands","Europe has an extensive transport infrastructure network where bridges play a vital role. Most of them were built as part of the post-World War II reconstruction effort, meaning that we, as society, are already facing the beginning of the end of their design life. This shows the necessity of efficient approaches, complementing visual inspections, for early detection of damage that might jeopardise structural integrity and ultimately might lead to loss of life. This work introduces a novel, pro-active structural health monitoring (SHM) approach to better identify and quantify representative damage types, on prestressed concrete bridges. Based on a numerical simulation of a comprehensive case study available in the literature: the Lezíria Bridge, the results show that damage can be identified with good accuracy for early stages of damaged bridges. Pier settlements and prestress losses are the damage types where the severity is quite accurately quantified even for lower damage severity levels. Once the damage type is identified, it is found that a pair of two vertical displacements reveals to be enough to quantify the damage extent. The results also show potential in the utilisation of the approach for a rational and efficient design of monitoring systems towards damage identification. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.","Bayesian statistics; bridge management; Bridges; damage; finite element modelling; measurement uncertainty; response surface modelling; structural health monitoring","Bayesian networks; Bridges; Chemical detection; Finite element method; Military operations; Prestressed concrete; Structural health monitoring; Uncertainty analysis; Bayesian statistics; Bridge management; damage; Finite element modelling; Measurement uncertainty; Response surface modelling; Damage detection",,,,,"European Cooperation in Science and Technology, COST: TU1402","This work was funded by European Cooperation in Science and Technology. The COST Action TU1402 on Quantifying the Value of Structural Health Monitoring is gratefully acknowledged for networking. This research was also conducted in the course of TNO ERP–SI: TNO Early Research Program–Structural Integrity. With respect to this, the valuable comments and suggestions of Agnieszka Bigaj van Vliet from the conception until the end of the project are greatly acknowledged.",,,,,,,,,,"Aitchison, J., Dunsmore, I.R., (1980) Statistical prediction analysis, , New York, NY: Cambridge University Press; Alborn, T., Kasper, J., Aktan, H., Koyuncu, Y., Rutyna, J., (2002) Causes & cures for prestressed concrete I-beam end deterioration, , Research Report RC-1412. Wayne State University, CEE Department; (2012) Design standard for structural health monitoring system (CECS 333:2012), , Beijing, China: Architecture & Building Press, China Association for Engineering Construction Standardization,. 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(in Portuguese; (2004) Procedures required for assessing highway structures, , Joint Report of Working Groups 2 and 3: Methods used European States to inspect and assess the condition of highway structures, Brussels, Belgium; Ebrahimian, H., Astroza, R., Conte, J.P., Papadimitriou, C., Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures (2018) Structural Control and Health Monitoring, 25 (4), p. e2128; Enckell, M., (2011) Lessons learned in structural health monitoring of bridges using advanced sensor technology, , KTH Royal Institute of Technology, Sweden: (PhD thesis; (2004) Design of concrete structures—Part 1-1: General rules and rules for buildings, , Brussels, Belgium: European Committee for Standardization CEN, Eurocode 2: EN 1992-1-1; Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B., (2003) Bayesian data analysis, , New York, NY: Chapman and Hall/CRC; Hoeting, J.A., Madigan, D., Raftery, A.E., Volinsky, C.T., Bayesian model averaging: A tutorial (1999) Statistical Science, 14 (4), pp. 382-417; Hou, J., An, Y., Wang, S., Wang, Z., Jankowski, L., Ou, J., Structural damage localization and quantification based on additional virtual masses and Bayesian theory (2018) Journal of Engineering Mechanics, 144 (10), p. 04018097; Johnson, D., Milliken, G., (2006) Encyclopedia of statistical sciences, , Hoboken, NJ: Wiley; Lophaven, S., Nielsen, H., Søndergaard, J., (2002) DACE—A MATLAB Kriging Toolbox—Version 2.0, , August 1)., Technical Report IMM-TR-2002-12, Technical University of Denmark; Manie, J., DIANA—Finite element analysis (2008) User’s manual, release 9.3, , Delft, Netherlands: TNO DIANA, BV,. 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(Eds; Sousa, C., Sousa, H., Neves, A., Figueiras, J., Numerical evaluation of the long-term behavior of precast continuous bridge decks (2012) Journal of Bridge Engineering, 17 (1), pp. 89-96; Sousa, H., Bento, J., Figueiras, J., Construction assessment and long-term prediction of prestressed concrete bridges based on monitoring data (2013) Engineering Structures, 52, pp. 26-37; Sousa, H., Bento, J., Figueiras, J., Assessment and management of concrete bridges supported by monitoring data-based finite-element modeling (2014) Journal of Bridge Engineering, 19 (6), p. 05014002; Sousa, H., Félix, C., Bento, J., Figueiras, J., Design and implementation of a monitoring system applied to a long-span prestressed concrete bridge (2011) Structural Concrete, 12 (2), pp. 82-93; Spiegelhalter, D., Rice, K., Bayesian statistics (2009) Scholarpedia, 4 (8), p. 5230; Vahedi, M., Khoshnoudian, F., Hsu, T.Y., Application of Bayesian statistical method in sensitivity-based seismic damage identification of structures: Numerical and experimental validation (2018) Structural Health Monitoring, 17 (5), pp. 1255-1276; Wang, T., (2018) Application of probabilistic damage identification to civil engineering structures: A marriage of Structural Health Monitoring and Bayesian statistics, , Delft University of Technology, Delft, The Netherlands: (MSc thesis; Wong, K., Design of a structural health monitoring system for long-span bridges (2007) Structure and Infrastructure Engineering, 3 (2), pp. 169-185; Zakic, B., Ryzynski, A., Guo-Hong, C., Jokela, J., Classification of damage in concrete bridges (1991) Materials and Structures, 24 (4), pp. 268-275","Sousa, H.; Research and Innovation, Portugal; email: mail@hfmsousa.com",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","",Scopus,2-s2.0-85070463204
"Abedin M., Farhangdoust S., Mehrabi A.","57211253861;57197801868;7005771645;","Fracture detection in steel girder bridges using self-powered wireless sensors",2019,"Risk-based Bridge Engineering - 10th NewYork City Bridge Conference, 2019",,,,"216","228",,15,"10.1201/9780367815646-18","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074352493&doi=10.1201%2f9780367815646-18&partnerID=40&md5=81fcaf0340431dfff2f2ac7baf459b3a","Department of Civil and Environmental Engineering, Florida International University, Miami, FL, United States","Abedin, M., Department of Civil and Environmental Engineering, Florida International University, Miami, FL, United States; Farhangdoust, S., Department of Civil and Environmental Engineering, Florida International University, Miami, FL, United States; Mehrabi, A., Department of Civil and Environmental Engineering, Florida International University, Miami, FL, United States","Fracture Critical members are steel tension components whose failure is expected to result in collapse of the bridge. It is required to inspect fracture-critical bridges using “arms-length” approach, which is costly and time consuming. Structural health monitoring can be used as alternative approach for inspection providing both accuracy and economy. This paper investigates the feasibility of using a handful of self-powered wireless sensors for continuous monitoring and detection of fracture in steel plate girder bridges. A detailed finite element analysis was carried out on a multi-girder bridge using available traffic data. The time histories of displacement obtained for intact and fractured scenarios show that vibration amplitude was significantly increased for fractured girder, and strain variation was recorded especially in the vicinity of fracture, conditions that can be detected with relevant sensors. Moreover, the amplitude and frequency of the vibration was significant enough to provide the required power for typical sensor(s). © 2019 Taylor & Francis Group, London, UK.","Bridges; Damage Detection; Fracture Critical; Health Monitoring; Self-powered Sensor; Steel Girder","Bridges; Damage detection; Plate girder bridges; Railroad bridges; Steel beams and girders; Structural health monitoring; Continuous monitoring; Fracture-critical bridges; Health monitoring; Multi-girder bridges; Self-powered; Steel girder; Steel plate girders; Vibration amplitude; Fracture",,,,,,,,,,,,,,,,"(2017) AASHTO LRFD Bridge Design Specifications. American Association of State Highway and Transportation Officials, , 8th Edition, Washington, D.C; (2014) Building Code Requirements for Structural Concrete (ACI 318-14): Commentary on Building Code Requirements for Structural Concrete (ACI 318R-14): An ACI Report, , American Concrete Institute. ACI; Antunes, P., Lima, H., Varum, H., André, P., Optical fiber sensors for static and dynamic health monitoring of civil engineering infrastructures: Abode wall case study (2012) Measurement, 45 (7), pp. 1695-1705. , ), pp; Connor, R.J., Martín, B., Francisco, J., Varma, A., Lai, Z., Korkmaz, C., (2018) Fracture-Critical System Analysis for Steel Bridges, , No. Project 12-87A); Ding, Y.L., Zhao, H.W., Li, A.Q., Temperature effects on strain influence lines and dynamic load factors in a steel-truss arch railway bridge using adaptive FIR filtering (2017) Journal of Performance of Constructed Facilities, 31 (4); Elvin, N.G., Lajnef, N., Elvin, A.A., Feasibility of structural monitoring with vibration powered sensors (2006) Smart Materials and Structures, 15 (4), p. 977. , ), p; Farhangdoust, S., Mehrabi, A., Younesian, D., Bistable wind-induced vibration energy harvester for self-powered wireless sensors in smart bridge monitoring systems (2019) Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII, , April, (Vol. 10971, p. 109710C). International Society for Optics and Photonics; Hebdon, M.H., Singh, J., Connor, R.J., Redundancy and Fracture Resilience of Built-Up Steel Girders (2017) Structures Congress, p. 2017; Huang, H.B., Yi, T.H., Li, H.N., Canonical correlation analysis based fault diagnosis method for structural monitoring sensor networks (2016) Smart Struct. Syst, 17 (6), pp. 1031-1053. , ), pp; Kathol, S., Azizinamini, A., Luedke, J., (1995) Strength Capacity of Steel Girder Bridges. Final Report (No. RES1 (0099) P469); Lubliner, J., Oliver, J., Oller, S., Oñate, E., A plastic-damage model for concrete (1989) International Journal of Solids and Structures, 25 (3), pp. 299-326. , ), pp; Moschas, F., Stiros, S., Noise characteristics of high-frequency, short-duration GPS records from analysis of identical, collocated instruments (2013) Measurement, 46 (4), pp. 1488-1506. , ), pp; Rahimi, A., Azimi, G., Asgari, H., Jin, X., Clustering Approach toward Large Truck Crash Analysis (2019) Transportation Research Record; Samimi, A., Rahimi, E., Amini, H., Jamshidi, H., Freight modal policies toward a sustainable society (2019) Scientia Iranica; Sazonov, E., Li, H., Curry, D., Pillay, P., Self-powered sensors for monitoring of highway bridges (2009) IEEE Sensors Journal, 9 (11), pp. 1422-1429. , ), pp; Simulia, D.S., (2013) ABAQUS 6.13 User’s Manual. Dassault Systems, , Providence, RI; Sohn, H., Noncontact laser sensing technology for structural health monitoring and nondestructive testing (Presentation video) (2014) Bioinspiration, Biomimetics, and Bioreplication 2014, , (Vol. 9055, p. 90550W). International Society for Optics and Photonics; Yi, T.H., Li, H.N., Gu, M., Optimal sensor placement for structural health monitoring based on multiple optimization strategies (2011) The Structural Design of Tall and Special Buildings, 20 (7), pp. 881-900. , ), pp; Yuen, K.V., Kuok, S.C., Efficient Bayesian sensor placement algorithm for structural identification: A general approach for multi-type sensory systems (2015) Earthquake Engineering & Structural Dynamics, 44 (5), pp. 757-774. , ), pp",,"Mahmoud K.M.",,"CRC Press/Balkema","10th NewYork City Bridge Conference, 2019","26 August 2019 through 27 August 2019",,236199,,9780367416737,,,"English","Risk-based Br. Eng. - NewYork City Br. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85074352493
"Sabamehr A., Lim C., Bagchi A.","55596413600;56927357200;10043301000;","System identification and model updating of highway bridges using ambient vibration tests",2018,"Journal of Civil Structural Health Monitoring","8","5",,"755","771",,15,"10.1007/s13349-018-0304-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056879545&doi=10.1007%2fs13349-018-0304-5&partnerID=40&md5=e017d81834b96d2e8e536bd0092f9ded","Department of Building, Civil and Environmental Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, QC  H3G 1M8, Canada","Sabamehr, A., Department of Building, Civil and Environmental Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, QC  H3G 1M8, Canada; Lim, C., Department of Building, Civil and Environmental Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, QC  H3G 1M8, Canada; Bagchi, A., Department of Building, Civil and Environmental Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, QC  H3G 1M8, Canada","Vibration-based structural health monitoring (SHM) has received significant attention in the past. Due to the existence of some defect of implementation, the measured response of a structure and the response from its finite element model may not match. There are a number of methods available for updating the Finite Element (FE) model of a structure such that the response calculated from the model agrees with field measurements, and identifying the system parameters like stiffness and mass based on dynamic response of the structure. These methods are categorized into physics based and data driven. In this study, the FE models of a 16-span Pre-Stress Concrete Box (PSCB) girder bridge with the total length of 780 m, a 3-span Void Slab Bridge with the total length is of 65 m, and a Steel Box (STB) with 380 m length and 12 m width, with 8 spans of equal length of 47.5 m bridge are constructed and updated using the measured vibration data. The objective of this study is to identify the system properties of the bridges using physics-based and data-driven methods and update the corresponding models using the data from ambient vibration tests and determine the efficacy of each method. A well-known and effective physics-based method, the matrix update method, is used for correlating the models by solving the relevant inverse problem through constrained optimization. In data-driven methods, the Neural Network and Genetic Algorithms are applied to find the correlations between the structural frequencies and changes in the sectional properties of the bridge segments. The outputs of these models are compared with certain target frequencies based on the measured data in order to adjust the section properties of the bridge elements. It is found that while the physics-based method has a better performance than the data-driven model in identifying the modal properties, the physics-based model is difficult to implement and there is a need for developing a hybrid method to achieve a better result. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.","Ambient vibration test (AVT); Frequency domain decomposition (FDD); Genetic algorithm (GA); Matrix update method; Neural network (NN)","Constrained optimization; Domain decomposition methods; Frequency domain analysis; Genetic algorithms; Inverse problems; Matrix algebra; Structural health monitoring; Structural properties; Vibration analysis; Ambient vibration test; Frequency domain decomposition; Matrix updates; Neural network (nn); Physics-based methods; Physics-based modeling; Structural frequencies; Vibration-based structural health monitoring; Finite element method",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC","Acknowledgements The support of the Natural Sciences and Engineering Research Council of Canada is gratefully acknowledged. The authors would also like to thank Korea Expressway Corporation for cooperation.",,,,,,,,,,"Liu, Y., Li, Y., Wang, D., Zhang, S., Model updating of complex structures using the combination of component mode synthesis and Kriging predictor (2014) Sci World J, 2014, p. 476219; Jaishi, B., Ren, W.X., Structural finite element model updating using ambient vibration test results (2005) J Struct Eng, 131 (4), pp. 617-628; Kodikara, K.A.T.L., Chan, T.H., Nguyen, T., Thambiratnam, D.P., Model updating of real structures with ambient vibration data (2016) J Civil Struct Health Monit, 6 (3), pp. 329-341; Kabe, A.M., Stiffness matrix adjustment using mode data (1985) AIAA J, 23 (9), pp. 1431-1436; Bagchi, A., Updating the mathematical model of a structure using vibration data (2005) J Vib Control, 11 (12), p. 1469; Entezami, A., Shariatmadar, H., (2014) Damage localization in shear building by direct updating of physical properties, , Springer, Berlin; Zeng, P., Applied mechanics review (1998) Neural Comput Mech, 51, pp. 173-197; Barai, S.V., Pandey, P.C., Vibration signature analysis using artificial neural networks (1995) J Comput Civil Eng, 9, pp. 259-265; Xu, B., Wu, Z.S., Yokoyama, K., Response time series based structural parametric assessment approach with neural networks (2003) Proceedings of the 1St International Conference on Structural Health Monitoring and Intelligent Infrastructure, 1, pp. 601-610. , Tokyo, Japan; Hasancebi, O., Dumlupinar, T., Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial networks (2013) Elsevier Comput Struct, 119, pp. 1-11; Tsai, C.H., Hsu, D.S., Diagnosis of reinforced concrete structural damage base on displacement time history using the back-propagation neural network technique (2002) J Comput Civ Eng, 16 (1), pp. 49-58; Kubalik, J., Lazansky, J., A genetic algorithms and their testing (1999) AIP Conf Proc, 465, pp. 217-229; Perera, R., Ruiz, A., A multistage FE updating procedure for damage identification in large-scale structures based on multi-objective evolutionary optimization (2008) Mech Syst Signal Process, 22, pp. 970-991; Yi, J.-H., Yun, C.-B., Comparative study on modal identification methods using output-only information (2004) Structural Engineering and Mechanics, 17 (3-4), pp. 445-466; Brincker, R., Zhang, L., Andersen, P., Modal identification from ambient responses using frequency domain decomposition (2000) International Modal Analysis Conference (IMAC), , San Antonio, Texas; Mottershead, J.E., Friswell, M.I., Model updating in structural dynamics: a survey (1993) J Sound Vib, 167 (2), pp. 347-375; Friswell, M.I., Mottershead, J.E., Finite Element Modelling (1995) Finite Element Model Updating in Structural Dynamics, pp. 7-35. , Springer Netherlands, Dordrecht; Maia, N.M.M., Silva, J.M.M., Sampaio, R.P.C., Localization of damage using curvature of the frequency-response-functions (1997) Proceedings of the 15Th International Modal Analysis Conference, 3089, p. 942; Link, M., Hanke, G., Model Quality Assessment and Model Updating (1999) Modal Analysis and Testing, pp. 305-324. , Springer Netherlands, Dordrecht; Baruch, M., Bar Itzhack, I.Y., Optimal weighted orttiogonalization of measured modes (1978) AIAA J, 16 (4), pp. 346-351; Berman, A., Nagy, E.J., Improvement of a large analytical model using test data (1983) AIAA J, 21 (8), pp. 1168-1173; Marwala, T., (2010) Finite-element-model updating using computational intelligence technique, , Springer, Berlin: (Book; Bendat, J.S., Piersol, A.G., (1980) Engineering applications of correlation and spectral analysis, 315, p. 1. , Wiley, New York; Newland, D.E., An introduction to random vibrations, spectral & wavelet analysis (2012) Courier Corporation; Otte, D., van De Ponseele, P., Leuridan, J., Operational shapes estimation as a function of dynamic loads (1990) Proceedings of the 8Th International Modal Analysis Conference; Farshchin, M., (2015) Frequency Domain Decomposition (FDD) MATLAB Code, , https://www.mathworks.com/matlabcentral/fileexchange/50988-frequency-domain-decomposition--fdd; Integrated software for structural analysis & design (2007) Computer and Structures, , Inc., Berkeley; Demuth, H., Beale, M., (1993) Neural Network Toolbox for Use with Matlab—User’S Guide Verion 3.0; Bagchi, A., Humar, J., Noman, A., Development of a finite element system for vibration based damage identification in structures (2007) J Appl Sci, 7 (17), pp. 2404-2413","Bagchi, A.; Department of Building, 1455 De Maisonneuve Blvd. West, Canada; email: ashutosh.bagchi@concordia.ca",,,"Springer Verlag",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85056879545
"Sun L., Li Y., Zhang W.","7403956279;57211568199;56646249600;","Experimental Study on Continuous Bridge-Deflection Estimation through Inclination and Strain",2020,"Journal of Bridge Engineering","25","5","04020020","","",,14,"10.1061/(ASCE)BE.1943-5592.0001543","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081962662&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001543&partnerID=40&md5=98744ecd4dcc2129edd1fcfe6824d376","Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China; Fujian Academy of Building Research, Fujian Key Laboratory of Green Building Technology, No. 52 Jintang Road, Fuzhou, Fujian, 350025, China","Sun, L., Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China; Li, Y., Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China; Zhang, W., Fujian Academy of Building Research, Fujian Key Laboratory of Green Building Technology, No. 52 Jintang Road, Fuzhou, Fujian, 350025, China","When monitoring structural data, incompleteness is a crucial issue that affects structural health monitoring (SHM). Information on displacement is particularly important for bridge state estimation, but it is difficult to measure. To obtain the required data at any position, a hybrid monitoring (HM) algorithm that combines the finite-element model (FEM) with the monitored data is proposed to extend these data from discrete points to the full structure. The aim of this study is to demonstrate the accuracy and adaptiveness of the algorithm by adopting a complex, large-scale bridge model and considering the modeling error and environmental noise. First, the basic idea and theoretical basis of HM is briefly introduced, and a multitype data-fusion method is proposed to improve the accuracy. Then the experimental equipment, FEM, and updating process are introduced. The influences of the global stiffness error and the boundary condition error are subsequently discussed, showing the algorithm robustness. Finally, the experimental results from two quasi-dynamic loading conditions confirm the HM accuracy using different data sources with high computational efficiency. The superiority of the HM method is also validated by comparing it with some existing methods. © 2020 American Society of Civil Engineers.","Deflection estimation; Hybrid monitoring; Inclination; Partial least-square regression; Strain","Computational efficiency; Data fusion; Dynamic loads; Errors; Strain; Continuous bridges; Data fusion methods; Environmental noise; Experimental equipments; Inclination; Large-scale bridges; Partial least square regression; Structural health monitoring (SHM); Structural health monitoring",,,,,"National Natural Science Foundation of China, NSFC: 51878482; Tongji University: SLDRCE15-A-02; State Key Laboratory for Disaster Reduction in Civil Engineering","The authors acknowledge support for the work reported in this paper from the National Natural Science Foundation of China (Grant No. 51878482) and the State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University (Grant No. SLDRCE15-A-02).",,,,,,,,,,"Alkayem, N.F., Cao, M., Zhang, Y., Bayat, M., Su, Z., Structural damage detection using finite element model updating with evolutionary algorithms: A survey (2018) Neural Comput. 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Aerospace Eng., 2017, p. 1326309. , https://doi.org/10.1155/2017/1326309; Zhou, L., Yan, G., Ou, J., Response surface method based on radial basis functions for modeling large-scale structures in model updating (2013) Comput.-Aided Civ. Infrastruct. Eng., 28 (3), pp. 210-226. , https://doi.org/10.1111/j.1467-8667.2012.00803.x","Li, Y.; Dept. of Bridge Engineering, China; email: 1710733@tongji.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85081962662
"Farhangdoust S.","57197801868;","Auxetic Cantilever Beam Energy Harvester",2020,"Proceedings of SPIE - The International Society for Optical Engineering","11382",,"113820V","","",,14,"10.1117/12.2559327","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087751301&doi=10.1117%2f12.2559327&partnerID=40&md5=76c37ae34984013a2a179c56d5c20e55","Department of Civil and Environmental Engineering, Florida International University, Miami, FL, United States","Farhangdoust, S., Department of Civil and Environmental Engineering, Florida International University, Miami, FL, United States","This paper develops an auxetic cantilever beam energy harvester (ACBEH) to enhance the harvesting power from ambient vibration sources. A finite element analysis was performed to verify the power increase mechanism of the ACBEH. The simulation model of the ACBEH comprises of three main components: Support, tip mass, and cantilever beam with a re-entrant hexagonal auxetic structure in which a piezoelectric element bonded to top of the auxetic region by using a thin elastic layer of epoxy. The performance of the ACBEH was computationally investigated and compared with an equivalent conventional energy harvester with a plain cantilever beam where they are attached to a bridge stay cable. The simulation result shows that the ACBEH excited by a harmonic acceleration of 1 m/s2 at 3 Hz is able to produce electric power of 427.22 μW, which is 2.51 times that of the power produced by the equivalent plain cantilever beam energy harvester (170.17 μW). This paper opens up a great potential of using auxetic cantilever beam applications for different energy harvesting systems in Metamaterials, Acoustics, Civil, Electrical, Aerospace, Biomedical, and Mechanical Engineering. © 2020 SPIE.","Auxetic cantilever beam; Cable-stayed bridge; Energy harvesting; Self-powered structural health monitoring; Smart bridge monitoring; Wireless sensor network","Cantilever beams; Energy harvesting; Industry 4.0; Smart city; Ambient vibrations; Auxetic structures; Bridge stay cables; Energy Harvester; Energy harvesting systems; Piezoelectric elements; Simulation model; Thin elastic layer; Nanocantilevers",,,,,,,,,,,,,,,,"HekmatiAthar, S., Taheri, M., Secrist, J., Taheri, H., Neural network for structural health monitoring with combined direct and indirect methods (2020) Journal of Applied Remote Sensing, 14 (1), p. 014511; Hasanian, M., Choi, S., Lissenden, C., Laser ultra sonics toward remote detection of stress corrosion cracking (2019) Materials Evaluation, 77 (9), pp. 1089-1098; Farhangdoust, S., Mehrabi, A., Non-destructive evaluation of closure joints in accelerated bridge construction using a damage etiology approach (2020) Applied Sciences, 10 (4), p. 1457; Farhangdoust, S., Mehrabi, A., Health monitoring of closure joints in accelerated bridge construction: A review of non-destructive testing application (2019) Journal of Advanced Concrete Technology, 17 (7), pp. 381-404; Cho, H., Hasanian, M., Shan, S., Lissenden, C.J., Nonlinear guided wave technique for localized damage detection in plates with surface-bonded sensors to receive Lamb waves generated by shear-horizontal wave mixing (2019) NDT & E International, 102, pp. 35-46; Hasanian, M., Choi, S., Lissenden, C., Laser ultrasonics toward remote detection of stress corrosion cracking (2019) Materials Evaluation, 77 (9), pp. 1089-1098; Farhangdoust, S., Mehrabi, A., NDT inspection of critical ABC details to assure life cycle performance and avoid future unforeseen excessive repairs (2019) Structures Congress 2019American Society of Civil Engineers, , April; Lee, J.L., Tyan, Y.Y., Wen, M.H., Wu, Y.W., Development of an iot-based bridge safety monitoring system (2017) 2017 International Conference on Applied System Innovation (ICASI), pp. 84-86. , (May). . IEEE; Xu, R., Kim, S.G., Modeling and experimental validation of bi-stable beam based piezoelectric energy harvester (2016) Energy Harvesting and Systems, 3 (4), pp. 313-321; Haluk, A., Sang-Gook, K., Xu, R., Buckled MEMS beams for energy harvesting from low frequency vibrations (2019) Research 2019, p. 1087946; Peigney, M., Siegert, D., Piezoelectric energy harvesting from traffic-induced bridge vibrations (2013) Smart Mater Struct, 22 (9), p. 095019; Zhang, Z., Xiang, H., Shi, Z., Zhan, J., Experimental investigation on piezoelectric energy harvesting from vehicle-bridge coupling vibration (2018) Energy Conversion and Management, 163, pp. 169-179; Maruccio, C., Quaranta, G., De Lorenzis, L., Monti, G., Energy harvesting from electrospun piezoelectric nanofibers for structural health monitoring of a cable-stayed bridge (2016) Smart Materials and Structures, 25 (8), p. 085040; Boakye, A., Chang, Y., Raji, R.K., Ma, P., A review on auxetic textile structures, their mechanism and properties (2019) Journal of Textile Science & Fashion Technology, 2 (1), pp. 1-10; Alderson, A., Alderson, K.L., Auxetic materials (2007) Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 221 (4), pp. 565-575; Zhong-Ming, L., Wei, S., Bang-Hu, X., Ming-Bo, Y., Yang, W., Review on auxetic materials (2004) Journal of Materials Science, 39 (10), pp. 3269-3279; Liu, Y., Hu, H., A review on auxetic structures and polymeric materials (2010) Scientific Research and Essays, 5 (10), pp. 1052-1063; Fernandes, M., Mhatre, S., Mesa, O., Bertoldi, K., Bechthold, M., Porous inclined auxetic structural material (2020) Bulletin of the American Physical Society; Rafsanjani, A., Pasini, D., Bistable auxetic mechanical metamaterials inspired by ancient geometric motifs (2016) Extreme Mechanics Letters, 9, pp. 291-296; Adeshara, J., (2013) Design Optimization of Geometrical Parameters and Material Properties of Vibrating Bimorph Cantilever Beams with Solid and Honeycomb Substrates for Maximum Energy Harvested; Pasini, Damiano, Abbasi, A.R., (2017) Bistable Auxetics, , U.S. Patent Application 15/612, 212, filed December 21; Li, Q., Kuang, Y., Zhu, M., Auxetic piezoelectric energy harvesters for increased electric power output (2017) Aip Advances, 7 (1), p. 015104; Ferguson, W.J., Kuang, Y., Evans, K.E., Smith, C.W., Zhu, M., Auxetic structure for increased power output of strain vibration energy harvester (2018) Sensors and Actuators A: Physical, 282, pp. 90-96; O'Hara, J.M., Brown, W.S., An investigation of the relative safety of alternative navigational system designs for the new sunshine skyway bridge: A CAORF (1985) National Maritime Research Center Kings Point Ny Computer Aided Operations Research Facility, , (Computer Aided Operations Research Facility) Simulation (No. CAORF-26-8232-04); http://www.pbs.org/wgbh/buildingbig/wonder/structure/sunshine_skyway.html, Online; Mehrabi, A.B., Farhangdoust, S., A laser-based noncontact vibration technique for health monitoring of structural cables: Background, success, and new developments (2018) Adv. In Aco. & Vib., 2018; Wei, R., (2014) A Vibrational Energy Harvesting System with Resonant Piezoelectric Devices and Low-Power Electronic Interface, , Doctoral dissertation, Case Western Reserve University; Alsaad, A.M., Ahmad, A.A., Al-Bataineh, Q.M., Daoud, N.S., Khazaleh, M.H., Design and analysis of MEMS based aluminum nitride (ALN), lithium niobate (LiNbO3) and zinc oxide (ZNO) cantilever with different substrate materials for piezoelectric vibration energy harvesters using COMSOL multi physics software (2019) Open Journal of Applied Sciences, 9 (4), pp. 181-197","Farhangdoust, S.; Department of Civil and Environmental Engineering, United States; email: Sfarh006@fiu.edu","Gath K.Meyendorf N.G.","The Society of Photo-Optical Instrumentation Engineers (SPIE)","SPIE","Smart Structures and NDE for Industry 4.0, Smart Cities, and Energy Systems 2020","27 April 2020 through 8 May 2020",,160844,0277786X,9781510635418,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85087751301
"Kefal A., Maruccio C., Quaranta G., Oterkus E.","56866304900;55579697400;50062304700;8345890200;","Modelling and parameter identification of electromechanical systems for energy harvesting and sensing",2019,"Mechanical Systems and Signal Processing","121",,,"890","912",,14,"10.1016/j.ymssp.2018.10.042","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058215315&doi=10.1016%2fj.ymssp.2018.10.042&partnerID=40&md5=27ba72d4159fe4fb4fffedb48d02d0c1","Faculty of Naval Architecture and Ocean Engineering, Istanbul Technical University, Istanbul, Turkey; Integrated Manufacturing Technologies Research and Application Center, Sabanci University, Istanbul, Turkey; Department of Innovation Engineering, University of Salento, Lecce, Italy; Department of Structural and Geotechnical Engineering, Sapienza University of Rome, Italy; Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, United Kingdom","Kefal, A., Faculty of Naval Architecture and Ocean Engineering, Istanbul Technical University, Istanbul, Turkey, Integrated Manufacturing Technologies Research and Application Center, Sabanci University, Istanbul, Turkey; Maruccio, C., Department of Innovation Engineering, University of Salento, Lecce, Italy; Quaranta, G., Department of Structural and Geotechnical Engineering, Sapienza University of Rome, Italy; Oterkus, E., Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, United Kingdom","Advanced modelling of electro-mechanical systems for energy harvesting (EH) and sensing is important to develop reliable self-powered autonomous electronic devices and for structural health monitoring (SHM). In this perspective, a novel computational approach is here proposed for both real-time and off-line parameter identification (PI). The system response is governed by a set of four partial differential equations (PDE) where the three displacement components and the electrical potential are the unknowns. Firstly, the finite element (FE) method is used to reduce the PDE problem into a set of ordinary differential equations (ODE). Then, a state-space model is derived with the aim to limit the PI problem to a subset of unknowns. After that, an identification error is introduced and the Lyapunov theory is used to derive the PI algorithm. The numerical implementation is based on a sensitivity analysis feedback block. The overall proposed computational strategy is robust and results in an exponential asymptotic convergence. The accuracy of the PI method is demonstrated by analysing the time-domain response of an array of piezoelectric bimorphs subjected to low-frequency structural random vibrations. The selected case-study is an existing cable-stayed bridge, for which an extensive dynamic monitoring campaign has provided the experimental data. Once time histories of the device response are obtained through time-dependent dynamic FE simulations, the PI algorithm is used to determine the unknown lumped coefficients of the state-space model. The comparison between FE method and lumped parameters model in terms of tip displacement and output voltage demonstrates the superior predictive capability of the new PI algorithm. As a result of the sensitivity analysis, guidelines to assess the optimal array configuration are also provided. © 2018 Elsevier Ltd","Energy harvesting; Finite element method; Parameter identification; Piezoelectric solid; Sensitivity analysis; State-space models","Cable stayed bridges; Computation theory; Energy harvesting; Identification (control systems); Ordinary differential equations; Parameter estimation; Piezoelectric devices; Piezoelectric materials; Piezoelectricity; Sensitivity analysis; State space methods; Structural health monitoring; Time domain analysis; Vibration analysis; Electromechanical systems; Lumped-parameters models; Numerical implementation; Ordinary differential equation (ODE); Partial differential equations (PDE); Piezoelectric solids; State - space models; Structural health monitoring (SHM); Finite element method",,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: RBFR107AKG; Sapienza Università di Roma","Claudio Maruccio acknowledges the support from the Italian MIUR through the project FIRB Futuro in Ricerca 2010 Structural mechanics models for renewable energy applications (RBFR107AKG). Giuseppe Quaranta acknowledges the support from Sapienza University of Rome through the project ""Smart solutions for the assessment of structures in seismic areas"".",,,,,,,,,,"Hoang, N.S., Ramm, A.G., Dynamical systems method for solving nonlinear equations with monotone operators (2010) Am. Math. Soc., Math. Comput., 79 (269), pp. 239-249; Ramm, A.G., Hoang, S.N., Dynamical Systems Method and Applications: Theoretical Developments and Numerical Examples (2011), Wiley; Ramm, A.G., Dynamical Systems Method for Solving Operator Equations (2007), Elsevier; Zadeh, L., On the identification problem (1956) IRE Trans. Circuit Theory, 3 (4), pp. 277-281; Keshavarz, M., Mojra, A., Dynamic modeling of breast tissue with application of model reference adaptive system identification technique based on clinical robot-assisted palpation (2015) J. Mech. Behav. Biomed. 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Control, 55, pp. 2104-2108; Kaltenbacher, M., Numerical Simulation of Mechatronic Sensors and Actuators (2015), Springer; Clark, V., Marepalli, P., Bansal, R., Modeling, Design and Simulation of N/MEMS by Integrating Finite Element, Lumped Element and System Level Analyses (2012) Chapter 3, Computational Finite Element Methods in Nanotechnology, pp. 41-84. , CRC Press; Bathe, K.J., Finite Element Procedures in Engineering Analysis (1982), Prentice-Hall; (1987), 176. , IEEE Standard on Piezoelectricity.., ANSI/IEEE Std; Maruccio, C., Montegiglio, P., Acciani, G., Carnimeo, L., Torelli, F., Identification of Piezoelectric Energy Harvester Parameters Using Adaptive Models (2018) Proceedings of the 2018 IEEE International Conference on Environment and Electrical Engineering, Palermo (Italy); Elvin, N.G., Elvin, A.A., Large deflection effects in flexible energy harvesters (2012) J. Intell. Mater. Syst. Struct., 23 (13), pp. 1475-1484; Tang, L., Yang, Y., Kiong, S.C., Broadband Vibration Energy Harvesting Techniques (2013) Advances in Energy Harvesting Methods, , E. Niell A. Erturk Springer; Blokhina, E., El Aroudi, E., Alarcon, E., Galayko, D., Nonlinearity in Energy Harvesting Systems: Micro- and Nanoscale Applications (2016), Springer; Sun, C., Shi, J., Bayerl, D., Wang, X., PVDF microbelts for harvesting energy from respiration (2011) Energy Environ. Sci., 4, p. 4508; Cahill, P., Hazra, B., Karoumi, R., Mathewson, A., Pakrashi, V., Vibration energy harvesting based monitoring of an operational bridge undergoing forced vibration and train passage (2018) Mech. Syst. Signal Process., 106, pp. 265-283; Cahill, P., Mathewson, A., Pakrashi, V., Experimental validation of piezoelectric energy harvesting device for built infrastructure application (2018) ASCE J. Bridge Eng., 23 (8). , 04018056-1-11; Sazonov, E., Curry, D., Pillay, P., Self-powered sensors for monitoring of highway bridges (2009) IEEE Sens. J., 9 (11), pp. 1422-1429; Peigney, M., Siegert, D., Piezoelectric energy harvesting from traffic induced bridge vibrations (2013) Smart Mater. Struct., 22 (9)","Maruccio, C.; Department of Innovation Engineering, Italy; email: claudio.maruccio@unisalento.it",,,"Academic Press",,,,,08883270,,MSSPE,,"English","Mech Syst Signal Process",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85058215315
"Hajializadeh D., OBrien E.J., O’Connor A.J.","55315244300;57218648462;7103379523;","Virtual structural health monitoring and remaining life prediction of steel bridges",2017,"Canadian Journal of Civil Engineering","44","4",,"264","273",,14,"10.1139/cjce-2016-0286","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016950435&doi=10.1139%2fcjce-2016-0286&partnerID=40&md5=9a844a95b8fc37d985fe2cb1501df754","Anglia Ruskin University, Essex, United Kingdom; Roughan O’Donovan Consulting Engineers, Ireland; University College Dublin, Dublin, Ireland; Trinity College Dublin, Dublin, Ireland","Hajializadeh, D., Anglia Ruskin University, Essex, United Kingdom; OBrien, E.J., Roughan O’Donovan Consulting Engineers, Ireland, University College Dublin, Dublin, Ireland; O’Connor, A.J., Roughan O’Donovan Consulting Engineers, Ireland, Trinity College Dublin, Dublin, Ireland","In this study a structural health monitoring (SHM) system is combined with bridge weigh-in-motion (B-WIM) measurements of the actual traffic loading on a bridge to carry out a fatigue damage calculation. The SHM system uses the ‘virtual monitoring’ concept, where all parts of the bridge that are not monitored directly using sensors, are ‘virtually’ monitored using the load information and a calibrated finite element (FE) model of the bridge. Besides providing the actual traffic loading on the bridge, the measurements are used to calibrate the SHM system and to update the FE model of the bridge. The newly developed virtual monitoring concept then uses the calibrated FE model of the bridge to calculate stress ranges and hence to monitor fatigue at locations on the bridge not directly monitored. The combination of a validated numerical model of the bridge with the actual site-specific traffic loading allows a more accurate prediction of the cumulative fatigue damage at the time of measurement and facilitates studies on the implications of traffic growth. To test the accuracy of the virtual monitoring system, a steel bridge with a cable-stayed span in the Netherlands was used for testing. © 2017, Canadian Science Publishing. All rights reserved.","Bridge FEM; Fatigue; SHM; Steel bridges; Virtual monitoring","Calibration; Electric measuring bridges; Fatigue damage; Fatigue of materials; Finite element method; Steel bridges; Steel testing; Structural health monitoring; Traffic surveys; Weigh-in-motion (WIM); Accurate prediction; Cumulative fatigue damage; Damage calculations; Load information; Measurements of; Monitoring system; Remaining life prediction; Structural health monitoring (SHM); Monitoring; bridge; damage; fatigue; health monitoring; numerical model; steel structure; Netherlands",,,,,"Seventh Framework Programme, FP7: 315629","This work was supported through the BridgeMon project. BridgeMon was funded by the European Commission 7th Framework Programme (grant agreement No. 315629). The authors acknowledge the Dutch Ministry of Transport and Infrastructure, Rijkswaterstaat, for their cooperation and support. The authors also gratefully acknowledge the contributions of the other BridgeMon consortium partners: CESTEL CESTNI INZENIRING DOO, KALIBRA INVESTMENTS BV, ADAPTRONICA ZOO SP, ZAVOD ZA GRADBENISTVO SLOVENIJE (ZAG), and CORNER STONE INTERNATIONAL SAGL.",,,,,,,,,,"(2010) LRFD Bridge Design Specifications, , 5th ed., Washington D. C.: American Association of State Highway and Transportation Officials; Alampalli, S., Special Issue on Nondestructive Evaluation and Testing for Bridge Inspection and Evaluation (2012) Journal of Bridge Engineering, 17 (6), pp. 827-828; Battista, R.C., Pfeil, M.S., Carvalho, E.M.L., Fatigue life estimates for a slender orthotropic steel deck (2008) Journal of Constructional Steel Research, 64 (1), pp. 134-143; (2003) EN 1991-2:2003, , Eurocode 1: Actions on Structures - Part 2: Traffic Loads on Bridges. 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Montreal, Canada; Guo, T., Chen, Y.W., Field stress/displacement monitoring and fatigue reliability assessment of retrofitted steel bridge details (2011) Engineering Failure Analysis, 18 (1), pp. 354-363; Guo, T., Chen, Y.-W., Fatigue reliability analysis of steel bridge details based on field-monitored data and linear elastic fracture mechanics (2013) Structure and Infrastructure Engineering, 9 (5), pp. 496-505; Guo, T., Frangopol, D.M., Chen, Y., Fatigue reliability assessment of steel bridge details integrating weigh-in-motion data and probabilistic finite element analysis (2012) Computers and Structures, 112-113, pp. 245-257; Guo, T., Liu, Z.-X., Zhu, J., Fatigue reliability assessment of orthotropic steel bridge decks based on probabilistic multi-scale finite element analysis (2015) Advanced Steel Construction, 11 (3), pp. 334-346; Hammad, A., Yan, J., Mostofi, B., (2007) Recent development of bridge management systems in Canada, , Annual Conference of the Transportation Association of Canada. 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S. Department of Transportation Federal Highway Administration. Charlottesville, Virginia Virginia Transportation Research Council; Matsuishi, M., Endo, T., (1968) Fatigue of metals subjected to varying stress, pp. 37-40. , Paper presented to Japan Society of Mechanical Engineers, Fukuoka, Japan; Messervey, T.B., Frangopol, D.M., Casciati, S., Application of the statistics of extremes to the reliability assessment and performance prediction of monitored highway bridges (2011) Structure and Infrastructure Engineering, 7 (1-2), pp. 87-99; Miao, T.J., Chan, T.H.T., Bridge live load models from WIM data (2002) Engineering Structures, 24 (8), pp. 1071-1084; Miner, M.A., (1945) Cumulative damage in fatigue Journal of Applied Mechanics, , Journal of Applied Mechanics; Moses, F., Weigh-In-Motion system using instrumented bridges (1979) Journal of Transportation Engineering, 105, pp. 233-249; Mufti, A.A., Structural health monitoring of innovative Canadian civil engineering structures (2002) Structural Health Monitoring, 1 (1), pp. 89-103; Newhook, J.P., Edalatmanesh, R., Integrating reliability and structural health monitoring in the fatigue assessment of concrete bridge decks (2013) Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance, 9 (7), pp. 619-633; Ni, Y.Q., Ye, X.W., Ko, J.M., Monitoring-based fatigue reliability assessment of steel bridges: Analytical model and application (2010) Journal of Structural Engineering, 136 (12), pp. 1563-1573; Nyman, W.E., Moses, F., Calibration of bridge fatigue design model (1985) Journal of Structural Engineering, 111 (6), pp. 1251-1266; Obrien, E.J., Enright, B., Modeling same-direction two-lane traffic for bridge loading (2011) Structural Safety, 33, pp. 296-304; Obrien, E.J., Znidaric, A., Ojio, T.A., (2008) Bridge-Weigh-in-Motion - Latest developments and applications world-wide. 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Mathematical Problems in Engineering, pp. 1-13. , Hindawi Publishing Corporation; Žnidarič, A., Kalin, J., (2014) Technical specifications for optimised Bridge WIM axle detection, pp. 2012-2014. , Deliverable D1.4, EU funded BridgeMon project (Bridge Monitoring), GA no. 315629; Žnidarič, A., Lavrič, I., Kalin, J., Kulauzović, B., (2011) SiWIM Bridge Wiegh-in-Motion Manual, Fourth edition, , Ljubljana: ZAG, Cestel","Hajializadeh, D.; Anglia Ruskin UniversityUnited Kingdom; email: donyaucd@gmail.com",,,"Canadian Science Publishing",,,,,03151468,,CJCEB,,"English","Can. J. Civ. Eng.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85016950435
"Caddemi S., Caliò I., Cannizzaro F., D’Urso D., Pantò B., Rapicavoli D., Occhipinti G.","6602721562;6603126726;36720027000;57221849251;36721847200;55745461400;57188969471;","3D discrete macro-modelling approach for masonry arch bridges",2019,"IABSE Symposium, Guimaraes 2019: Towards a Resilient Built Environment Risk and Asset Management - Report",,,,"1825","1835",,13,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065258779&partnerID=40&md5=553482a74d7241e5c2ff0ddb5456ef33","Dept. of Engineering and Architecture, University of Catania, Catania, Italy; Italian National Research Council, Institute of Environmental Geology and Geoengineer, Rome, Italy","Caddemi, S., Dept. of Engineering and Architecture, University of Catania, Catania, Italy; Caliò, I., Dept. of Engineering and Architecture, University of Catania, Catania, Italy; Cannizzaro, F., Dept. of Engineering and Architecture, University of Catania, Catania, Italy; D’Urso, D., Dept. of Engineering and Architecture, University of Catania, Catania, Italy; Pantò, B., Dept. of Engineering and Architecture, University of Catania, Catania, Italy; Rapicavoli, D., Dept. of Engineering and Architecture, University of Catania, Catania, Italy; Occhipinti, G., Italian National Research Council, Institute of Environmental Geology and Geoengineer, Rome, Italy","Masonry multi-span arch bridges are historical structures still playing a key role in many transportation networks of numerous countries. Most of these bridges are several decades old and have been subjected to continuous dynamic loadings, due to the vehicular traffic, and in many cases their maintenance required structural modifications. The currently adopted health monitoring strategies are based on in situ inspections as well as structural assessments based on numerical models characterised by different levels of reliability according to the required purpose. Simplified approaches are generally adopted for fast structural evaluation, on the other hand more rigorous approaches are fundamental for a reliable structural assessment of these particular structures, often characterized by very complex geometrical layouts and structural alterations not always sufficiently documented. This paper presents an original Discrete Macro-Element Method (DMEM) that allows a reliable simulation of the linear and nonlinear response of masonry structures and masonry bridges characterised by a lower computational burden, compared to classical nonlinear FEM analyses, although maintaining a good accuracy. The method is applied to a real masonry bridges and the results are compared with those obtained from a more sophisticated three-dimensional nonlinear FEM model both in linear and nonlinear context. © 2019 IABSE. All rights reserved.","Discrete Element Method (DEM); Discrete Macro-Element Method (DMEM); HiStrA software; Masonry arch bridges; Nonlinear analysis; Railway bridges","Arches; Asset management; Dynamic loads; Environmental management; Finite difference method; Macros; Masonry bridges; Masonry construction; Masonry materials; Nonlinear analysis; Structural health monitoring; Macro element; Masonry arch bridges; Nonlinear FEM analysis; Railway bridges; Structural alterations; Structural assessments; Structural modifications; Transportation network; Arch bridges",,,,,,,,,,,,,,,,"Orban, Z., Assessment, reliability and maintenance of masonry arch railway bridges in Europe (2004) Proc. Of ARCH'04, 4th Int. Conf. Of Arch Bridges, , Roca, E. Oñate eds., CIMNE, Barcelona; Orban, Z., Gutermann, M., Assessment of masonry arch railway bridges using nondestructive in-situ testing methods (2009) Engineering Structures, 31, pp. 2287-2298; Benedetti, A., Colla, C., Pignagnoli, G., Tarozzi, M., Static and Dynamic Investigation of the Taro Masonry Bridge in Parma"" Simplified seismic assessment of multi-span masonry arch bridges (2015) Bulletin of Earthquake Engineering, 13 (9), pp. 2629-2646; Sarhosis, V., De Santis, S., De Felice, G., A review of experimental investigations and assessment methods for masonry arch bridges (2016) Structure and Infrastructure Engineering, 12 (11), pp. 1439-1464; Brencich, A., Sabia, D., Experimental identification of a multi-span masonry bridge: The Tanaro Bridge (2008) Construction and Building Materials, 22 (10), pp. 2087-2099; Gentile, C., Modal and structural identification of a R.C. Arch bridge (2006) Structural Engineering and Mechanics, 22 (1), pp. 53-70; Cavicchi, A., Gambarotta, L., Lower bound limit analysis of masonry bridges including arch–fill interaction (2007) Eng. Struct., 29, pp. 3002-3014; De Felice, G., Assessment of the load-carrying capacity of multi-span masonry arch bridges using fibre beam elements (2009) Engineering Structures, 31 (8), pp. 1634-1647; Audenaert, A., Fanning, P., Sobczak, L., Peremans, H., 2-D analysis of arch bridges using an elasto-plastic material model (2008) Eng. Struct., 30, pp. 845-855; Gilbert, M., Casapulla, C., Ahmed, H.M., Limit analysis of masonry block structures with non-associative frictional joints using linear programming (2006) Computers & Structures, 84 (13-14), pp. 873-887; Gilbert, M., Ring: A 2D rigid block analysis program for masonry arch bridges (2001) Proc. 3rd International Arch Bridges Conference, pp. 109-118. , Paris, France; Reccia, E., Milani, G., Cecchi, A., Tralli, A., Full 3D homogenization approach to investigate the behavior of masonry arch bridges: The Venice trans-lagoon railway bridge (2014) Construction and Building Materials, 66, pp. 567-586; Milani, G., Lourenço, P.B., 3D non-linear behavior of masonry arch bridges (2012) Computers & Structures, 110 (111), pp. 133-150; Drosopoulos, G.A., Stavroulakis, G.E., Massalas, C.V., Limit analysis of a single span masonry bridge with unilateral frictional contact interfaces (2006) Eng. Struct., 28 (13), pp. 1864-1873; Fanning, P.J., Boothby, T.E., Three dimensional modelling and full scale testing of stone arch bridges (2001) Comput. Struct., 79 (29-30), pp. 2645-2662; Oliveira, D.V., Lourenço, P.B., Lemos, C., Geometric issues and ultimate load of masonry arch bridges from the northwest Iberian Peninsula (2010) Eng. Struct., 32 (12), pp. 3955-3965; Zhang, Y., Tubaldi, E., Macorini, L., Izzuddin, B.A., Mesoscale partitioned modelling of masonry bridges allowing for arch-backfill interaction (2018) Construction and Building Materials, 173, pp. 820-842; Tubaldi, E., Macorini, L., Izzuddin, B.A., Three-dimensional mesoscale modelling of multi-span masonry arch bridges subjected to scour (2018) Eng Struct, 165, pp. 486-500; Caliò, I., Marletta, M., Pantò, B., A new discrete element model for the evaluation of the seismic behaviour of unreinforced masonry buildings (2012) Engineering Structures, 40, pp. 327-338; Pantò, B., Cannizzaro, F., Caliò, I., Lourenço, P.B., Numerical and experimental validation of a 3D macro-model element method for the in-plane and out-of-plane behaviour of unreinforced masonry walls (2017) International Journal of Architectural Heritage, 11 (7), pp. 946-964; Pantò, B., Giresini, L., Sassu, M., Caliò, I., Non linear modeling of masonry churches through a discrete macro-element approach (2017) Earthquake and Structures, 12 (2), pp. 223-236; Pantò, B., Cannizzaro, F., Caddemi, S., Caliò, I., 3D macro-element modelling approach for seismic assessment of historical masonry churches (2016) Advances in Engineering Software, 97, pp. 40-59; Caddemi, S., Caliò, I., Cannizzaro, F., Pantò, B., New frontiers on seismic modeling of masonry structures (2017) Front. Built Environ; Cannizzaro, F., Pantò, B., Caddemi, S., Caliò, I., A Discrete Macro-Element Method (DMEM) for the nonlinear structural assessment of masonry arches (2018) Engineering Structures, 168, pp. 243-256; Sismica, G., (2017) HiStrA Software (Historical Structures Analysis) Release 4.6.0, , Catania, Italy, September; LUSAS - Theory Manuals, , FEA ltd, Lusas Vertion 16.0; (2006) Guide Line RFI DIN ICI LG IFS 001 A ""Linee Guida Per La Verifica Strutturale Dei Ponti Ad Arco in Muratura, , RFI Italian","Caliò, I.; Dept. of Engineering and Architecture, Italy; email: icalio@dica.unict.it",,"Allplan;Brisa;Maurer;S and P","International Association for Bridge and Structural Engineering (IABSE)","IABSE Symposium 2019 Guimaraes: Towards a Resilient Built Environment - Risk and Asset Management","27 March 2019 through 29 March 2019",,147396,,9783857481635,,,"English","IABSE Symp., Guimaraes: Towards Resilient Built Environ. Risk Asset Manag. - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85065258779
"Mustafa S., Matsumoto Y., Yamaguchi H.","56730098100;7404546455;56573240400;","Vibration-Based Health Monitoring of an Existing Truss Bridge Using Energy-Based Damping Evaluation",2018,"Journal of Bridge Engineering","23","1","04017114","","",,13,"10.1061/(ASCE)BE.1943-5592.0001159","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032451235&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001159&partnerID=40&md5=1d2e4ded83a0b9773cda0b85c3b7c02a","Dept. of Civil Engineering IIT Indore Simrol, Indore, 453552, India; Dept. of Civil and Environmental Engineering, Saitama Univ., 255 Shimo-Okubo, Sakura-ku, Saitama, 338-8570, Japan","Mustafa, S., Dept. of Civil Engineering IIT Indore Simrol, Indore, 453552, India, Dept. of Civil and Environmental Engineering, Saitama Univ., 255 Shimo-Okubo, Sakura-ku, Saitama, 338-8570, Japan; Matsumoto, Y., Dept. of Civil and Environmental Engineering, Saitama Univ., 255 Shimo-Okubo, Sakura-ku, Saitama, 338-8570, Japan; Yamaguchi, H., Dept. of Civil and Environmental Engineering, Saitama Univ., 255 Shimo-Okubo, Sakura-ku, Saitama, 338-8570, Japan","This paper proposes an analytical framework for vibration-based structural health monitoring (SHM) by introducing an energy-based damping evaluation (EBDE). The damage detection by the proposed EBDE was carried out by estimating the contribution of modal damping ratios from different structural elements utilizing experimentally identified modal damping ratios, and estimating modal strain and modal potential energies from an updated finite-element (FE) model of the structure under consideration. Model updating was performed using modal frequencies and mode shapes that are generally not sensitive to local damage. The advantage of using damping as a damage indicator is that the damping change in global modes affected by the local damage can be identified with a small number of sensors. A previous study reported that the studied bridge with damage at the local diagonal member showed a significant increase in the damping of global vibration mode of the structure. The present study utilized the EBDE to identify the cause of the modal damping increase by observing the change in the contribution from different structural elements on the modal damping ratios. © 2017 American Society of Civil Engineers.","Damage detection; Damping analysis; Modal data; Steel bridges; Vibration-based health monitoring","Damping; Finite element method; Modal analysis; Potential energy; Steel bridges; Structural health monitoring; Structural panels; Trusses; Vibration analysis; Damage indicator; Damping analysis; Health monitoring; Modal damping ratios; Modal data; Small number of sensors; Structural elements; Vibration-based structural health monitoring; Damage detection",,,,,,,,,,,,,,,,"Beck, J.L., Katafygiotis, L.S., Updating models and their uncertainties. I: Bayesian statistical framework (1998) J. Eng. Mech., pp. 455-461; Chen, G.W., Beskhyroun, S., Omenzetter, P., Experimental investigation into amplitude-dependent modal properties of an eleven-span motorway bridge (2016) Eng. Struct., 107, pp. 80-100; Curadelli, R.O., Riera, J.D., Ambrosini, D., Amani, M.G., Damage detection by means of structural damping identification (2008) Eng. Struct., 30 (12), pp. 3497-3504; Dammika, A.J., Kawarai, K., Yamaguchi, H., Matsumoto, Y., Yoshioka, T., Analytical damping evaluation complementary to experimental structural health monitoring of bridges (2015) J. Bridge Eng., p. 04014095; Doebling, S.W., Farrar, C.R., Prime, M.B., A summary review of vibration-based damage identification methods (1998) Shock Vib. Digest, 30 (2), pp. 91-105; Fan, W., Qiao, P., Vibration-based damage identification methods: A review and comparative study (2011) Struct. Health Monit., 10 (1), pp. 83-129; Feng, D., Feng, M.Q., Model updating of railway bridge using in situ dynamic displacement measurement under trainloads (2015) J. Bridge Eng., p. 04015019. , 1-12; Feng, D., Feng, M.Q., Output-only damage detection using vehicle-induced displacement response and mode shape curvature index (2016) Struct. Control Health Monit., 23 (8), pp. 1088-1107; Frizzarin, M., Feng, M.Q., Franchetti, P., Soyoz, S., Modena, C., Damage detection based on damping analysis of ambient vibration data (2010) Struct. Control Health Monit., 17 (4), pp. 368-385; Fujino, Y., Siringoringo, D.M., Structural health monitoring of bridges in Japan: An overview of the current trend (2008) Proc. 4th Int. Conf. on FRP Composites in Civil Engineering, , Empa, Structural Engineering Research Laboratory, Switzerland; Govers, Y., Link, M., Stochastic model updating-covariance matrix adjustment from uncertain experimental modal data (2010) Mech. Syst. Signal Process., 24 (3), pp. 696-706; Juang, J.N., Pappa, R.S., An eigensystem realization algorithm for modal parameter identification and model reduction (1985) J. Guidance Control Dyn., 8 (5), pp. 620-627; Kawashima, K., Nagashima, H., Iwasaki, H., Evaluation of modal damping ratio based on strain energy proportional damping method (1994) Proc. 9th U.S.-Japan Bridge Engineering Workshop, Public Works Research Institute (PWRI), pp. 211-226. , Public Works Research Institute, Tsukuba, Japan; MATLAB [Computer Software], , MathWorks, Natick, MA; Modena, C., Sonda, D., Zonta, D., Damage localization in reinforced concrete structures by using damping measurements, Damage assessment of structures (1999) Proc. Int. Conf. on Damage Assessment of Structures, DAMAS 99, pp. 132-141. , Trans Tech Publisher, Switzerland; Mustafa, S., Debnath, N., Dutta, A., Bayesian probabilistic approach for model updating and damage detection for a large truss bridge (2015) Int. J. Steel Struct., 15 (2), pp. 473-485; Mustafa, S., Matsumoto, Y., Bayesian model updating and its limitations for detecting local damage of an existing truss bridge (2017) J. Bridge Eng., p. 04017019; Nair, K.K., Kiremidjian, A.S., Law, K.H., Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure (2006) J. Sound Vib., 291 (12), pp. 349-368; Nakamura, M., Masri, S.F., Chassiakos, A.G., Caughey, T.K., A method for non-parametric damage detection through the use of neural networks (1998) Earthquake Eng. Struct. Dyn., 27 (9), pp. 997-1010; Nashif, A.D., Jones, D.I., Henderson, J.P., (1985) Vibration Damping, pp. 45-50. , Wiley, New York; Overschee, P.V., Moor, B.D., Subspace algorithms for the stochastic identification problem (1993) Automatica, 29 (3), pp. 649-660; Salawu, O., Detection of structural damage through changes in frequency: A review (1997) Eng. Struct., 19 (9), pp. 718-723; Simoen, E., De Roeck, G., Lombaert, G., Dealing with uncertainty in model updating for damage assessment: A review (2015) Mech. Syst. Signal Process., 5657, pp. 123-149; Sohn, H., Law, K.H., Bayesian probabilistic damage detection of a reinforced-concrete bridge column (2000) Earthquake Eng. Struct. Dyn., 29 (8), pp. 1131-1152; Soize, C., Capiez-Lernout, E., Ohayon, R., Robust updating of uncertain computational models using experimental modal analysis (2008) AIAA J., 46 (11), pp. 2955-2965; Ungar, E.E., Kerwin, E.M., Loss factor of viscoelastic systems in terms of energy concepts (1962) J. Acoust. Soc. Am., 34 (5), pp. 954-957; Vanik, M.W., Beck, J.L., Au, S.K., Bayesian probabilistic approach to structural health monitoring (2000) J. Eng. Mech., pp. 738-745; Yamaguchi, H., Ito, M., Mode-dependence of structural damping in cable-stayed bridges (1997) J. Wind Eng. Indust. Aerodyn., 72, pp. 289-300; Yamaguchi, H., Jayawardena, L., Analytical estimation of structural damping in cable structures (1992) J. Wind Eng. Ind. Aerodyn., 43 (13), pp. 1961-1972; Yamaguchi, H., Matsumoto, Y., Yoshioka, T., Effects of local structural damage in a steel truss bridge on internal dynamic coupling and modal damping (2015) Smart Struct. Syst., 15 (3), pp. 523-541; Yamaguchi, H., Nagahawatta, H.D., Damping effects of cable cross ties in cable-stayed bridges (1995) J. Wind Eng. Ind. Aerodyn., 5455, pp. 35-43; Yamaguchi, H., Takano, H., Ogasawara, M., Shimosato, T., Kato, M., Kato, H., Energy-based damping evaluation of cable-stayed bridges and application to Tsurumi Tsubasa bridge (1997) Struct. Eng. Earthquake Eng., 14 (2), pp. 201s-213s; Yoshioka, T., Yamaguchi, H., Matsumoto, Y., Structural health monitoring of steel truss bridges based on modal damping changes in local and global modes (2010) Proc. 5th World Conf. on Structural Control and Monitoring, International Association for Structural Control and Monitoring, p. 167. , Earthquake Engineering Program of the U.S. National Science Foundation, Oakland, CA; Yuen, K.V., Au, S.K., Beck, J.L., Two-stage structural health monitoring approach for Phase i benchmark studies (2004) J. Eng. Mech., pp. 16-33; Zoghi, M., (2014) The International Handbook of FRP Composites in Civil Engineering, , CRC Press, Boca Raton, FL","Mustafa, S.; Dept. of Civil Engineering IIT Indore SimrolIndia; email: samim@alumni.iitg.ernet.in",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85032451235
"Li S., He S., Li H., Jin Y.","57218879558;57191955792;57202721115;56802954600;","Scour Depth Determination of Bridge Piers Based on Time-Varying Modal Parameters: Application to Hangzhou Bay Bridge",2017,"Journal of Bridge Engineering","22","12","04017107","","",,13,"10.1061/(ASCE)BE.1943-5592.0001154","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031508390&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001154&partnerID=40&md5=c9c386106691e45f5d7fdfe203a344e4","Key Lab of Intelligent Disaster Mitigation of the Min. of Indust, School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China; CCCC Highway Consultants CO., Ltd. (HPDI), Beijing, 100088, China","Li, S., Key Lab of Intelligent Disaster Mitigation of the Min. of Indust, School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China; He, S., CCCC Highway Consultants CO., Ltd. (HPDI), Beijing, 100088, China; Li, H., Key Lab of Intelligent Disaster Mitigation of the Min. of Indust, School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China; Jin, Y., CCCC Highway Consultants CO., Ltd. (HPDI), Beijing, 100088, China","Scour of bridge piers has been demonstrated to be one of the most common causes of bridge instability and destruction. This article presents a long-term approach to scour depth determination for a cable-stayed bridge based on time-varying modal parameters generated from a structural health monitoring system. The finite-element model, updated according to dynamic testing before opening the bridge to traffic, was employed to identify the sensitive modes for scour depth variation and establish the numerical relationship between scour depth and the modal parameters. Afterward, the time-varying modal parameters were determined using monitored acceleration data sets. Numerical investigation has indicated that these modal parameters are also significantly impacted by environmental conditions, which will cause confusion when determining scour depth. Nonlinear principal component analysis (NLPCA) was chosen to separate the environmental influence from the scour depth influence on modal parameters. Finally, comparison of the scour depth determined based on modal parameters with the results of visual inspection by divers exhibits the effectiveness of the proposed approach. Moreover, this study could provide guidelines for future decision making regarding the early warning and maintenance of bridge piers. © 2017 American Society of Civil Engineers.","Environmental conditions; Modal parameters; Nonlinear principal component analysis; Scour depth determination; Structural health monitoring","Bridge piers; Cable stayed bridges; Composite beams and girders; Decision making; Finite element method; Modal analysis; Principal component analysis; Structural health monitoring; Environmental conditions; Environmental influences; Hangzhou Bay Bridge; Modal parameters; Nonlinear principal component analysis; Numerical investigations; Scour depth; Structural health monitoring systems; Scour",,,,,"National Natural Science Foundation of China, NSFC: 51478149, 51638007, 51678204; Ministry of Science and Technology of the People's Republic of China, MOST: 2013CB036305, 2014AA110401, 2015DFG82080; Ningbo Municipal Bureau of Science and Technology: 2015C110020","This study was financially supported by the National Natural Science Foundation of China (NSFC) (Grants 51478149, 51678204, and 51638007), the Ministry of Science and Technology of the People’s Republic of China (MOST) (Grants 2013CB036305, 2015DFG82080, and 2014AA110401), and the Ningbo Science and Technology Project (Grant 2015C110020).",,,,,,,,,,"Anderson, I., (2015) Multivariate Feature Selection for Predicting Scour-related Bridge Damage Using A Genetic Algorithm, , Proc. 2015 Fall Meeting of American Geophysical Union, American Geophysical Union, Washington, DC; Briaud, J.-L., (2011) Realtime Monitoring of Bridge Scour Using Remote Monitoring Technology, , et al. 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Bridge Eng., pp. 295-304; Friswell, M.I., Mottershead, J.E., (1995) Finite Element Model Updating in Structural Dynamics, , Springer, Berlin; Hamill, L., (1998) Bridge Hydraulics, , CRC, Boca Raton, FL; Heza, Y.B.M., Soliman, A.M., Saleh, S.A., Prediction of the scour hole geometry around exposed bridge circular-pile foundation (2007) J. Eng. Appl. Sci., 54 (4), pp. 375-392; Ismail, A., Jeng, D.-S., Zhang, L.L., Zhang, J.-S., Predictions of bridge scour: Application of a feed-forward neural network with an adaptive activation function (2013) Eng. Appl. Artif. Intell., 26 (56), pp. 1540-1549; Kassem, A., Salaheldin, T., Imran, J., Chaudhry, M., Numerical modeling of scour in cohesive soils around artificial rock island of Cooper River Bridge (2003) Transp. Res. Rec., 1851, pp. 45-50; Kattell, J., Eriksson, M., Bridge scour evaluation: Screening, analysis, and countermeasures (1998) Rep. No. 9877, , U.S. Dept. of Agriculture Forest Services, Washington, DC; Laursen, E.M., Toch, A., (1956) Scour around bridge piers and abutments, , Iowa Highway Research Board, Ames, IA; Li, H., Li, S., Ou, J., Li, H., Modal identification of bridges under varying environmental conditions: Temperature and wind effects (2010) Struct. Control Health Monit., 17 (5), pp. 495-512; Lim, S.-Y., Equilibrium clear-water scour around an abutment (1997) J. Hydraul. Eng., pp. 237-243; Lin, Y.-B., Lai, J.-S., Chang, K.-C., Chang, W.-Y., Lee, F.-Z., Tan, Y.-C., Using MEMS sensors in the bridge scour monitoring system (2010) J. Chin. Inst. Eng., 33 (1), pp. 25-35; Lin, Y.-B., Lai, J.-S., Chang, K.-C., Li, L.-S., Flood scour monitoring system using fiber Bragg grating sensors (2006) Smart Mater. Struct., 15 (6), p. 1950; Melville, B.W., Sutherland, A.J., Design method for local scour at bridge piers (1988) J. Hydraul. Eng., pp. 1210-1226; midas Civil [Computer software]. 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ASCE, Reston, VA; Saegusa, R., Sakano, H., Hashimoto, S., Nonlinear principal component analysis to preserve the order of principal components (2004) Neurocomput., 61, pp. 57-70; Scholz, M., Kaplan, F., Guy, C.L., Kopka, J., Selbig, J., Non-linear PCA: A missing data approach (2005) Bioinf., 21 (20), pp. 3887-3895; Sheppard, D.M., Large scale and live bed local pier scour experiments (2003) Rep No. 133, , Dept. of Civil and Coastal Engineering, Univ. of Florida, Gainesville, FL; Sheppard, D.M., Miller, W., Jr., Live-bed local pier scour experiments (2006) J. Hydraul. Eng., pp. 635-642; Shirhole, A.M., Holt, R.C., (1991) Planning for A Comprehensive Bridge Safety Program, pp. 39-50. , Proc. 3rd Conf. of Bridge Engineering, Transportation Research Board, Washington, DC; Sohn, H., (2004) A Review of Structural Health Monitoring Literature: 1996-2001, , et al. Los Alamos National Laboratory, Los Alamos, NM; Young, G., Dou, X., Saffarinia, K., Jones, J., (1998) Proc., Water Resources Engineering, pp. 180-185. , Testing abutment scour model."" ASCE, Reston, VA; Yu, X., Yu, X., Time domain reflectometry automatic bridge scour measurement system: Principles and potentials (2009) Struct. Health Monit., 8 (6), pp. 463-476","Li, S.; Key Lab of Intelligent Disaster Mitigation of the Min. of Indust, China; email: lishunlong@hit.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85031508390
"Zhou Y., Zhang J., Yi W., Jiang Y., Pan Q.","57061050800;56990761600;7102120106;56660101500;8866010900;","Structural Identification of a Concrete-Filled Steel Tubular Arch Bridge via Ambient Vibration Test Data",2017,"Journal of Bridge Engineering","22","8","04017049","","",,13,"10.1061/(ASCE)BE.1943-5592.0001086","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020716172&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001086&partnerID=40&md5=97c01ed2237d70aca3643d28370cbd48","College of Civil Engineering, Hunan Provincial Key Lab on Damage Diagnosis for Engineering Structures, Hunan Univ., Changsha, Hunan, 410082, China; Drexel Univ., Philadelphia, PA  19104, United States","Zhou, Y., College of Civil Engineering, Hunan Provincial Key Lab on Damage Diagnosis for Engineering Structures, Hunan Univ., Changsha, Hunan, 410082, China; Zhang, J., College of Civil Engineering, Hunan Provincial Key Lab on Damage Diagnosis for Engineering Structures, Hunan Univ., Changsha, Hunan, 410082, China; Yi, W., College of Civil Engineering, Hunan Provincial Key Lab on Damage Diagnosis for Engineering Structures, Hunan Univ., Changsha, Hunan, 410082, China; Jiang, Y., College of Civil Engineering, Hunan Provincial Key Lab on Damage Diagnosis for Engineering Structures, Hunan Univ., Changsha, Hunan, 410082, China; Pan, Q., Drexel Univ., Philadelphia, PA  19104, United States","Structural identification (St-Id) is an effective structural evaluation approach for health monitoring and performance-based engineering. However, various uncertainties may significantly influence the reliability of St-Id. This paper presents ambient vibration measurements to develop a baseline model for a newly constructed arch bridge over Hongshui River in Guangxi, China. In this study, modal parameter identification was performed using the random decrement (RD) technique together with the complex mode indicator function (CMIF) algorithm, and the results were compared with those from stochastic subspace identification (SSI). First, a three-dimensional (3D) finite-element (FE) model was constructed to obtain the analytical frequencies and mode shapes. Then, the FE model of the arch bridge was tuned to minimize the difference between the analytical and experimental modal properties. Three artificial intelligence algorithms were used to calibrate uncertain parameters: the simple genetic algorithm (SGA), the simulated annealing algorithm (SAA), and the genetic annealing hybrid algorithm (GAHA). The simulation results showed that GAHA exhibited the best performance in mathematic function tests among the three methods and that the large-scale arch bridge could be efficiently calibrated using a hybrid strategy that combines SGA and SAA. To verify the admissibility of the calibration procedure, a sensitivity analysis was performed for the Young's modulus of the steel members, and the relative error for the static deformation of the bridge deck was determined. Finally, to verify the accuracy of the results, a multimodel updating method based on Bayesian statistical detection was analyzed for further validation. Through a detailed St-Id study using precise modeling, operational modal analysis (OMA), and the artificial intelligence algorithms, the authors confirmed the accuracy of the updated FE model for further structural performance prediction. © 2017 American Society of Civil Engineers.","Concrete-filled steel tubular arch bridge; Epistemic uncertainty; Finite-element model; Model calibration; Operational modal analysis","Arch bridges; Arches; Artificial intelligence; Concretes; Elastic moduli; Genetic algorithms; Identification (control systems); Modal analysis; Parameter estimation; Sensitivity analysis; Simulated annealing; Stochastic systems; Structural analysis; Structural health monitoring; Uncertainty analysis; Artificial intelligence algorithms; Complex mode indicator functions; Concrete filled steel tubular arch bridges; Epistemic uncertainties; Model calibration; Operational modal analysis; Simulated annealing algorithm(SAA); Stochastic subspace identification; Finite element method",,,,,,,,,,,,,,,,"Abdel-Ghaffar, A.M., Scanlan, R.H., Ambient vibration studies of Golden Gate Bridge. 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Eng., 28 (7), pp. 522-530; Zhong, S.T., (2003) The Concrete-filled Steel Tubular Structures, , Tsinghua University Press, Beijing; Zhou, Y., (2008) Parameter Identification Experiment and Research on Elastic Foundation Slab and Reinforced Concrete Frame Structure, , Ph.D. thesis, College of Civil Engineering, Hunan Univ. Changsha, Hunan, China; Zhu, G., Kwong, S., Gbest-guided artificial bee colony algorithm for numerical function optimization (2010) Appl. Math. Comput., 217 (7), pp. 3166-3173","Zhou, Y.; College of Civil Engineering, China; email: zhouyun05@gmail.com",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85020716172
"Hossain T., Segura S., Okeil A.M.","55669509000;57214245521;6602375318;","Structural effects of temperature gradient on a continuous prestressed concrete girder bridge: analysis and field measurements",2020,"Structure and Infrastructure Engineering","16","11",,"1539","1550",,12,"10.1080/15732479.2020.1713167","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078530023&doi=10.1080%2f15732479.2020.1713167&partnerID=40&md5=ad27d20972ee29e30ac5ebc7c1a5d229","Arcadis USA, Inc., Houston, TX, United States; MBI Companies Inc, Knoxville, TN, United States; Department of Civil & Environmental Engineering, Louisiana State University, Baton Rouge, LA, United States","Hossain, T., Arcadis USA, Inc., Houston, TX, United States; Segura, S., MBI Companies Inc, Knoxville, TN, United States; Okeil, A.M., Department of Civil & Environmental Engineering, Louisiana State University, Baton Rouge, LA, United States","The temperature of a structure exposed to the atmosphere depends on many factors such as geographical location, climatological condition, structure’s orientation, materials and surface condition, and its surroundings. In this paper, the temperature distribution at a particular segment of a prestressed concrete girder bridge from the John James Audubon Bridge Project in Louisiana is quantified for different days of the year. Computed temperatures, actual observed temperature at the bridge site, and AASHTO specified gradients are presented and compared. It was found that AASHTO temperature gradient matches the measured temperature well at the site with some exceptions. The restraint moment caused by the temperature gradient was quantified and compared with the cracking moment of girder ends. Primary and secondary thermally induced stresses were then calculated for different girders. It was found that temperature gradient alone does not produce stresses that exceed the girder section’s cracking limits for the investigated bridge. However, the cumulative effect of the primary thermal stresses and additional positive restraint moment due to thermal gradients and other long-term effects may well exceed the tensile strength of concrete and cause cracking. © 2020 Informa UK Limited, trading as Taylor & Francis Group.","concrete bridges; continuous bridges; diurnal and seasonal temperature variations; finite element method; prestressed concrete; structural behaviour; structural health monitoring; Thermal effects","Concrete beams and girders; Concrete bridges; Finite element method; Prestressed concrete; Structural health monitoring; Temperature distribution; Tensile strength; Thermal effects; Thermal gradients; Climatological conditions; Continuous bridges; Geographical locations; Measured temperatures; Seasonal temperature variations; Strength of concrete; Structural behaviour; Thermally induced stress; Atmospheric temperature",,,,,"Louisiana Transportation Research Center, LTRC: 08-1ST","Field data used in this study was generated as part of a research project sponsored by Louisiana Transportation Research Center (LTRC Project No. 08-1ST) with Dr. Walid Alaywan as Project Manager. The license key provided by the ThermoAnalytics Inc. to perform the thermal analysis is gratefully acknowledged. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsoring agencies.",,,,,,,,,,"(2008) LRFD bridge design specifications, , Washington, DC: Author; Barr, P.J., Stanton, J.F., Eberhard, M.O., Effects of temperature variations on precast, prestressed concrete bridge girders (2005) Journal of Bridge Engineering, 10 (2), pp. 186-194; Batla, F.A., Reisnour, P.R., Pathak, D.V., Deformations and stresses in flanged concrete structures due to temperature differentials (1985) ACI SP-086: Deflections of Concrete Structures, 86, pp. 395-406; de Battista, N., Brownjohn, J.M.W., Tan, H.P., Koo, K.-Y., Measuring and modelling the thermal performance of the Tamar suspension bridge using a wireless sensor network (2015) Structure and Infrastructure Engineering, 11 (2), pp. 176-193; Dilger, W.H., Ghali, A., Chan, M., Cheung, M.S., Maes, M.A., Temperature stresses in composite box girder bridges (1983) Journal of Structural Engineering, 109 (6), pp. 1460-1478; Du, J., Luo, X., Ng, P.L., Au, F.T.K., Early age temperature rise and thermal stresses induced in concrete bridge pier (2011) 2011 International Conference on Structures and Building Materials, , January 7–9).,. Trans Tech Publications Ltd, Guangzhou, China; Duffie, J.A., Beckman, W.A., (1991) Solar engineering of thermal processes, , New York, NY: John Wiley & Sons; Elbadry, M.M., Ghali, A., Temperature variations in concrete bridges (1983) Journal of Structural Engineering, 109 (10), pp. 2355-2374; Elbadry, M., Ghali, A., Thermal stresses and cracking of concrete bridges (1986) Journal of the American Concrete Institute, 83 (6), pp. 1001-1009; Hambly, E.C., Temperature distributions and stresses in concrete bridges (1978) Structural Engineer, 56, pp. 143-148; Hossain, T., Okeil, A.M., Force transfer mechanism in positive moment continuity details for prestressed concrete girder bridges (2014) Computers and Concrete, 14 (2), pp. 109-125; Hossain, T., Okeil, A.M., Cai, C.S., Calibrated finite element modeling of creep behavior of prestressed concrete bridge girders (2014) ACI Structural Journal, 111 (6). , 1287-1296; Hossain, T., Okeil, A.M., Cai, C.S., Field test and finite-element modeling of a three-span continuous-girder bridge (2014) Journal of Performance of Constructed Facilities, 28 (1), pp. 136-148. , –,. 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Retrieved from; Miller, R.A., Castrodale, R., Mirmiran, A., Hastak, M., (2004) Connection of simple-span precast concrete girders for continuity, , Washington, DC: Transportation Research Board, &, (NCHRP Report 519; Nilson, A.H., (1987) Design of prestressed concrete, , 2nd ed, Hoboken, NJ: Wiley; Okeil, A.M., Hossain, T., Cai, C.S., Field monitoring of positive moment continuity detail in a skewed prestressed concrete bulb-T girder bridge (2013) PCI Journal, 58 (2), pp. 80-90; Okeil, A.M., (2014) Data collection and evaluation of continuity detail for John James Audubon Bridge No. 61390613004101 (526), , Baton Rouge, LA: Louisiana Transportation Research Center; Okeil, A.M., A monitoring system for long-term performance of positive moment continuity detail in prestressed girder bridges (2009) 88th Annual Meeting of the Transportation Research Board, Washington, D.C, , & Cai, C. S; Okeil, A.M., Cai, C.S., Chebole, V., Hossain, T., (2011) Evaluation of continuity detail for precast prestressed girders (477), , Baton Rouge, LA: Louisiana Transportation Research Center; Paltridge, G.W., Platt, C.M.R., (1976) Radiative processes in meteorology and climatology, , Elsevier Scientific Publ, &,. Chicago, IL; Potgieter, I.C., Gamble, W.L., Nonlinear temperature distributions in bridges at different locations in the United States (1989) PCI Journal, 34 (4), pp. 80-103; Priestley, M.J.N., Long term observations of concrete structures. Analysis of temperature gradient effects (1985) Materials and Structures, 106, pp. 309-316; (2009) RadTherm® manual, , Calumet, MI: ThermoAnalytics, Inc; Rostásy, F.S., Budelmann, H., Verification of thermal restraint of a railways trough structure by long-term monitoring (2007) Structure and Infrastructure Engineering, 3 (3), pp. 237-244; Schlaich, J., Schäfer, K., Jennewein, M., Toward a consistent design of structural concrete (1987) PCI Journal, 32 (3), pp. 74-150; (1978) On the Nature and Distribution of Solar Radiation, , Watt Engineering Ltd, (Report for US DOE). US Government Printing Office Stock No. 016-000-00044-5, United States Government Printing Office, Washington, D.C; Zhou, Y., Sun, L., Insights into temperature effects on structural deformation of a cable-stayed bridge based on structural health monitoring (2019) Structural Health Monitoring, 18 (3), pp. 778-791","Okeil, A.M.; Department of Civil and Environmental Engineering, 3255-D Patrick F. Taylor Hall, United States; email: aokeil@lsu.edu",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","",Scopus,2-s2.0-85078530023
"Pan C., Yu L.","55834544500;35729477500;","Sparse regularization-based damage detection in a bridge subjected to unknown moving forces",2019,"Journal of Civil Structural Health Monitoring","9","3",,"425","438",,12,"10.1007/s13349-019-00343-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068925534&doi=10.1007%2fs13349-019-00343-w&partnerID=40&md5=6749172c00592e2a3b76db86df74ea85","School of Civil Engineering, Guangzhou University, Guangzhou, 510006, China; MOE Key Laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University, Guangzhou, 510632, China","Pan, C., School of Civil Engineering, Guangzhou University, Guangzhou, 510006, China, MOE Key Laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University, Guangzhou, 510632, China; Yu, L., MOE Key Laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University, Guangzhou, 510632, China","Output-only structural damage detection (SDD) is an important issue in the field of structural health monitoring (SHM). As an attempt, this study aims to propose a sparse regularization-based method for detecting the structural damage using structural responses caused by unknown moving forces. First, a transmissibility matrix between two sensor sets is constructed using a known bridge model and least square-based moving force identification algorithm. Second, the measured responses are used as inputs to estimate the reconstructed responses with the help of the transmissibility matrix. Then, the damage detection procedure can be regarded as an optimization problem trying to find a possible damage vector, which makes the difference between the measured and reconstructed responses minimum. Lp-norm (0 &lt; p ≤ 1) sparse regularization is adopted to improve the ill-conditioned SDD problem. To assess the feasibility of the proposed method, damaged bridges subjected to moving forces are taken as examples for numerical simulations. Differences between finite element model (FEM) used for model updating and the one applied to simulate the true damage conditions are considered. The illustrated results show that the proposed method can identify structural damages with a strong robustness. Some related issues, such as regularization parameters, finite element models, Lp-norm (0 &lt; p ≤ 1) penalty terms, noise levels and damage patterns, are discussed as well. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.","Sparse regularization; Structural damage detection (SDD); Structural health monitoring (SHM); Unknown moving force","Finite element method; Numerical methods; Structural analysis; Structural health monitoring; Moving force identification; Moving forces; Optimization problems; Regularization parameters; Sparse regularizations; Structural damage detection; Structural health monitoring (SHM); Structural response; Damage detection",,,,,"National Natural Science Foundation of China, NSFC: 51278226, 51678278","This work was jointly supported by the National Natural Science Foundation of China with Grant numbers 51678278 and 51278226. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.",,,,,,,,,,"Doebling, S.W., Farrar, C.R., Prime, M.B., (1996) Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review, , Los Alamos National Laboratory report, (LA-13070-MS); Fan, W., Qiao, P., Vibration-based damage identification methods: a review and comparative study (2011) Struct Health Monit, 10 (1), pp. 83-111; Ou, J., Li, H., Structural health monitoring in mainland China: review and future trends (2010) Struct Health Monit, 9 (3), pp. 219-231; Yang, Y., Yang, J.P., State-of-the-art review on modal identification and damage detection of bridges by moving test vehicles (2018) Int J Struct Stab Dyn, 18 (2), p. 1850025; Zhu, X.Q., Law, S.S., Recent developments in inverse problems of vehicle–bridge interaction dynamics (2016) J Civil Struct Health Monit, 6 (1), pp. 107-128; Jamali, S., Chan, T.H.T., Nguyen, A., Modelling techniques for structural evaluation for bridge assessment (2018) J Civil Struct Health Monit, 8 (2), pp. 1-13; Hosseini, M., Khoshnoudian, F., Esfandiari, A., Improved data expansion method used in damage detection method (2017) J Civil Struct Health Monit, 7 (1), pp. 15-27; Pan, C.D., Yu, L., Chen, Z.P., A hybrid self-adaptive Firefly–Nelder–Mead algorithm for structural damage detection (2016) Smart Struct Syst, 17 (6), pp. 957-980; Pedram, M., Esfandiari, A., Khedmati, M.R., Damage detection by a FE model updating method using power spectral density: numerical and experimental investigation (2017) J Sound Vib, 397, pp. 51-76; Neves, A.C., González, I., Leander, J., Structural health monitoring of bridges: a model-free ANN-based approach to damage detection (2017) J Civil Struct Health Monit, 7 (5), pp. 689-702; Yu, L., Zhu, J.H., Nonlinear damage detection using higher statistical moments of structural responses (2015) Struct Eng Mech., 54 (2), pp. 221-237; 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Xu, Z., Zhang, H., Wang, Y., L 1/2 regularization (2010) Sci China Inf Sci, 53 (6), pp. 1159-1169; Law, S.S., Chan, T.H.T., Zeng, Q.H., Moving force identification: a time domain method (1997) J Sound Vib, 201 (1), pp. 1-22; Pan, C.D., Yu, L., Liu, H.L., Identification of moving vehicle forces on bridge structures via moving average Tikhonov regularization (2017) Smart Mater Struct, 26 (8), p. 085041; Pan, C.D., Yu, L., Liu, H.L., Moving force identification based on redundant concatenated dictionary and weighted l 1-norm regularization (2018) Mech Syst Signal Process., 98, pp. 32-49; Liddle, A.R., Information criteria for astrophysical model selection (2010) Mon Not R Astron Soc Lett., 377 (1), pp. L74-L78","Yu, L.; MOE Key Laboratory of Disaster Forecast and Control in Engineering, China; email: lyu1997@163.com",,,"Springer Verlag",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85068925534
"Ali A., Sandhu T.Y., Usman M.","57217431052;57377128600;57192299057;","Ambient vibration testing of a pedestrian bridge using low-cost accelerometers for shm applications",2019,"Smart Cities","2","1",,"20","30",,12,"10.3390/smartcities2010002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073597811&doi=10.3390%2fsmartcities2010002&partnerID=40&md5=625209d357d9a427b6005d9dbac03947","School of Civil and Environmental Engineering, National University of Sciences and Technology, Sector H-12, Islamabad, 44000, Pakistan","Ali, A., School of Civil and Environmental Engineering, National University of Sciences and Technology, Sector H-12, Islamabad, 44000, Pakistan; Sandhu, T.Y., School of Civil and Environmental Engineering, National University of Sciences and Technology, Sector H-12, Islamabad, 44000, Pakistan; Usman, M., School of Civil and Environmental Engineering, National University of Sciences and Technology, Sector H-12, Islamabad, 44000, Pakistan","Damage detection and structural health monitoring have always been of great importance to civil engineers and researchers. Vibration-based damage detection has several advantages compared to traditional methods of non-destructive evaluation, such as ground penetrating radar (GPR) or ultrasonic testing, since they give a global response and are feasible for large structures. Damage detection requires a comparison between two systems states, the baseline or “healthy state”, i.e., the initial modal parameters, and the damaged state. In this study, system identification (SI) was carried out on a pedestrian bridge by measuring the dynamic response using six low-cost triaxial accelerometers. These low-cost accelerometers use a micro-electro-mechanical system (MEMS), which is cheaper compared to a piezoelectric sensor. The frequency domain decomposition algorithm, which is an output-only method of modal analysis, was used to obtain the modal properties, i.e., natural frequencies and mode shapes. Three mode shapes and frequencies were found out using system identification and were compared with the finite element model (FEM) of the bridge, developed using the commercial finite element software, Abaqus. A good comparison was found between the FEM and SI results. The frequency difference was nearly 10%, and the modal assurance criterion (MAC) of experimental and analytical mode shapes was greater than 0.80, which proved to be a good comparison despite the small number of accelerometers available and the simplifications and idealizations in FEM. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.","Finite element modelling; Frequency domain decomposition; Low-cost accelerometers; System identification; Vibration-based damage detection",,,,,,,,,,,,,,,,,"Pan, H., Azimi, M., Gui, G., Yan, F., Lin, Z., Vibration-Based Support Vector Machine for Structural Health Monitoring (2018) International Conference on Experimental Vibration Analysis for Civil Engineering Structures, pp. 167-178. , Springer: Cham, Switzerland; Rasheed, A., Farooq, S.H., Usman, M., Hanif, A., Khan, N.A., Khushnood, R.A., Structural reliability analysis of superstructure of highway bridges on China-Pakistan Economic Corridor (CPEC): A case study (2018) J. Struct. Integr. Maint, 5314, pp. 197-207. , [CrossRef]; Farrar, C.R., Worden, K., An introduction to structural health monitoring (2007) Phil. Trans. R. Soc. 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Signal Process, 25, pp. 1431-1450. , [CrossRef]; Chen, G.W., Omenzetter, P., Beskhyroun, S., Operational modal analysis of an eleven-span concrete bridge subjected to weak ambient excitations (2017) Eng. Struct, 151, pp. 839-860. , [CrossRef]; Allemang, R.J., The modal assurance criterion—Twenty years of use and abuse (2003) Sound Vib, 37, pp. 14-21","Usman, M.; School of Civil and Environmental Engineering, Sector H-12, Pakistan; email: m.usman@kaist.ac.kr",,,"MDPI",,,,,26246511,,,,"English","Smart. Cities.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85073597811
"Savino P., Tondolo F., Gherlone M., Tessler A.","57211552185;23668913100;6505966217;56231364300;","Application of inverse finite element method to shape sensing of curved beams",2020,"Sensors (Switzerland)","20","24","7012","1","16",,11,"10.3390/s20247012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097581792&doi=10.3390%2fs20247012&partnerID=40&md5=1c5358c8018857e535b6f56f09cd059b","Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Structural Mechanics and Concepts Branch, NASA Langley Research Center, Mail Stop 190, Hampton, VA  23681-2199, United States","Savino, P., Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Tondolo, F., Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Gherlone, M., Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Tessler, A., Structural Mechanics and Concepts Branch, NASA Langley Research Center, Mail Stop 190, Hampton, VA  23681-2199, United States","Curved beam, plate, and shell finite elements are commonly used in the finite element modeling of a wide range of civil and mechanical engineering structures. In civil engineering, curved elements are used to model tunnels, arch bridges, pipelines, and domes. Such structures provide a more efficient load transfer than their straight/flat counterparts due to the additional strength provided by their curved geometry. The load transfer is characterized by the bending, shear, and membrane actions. In this paper, a higher-order curved inverse beam element is developed for the inverse Finite Element Method (iFEM), which is aimed at reconstructing the deformed structural shapes based on real-time, in situ strain measurements. The proposed two-node inverse beam element is based on the quintic-degree polynomial shape functions that interpolate the kinematic variables. The element is C2 continuous and has rapid convergence characteristics. To assess the element predictive capabilities, several circular arch structures subjected to static loading are analyzed, under the assumption of linear elasticity and isotropic material behavior. Comparisons between direct FEM and iFEM results are presented. It is demonstrated that the present inverse beam finite element is both efficient and accurate, requiring only a few element subdivisions to reconstruct an accurate displacement field of shallow and deep curved beams. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Curved beam; IFEM; Shape sensing; Structural health monitoring","Arch bridges; Arches; Curved beams and girders; Inverse problems; Beam finite elements; Displacement field; Inverse finite element methods; Isotropic materials; Kinematic variables; Predictive capabilities; Rapid convergence; Shell finite elements; Finite element method; article; elasticity; inverse finite element analysis",,,,,,,,,,,,,,,,"Tessler, A., Spangler, J.L., (2003) A Variational Principle for Reconstruction of Elastic Deformations in Shear Deformable Plates and Shells, , NASA TM-2003-212445; NASA: Hampton, VA, USA; Tessler, A., Spangler, J.L., Inverse FEM for full-field reconstruction of elastic deformations in shear deformable plates and shells (2004) Proceedings of the 2nd European Workshop on Structural Health Monitoring, , Munich, Germany, 7–9 July; Tessler, A., Spangler, J.L., A least-squares variational method for full-field reconstruction of elastic deformations in shear-deformable plates and shells (2005) Comput. Meth. Appl. Mech. Eng, 194, pp. 327-339. , [CrossRef]; Cerracchio, P., Gherlone, M., Tessler, A., Real-time displacement monitoring of a composite stiffened panel subjected to mechanical and thermal loads (2015) Meccanica, 50, pp. 2487-2496. , [CrossRef]; Kefal, A., Oterkus, E., Tessler, A., Spangler, J.L., A quadrilateral inverse-shell element with drilling degrees of freedom for shape sensing and structural health monitoring (2016) Eng. Sci. Technol. Int. J, 19, pp. 1299-1313. , [CrossRef]; Cerracchio, P., Gherlone, M., Di Sciuva, M., Tessler, A., A novel approach for displacements and stress monitoring of sandwich structures based on the inverse Finite Element Method (2015) Comput. Struct, 127, pp. 69-76. , [CrossRef]; Kefal, A., Oterkus, E., Displacement and stress monitoring of a chemical tanker based on inverse finite element method (2016) Ocean Eng, 112, pp. 33-46. , [CrossRef]; Kefal, A., Oterkus, E., Displacement and stress monitoring of a Panamax containership using inverse finite element method (2016) Ocean Eng, 119, pp. 16-29. , [CrossRef]; Gherlone, M., Beam Inverse Finite Element Formulation, , Research Report 1 2008; Department of Aeronautics and Space Engineering, Politecnico di Torino: Torino, Italy, 2018; Gherlone, M., Cerracchio, P., Mattone, M., Di Sciuva, M., Tessler, A., Shape sensing of 3D frame structures using an inverse Finite Element Method (2012) Int. J. Solids Struct, 49, pp. 3100-3112. , [CrossRef]; Savino, P., Gherlone, M., Tondolo, F., Shape sensing with inverse finite element method for slender structures (2019) Struct. Eng. Mech, 72, pp. 217-227; Tessler, A., Structural analysis methods for structural health management of future aerospace vehicles (2007) Key Eng. Mat, 347, pp. 57-66. , [CrossRef]; Tessler, A., Spangler, J.L., Gherlone, M., Mattone, M., Di Sciuva, M., Real-time characterization of aerospace structures using onboard strain measurement technologies and inverse finite element method (2011) Proceedings of the 8th International Workshop on Structural Health Monitoring, , Stanford, CA, USA, 13–15 September; Quach, C.C., Vazquez, S.L., Tessler, A., Moore, J.P., Cooper, E.G., Spangler, J.L., Structural anomaly detection using fiber optic sensors and inverse finite element method (2005) Proceedings of the AIAA Guidance Navigation, and Control Conference and Exhibit, , San Francisco, CA, USA, 15–18 August; Gherlone, M., Cerracchio, P., Mattone, M., Di Sciuva, M., Tessler, A., An inverse finite element method for beam shape sensing: Theoretical framework and experimental validation (2014) Smart Mater. Struct, 23, p. 045027. , [CrossRef]; Kefal, A., Mayang, B.J., Oterkus, E., Yildiz, M., Three dimensional shape and stress monitoring of bulk carriers based on iFEM methodology (2018) Ocean Eng, 147, pp. 256-267. , [CrossRef]; Kefal, A., Tessler, A., Oterkus, E., An enhanced inverse finite element method for displacement and stress monitoring of multi-layered composite and sandwich structures (2017) Comput. Struct, 179, pp. 514-540. , [CrossRef]; Kefal, A., Yildiz, M., Modeling of sensor placement strategy for shape sensing and Structural Health Monitoring of a wing-shaped sandwich panel using inverse Finite Element Method (2017) Sensors, 17, p. 2775. , [CrossRef] [PubMed]; Cyrus, N.J., Eppink, R.T., Fulton, R.E., Walz, J.E., (1970) Accuracy of Finite Element Approximation to Structural Problems, , NASA Technical Note D-5728; NASA: Washington, DC, USA; Kikuchi, F., On the validity of the finite element analysis of circular arches represented by an assemblage of beam elements (1975) Comput. Meth. Appl. Mech. Eng, 5, pp. 253-276; Ashwell, D.G., Sabir, A.B., Limitations of certain curved finite elements when applied to arches (1971) Int. J. Mech. Sci, 13, pp. 133-139. , [CrossRef]; Ashwell, D.G., Sabir, A.B., Roberts, T.M., Further studies in the application of curved finite elements to circular arches (1971) Int. J. Mech. Sci, 13, pp. 507-517. , [CrossRef]; Dawe, D.J., Rigid-body motions and strain-displacement equations of curved shell finite elements (1972) Int. J. Mech. Sci, 14, pp. 569-578. , [CrossRef]; Davis, R., Henshell, R.D., Warburton, G.N., Constant curvature beam finite elements for in-plane vibration (1972) J. Sound Vib, 25, pp. 561-576. , [CrossRef]; Dawe, D.J., Curved finite elements for the analysis of shallow and deep arches (1974) Comput. Struct, 4, pp. 559-580. , [CrossRef]; Dawe, D.J., Numerical studies using circular arch finite elements (1974) Comput. Struct, 4, pp. 729-740. , [CrossRef]; Meck, H.R., An accurate polynomial displacement function for finite ring elements (1980) Comput. Struct, 11, pp. 265-269. , [CrossRef]; Stolarski, H., Belytschko, T., Shear and membrane locking in curved C0 elements (1983) Comput. Meth. Appl. Mech. Eng, 41, pp. 279-296. , [CrossRef]; Saleeb, A.F., Chang, T.Y., On the hybrid-mixed formulations of C0 curved beam elements (1987) Comput. Meth. Appl. Mech. Eng, 60, pp. 95-121. , [CrossRef]; Saffari, H., Tabatabaei, R., A finite circular arch element based on trigonometric shape functions (2007) Math. Prob. Eng, p. 78507. , [CrossRef]; Gimena, L., Gimena, F.N., Gonzaga, P., Structural analysis of a curved beam element defined in global coordinates (2008) Eng. Struct, 30, pp. 3355-3364. , [CrossRef]; Tufekci, E., Arpaci, A., Analytical solutions of in-plane static problems for non-uniform curved beams including axial and shear deformations (2006) Struct. Eng. Mech, 22, pp. 131-150. , [CrossRef]; Tufekci, E., Eroglu, U., Aya, S.A., A new two-noded curved beam finite element formulation based on exact solution (2017) Eng. Comput, 33, pp. 261-273. , [CrossRef]; Krishnan, A., Suresh, Y.J., A simple cubic linear element for static and free vibration analyses of curved beams (1998) Comput. Struct, 68, pp. 473-489. , [CrossRef]; (1995) LUSAS Finite Element System V15.1(1995) User Manual, , FEA Ltd.: London, UK","Tessler, A.; Structural Mechanics and Concepts Branch, Mail Stop 190, United States; email: alexander.tessler-1@nasa.gov",,,"MDPI AG",,,,,14248220,,,"33302401","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85097581792
"Moravej H., Chan T.H.T., Nguyen K.-D., Jesus A.","57188768669;7402687570;39262319400;56150440500;","Vibration-based Bayesian model updating of civil engineering structures applying Gaussian process metamodel",2019,"Advances in Structural Engineering","22","16",,"3487","3502",,11,"10.1177/1369433219858723","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068622600&doi=10.1177%2f1369433219858723&partnerID=40&md5=7ce6dbdeb7144842a0bde0242fa343e5","School of Civil Engineering and Built Environment, Faculty of Science and Engineering, Queensland University of Technology (QUT), Brisbane, QLD, Australia; University of West London, London, United Kingdom","Moravej, H., School of Civil Engineering and Built Environment, Faculty of Science and Engineering, Queensland University of Technology (QUT), Brisbane, QLD, Australia; Chan, T.H.T., School of Civil Engineering and Built Environment, Faculty of Science and Engineering, Queensland University of Technology (QUT), Brisbane, QLD, Australia; Nguyen, K.-D., School of Civil Engineering and Built Environment, Faculty of Science and Engineering, Queensland University of Technology (QUT), Brisbane, QLD, Australia; Jesus, A., University of West London, London, United Kingdom","Structural health monitoring plays a significant role in providing information regarding the performance of structures throughout their life spans. However, information that is directly extracted from monitored data is usually susceptible to uncertainties and not reliable enough to be used for structural investigations. Finite element model updating is an accredited framework that reliably identifies structural behavior. Recently, the modular Bayesian approach has emerged as a probabilistic technique in calibrating the finite element model of structures and comprehensively addressing uncertainties. However, few studies have investigated its performance on real structures. In this article, modular Bayesian approach is applied to calibrate the finite element model of a lab-scaled concrete box girder bridge. This study is the first to use the modular Bayesian approach to update the initial finite element model of a real structure for two states—undamaged and damaged conditions—in which the damaged state represents changes in structural parameters as a result of aging or overloading. The application of the modular Bayesian approach in the two states provides an opportunity to examine the performance of the approach with observed evidence. A discrepancy function is used to identify the deviation between the outputs of the experimental and numerical models. To alleviate computational burden, the numerical model and the model discrepancy function are replaced by Gaussian processes. Results indicate a significant reduction in the stiffness of concrete in the damaged state, which is identical to cracks observed on the body of the structure. The discrepancy function reaches satisfying ranges in both states, which implies that the properties of the structure are predicted accurately. Consequently, the proposed methodology contributes to a more reliable judgment about structural safety. © The Author(s) 2019.","Bayesian framework; box Girder bridge; finite element model updating; Gaussian process; structural health monitoring; vibration analysis","Bayesian networks; Box girder bridges; Bridge decks; Concrete bridges; Concretes; Gaussian distribution; Gaussian noise (electronic); Numerical models; Steel bridges; Structural health monitoring; Vibration analysis; Bayesian frameworks; Bayesian model updating; Civil engineering structures; Concrete box girder bridge; Finite-element model updating; Gaussian Processes; Probabilistic technique; Structural investigation; Finite element method",,,,,"Australian Research Council, ARC: DP160101764; Queensland University of Technology, QUT","The first author would like to express his sincere appreciation to Queensland University of Technology (QUT) for the financial support for his research. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The support provided by Australian Research Council (ARC) via a Discovery Project (DP160101764) is gratefully acknowledged. Also, the support provided by technical support from FEMtools is acknowledged.",,,,,,,,,,"Abaqus, F.E.A., (2017) Abaqus Inc, , Providence, RI, Abaqus Inc; Arendt, P.D., Apley, D.W., Chen, W., Quantification of model uncertainty: calibration, model discrepancy, and identifiability (2012) Journal of Mechanical Design, 134 (10), p. 100908. , (, a; Arendt, P.D., Apley, D.W., Chen, W., Improving identifiability in model calibration using multiple responses (2012) Journal of Mechanical Design, 134 (10), p. 100909. , (, b; (2005) General principles on reliability for structures; Bayarri, M.J., Berger, J.O., Paulo, R., A framework for validation of computer models (2007) Technometrics, 49 (2), pp. 138-154; Beck, J.L., Au, S.K., Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation (2002) Journal of Engineering Mechanics, 128 (4), pp. 380-391; Beck, J.L., Katafygiotis, L.S., Updating models and their uncertainties. I: Bayesian statistical framework (1998) Journal of Engineering Mechanics, 124 (4), pp. 455-461; Conde, B., Eguía, P., Stavroulakis, G.E., Parameter identification for damaged condition investigation on masonry arch bridges using a Bayesian approach (2018) Engineering Structures, 172, pp. 275-284; Conti, S., Gosling, J.P., Oakley, J.E., Gaussian process emulation of dynamic computer codes (2009) Biometrika, 96 (3), pp. 663-676; Darmawan, M.S., Stewart, M.G., Spatial time-dependent reliability analysis of corroding pretensioned prestressed concrete bridge girders (2007) Structural Safety, 29 (1), pp. 16-31; (2012) FEMtools”Dynamic Design Solutions N.V, , Charlotte, NC, Dynamic Design Solutions; Frangopol, D.M., Life-cycle performance, management, and optimisation of structural systems under uncertainty: accomplishments and challenges 1 (2011) Structure and Infrastructure Engineering, 7 (6), pp. 389-413; Friswell, M., Mottershead, J.E., (2013) Finite element model updating in structural dynamics (Vol.38), , New York, Springer; Higdon, D., Gattiker, J., Williams, B., Computer model calibration using high-dimensional output (2008) Journal of the American Statistical Association, 103 (482), pp. 570-583; Jamali, S., Chan, T.H., Nguyen, A., Reliability-based load-carrying capacity assessment of bridges using structural health monitoring and nonlinear analysis (2018) Structural Health Monitoring, 18 (1), pp. 20-34; Jamali, S., Chan, T.H., Thambiratnam, D.P., (2016) Pre-test finite element modelling of box girder overpass-application for bridge condition assessment, p. 457. , Proceedings of the Australasian Structural Engineering Conference, Brisbane, QLD, 23–25 November, Brisbane, QLD, Australia, ASEC,. In:, p; Jesus, A., Brommer, P., Westgate, R., Bayesian structural identification of a long suspension bridge considering temperature and traffic load effects (2018) Structural Health Monitoring, 18, pp. 1310-1323; Jesus, A., Brommer, P., Westgate, R., Modular Bayesian damage detection for complex civil infrastructure (2019) Journal of Civil Structural Health Monitoring, 9, pp. 201-215; Jesus, A., Brommer, P., Zhu, Y., Comprehensive Bayesian structural identification using temperature variation (2017) Engineering Structures, 141, pp. 75-82; Jesus, A.H., Dimitrovová, Z., Silva, M.A., A statistical analysis of the dynamic response of a railway viaduct (2014) Engineering Structures, 71, pp. 244-259; Kennedy, M., O’Hagan, A., Bayesian calibration of computer models (2001) Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63 (3), pp. 425-464. , (, a; Kennedy, M., O’Hagan, A., Supplementary details on Bayesian calibration of computer (2001) Technical report, University of Nottingham, Statistics Section, , http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.28.2835&rep=rep1&type=pdf, (, b; Li, H.N., Li, D.S., Ren, L., Structural health monitoring of innovative civil engineering structures in Mainland China (2016) Structural Monitoring and Maintenance, 3 (1), pp. 1-32; Liu, F., Bayarri, M.J., Berger, J.O., Modularization in Bayesian analysis, with emphasis on analysis of computer models (2009) Bayesian Analysis, 4 (1), pp. 119-150; Lophaven, S.N., Nielsen, H.B., Søndergaard, J., (2002) DACE: a Matlab kriging toolbox, 2. , Lyngby, IMM, Informatics and Mathematical Modelling, the Technical University of Denmark; Mirza, S.A., Kikuchi, D.K., MacGregor, J.G., Flexural strength reduction factor for bonded prestressed concrete beams (1980) Journal Proceedings, 77 (4), pp. 237-246; Moravej, H., Jamali, S., Chan, T.H.T., (2017) Finite element model updating of civil engineering infrastructures: a review literature, , Proceedings of the international conference on structural health monitoring of intelligent infrastructure, Brisbane, QLD, Australia, 5–8 December,. In; Mottershead, J.E., Link, M., Friswell, M.I., The sensitivity method in finite element model updating: a tutorial (2011) Mechanical Systems and Signal Processing, 25 (7), pp. 2275-2296; O’Hagan, A., Bayesian analysis of computer code outputs: a tutorial (2006) Reliability Engineering & System Safety, 91 (10-11), pp. 1290-1300; Pathirage, T.S., (2017) Identification of prestress force in prestressed concrete box girder bridges using vibration-based techniques, , Queensland University of Technology, Brisbane, QLD, Australia, PhD Thesis; Rasmussen, C., Williams, C., (2006) Gaussian processes for machine learning, , Cambridge, MA, The MIT Press, (Adaptive Computation and Machine Learning; Shahidi, S.G., Pakzad, S.N., Generalized response surface model updating using time domain data (2013) Journal of Structural Engineering, 140 (8), p. A4014001; Simoen, E., Papadimitriou, C., Lombaert, G., On prediction error correlation in Bayesian model updating (2013) Journal of Sound and Vibration, 332 (18), pp. 4136-4152; (2011) SVS-ARTeMIS extractor-release 5.3, user’s manual, , Aalborg, Structural Vibration Solutions A/S; Weng, S., Xia, Y., Zhou, X.Q., Inverse substructure method for model updating of structures (2012) Journal of Sound and Vibration, 331 (25), pp. 5449-5468","Moravej, H.; School of Civil Engineering and Built Environment, Australia; email: h.moravej@qut.edu.au",,,"SAGE Publications Inc.",,,,,13694332,,ASEDD,,"English","Adv. Struct. Eng.",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85068622600
"Wang C., Wang Y., Duan L., Wang S., Zhai M.","57196394009;54380928100;30467582500;56119469300;55507880600;","Fatigue Performance Evaluation and Cold Reinforcement for Old Steel Bridges",2019,"Structural Engineering International","29","4",,"563","569",,11,"10.1080/10168664.2019.1593069","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067447791&doi=10.1080%2f10168664.2019.1593069&partnerID=40&md5=c3994fa7dc86bbf335f18e7db648ed5d","Institute of Bridge Engineering, College of Highways, Chang’an University, Xi’an, China; School of Civil Engineering, Suzhou University of Science and Technology, Suzhou, China","Wang, C., Institute of Bridge Engineering, College of Highways, Chang’an University, Xi’an, China; Wang, Y., Institute of Bridge Engineering, College of Highways, Chang’an University, Xi’an, China; Duan, L., Institute of Bridge Engineering, College of Highways, Chang’an University, Xi’an, China; Wang, S., Institute of Bridge Engineering, College of Highways, Chang’an University, Xi’an, China; Zhai, M., School of Civil Engineering, Suzhou University of Science and Technology, Suzhou, China","Fatigue and corrosion damage in existing old steel bridges are accumulated continuously in service stage, probably leading to structural failure. One old welded steel bridge and one old riveted steel bridge are taken as examples for structural performance and safety evaluation. A large number of out plan deformation-induced long fatigue cracks were found at the connection plates connecting the bottom flange of end cross-frames and main girders of the two old steel bridges, and serious corrosion damage was found at such details of the old riveted steel bridge. Cold reinforcement techniques of drilling stop holes and bonding and bolting steel plates were adopted to strengthen the old steel bridges. Fracture mechanical models using the short-term monitoring data were established to assess the fatigue lives before and after the reinforcement, considering the coupling effect of fatigue and corrosion, and the evaluation results indicate that corrosion and fatigue coupling is very significant for old steel bridges. Monitoring and evaluation results show that bonding and bolting steel plates leads to about 90% reduction in the maximum tensile stress and about 8 times fatigue lives extension, while drilling stop holes can only temporarily arrest the crack growth. Additionally, the future maintenance strategies recommendations are provided. © 2019, © 2019 International Association for Bridge and Structural Engineering (IABSE).","cold reinforcement; corrosion fatigue; finite element model; out plan deformation-induced fatigue; steel bridges; structural health monitoring","Beams and girders; Boreholes; Corrosion fatigue; Cracks; Deformation; Failure (mechanical); Fatigue damage; Finite element method; Fracture mechanics; Infill drilling; Plates (structural components); Reinforcement; Steel corrosion; Structural health monitoring; Fatigue performance; Fracture mechanical model; Maintenance strategies; Monitoring and evaluations; Reinforcement technique; Riveted steel bridges; Short-term monitoring; Structural performance; Steel bridges",,,,,,,,,,,,,,,,"Wang, C.S., Chen, W.Z., Chen, A.R., Fatigue safety assessment of existing steel bridges in China (2009) Struct. Eng. Int, 19 (2), pp. 174-179; Wang, C.S., Chen, A.R., Chen, W.Z., Xu, Y., Application of probabilistic fracture mechanics in evaluation of existing riveted bridges (2006) Bridge Struct, 2 (4), pp. 223-232; Biezma, M.V., Schanack, F., Collapse of steel bridges (2007) J. Perform. Constr. Fac, 21 (5), pp. 398-405; Connor, R.J., Fisher, J.W., Identifying effective and ineffective retrofits for distortion fatigue cracking in steel bridges using field instrumentation (2006) J. Bridge Eng, 11 (6), pp. 745-752; Fisher, J.W., (1984) Fatigue and Fracture in Steel Bridges, , New York: John Wiley & Sons; Barsom, J.M., Rolfe, S.T., (1999) Fracture and Fatigue Control of Structures: Applications and Fracture Mechanics, , West Conshohocken: ASTM; Kayser, J.R., Nowak, A.S., Capacity loss due to corrosion in steel-girder bridges (1989) J. Struct. Eng, 115 (6), pp. 1525-1537; Fisher, J.W., Kaufmann, E.J., Pense, A.W., Effect of corrosion on crack development and fatigue life (1624) Transp. Res. Rec.: J. Transp. Res. Board, 1, pp. 110-117; Yazdani, N., Albrecht, P., Crack growth rates of structural steel in air and aqueous environments (1989) Eng. Fract. Mech, 32 (6), pp. 997-1007; Albrecht, P., Cheng, J.G., Fatigue tests of 8-yr weathered A588 steel Weldment (1983) J. Struct. Eng, 109 (9), pp. 2048-2065; Macho, M., Ryjáček, P., Matos, J.C., Static and fatigue test on real steel bridge components deteriorated by corrosion (2018) Int. J. Steel Struct, 18 (11), pp. 1-21; Aghoury, I.M.E., Galal, K., Corrosion-fatigue strain-life model for steel bridge girders under various weathering conditions (2014) J. Struct. Eng, 140 (6), p. 04014026; Wang, C.S., Zhai, M.S., Duan, L., Wang, Y.Z., Cold reinforcement and evaluation of steel bridges with fatigue cracks (2018) J. Bridge Eng, 23 (4), p. 04018014; Zhao, Y., Roddis, W.M.K., Fatigue behavior and retrofit investigation of distortion-induced web gap cracking (2007) J. Bridge Eng, 12 (6), pp. 737-745; Gregory, E., Slater, G., Woodley, C., Welded repair of cracks in steel bridge members, , NCHRP Rep. 321. Washington, DC: Transportation Research Board; 1989; Keating, P., Wilson, S., Kohutek, T., Evaluation of repair procedures for web gap fatigue damage, , Research Rep. 1360-1. College station, Texas: Texas Transportation Institute Texas A&M University system; 1996; Alemdar, F., (2011) Repair of Bridge Steel Girders Damaged by Distortion Induced Fatigue, , University of Kansas, Lawrence; Bowman, M.D., Fu, G., Zhou, Y.E., Connor, R.J., Godbole, A.A., Fatigue evaluation of steel bridges, , NCHRP Rep. 721. Washington, DC: Transportation Research Board; 2012; Liu, H., Zhou, J., Bun, S.H., Effectiveness of crack-arrest holes under distortion-induced fatigue loading (2018) J. Bridge Eng, 23 (2), p. 04017141; Ghafoori, E., Prinz, G.S., Mayor, E., Finite element analysis for fatigue damage reduction in metallic riveted bridges using pre-stressed CFRP plates (2014) Polymers (Basel), 6 (4), pp. 1096-1118; Ghafoori, E., Hosseini, A., Al-Mahaidi, R., Zhao, X.L., Motavalli, M., Prestressed CFRP-strengthening and long-term wireless monitoring of an old roadway metallic bridge (2018) Eng. Struct, 176, pp. 585-605; Lesiuk, G., Katkowski, M., Correia, J., De Jesus, A., Blazejewski, W., Fatigue crack growth rate in CFRP reinforced constructional old steel (2018) Int. J. Struct. Integrity, 9 (3), pp. 381-395; Newman, J.C., Raju, I.S., An empirical stress-intensity factor equation for the surface crack (1981) Eng. Fract. Mech, 15 (1-2), pp. 185-192; Hobbacher, A., Stress intensity factors of welded joints (1993) Eng. Fract. Mech, 46 (2), pp. 173-182; Shi, P., (2001) Corrosion Fatigue Reliability of Aging Structures, , University of Vanderbilt, Nashville; (1993) Handbook of Stress Intensity Factor, , Beijing: Science Press, (in Chinese; Sedlacek, G., Hensen, W., Bild, J., Dahl, W., Langenberg, P., Verfahren zur Ermittlung der Sicherheit von alten Stahlbrücken unter Verwendung neuester Erkenntnisse der Werkstofftechnik (1992) Bauingenieur, 67 (3), pp. 129-136","Wang, C.; Institute of Bridge Engineering, China; email: wcs2000wcs@163.com",,,"Taylor and Francis Ltd.",,,,,10168664,,,,"English","Struct Eng Int J Int",Article,"Final","",Scopus,2-s2.0-85067447791
"Ye X.W., Su Y.H., Jin T., Chen B., Han J.P.","14829893000;56069855200;57210249528;55723031600;55639619200;","Master S-N Curve-Based Fatigue Life Assessment of Steel Bridges Using Finite Element Model and Field Monitoring Data",2019,"International Journal of Structural Stability and Dynamics","19","1","1940013","","",,11,"10.1142/S0219455419400133","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049777378&doi=10.1142%2fS0219455419400133&partnerID=40&md5=21c61490a15a8371b5deaf2ff2b91f4b","Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China; School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China","Ye, X.W., Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China; Su, Y.H., Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China; Jin, T., Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China; Chen, B., Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China; Han, J.P., School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China","The accuracy of fatigue life assessment for the welded joint in a steel bridge is largely dependent on an appropriate S-N curve. In this paper, a master S-N curve-based fatigue life assessment approach for the welded joint with an open-rib in orthotropic steel bridge deck is proposed based on the finite element model (FEM) and field monitoring data from structural health monitoring (SHM) system. The case studies on fatigue life assessment by use of finite element analysis (FEA) for constant-Amplitude cyclic loading mode and field monitoring data under variable-Amplitude cyclic loading mode are addressed. In the case of FEA, the distribution of structural stress at fatigue-prone weld toe is achieved using 4-node shell element model and then transformed into equivalent structural stress by fracture mechanics theory. The fatigue life of the welded joint is estimated with a single master S-N curve in the form of equivalent structural stress range versus the cycles to failure. In the case of monitoring data-based fatigue life assessment, the daily history of structural stress at diaphragm to U-rib is derived from the raw strain data measured by the instrumented fiber Bragg grating (FBG) sensors and transformed into equivalent structural stress. The fatigue life of the investigated welded joint is calculated by cyclic counting method and Palmgren-Miner linear damage cumulative rule. The master S-N curve method provides an effective fatigue life assessment process, especially when the nominal stress is hard to be defined. A single master S-N curve will facilitate to solve the difficulty in choosing a proper S-N curve which is required in the traditional fatigue life assessment methods. © 2019 World Scientific Publishing Company.","fatigue life assessment; finite element analysis; master S-N curve method; Steel bridge; structural health monitoring; welded joint","Cyclic loads; Fiber Bragg gratings; Finite element method; Fracture mechanics; Monitoring; Steel bridges; Stresses; Structural health monitoring; Welded steel structures; Welding; Welds; Curve method; Fatigue life assessment; Fiber Bragg Grating Sensors; Field monitoring data; Fracture mechanics theory; Orthotropic steel bridge decks; Structural health monitoring (SHM); Variable amplitudes; Fatigue of materials",,,,,"National Natural Science Foundation of China, NSFC: 51778574; Ministry of Education, MOE; Harbin Institute of Technology, HIT; National Key Research and Development Program of China, NKRDPC: 2017YFC0806100; Fundamental Research Funds for the Central Universities: 2017QNA4024","The work described in this paper was jointly supported by the National Key R&D Program of China (Grant No. 2017YFC0806100), the National Science Foundation of China (Grant No. 51778574), the Fundamental Research Funds for the Central Universities of China (Grant No. 2017QNA4024), and the Key Lab of Structures Dynamic Behavior and Control (Harbin Institute of Technology), Ministry of Education, Harbin 150090, China.",,,,,,,,,,"Fisher, J.W., (1984) Fatigue and Fracture in Steel Bridges: Case Studies, , Wiley New York; Stephens, R.I., Fatemi, A., Stephens, R.R., Fuchs, H.O., (2001) Metal Fatigue in Engineering, , Wiley, New York; Ye, X.W., Ni, Y.Q., Wong, K.Y., Ko, J.M., Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data (2012) Eng Struct, 45, pp. 166-176; (1980) BSI BS5400: Steel, Concrete and Composite Bridges, Part 10: Code of Practice for Fatigue, , British Standards Institution, London; (1992), CEN. Eurocode 3: Design of Steel Structures, Part 1.1: General Rules and Rules for Buildings, ENV 1993-1-1, European Committee for Standardization, Brussels; (2005), AASHTO. LRFD Bridge Design Specifications American Association of State Highway and Transportation Oficials Washington D.C; Niemi, E., Fricke, W., Maddox, S.J., (2006) Fatigue Analysis of Welded Components: Designer's Guide to the Structural Hot-Spot Stress Approach (IIW-1430-00), , Woodhead Publishing Cambridge; Xiao, Z.G., Yamada, K., A method of determining geometric stress for fatigue strength evaluation of steel welded joints (2004) Int. J. Fatigue, 26 (12), pp. 1277-1293; Aygul, M., Bokesj, M., Heshmati, M., Al-Emrani, M., A comparative study of different fatigue failure assessments of welded bridge details (2013) Int. J. Fatigue 49, pp. 62-72; Ni, Y.Q., Ye, X.W., Ko, J.M., Monitoring-based fatigue reliability assessment of steel bridges: Analytical model and application (2010) J Struct. Eng, 136 (12), pp. 1563-1573; Radaj, D., Sonsino, C.M., (1998) Fatigue Assessment of Welded Joints by Local Approaches, , Abington Publishing, Cambridge; Fricke, W., Kahl, A., Comparison of different structural approaches for fatigue assessment of welded ship structures (2005) Mar. Struct, 18 (7), pp. 473-488; Poutiainen, I., Tanskanen, P., Marquis, G., Finite element methods for structural hot spot stress determination-A comparison of procedures (2004) Int. J. Fatigue, 26 (11), pp. 1147-1157; Doerk, O., Fricke, W., Weissenbom, C., Comparison of different calculation methods for structural stress at welded joints (2003) Int. J. Fatigue, 25 (5), pp. 359-369; Dong, P., A structural stress definition and numerical implementation for fatigue analysis of welded joints (2001) Int. J. Fatigue, 23 (10), pp. 865-876; Dong, P., Hong, J.K., Osage, D., Prager, M., (2002) Master S-N Curve Method for Fatigue Evaluation of Welded Components, , WRC Bulletin 474 (Welding Research Council, New York); Dong, P., Hong, J.K., Osage, D., Prager, M., Assessment of ASME's FSRF rules for vessel and piping welds using a new structural stress method (2003) Weld. World 47, pp. 31-43; Kang, H.T., Dong, P., Hong, J.K., Fatigue analysis of spot welds using a meshinsensitive structural stress approach (2007) Int J. Fatigue, 29 (8), pp. 1546-1553; Kyuba, H., Dong, P., Equilibrium-equivalent structural stress approach to fatigue analysis of a rectangular hollow section joint (2005) Int. J. Fatigue, 27 (1), pp. 85-94; Yaghoubshahi, M., Alinia, M.M., Milani, A.S., Master S-N curve approach to fatigue prediction of breathing web panels (2017) J Constr. Steel Res, 128, pp. 789-799; Mei, J., Dong, P., An equivalent stress parameter for multi-Axial fatigue evaluation of welded components including non-proportional loading effects (2017) Int. J. Fatigue 101, pp. 297-311; Boiler, A., Vessel Code, P., Viii, S., Rules for Construction of Pressure Vessels (2015) Division 2-Alternate Rules, American Society of Mechanical Engineers","Ye, X.W.; Department of Civil Engineering, China; email: cexwye@zju.edu.cn",,,"World Scientific Publishing Co. Pte Ltd",,,,,02194554,,,,"English","Int. J. Struct. Stab. Dyn.",Article,"Final","",Scopus,2-s2.0-85049777378
"Morgenthal G., Rau S., Taraben J., Abbas T.","16304701500;57196719520;57199260229;56533472800;","Determination of Stay-Cable Forces Using Highly Mobile Vibration Measurement Devices",2018,"Journal of Bridge Engineering","23","2","04017136","","",,11,"10.1061/(ASCE)BE.1943-5592.0001166","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037743582&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001166&partnerID=40&md5=17f23a490afc5c7761561b9f44c39ce2","Department of Modelling and Simulation of Structures, Institute of Structural Engineering, Bauhaus Univ. Weimar, Marienstraße 13, Weimar, 99423, Germany","Morgenthal, G., Department of Modelling and Simulation of Structures, Institute of Structural Engineering, Bauhaus Univ. Weimar, Marienstraße 13, Weimar, 99423, Germany; Rau, S., Department of Modelling and Simulation of Structures, Institute of Structural Engineering, Bauhaus Univ. Weimar, Marienstraße 13, Weimar, 99423, Germany; Taraben, J., Department of Modelling and Simulation of Structures, Institute of Structural Engineering, Bauhaus Univ. Weimar, Marienstraße 13, Weimar, 99423, Germany; Abbas, T., Department of Modelling and Simulation of Structures, Institute of Structural Engineering, Bauhaus Univ. Weimar, Marienstraße 13, Weimar, 99423, Germany","Vibration measurements are an established method for determining the tension force of stay cables. Whereas stay cables are structural members exhibiting significant geometrical nonlinearities, their behavior can be linearized to approximate their vibration characteristics. Based on measured natural frequencies, the cable force can thus be identified. This paper presents methods to facilitate such force identification using highly mobile measurement equipment - namely, modern microelectromechanical systems (MEMS)-based acceleration sensors connected to battery-operated microcontrollers as well as those integrated in smartphones. The paper systematically investigates the accuracy of measurement data obtained from such systems and the effect on the tension force calculated. It is shown that sensor resolution and sampling rate directly affect the accuracy of the force measurement and are discriminating criteria between competing systems. Furthermore, a study of the amplitude of excitation arising from manual and ambient wind excitation shows that resolution limitations of the sensor may prohibit a reliable identification of natural frequencies. A novel smartphone-based software framework is presented, which integrates measurements from the different types of sensor systems. Furthermore, it allows use of nonlinear finite-element-based analyses for the cable-force identification. The proposed methodology and implementation were validated on cables of the Queensferry Crossing during construction, for which direct force measurements were available to compare with vibration-based results. The technology has shown potential to allow very simple and cost-effective yet accurate tension-force measurements. © 2017 American Society of Civil Engineers.","Accelerometer; Cable forces; Fast Fourier transform (FFT); Nonlinear analysis; Smartphone; Stay cables; Structural health monitoring","Accelerometers; Cable stayed bridges; Computer programming; Cost effectiveness; Electromechanical devices; Fast Fourier transforms; Finite element method; Force measurement; Mathematical transformations; MEMS; Natural frequencies; Nonlinear analysis; Smartphones; Structural health monitoring; Vibration measurement; Accuracy of measurements; Cable forces; Direct force measurements; Geometrical non-linearity; Micro electromechanical system (MEMS); Non-linear finite elements; Stay cable; Vibration characteristics; Cables",,,,,"Deutsche Forschungsgemeinschaft, DFG: GRK 1462","This research is supported by the German Research Foundation (DFG) via Research Training Group Evaluation of Coupled Numerical and Experimental Partial Models in Structural Engineering (GRK 1462), which is gratefully acknowledged. Furthermore, the contribution of Paul Debus to the RPi sensor framework is acknowledged. The authors are also grateful to Martin Romberg and Lukas Kohler form Leonhardt, Andrä und Partner and to Transport Scotland for permission to conduct measurements at Queensferry Crossing.",,,,,,,,,,"Bathe, K.-J., (1996) Finite Element Procedures, , Prentice Hall, Englewood Cliffs, NJ; Caetano, E.D.S., (2007) Cable Vibrations in Cable-stayed Bridges, Vol. 9 of Structural Engineering Documents, , IABSE, Zürich, Switzerland; Chang, W.-S., Reynolds, T., Harris, R., Mosalam, K., Using smartphone to identify dynamic characteristics of timber bridges (2014) Proc., COST - Timber Bridges Conf., , Bern Univ. of Applied Sciences, Switzerland Institute for Timber Constructions, Structures, and Architecture, Bern, Switzerland; Chen, S.E., Petro, S., Nondestructive bridge cable tension assessment using laser vibrometry (2005) Exp. Tech., 29 (2), pp. 29-32; Chen, S.-R., Yan, Q.-S., A method of measuring the cable tension force with the application of smart phones (2013) Comput. Modell. New Technol., 17 (5 D), pp. 11-18; Chopra, A.K., (2012) Dynamics of Structures: Theory and Applications to Earthquake Engineering, , 4th Ed. Prentice Hall Pearson, Upper Saddle River, NJ; Clough, R.W., Penzien, J., (1975) Dynamics of Structures, , McGraw-Hill, New York; Cunha, A., Caetano, E., Dynamic measurements on stay cables of cable-stayed bridges using an interferometry laser system (1999) Exp. Tech., 23 (3), pp. 38-43; Cunha, A., Caetano, E., Delgado, R., Dynamic tests on large cable-stayed bridge (2001) J. Bridge Eng., pp. 54-62; Debora, S., Parivallal, S., Ravisankar, K., Hemalatha, G., Evaluation of cable tension using vibration based methodologies for health monitoring of structures (2015) Int. J. Innovative Res. Sci., Eng. Technol. (IJIRSET), 4 (6), pp. 506-514; Fang, Z., Practical formula for cable tension estimation by vibration method (2012) J. Bridge Eng., pp. 161-164; Feng, M., Fukuda, Y., Mizuta, M., Ozer, E., Citizen sensors for SHM: Use of accelerometer data from smartphones (2015) Sensors (Basel, Switzerland), 15 (2), pp. 2980-2998; Gibbs, M., Dynamic characteristics of suspension footbridges: A novel sensing approach (2014) Proc., 6th World Conf. on Structural Control and Monitoring, , International Center for Numerical Methods in Engineering (CIMNE), Barcelona, Spain; Gibbs, M., Trost, D., Morgenthal, G., Kareem, A., Monitoring dynamics of suspension footbridges using novel sensing techniques (2014) Footbridge: Footbridges: Past, Present and Future, Hemming Information Services, London; Gimsing, N.J., Georgakis, C.T., (2012) Cable Supported Bridges: Concept and Design, , 3rd Ed. John Wiley & Sons, Chichester, U.K; (2013) MPU-6000 and MPU-6050: Product Specification: Datasheet PS-MPU-6000A-00, , InvenSense Inc. San Jose, CA; Irvine, H.M., Caughey, T.K., The linear theory of free vibrations of a suspended cable (1974) Proc. R. Soc. A: Math., Phys. Eng. Sci., 341 (1626), pp. 299-315; Mehrabi, A.B., Tabatabai, H., Unified finite difference formulation for free vibration of cables (1998) J. Struct. Eng., pp. 1313-1322; Morgenthal, G., The application of smartphones in bridge inspection and monitoring (2012) IABSE Congr. Rep., 18 (23), pp. 610-618; Morgenthal, G., Hallermann, N., Strukturidentifikation und monitoring mit smartphone-basierten sensorsystemen [Structural identification and monitoring with smartphone-based sensor systems] (2013) Ernst Sohn Special: Messtechnik im Bauwesen, (4), pp. 25-29; Morgenthal, G., Höpfner, H., The application of smartphones to measuring transient structural displacements (2012) J. Civ. Struct. Health Monit., 2 (34), pp. 149-161; Morgenthal, G., Sham, R., West, B., Engineering the tower and main span construction of Stonecutters Bridge (2010) J. Bridge Eng., pp. 144-152; Morgenthal, G., Yamasaki, Y., Behaviour of very long cable-stayed bridges during erection (2010) Proc. Inst. Civ. Eng. Bridge Eng., 163 (4), pp. 213-224; Ren, W.-X., Chen, G., Hu, W.-H., Empirical formulas to estimate cable tension by cable fundamental frequency (2005) Struct. Eng. Mech., 20 (3), pp. 363-380; Schmieder, M., Taylor-Noonan, A., Heere, R., Rapid non-contact tension force measurements on stay cables (2012) Bridge Maintenance, Safety, Management, Resilience and Sustainability, pp. 3799-3805. , F. Biondini and D. Frangopol, eds. Vol. 20125550, CRC Press, Boca Raton, FL; Strømmen, E.N., (2006) Theory of Bridge Aerodynamics, , Springer, Berlin, Heidelberg; Svensson, H., (2012) Cable-stayed Bridges: 40 Years of Experience Worldwide, , Ernst & Sohn, Berlin; Svensson, S.E., (2004) Bygningsstatiske Meddelelser, Dansk Selskab for Bygningsstatik, 4, pp. 79-106. , Vibration of cable stays."" Copenhagen, Denmark; Tabatabai, H., (2005) Inspection and Maintenance of Bridge Stay Cable Systems, , Transportation Research Board, Washington, DC; Yu, Y., Initial validation of mobile-structural health monitoring method using smartphones (2015) Int. J. Distrib. Sens. Netw., 11 (2), pp. 1-14; Yu, Y., Zhao, X., Ou, J., A new idea: Mobile structural health monitoring using smart phones (2012) Proc., Third Int. Conf. on Intelligent Control and Information Processing (ICICIP), pp. 714-716; Zhao, X., Cable force monitoring system of cable stayed bridges using accelerometers inside mobile smart phone (2015) SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, , J. P. Lynch, ed. SPIE Proceedings, Society of Photo-Optical Instrumentation Engineers, Bellingham, WA; Zhao, X., Portable and convenient cable force measurement using smartphone (2015) J. Civ. Struct. Health Monit., 5 (4), pp. 481-491; Zui, H., Shinke, T., Namita, Y., Practical formulas for estimation of cable tension by vibration method (1996) J. Struct. Eng., pp. 651-656","Rau, S.; Department of Modelling and Simulation of Structures, Marienstraße 13, Germany; email: sebastian.rau@uni-weimar.de",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85037743582
"Abedin M., De Caso y Basalo F.J., Kiani N., Mehrabi A.B., Nanni A.","57211253861;54899631700;57211254647;7005771645;7005344499;","Bridge load testing and damage evaluation using model updating method",2022,"Engineering Structures","252",,"113648","","",,10,"10.1016/j.engstruct.2021.113648","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120463519&doi=10.1016%2fj.engstruct.2021.113648&partnerID=40&md5=94e9b6b3131a4f133c0927340fe3af54","Department of Civil and Environmental Engineering, Florida International University, 10555 W. Flagler Street, Miami, FL  33174, United States; Department of Civil, Architectural and Environmental Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL  33146, United States","Abedin, M., Department of Civil and Environmental Engineering, Florida International University, 10555 W. Flagler Street, Miami, FL  33174, United States; De Caso y Basalo, F.J., Department of Civil, Architectural and Environmental Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL  33146, United States; Kiani, N., Department of Civil, Architectural and Environmental Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL  33146, United States; Mehrabi, A.B., Department of Civil and Environmental Engineering, Florida International University, 10555 W. Flagler Street, Miami, FL  33174, United States; Nanni, A., Department of Civil, Architectural and Environmental Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL  33146, United States","In this paper, the performance of a precast-prestressed box-beam bridge that had been in service for more than 50 years was investigated using a series of static and dynamic load tests. The bridge was instrumented to record individual panel deflections under live loads. A detailed finite element (FE) model was developed for better understanding the bridge behavior with suspected damage at deck panel joints. A comparison between the results of the FE model and actual bridge response confirmed damage at the deck longitudinal joints, also inferred by the observation of reflective cracking on the deck surface. Using the FE analysis and load testing results, a new damage detection method for structural health monitoring of these bridges with precast deck panels was introduced. This method can effectively identify locations and significance of potential deck joint damage based on the measured changes in bridge response and model updating. The results showed that such joint damage affects the bridge integrity, alters the live load distribution, and can potentially reduce the bridge load-carrying capacity. © 2021 Elsevier Ltd","Adjacent box girder; Bridge performance; Damage detection; Finite element analysis; Live load testing; Precast concrete bridge","Box girder bridges; Bridge decks; Damage detection; Dynamic loads; Load testing; Precast concrete; Steel bridges; Structural health monitoring; Adjacent box girde; Box girder; Bridge performance; Bridge response; Finite element analyse; Finite element modelling (FEM); Live load testing; Live loads; Model updating; Precast concrete bridge; Finite element method; bridge; concrete structure; damage mechanics; detection method; finite element method; loading test; numerical model; performance assessment; structural analysis",,,,,"1916342","The authors gratefully acknowledge the partial financial support from the NSF Center for Integration of Composites into Infrastructure (CICI) under grant #1916342. In addition to the technical staff of the Structures and Materials Laboratory at UM, several individuals contributed to the execution of the load test, including Roberto Rodriguez, Christian Steputat, Babak Vafaei, Dr. Amin Sarafraz, and Prof. Alfredo Cigada.",,,,,,,,,,"Riccitelli, F., Mehrabi, A., Abedin, M., Farhangdoust, S., (2020), Khedmatgozar Dolati SS Performance of Existing Abc Projects: Inspection Case Studies. Miami, FL, USA: Accel Bridg Constr Univ Transp Cent;; (2009), FDOT. Index 20350 series: Prestressed Slab Unit. 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"Siwowski T., Rajchel M., Howiacki T., Sieńko R., Bednarski Ł.","25029342900;57194606221;56790057700;56866373700;24365952700;","Distributed fibre optic sensors in FRP composite bridge monitoring: Validation through proof load tests",2021,"Engineering Structures","246",,"113057","","",,10,"10.1016/j.engstruct.2021.113057","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113385380&doi=10.1016%2fj.engstruct.2021.113057&partnerID=40&md5=287657077c7ba575c58bd36158ae3288","Rzeszow University of Technology, al. Powstancow Warszawy 12, Rzeszow, 35-959, Poland; Cracow University of Technology, Warszawska St. 24, Krakow, 31-155, Poland; AGH University of Science and Technology in Krakow, al. Mickiewicza 30, Krakow, 30-059, Poland","Siwowski, T., Rzeszow University of Technology, al. Powstancow Warszawy 12, Rzeszow, 35-959, Poland; Rajchel, M., Rzeszow University of Technology, al. Powstancow Warszawy 12, Rzeszow, 35-959, Poland; Howiacki, T., Cracow University of Technology, Warszawska St. 24, Krakow, 31-155, Poland; Sieńko, R., Cracow University of Technology, Warszawska St. 24, Krakow, 31-155, Poland; Bednarski, Ł., AGH University of Science and Technology in Krakow, al. Mickiewicza 30, Krakow, 30-059, Poland","The distributed fibre optic sensors (DFOS) technique based on Rayleigh scattering has been chosen as the basic structural health monitoring (SHM) system of the first Polish fibre reinforced polymer (FRP) composite bridge. The validation of the DFOS technique is critical to address the development of a monitoring system with improved accuracy and spatial resolution tailored for the FRP bridge application. The validation through proof load tests and finite element analysis (FEA) has been performed to ensure accurate and reliable DFOS readings. Two additional strain measurements techniques were implemented to validate DFOS, i.e. common foil strain gauges and vibrating wire strain gauges. The DFOS strain profiles were first compared with discrete strain measurements and then converted into deflection profiles and validated against discrete deflection measurements performed with common linear variable differential transducers. The additional validation based on the comparison of the measurement profiles with the results of FEA has been also performed. Based on the experimental and numerical results it has been revealed the DFOS technique is a reliable and accurate method to be implemented for FRP bridge monitoring. © 2021 The Authors","Distributed fibre optic sensors; Fibre optic sensing; Finite element analysis; FRP composite bridge; Measurement validation; Structural health monitoring","Composite bridges; Fiber optic sensors; Fiber optics; Fiber reinforced plastics; Fibers; Load testing; Numerical methods; Strain gages; Strain measurement; Structural health monitoring; Bridge monitoring; Distributed fiber-optic sensors; Fiber reinforced polymer composite bridge; Fiber reinforced polymer composites; Fiber-optic sensing; Finite elements analysis; Health monitoring; Measurement validation; Sensors technique; Structural health; Finite element method; bridge; finite element method; health monitoring; loading; loading test; model validation; strain analysis",,,,,"Narodowe Centrum Badań i Rozwoju, NCBR: DEM-1–041/001","This work was created within the framework of the project: “COMBRIDGE – An innovative FRP composite road bridge”. The project was implemented as part of a pilot programme entitled: “Support for Research and Development Works in the Demonstrative Scale DEMONSTRATOR+” and was co-founded by the National Centre for Research and Development – Poland ( NCBiR ) as well as the industry partners: Mostostal Warszawa S.A. and Promost Consulting, Rzeszow (Contract No. DEM-1–041/001 ).",,,,,,,,,,"Keller, T., Overview of Fibre-Reinforced Polymers in Bridge Construction (2002) Struct Eng Int, 12 (2), pp. 66-70; Zoghi, M., The International Handbook of FRP Composites in Civil Engineering (2013), CRC Press, Taylor & Francis Group Boca Raton, FL; Uddin, N., Developments in Fiber-Reinforced Polymer (FRP) Composites for Civil Engineering (2013) Woodhead Publishing Limited; Siwowski, T., FRP composite bridges. 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In: Proceedings of SPIE - The International Society for Optical Engineering 2011; 7753; Bednarski Ł, Sieńko R, Grygierek M, Howiacki T. New distributed fibre optic 3D Sensor with thermal self-compensation system: design, research and field proof application inside geotechnical structure. Sensors 2021, The Special Issue “Sensors and Measurements in Geotechnical Engineering” (to be published); (2013), Optical Backscatter Reflectometer (model OBR 4600). User Guide 6, OBR 4600 Software 3.10.1 Luna Innovations, 3157 State Street, Blacksburg, VA 24060, USA","Siwowski, T.; Rzeszow University of Technology, al. Powstancow Warszawy 12, Poland; email: siwowski@prz.edu.pl",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85113385380
"Dang H.V., Raza M., Nguyen T.V., Bui-Tien T., Nguyen H.X.","57218766707;57197446345;57220758433;57204859112;57199967463;","Deep learning-based detection of structural damage using time-series data",2021,"Structure and Infrastructure Engineering","17","11",,"1474","1493",,10,"10.1080/15732479.2020.1815225","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090240654&doi=10.1080%2f15732479.2020.1815225&partnerID=40&md5=43cca8527c7d9d0849e1f69e55b34b03","Faculty of Science and Technology, Middlesex University, London, United Kingdom; Department of Computer Science, Edge Hill University, Ormskirk, United Kingdom; Modeling & Simulation Team, Schlumberger, Clamart, France; Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Viet Nam; Faculty of Building and Industrial Construction, National University of Civil Engineering, Hanoi, Viet Nam","Dang, H.V., Faculty of Science and Technology, Middlesex University, London, United Kingdom, Faculty of Building and Industrial Construction, National University of Civil Engineering, Hanoi, Viet Nam; Raza, M., Department of Computer Science, Edge Hill University, Ormskirk, United Kingdom; Nguyen, T.V., Modeling & Simulation Team, Schlumberger, Clamart, France; Bui-Tien, T., Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Viet Nam; Nguyen, H.X., Faculty of Science and Technology, Middlesex University, London, United Kingdom","Previously, it was nearly impossible to use raw time series sensory signals for structural health monitoring due to the inherent high dimensionality of measured data. However, recent developments in deep learning techniques have overcome the need of complex preprocessing in time series data. This study extends the applicability of four prominent deep learning algorithms: Multi-Layer Perceptron, Long Short Term Memory network, 1D Convolutional Neural Network, and Convolutional Neural Network to structural damage detection using raw data. Three structures ranging from relatively small structures to considerably large structures are extensively investigated, i.e., 1D continuous beam under random excitation, a 2D steel frame subjected to earthquake ground motion, and a 3D stayed-cable bridge under vehicular loads. In addition, a modulated workflow is designed to ease the switch of different DL algorithms and the fusion of data from sensors. The results provide a more insightful picture of the applicability of Deep Learning algorithms in performing structural damage detection via quantitative evaluations of detection accuracy, time complexity, and required data storage in multi-damage scenarios. Moreover, these results emphasize the high reliability of 2DCNN, as well as the good balance between accuracy and complexity of Long Short Term Memory and 1D Convolutional Neural Network. © 2020 Informa UK Limited, trading as Taylor & Francis Group.","finite element methods; machine learning; neural network; signal processing; structural analysis; structural health monitoring; time-series; Vibration","Brain; Cable stayed bridges; Complex networks; Convolution; Convolutional neural networks; Damage detection; Deep learning; Digital storage; Earthquakes; Long short-term memory; Multilayer neural networks; Structural analysis; Structural health monitoring; Time series; Earthquake ground motions; High dimensionality; Learning techniques; Multi layer perceptron; Quantitative evaluation; Stayed cable-bridge; Structural damage detection; Structural damages; Learning algorithms",,,,,"Newton Fund; British Council: 429715093","This work was supported by the Newton Fund Institutional Links through the U.K. Department of Business, Energy, and Industrial Strategy and managed by the British Council under Grant 429715093.",,,,,,,,,,"(1998) AASHTO LRFD bridge design specifications, , USA: American Association of State Highway and Transportation Officials; Abdeljaber, O., Avci, O., Kiranyaz, M.S., Boashash, B., Sodano, H., Inman, D.J., 1-d cnns for structural damage detection: Verification on a structural health monitoring benchmark data (2018) Neurocomputing, 275, pp. 1308-1317; Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Inman, D.J., Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks (2018) Journal of Sound and Vibration, 424, pp. 158-172; Awkar, J.C., Lui, E.M., Seismic analysis and response of multistory semirigid frames (1999) Engineering Structures, 21 (5), pp. 425-441; Brownlee, J., (2019), https://machinelearningmastery.com/what-is-deep-learning, What is, deep learning, ? 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Retrieved from; Olamigoke, O., (2018) Structural response of cable-stayed bridges to cable loss, , University of Surrey, (Doctoral dissertation; Pan, S.J., Yang, Q., A survey on transfer learning (2010) IEEE Transactions on Knowledge and Data Engineering, 22 (10), pp. 1345-1359; Pedro, J., Oliveira, J., Reis, A.J., Nonlinear analysis of composite steel–concrete cable-stayed bridges (2010) Engineering Structures, 32 (9), pp. 2702-2716; Sekulovic, M., Salatic, R., Nonlinear analysis of frames with flexible connections (2001) Computers & Structures, 79 (11), pp. 1097-1107; Tang, Z., Chen, Z., Bao, Y., Li, H., Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring (2019) Structural Control and Health Monitoring, 26 (1), p. e2296; (2011), https://arab-trip.com/wp-content/uploads/2019/07, Retrieved from; Wang, Y.R.P., Gao, R., Virtualization and deep recognition for system fault classification (2017) Journal of Manufacturing Systems, 44, pp. 310-316; Yuan, M., Wu, Y., Lin, L., Fault diagnosis and remaining useful life estimation of aero engine using lstm neural network (2016) IEEE International Conference on Aircraft Utility Systems, , In; Yun, C.B., Yi, J.H., Bahng, E.Y., Joint damage assessment of framed structures using a neural networks technique (2001) Engineering Structures, 23 (5), pp. 425-435; Zhang, Y., Miyamori, Y., Mikami, S., Saito, T., Vibration-based structural state identification by a 1-dimensional convolutional neural network (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (9), pp. 822-839; Zhao, R., Wang, J., Yan, R., Mao, K., Machine health monitoring with LSTM networks (2016) 10th International Conference on Sensing Technology, , In","Dang, H.V.; Faculty of Science and Technology, United Kingdom",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85090240654
"Hasni H., Jiao P., Lajnef N., Alavi A.H.","56964369900;55604705500;14047090600;33867483600;","Damage localization and quantification in gusset plates: A battery-free sensing approach",2018,"Structural Control and Health Monitoring","25","6","e2158","","",,10,"10.1002/stc.2158","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043391372&doi=10.1002%2fstc.2158&partnerID=40&md5=123cf7c93dd89eb03f3247c4e525694b","Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48824, United States; Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA  19104, United States; Department of Civil and Environmental Engineering, University of Missouri, Columbia, MO  65211, United States","Hasni, H., Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48824, United States; Jiao, P., Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA  19104, United States; Lajnef, N., Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48824, United States; Alavi, A.H., Department of Civil and Environmental Engineering, University of Missouri, Columbia, MO  65211, United States","This study presents a numerical approach for damage localization and quantification in gusset plates using data provided by a battery-free sensing technology. A series of 3D finite element simulations are conducted to obtain the response of a gusset plate with a geometrical structure similar to U10W gusset plate of the I-35W Highway Bridge in Minneapolis, MN, USA. Crack propagation is simulated using the extended finite element method. A network of sensing nodes is placed on the surface of the plate to extract strain-time history for different damage stages. Subsequently, probability density functions (PDFs) are defined to characterize the output of the self-powered sensor. A crack localization and quantification approach is proposed based on fusion of the data from a network of sensors. Based on the results, damage can be precisely detected and localized through tracking relative shifts of PDFs over time. Moreover, features extracted from PDFs can be used to measure the crack size. Copyright © 2018 John Wiley & Sons, Ltd.","damage localization; damage quantification; finite element method; gusset plates; self-powered sensor; structural health monitoring","Crack propagation; Cracks; Damage detection; Electric batteries; Plates (structural components); Probability density function; Structural health monitoring; 3D finite-element simulation; Damage localization; Damage quantification; Extended finite element method; Geometrical structure; Gusset plates; Probability density functions (PDFs); Self-powered; Finite element method",,,,,"Federal Highway Administration, FHWA: DTFH61‐ 13‐H00009","The presented work is supported by a research grant from the Federal Highway Administration (FHWA; Grant DTFH61‐ 13‐H00009).",,,,,,,,,,"Maia, N.M.M., Almeida, R.A.B., Urgueira, A.P.V., Sampaio, R.P.C., (2011) Mech. Syst. Signal Proc., 25, p. 2475; Zou, Y., Tong, L., Steven, G.P., (2000) J. Sound Vib., 230 (2), p. 357; Lee, J.J., Lee, J.W., Yi, J.H., Yun, C.B., Jung, H.Y., (2005) J. Sound Vib., 280, p. 555; Li, J., Hao, H., (2016) Meas., 88, p. 360; Liu, R.M., Babanajad, S.K., Taylor, T., Ansari, F., (2015) Smart Mater. Struct., 24; Fukuda, Y., Feng, M.Q., Shinozuka, M., (2010) Struct. Control Health Monit., 17 (8), p. 918; Yeum, C.M., Dyke, S.J., (2015) Computer-Aided Civ. Infrastruct. Eng., 30, p. 759; Shahidi, S.G., Gulgec, N.S., Pakzad, S.N., (2016) Dyn. Civil Struct. 2 Conf. Proc. Society Expe. Mech. Series, , https://doi.org/10.1007/978-3-319-29751-4_3; Yang, Y., Dorn, C., Mancini, T., Talken, Z., Theiler, J., Kenyon, G., Farrar, C., Mascarenas, D., (2017) Struct. Health Monit., p. 1. , https://doi.org/10.1177/1475921717704385; Roeder, A., Zhang, H., Sanchez, L., Yang, Y., Farrar, C., Mascarenas, D., (2017) Stadia, arenas and grandstands, , In, (Eds, J. M. Harvie, J. Baqersad,), Shock &amp; vibration, aircraft/aerospace, energy harvesting, acoustic &amp; optics., Proc. 35th IMAC, A Conf. Expo. Struct. Dyn; Raghavan, A., Kessler, S.S., Dunn, C.T., Barber, D., Wicks, S., Wardle, B.L., (2009) Proc. 7th Int. Workshop Structural Health Monit, , Stanford, CA, USA; Loh, K.J., Hou, T.C., Lynch, J.P., Kotov, N.A., (2009) J. Nondestruct. Evaluation, 28 (1), p. 9; Saafi, M., (2009) Nanotechnology, 20 (39); Zha, J.W., Zhang, B., Li, R.K., Dang, Z.M., (2016) Compos. Sci. Technol., 123, p. 32; Chen, S., Dong, X., Kim, J.Y., Wu, S., Wang, Y., (2016) Adv. Struct. Eng., 19 (2), p. 270; Kurata, N., Spencer, B.F., Ruiz-Sandoval, M., (2004) In Proc. 13th World Conference on Earthquake Eng, , (1406) (, August); Cho, S., Giles, R.K., Spencer, B.F., (2015) Struct. Control Health Monit., 22 (2), p. 255; Lynch, J.P., Law, K.H., Kiremidjian, A.S., Kenny, T.W., Carryer, E., Partridge, A., (2001) Proc. 3rd Int. Workshop Struct. Health Monit.; Yao, Y., Tung, S.E., Glisic, B., (2014) Struct. Control Health Monit., 21, p. 1387; Lynch, J.P., Loh, K.J., (2006) Shock Vib. Digest, 38 (2), p. 91; Lynch, J.P., (2005) Struct. Control Health Monit., 12, p. 405; Hasni, H., Jiao, P., Alavi, A.H., Lajnef, N., Masri, S.F., (2018) Auto Construct., 85, p. 344; Hasni, H., Alavi, A.H., Jiao, P., Lajnef, N., Chatti, K., Aono, K., Chakrabartty, S., (2017) Meas., 110, p. 217; Jiao, P., Borchani, W., Hasni, H., Lajnef, N., (2017) Meas. Sci. Tech., 28 (8); Liu, X., Dong, X., Wang, Y., (2016) Proc. Health Monit. Struct. Bio. Syst., , Las Vegas, NV, USA; Ampeliotis, D., Bogdanovic, N., Berberidis, K., Casciati, F., Al-Saleh, R., (2012) Power-efficient wireless sensor reachback for SHM, bridge maintenance, safty, management, resilience and sustainability (rammed earth conservation), p. 93. , CRC Press, USA; Chen, Z.C., Casciati, F., (2014) Struct. Control Health Monit., 21 (7), p. 1118; Bogdanovic, N., Ampeliotis, D., Berberidis, K., Casciati, F., Plata-Chaves, J., (2014) Smart Struct. Syst., 14 (1), p. 1; Crosti, C., Duthinh, D., (2014) Eng. Struct., 62-63, p. 135; Cheng, R., Xu, L., Jin, S., Shi, Y., (2016) J. Struct. Eng., 142 (3); Fang, C., Yam, M.C.H., Zhou, X., Zhang, Y., (2015) Eng. Struct., 99, p. 9; Meng, D., Ansari, F., Feng, X., (2015) Appl. Optics, 54 (16), p. 4972; Yao, R., Pakzad, S.N., Venkitasubramaniam, P., (2016) Struct. Control Health Monit., 24. , https://doi.org/10.1002/stc.1881; Chakrabartty, S., Lajnef, N., Elvin, N., Elvin, A., Gore, A., (2011) Self-powered sensor; Lajnef, N., Chatti, K., Chakrabartty, S., Rhimi, M., Sarkar, P., (2013) Report: FHWA-HRT-12-072, , in, Federal Highway Administration (FHWA), Washington, DC; Alavi, A.H., Hasni, H., Lajnef, N., Chatti, K., (2016) Eng. Struct., 128, p. 124; Alavi, A.H., Hasni, H., Lajnef, N., Chatti, K., Faridazar, F., (2016) Aut Construct, 62, p. 24; Hasni, H., Alavi, A.H., Jiao, P., Lajnef, N., (2017) Archi. Civil Mech. Eng., 17 (3), p. 609; Borchani, W., Aono, K., Lajnef, N., Chakrabartty, S., (2016) IEEE Biomed. Eng., 63 (7), p. 1463; Lajnef, N., Rhimi, M., Chatti, K., Mhamdi, L., (2011) Computer-Aided Civil Infrastruct. Eng., 26, p. 513; Elvin, N., Leung, C.K.Y., (1999) Eng. Fract. Mech., 6 (5), p. 631; Rosenstrauch, P.L., Sanayei, M., Brenner, B.R., (2013) Eng. Struct., 48, p. 543","Hasni, H.; Department of Civil and Environmental Engineering, United States; email: hasniha1@msu.edu",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-85043391372
"Mongelli M., De Canio G., Roselli I., Malena M., Nacuzi A., De Felice G.","7005882343;36100105000;6507903563;23501781800;57195313212;57213360105;","3D photogrammetric reconstruction by drone scanning for FE analysis and crack pattern mapping of the ""bridge of the Towers"", Spoleto",2017,"Key Engineering Materials","747 KEM",,,"423","430",,10,"10.4028/www.scientific.net/KEM.747.423","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027019440&doi=10.4028%2fwww.scientific.net%2fKEM.747.423&partnerID=40&md5=441a79378880a769bbf3b5984dc75e42","Enea, Casaccia Research Center, Via Anguillarese 301, Rome, 00123, Italy; Department of Engineering, Roma Tre University, Via Vito Volterra 62, Rome, 00146, Italy","Mongelli, M., Enea, Casaccia Research Center, Via Anguillarese 301, Rome, 00123, Italy; De Canio, G., Enea, Casaccia Research Center, Via Anguillarese 301, Rome, 00123, Italy; Roselli, I., Enea, Casaccia Research Center, Via Anguillarese 301, Rome, 00123, Italy; Malena, M., Department of Engineering, Roma Tre University, Via Vito Volterra 62, Rome, 00146, Italy; Nacuzi, A., Department of Engineering, Roma Tre University, Via Vito Volterra 62, Rome, 00146, Italy; De Felice, G., Department of Engineering, Roma Tre University, Via Vito Volterra 62, Rome, 00146, Italy","Technological advances in the digital camera industry and computing resources make the use of photogrammetry a very fast, low-cost, contactless and non-destructive technique. It can represent a good alternative to obtain 3D information for monitoring and conservation of cultural heritage assets, especially where it is not possible to use 3D laser scanners and also in situations where areas to be inspected are not easily accessible [1]. Resolution generally depends on the number of images, their quality and the level of overlap between them, as well as hardware and software capabilities. Starting from 2D aerial or terrestrial photographic images, photogrammetry allows to reconstruct a 3D model in the form of a ""point cloud"" and also to derive accurate 3D measurements of large architectural elements. This paper is about stereo-photogrammetric scanning by drone performed by MENCI software s.r.l. aimed at the definition of the state of conservation of the ""Bridge of the Towers"" in Spoleto and its long term preservation without building scaffoldings. It was performed within the RoMA (Resilience enhancement of a Metropolitan Area) project, through an agreement between the ""Italian National Agency for New Technologies, Energy and Sustainable Economic Development"" (ENEA) and the ""Italian Ministry of Cultural Heritage and Activities"" (MIBACT). Photogrammetric scanning and FE modelling were applied within the project together with many other monitoring techniques in order to assess the bridge cracks pattern and its structural health by a multidisciplinary approach that allows their mutual validation [2]. As one of the most important problems in the use of photogrammetric 3D reconstruction is the considerable demand in terms of hardware and software resources for images processing and data storage, thanks to the HPC (High Performance Computing) resources provided by the CRESCO infrastructure (Research Computational Centre on Complex Systems), it was possible to analyse and process a large amount of high-resolution photos in order to detect the crack pattern and to assess the actual damage state to be monitored over time [3]. © 2017 Trans Tech Publications.","3D reconstruction; Cultural heritage; FE analysis; Photogrammetry; SfM","3D modeling; Antennas; Costs; Cracks; Data handling; Digital storage; Drones; Finite element method; Glass ceramics; Historic preservation; Masonry materials; Nondestructive examination; Photogrammetry; Photography; Scanning; Stereo image processing; Structural health monitoring; Three dimensional computer graphics; 3D reconstruction; Conservation of cultural heritages; Cultural heritages; FE analysis; High performance computing; Multi-disciplinary approach; Non-destructive technique; Sustainable economic development; Image reconstruction",,,,,,,,,,,,,,,,"Arias, P., Herraez, J., Lorenzo, H., Ordonez, C., Control of structural problems in cultural heritage monuments using close-range photogrammetry and computer methods (2005) Computers and Structures, 83, pp. 1754-2176; De Canio, G., Roselli, I., Giocoli, A., Mongelli, M., Tatì, A., Pollino, M., Martini, S., Borfecchia, F., Seismic monitoring of the cathedral of orvieto: Combining satellite InSAR with in-situ techniques (2015) Proceedings of SHMII-7, , Turin, Italy; Ponti, G., The role of medium size facilities in the HPC ecosystem: The case of the new CRESCO4 cluster integrated in the ENEAGRID infrastructure (2014) Proceedings of the International Conference on High Performance Computing and Simulation (HPCS), , paper 6903807; De Canio, G., Mongelli, M., Roselli, I., Tatì, A., Addessi, D., Nocera, M., Liberatore, D., Numerical and operational modal analyses of the ""Ponte delle torri"", Spoleto, Italy Proceedings of 10th SAHC, , Leuven, Belgium; Gioffrè, M., Gusella, V., Cluni, F., Performance evaluation of monumental bridges: Testing and monitoring 'Ponte delle torri' in spoleto (2007) Structure and Infrastructure Engineering (2008), 4 (2), pp. 95-106. , Maintenance, Management, Life-Cycl August (12); Araiza Garaygordobil, J.C., Dynamic identification and model updating of historical buildings. State-of-the-art review (2004) Proceedings of 4th International Seminar on Structural Analysis of Historical Constructions, p. 499. , 10-13 November Padua, Italy; Brincker, R., De Stefano, A., Piombo, B., Ambient data to analyse the dynamic behaviour of bridges: A first comparison between different techniques (1996) Proceedings of 14th International Modal Analysis Conference (IMAC), pp. 477-482. , 12-15 February Dearborn, Michigan","Mongelli, M.; Enea, Via Anguillarese 301, Italy; email: marialuisa.mongelli@enea.it","Di Tommaso A.Gentilini C.Castellazzi G.",,"Trans Tech Publications Ltd","International Conference on Mechanics of Masonry Structures Strengthened with Composites Materials, MuRiCo5 2017","28 June 2017 through 30 June 2017",,195699,10139826,9783035711646,KEMAE,,"English","Key Eng Mat",Conference Paper,"Final","",Scopus,2-s2.0-85027019440
"Nguyen T.Q.","56937120700;","A Data-Driven Approach to Structural Health Monitoring of Bridge Structures Based on the Discrete Model and FFT-Deep Learning",2021,"Journal of Vibration Engineering and Technologies","9","8",,"1959","1981",,9,"10.1007/s42417-021-00343-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112865867&doi=10.1007%2fs42417-021-00343-5&partnerID=40&md5=8c5e6d7479afbe8621d9766954dfffa8","Faculty of Engineering and Technology, Thu Dau Mot University, Binh Duong, Thủ Dầu Một, Viet Nam; Thu Dau Mot University, Binh Duong, Thủ Dầu Một, Viet Nam","Nguyen, T.Q., Faculty of Engineering and Technology, Thu Dau Mot University, Binh Duong, Thủ Dầu Một, Viet Nam, Thu Dau Mot University, Binh Duong, Thủ Dầu Một, Viet Nam","In this paper, we investigate changes in the mechanical properties of complex structures using a combination of the discrete model, Fast Fourier Transform (FFT) analysis and deep learning. The first idea from this research utilizes the discrete model from a perspective that is different from the finite element method (FEM) of previous works. As the method in this paper only models the mechanical properties of structures with finite degrees of freedom instead of dividing them into smaller elements, it reduces error in evaluation and produces more realistic results compared to the FEM model. Another advantage is how it allows the research to survey both parameters that affect the mechanical properties of structures—the overall stiffness (K) and the damping coefficient (c)—during vibration, while previous researches focus only on one of these two parameters. The second idea is to use FFT analysis to increase the sensitivity of the signal received during vibration. FFT analysis simplifies calculations, thereby reducing the effect of noise or errors. The sensitivity achieved in FFT analysis increases by 25% compared to traditional Fourier Transform (FT) analysis; moreover, the error in FFT analysis compared to experimental results is quite small, less than 2%. This shows that FFT is a suitable method to identify sensitive characteristics in evaluating changes in the mechanical properties. When FFT is combined with the discrete model, results are much better than those of several existing approaches. For the last idea, the manuscript applies deep learning (FFT-deep learning) in the noise reduction process for the original data. This makes the results much more accurate than in previous studies. The results of this research are shown through the monitoring of spans of the Saigon Bridge—the biggest and most important bridge in Ho Chi Minh City, Vietnam—during the past 11 years. The correspondence between the theoretically obtained result and the experimental one at the Saigon Bridge suggests a new area for development in evaluating and forecasting structural changes in the future. © 2021, Krishtel eMaging Solutions Private Limited.","Deep learning; Fast Fourier Transform (FFT); Fourier Transform (FT); Structural health monitoring; Structured discrete models",,,,,,,,,,,,,,,,,"Bhardwaj, G., Singh, I.V., Mishra, B.K., Stochastic fatigue crack growth simulation of interfacial crack in bi-layered FGMs using XIGA (2015) Comput Methods Appl Mech Eng, 84, pp. 186-229; Kunin, B., Stochastic model of brittle crack growth under cyclic load (2013) Int J Pure Appl Math, 84, pp. 163-174; Yu, H., Wu, L., Guo, L., Wu, H., Du, S., An interaction integral method for 3D curved cracks in nonhomogeneous materials with complex (2010) Int J Solids Struct, 47, pp. 2178-2189; Price, R.J., Trevelyan, J., Boundary element simulation of fatigue crack growth in multi-site damage (2014) Eng Anal Boundary Elem, 43, pp. 67-75; Bittencourt, T., Wawrzynek, P., Ingraffea, A., Sousa, J., Quasi-automatic simulation of crack propagation for 2D LEFM problems (1996) Eng Fract Mech, 55, pp. 321-334; Moës, N., Dolbow, J., Belytschko, T., A finite element method for crack growth without re-meshing (1999) Int J Numer Methods Eng, 46, pp. 131-150; Sunde, S.L., Berto, F., Haugen, B., Predicting fretting fatigue in engineering design (2018) Int J Fatigue, 117, pp. 314-326; Vazquez, J., Carpinteri, A., Bohorquez, L., Vantadori, S., Fretting fatigue investigation on Al 7075–T651 alloy: experimental, analytical and numerical analysis (2019) Tribol Int, 135, pp. 478-487; Aeran, A., Vantadori, S., Carpinteri, A., Siriwardane, S., Scorza, D., Novel non-linear relationship to evaluate the critical plane orientation (2019) Int J Fatigue, 124, pp. 537-543; Bhatti, N.A., Wahab, M.A., Finite element analysis of fretting fatigue under out of phase loading conditions (2017) Tribol Int, 109, pp. 552-562; Bhatti, N.A., Wahab, M.A., Fretting fatigue crack nucleation: a review (2018) Tribol Int, 121, pp. 121-138; Hojjati-Talemi, R., Wahab, M.A., De Pauw, J., De Baets, P., Prediction of fretting fatigue crack initiation and propagation lifetime for cylindrical contact configuration (2014) Tribol Int, 76, pp. 73-91; Nguyen, T.Q., Nguyen, H.B., Detecting and evaluating defects in beams by correlation coefficients (2021) Shock Vib, 2021; Nguyen, T.Q., Tran, L.Q., Nguyen-Xuan, H., Ngo, N.K., A statistical approach for evaluating crack defects in structures under dynamic responses (2021) Nondestruct. Test Eval, 36 (2), pp. 113-144; Ngo, N.K., Nguyen, T.Q., Vu, T.V., Nguyen-Xuan, H., An fast Fourier transform–based correlation coefficient approach for structural damage diagnosis (2020) Struct Health Monit; Nguyen, T.Q., Nguyen, T.T.D., Nguyen-Xuan, H., Ngo, N.K., A correlation coefficient approach for evaluation of stiffness degradation of beams under moving load (2019) Comput Mater Continua, 61 (1), pp. 27-53; Kolekar, S., Venkatesh, K., Oh, J.S., Vibration controllability of sandwich structures with smart materials of electrorheological fluids and magnetorheological materials: a review (2019) J Vib Eng Technol, 7, pp. 359-377; Khiem, N.T., Lien, T.V., A simplified method for natural frequency analysis of a multiple cracked beam (2001) J Sound Vib, 245 (4), pp. 737-751; Ebrahimi, A., Heydari, M., Behzad, M., Optimal vibration control of rotors with an open edge crack using an electromagnetic actuator (2018) J Vib Control, 14 (1), pp. 37-59; Zhang, Q.W., Statistical damage identification for bridges using ambient vibration data (2007) Comput Struct, 85 (7-8), pp. 476-485; Li, Z.X., Chan, H.T., Zheng, T.R., Statistical analysis of online strain response and its application in fatigue assessment of a long-span steel bridge (2003) Eng Struct, 25 (14), pp. 1731-1741; Ayaho, M., Kei, K., Hideaki, N., Bridge management system and maintenance optimization for existing bridges (2000) Comput-Aided Civil Infrastruct Eng, 15 (3), pp. 45-55; Furuta, H., He, J., Watanabe, E., A fuzzy expert system for damage assessment using genetic algorithms and neural networks (1996) Microcomput Civil Eng, 11 (1), pp. 37-45; Mares, C., Surace, C., Application of genetic algorithms to identify damage in elastic structures (1996) J Sound Vib, 195 (5), pp. 195-215; Sharma, V., Raghuwanshi, N.K., Jain, A.K., Sensitive sub-band selection criteria for empirical wavelet transform to detect bearing fault based on vibration signals (2021) J Vib Eng Technol; Yun, C.B., Bahng, E.Y., Structural identification using neural networks (2000) Comput Struct, 77 (1), pp. 41-52; Chang, C.C., Chang, T.Y., Xu, Y.G., Structural damage detection using an iterative neural network (2000) J Intell Mater Syst Struct, 11 (1), pp. 32-42; Huang, C.S., Hung, S.L., Wen, C.M., Tu, T.T., A neural network approach for structural identification and diagnoses of a building from seismic response data (2003) Earthquake Eng Struct Dynam, 32 (2), pp. 187-206; Zhu, X.Q., Law, S.S., Wavelet-based crack identification of bridge beam from operational deflection time history (2006) Int J Solids Struct, 43 (7-8), pp. 2299-2317; Douka, E., Loutridis, S., Trochidis, A., Crack identification in beams using wavelet analysis (2003) Int J Solids Struct, 40 (13-14), pp. 3557-3569; Anantha Ramu, S., Johnson, V.T., Damage assessment of composite structures: a fuzzy logic integrated neural network approach (1995) Comput Struct, 57 (3), pp. 491-502; Ambhore, N., Kamble, D., Chinchanikar, S., Evaluation of cutting tool vibration and surface roughness in hard turning of AISI 52100 steel: an experimental and ANN approach (2020) J Vib Eng Technol, 8, pp. 455-462; Malla, C., Panigrahi, I., Review of condition monitoring of rolling element bearing using vibration analysis and other techniques (2019) J Vib Eng Technol, 7, pp. 407-414; Nguyen, T.Q., Vuong, L.C., Le, C.M., Ngo, N.K., Nguyen-Xuan, H., A data-driven approach based on wavelet analysis and deep learning for identification of multiple-cracked beam structures under moving load (2020) Meas J Int Meas Confed, 162; Heydari, M., Ebrahimi, A., Behzad, M., Forced vibration analysis of a Timoshenko cracked beam using a continuous model for the crack (2014) Eng Sci Technol Int J, 17, pp. 194-204; Nguyen, S.D., Ngo, K.N., Tran, Q.T., Choi, S.B., A new method for beam-damage-diagnosis using adaptive fuzzy neural structure and wavelet analysis (2013) Mech Syst Signal Process, 39 (1-2), pp. 181-194","Nguyen, T.Q.; Faculty of Engineering and Technology, Binh Duong, Viet Nam; email: nguyenquangthanh@tdmu.edu.vn",,,"Springer",,,,,25233920,,,,"English","J. Vib. Eng. Technol.",Article,"Final","",Scopus,2-s2.0-85112865867
"Buitrago M., Bertolesi E., Calderón P.A., Adam J.M.","56708321400;56395484400;16744940500;56216939800;","Robustness of steel truss bridges: Laboratory testing of a full-scale 21-metre bridge span",2021,"Structures","29",,,"691","700",,9,"10.1016/j.istruc.2020.12.005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097530655&doi=10.1016%2fj.istruc.2020.12.005&partnerID=40&md5=f2fd02cbe6f896874c640c2cad46cb94","ICITECH, Universitat Politècnica de València. Camino de Vera s/n, Valencia, 46022, Spain","Buitrago, M., ICITECH, Universitat Politècnica de València. Camino de Vera s/n, Valencia, 46022, Spain; Bertolesi, E., ICITECH, Universitat Politècnica de València. Camino de Vera s/n, Valencia, 46022, Spain; Calderón, P.A., ICITECH, Universitat Politècnica de València. Camino de Vera s/n, Valencia, 46022, Spain; Adam, J.M., ICITECH, Universitat Politècnica de València. Camino de Vera s/n, Valencia, 46022, Spain","This study aimed to experimentally analyse the robustness of riveted steel bridges based on truss-type structures and to define practical recommendations for early detection of local failures before they cause progressive structural collapse. Although there are many experimental studies on robustness and progressive collapse on buildings, those on bridges are either theoretical or deal with actual collapses. This paper describes a unique case of a 21 m full-scale bridge span tested under laboratory conditions with an extensive monitoring system, together with an experimental study to evaluate structural behaviour and robustness as damage or failure progressed in its elements. A linear-static finite-element analysis was also included to examine other possible causes not included in the experiment. The results proved the structural redundancy of this type of truss structure based on the joints’ resistance to bending moments and gave rise to a series of practical structural health recommendations to identify early failures and avoid progressive or sudden bridge collapse. The study carried out and the recommendations it produced are now being applied in three similar bridge case studies. © 2020 Institution of Structural Engineers","Experimental test; Progressive collapse; Riveted joints; Robustness; Steel truss bridges; Structural health monitoring",,,,,,,"We would like to express our gratitude to the FGV (Ferrocarrils de la Generalitat Valenciana) and FCC Construcción S.A. CHM Obras e Infraestructuras S.A. Contratas y Ventas S.A. and CALSENS S.L. for giving us the opportunity to test a bridge at the ICITECH facilities, also to Juan Antonio García Cerezo, of FGV, for his invaluable cooperation and recommendations. We also wish to show our gratitude for the magnificent work on the bridge by Jesús Martínez, Eduardo Luengo and Daniel Tasquer. The tests on the bridge meant that much of the Structures Laboratory was out of service for other work, for which we owe a debt of gratitude to our ICITECH colleagues for their infinite patience and understanding.",,,,,,,,,,"Ghali, A., Tadros, G., Bridge progressive collapse vulnerability (1997) J Struct Eng, 123 (2), pp. 227-231; Cha, E.J., Ellingwood, B.R., Risk-averse decision-making for civil infrastructure exposed to low-probability, high-consequence events (2012) Reliab Eng Syst Saf, 104, pp. 27-35; Apostolopoulos, C., Colajanni, P., Recupero, A., Ricciardi, G., Spinella, N., Failure by corrosion in PC bridges: a case history of a viaduct in Italy (2016) Int J Struct Integrity, 7 (2), pp. 181-193; Zhuang, M., Miao, C., Fatigue reliability assessment for hangers of a special-shaped CFST arch bridge (2020) Structures, 28, pp. 235-250; Starossek, U., Avoiding disproportionate collapse of major bridges (2009) Struct Eng Int, 19 (3), pp. 289-297; Russell, J.M., Sagaseta, J., Cormie, D., Jones, A.E.K., Historical review of prescriptive design rules for robustness after the collapse of Ronan Point (2019) Structures, 20, pp. 365-373; Bontempi, F., Elementary concepts of structural robustness of bridges and viaducts (2019) J Civil Struct Health Monit, 9 (5), pp. 703-717; Deng, L.U., Wang, W., Yu, Y., State-of-the-art review on the causes and mechanisms of bridge collapse (2016) J Perform Constr Facil, 30 (2), p. 04015005; Bi, K., Ren, W.-X., Cheng, P.-F., Hao, H., Domino-type progressive collapse analysis of a multi-span simply-supported bridge: a case study (2015) Eng Struct, 90, pp. 172-182; Rania, N., Coppola, I., Martorana, F., Migliorini, L., The Collapse of the Morandi Bridge in Genoa on 14 August 2018: a collective traumatic event and its emotional impact linked to the place and loss of a symbol (2019) Sustainability, 11, p. 6822; Buitrago, M., Sagaseta, J., Adam, J.M., Avoiding failures during building construction using structural fuses as load limiters on temporary shoring structures (2020) Eng Struct, 204, p. 109906; Adam, J.M., Parisi, F., Sagaseta, J., Lu, X., Research and practice on progressive collapse and robustness of building structures in the 21st century (2018) Eng Struct, 173, pp. 122-149; Adam, J.M., Buitrago, M., Bertolesi, E., Sagaseta, J., Moragues, J.J., Dynamic performance of a real-scale reinforced concrete building test under corner-column failure scenario (2020) Eng Struct, 210; Alshaikh, I.M.H., Bakar, B.H.A., Alwesabi, E.A.H., Akil, H.M., Experimental investigation of the progressive collapse of reinforced concrete structures: an overview (2020) Structures, 25, pp. 881-900; Fu, Q., Tan, K.-H., Numerical study on steel-concrete composite floor systems under corner column removal scenario (2019) Structures, 21, pp. 33-44; Mucedero, G., Brunesi, E., Parisi, F., Nonlinear material modelling for fibre-based progressive collapse analysis of RC framed buildings (2020) Eng Fail Anal, 118, p. 104901; Bao, Y., Main, J.A., Noh, S.-Y., Evaluation of structural robustness against column loss: methodology and application to RC frame buildings (2017) J Struct Eng, 143 (8), p. 04017066; Eren, N., Brunesi, E., Nascimbene, R., Influence of masonry infills on the progressive collapse resistance of reinforced concrete framed buildings (2019) Eng Struct, 178, pp. 375-394; Wang, M.R., Zhou, Z.J., Progressive collapse and structural robustness of bridges (2012) Appl Mech Mater, 193-194, pp. 1021-1024; Jiang, H., Wang, J., Chorzepa, M.G., Zhao, J., Numerical investigation of progressive collapse of a multispan continuous bridge subjected to vessel collision (2017) J Bridge Eng, 22 (5), p. 04017008; Miyachi, K., Nakamura, S., Manda, A., Progressive collapse analysis of steel truss bridges and evaluation of ductility (2012) J Constr Steel Res, 78, pp. 192-200; Garavaglia, E., Sgambi, L., Basso, N., Selective maintenance strategies applied to a bridge deteriorating steel truss (2012) Bridg Maintenance, Safety, Manag. Resil. Sustain. - Proc. Sixth Int. Conf. Bridg. Maintenance, Saf. Manag., pp. 1764-1770; Khuyen, H.T., Iwasaki, E., An approximate method of dynamic amplification factor for alternate load path in redundancy and progressive collapse linear static analysis for steel truss bridges (2016) Case Stud Struct Eng, 6, pp. 53-62; Olmati, P., Brando, F., Gkoumas, K., (2013) Robustness assessment of a steel truss bridge. Struct. Congr., pp. 250-261. , American Society of Civil Engineers Reston, VA; Trong Khuyen, H., Eiji, I., Linear redundancy analysis method considering plastic region for steel truss bridges (2017) J Bridge Eng, 22 (3), p. 05016011; Garavaglia, E., Sgambi, L., Selective maintenance planning of a steel truss bridge based on the Markovian approach (2016) Eng Struct, 125, pp. 532-545; Ma, X., Han, B., Analysis and mitigation of progressive collapse for steel truss girdersAnalysis and mitigation of progressive collapse for steel truss girders (2011) Second Int. Conf. Mech. Autom. Control Eng IEEE, pp. 1931-1934; Olmati, P., Gkoumas, K., Brando, F., Cao, L., Consequence-based robustness assessment of a steel truss bridge (2013) Steel Compos Struct, 14 (4), pp. 379-395; Azizinamini, A., Full scale testing of old steel truss bridge (2002) J Constr Steel Res, 58 (5-8), pp. 843-858; Sagaseta, J., Olmati, P., Micallef, K., Cormie, D., Punching shear failure in blast-loaded RC slabs and panels (2017) Eng Struct, 147, pp. 177-194; (2016), ABAQUS v16.4. Abaqus, Theory manual","Adam, J.M.; ICITECH, Spain; email: joadmar@upv.es",,,"Elsevier Ltd",,,,,23520124,,,,"English","Structures",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85097530655
"Fang C., Tang H., Phd, Li Y.","57192909272;55602376400;36067034900;","Stochastic Response Assessment of Cross-Sea Bridges under Correlated Wind and Waves via Machine Learning",2020,"Journal of Bridge Engineering","25","6","04020025","","",,9,"10.1061/(ASCE)BE.1943-5592.0001554","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083172794&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001554&partnerID=40&md5=ff99e58d46a5da115337adde8d0ed2a3","Dept. of Bridge Engineering, Southwest Jiaotong Univ., Chengdu, 610031, China","Fang, C., Dept. of Bridge Engineering, Southwest Jiaotong Univ., Chengdu, 610031, China; Tang, H., Phd, Dept. of Bridge Engineering, Southwest Jiaotong Univ., Chengdu, 610031, China; Li, Y., Dept. of Bridge Engineering, Southwest Jiaotong Univ., Chengdu, 610031, China","The stochastic response of cross-sea bridges is susceptible to the significant effects of wind and waves. In this study, an efficient probabilistic assessment framework for cross-sea bridges was developed by combining a wind-wave bridge (WWB) model with machine learning methods. The WWB model was first proposed based on finite element analysis (FEA) where the wind and wave parameters were obtained by structural health monitoring (SHM) and then correlated using copula models. The coupling effects in the wind-bridge and the wave-bridge were solved using the Newmark-β method. Taking a cable-stayed bridge as an example to illustrate the accuracy and efficiency of the proposed method, the WWB model was established and then performed to compute the dynamic response at different positions on the bridge. To deal with the time-consuming issues, a learning machine including support vector regression (SVR) and Latin hypercube sampling (LHS) was implemented to substitute further finite element calculations. The WWB model was simplified parametrically as response surfaces for stochastic wind and wave variables, and probabilistic simulations with a large number of samples were performed. The results show that the wind load controlled the displacement response of the girder, while the wave load dominated the base shear response of the foundation. The bridge response, considering when wind and waves were correlated, was 6%-25% lower than that when wind and waves were independent. Further response contour analysis demonstrated a direct relationship between the environmental parameters and the structural response to quickly estimate the bridge's maximum response in different return periods. © 2020 American Society of Civil Engineers.","Copula model; Cross-sea bridge; Machine learning; Stochastic response; Wind-wave bridge model","Cable stayed bridges; Machine learning; Stochastic models; Stochastic systems; Structural health monitoring; Support vector regression; Displacement response; Environmental parameter; Latin hypercube sampling; Machine learning methods; Probabilistic assessments; Probabilistic simulation; Structural health monitoring (SHM); Support vector regression (SVR); Shear flow",,,,,"National Natural Science Foundation of China, NSFC: 51525804","This work was supported financially by the National Natural Science Foundation of China (Grants No. 51525804).",,,,,,,,,,"An, Y., Pandey, M.D., The r largest order statistics model for extreme wind speed estimation (2007) J. 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Div., 88 (3), pp. 233-270; Dupuis, D.J., Using copulas in hydrology: Benefits, cautions, and issues (2007) J. Hydrol. Eng., 12 (4), pp. 381-393. , https://doi.org/10.1061/(ASCE)1084-0699(2007)12:4(381); Durante, F., Kolesárová, A., Mesiar, R., Sempi, C., Copulas with given diagonal sections: Novel constructions and applications (2007) Int. J. Uncertainty Fuzziness Knowledge Based Syst., 15 (4), pp. 397-410. , https://doi.org/10.1142/S0218488507004753; Durante, F., Sempi, C., (2015) Principles of Copula Theory, , London: Chapman and Hall/CRC; Ewans, K., Jonathan, P., Evaluating environmental joint extremes for the offshore industry using the conditional extremes model (2014) J. Mar. Syst., 130, pp. 124-130. , https://doi.org/10.1016/j.jmarsys.2013.03.007; Fang, C., Li, Y., Wei, K., Zhang, J., Liang, C., Vehicle-bridge coupling dynamic response of sea-crossing railway bridge under correlated wind and wave conditions (2018) Adv. Struct. Eng., 22 (4), pp. 893-906; Goda, Y., Suzuki, Y., (1975) Computation of Refraction and Diffraction of Sea Waves with Mitsuyasu's Directional Spectrum, , Technical Note No. 155. Yokosuka, Japan: Port and Harbour Research Institute; Guo, A., Liu, J., Chen, W., Bai, X., Liu, G., Liu, T., Chen, S., Li, H., Experimental study on the dynamic responses of a freestanding bridge tower subjected to coupled actions of wind and wave loads (2016) J. Wind Eng. Ind. Aerodyn., 159, pp. 36-47. , https://doi.org/10.1016/j.jweia.2016.10.003; Hristov, T.S., Miller, S.D., Friehe, C.A., Dynamical coupling of wind and ocean waves through wave-induced air flow (2003) Nature, 422 (6927), pp. 55-58. , https://doi.org/10.1038/nature01382; Lambert, P., Vandenhende, F., A copula-based model for multivariate non-normal longitudinal data: Analysis of a dose titration safety study on a new antidepressant (2002) Stat. Med., 21 (21), pp. 3197-3217. , https://doi.org/10.1002/sim.1249; Li, Y., Qiang, S., Liao, H., Xu, Y.L., Dynamics of wind-rail vehicle-bridge systems (2005) J. Wind Eng. Ind. Aerodyn., 93 (6), pp. 483-507. , https://doi.org/10.1016/j.jweia.2005.04.001; Lin, K.-P., Pai, P.-F., Lu, Y.-M., Chang, P.-T., Revenue forecasting using a least-squares support vector regression model in a fuzzy environment (2013) Inf. Sci., 220, pp. 196-209. , https://doi.org/10.1016/j.ins.2011.09.003; Liu, S., Li, Y., Li, G., Wave current forces on the pile group of base foundation for the East Sea Bridge, China (2007) J. Hydrodyn., Ser. B, 19 (6), pp. 661-670. , https://doi.org/10.1016/S1001-6058(08)60001-3; Liu, G., Liu, T., Guo, A., Chen, S., Bai, X., Dynamic elastic response testing method of bridge structure under wind-wave-current action (2015) Proc. 25th Int. Ocean and Polar Engineering Conf., pp. 523-530. , Mountain View, CA: International Society of Offshore and Polar Engineers; Meng, S., Ding, Y., Zhu, H., Stochastic response of a coastal cable-stayed bridge subjected to correlated wind and waves (2018) J. Bridge Eng., 23 (12), p. 04018091. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0001308; Nelsen, R., (2006) An Introduction to Copulas, , Berlin: Springer Science & Business Media; Neves, A.C., González, I., Leander, J., Karoumi, R., Structural health monitoring of bridges: A model-free ANN-based approach to damage detection (2017) J. Civ. Struct. Health Monit., 7 (5), pp. 689-702. , https://doi.org/10.1007/s13349-017-0252-5; Schoelzel, C., Friederichs, P., Multivariate non-normally distributed random variables in climate research - Introduction to the copula approach (2008) Nonlinear Processes Geophys., 15 (5), pp. 761-772; Shafaei, M., Kisi, O., Lake level forecasting using wavelet-SVR, wavelet-ANFIS and wavelet-ARMA conjunction models (2016) Water Resour. Manage., 30 (1), pp. 79-97. , https://doi.org/10.1007/s11269-015-1147-z; Siringoringo, D.M., Fujino, Y., Observed along-wind vibration of a suspension bridge tower (2012) J. Wind Eng. Ind. Aerodyn., 103, pp. 107-121. , https://doi.org/10.1016/j.jweia.2012.03.007; Subbaraj, K., Dokainish, M.A., A survey of direct time-integration methods in computational structural dynamics - II. Implicit methods (1989) Comput. Struct., 32 (6), pp. 1387-1401. , https://doi.org/10.1016/0045-7949(89)90315-5; Ti, Z., Wei, K., Qin, S., Li, Y., Mei, D., Numerical simulation of wave conditions in nearshore island area for sea-crossing bridge using spectral wave model (2017) Adv. Struct. Eng., 21 (5), pp. 756-768; Valamanesh, V., Myers, A.T., Arwade, S.R., Multivariate analysis of extreme metocean conditions for offshore wind turbines (2015) Struct. Saf., 55, pp. 60-69. , https://doi.org/10.1016/j.strusafe.2015.03.002; Yang, X., Zhang, Q., Joint probability distribution of winds and waves from wave simulation of 20 years (1989-2008) in Bohai Bay (2013) Water Sci. Eng., 6 (3), pp. 296-307; Zaheer, M.M., Islam, N., Dynamic response of articulated towers under correlated wind and waves (2017) Ocean Eng., 132, pp. 114-125. , https://doi.org/10.1016/j.oceaneng.2017.01.019; Zhai, J., Yin, Q., Dong, S., Metocean design parameter estimation for fixed platform based on copula functions (2017) J. Ocean Univ. China, 16 (4), pp. 635-648. , https://doi.org/10.1007/s11802-017-3327-3; Zhu, J., Zhang, W., Wu, M.X., Coupled dynamic analysis of the vehicle-bridge-wind-wave system (2018) J. Bridge Eng., 23 (8), p. 04018054. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0001268","Tang, H.; Dept. of Bridge Engineering, China; email: thj@swjtu.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85083172794
"Cocking S., Acikgoz S., Dejong M.","57202579978;55126184000;16645691200;","Interpretation of the Dynamic Response of a Masonry Arch Rail Viaduct Using Finite-Element Modeling",2020,"Journal of Architectural Engineering","26","1","05019008","","",,9,"10.1061/(ASCE)AE.1943-5568.0000369","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076116971&doi=10.1061%2f%28ASCE%29AE.1943-5568.0000369&partnerID=40&md5=6c8cc893f13fb8cbf1672301680a61e2","Dept. of Engineering, Univ. of Cambridge, Civil Engineering Bldg., JJ Thompson Ave. 7a, Cambridge, CB3 0FA, United Kingdom; Dept. of Engineering Science, Univ. of Oxford, Parks Rd., Oxford, OX1 3PJ, United Kingdom; Dept. of Civil Engineering, Univ. of California, 760 Davis Hall, Berkeley, CA  94720-1710, United States","Cocking, S., Dept. of Engineering, Univ. of Cambridge, Civil Engineering Bldg., JJ Thompson Ave. 7a, Cambridge, CB3 0FA, United Kingdom; Acikgoz, S., Dept. of Engineering Science, Univ. of Oxford, Parks Rd., Oxford, OX1 3PJ, United Kingdom; Dejong, M., Dept. of Civil Engineering, Univ. of California, 760 Davis Hall, Berkeley, CA  94720-1710, United States","Linear-elastic finite-element analysis is sometimes used to assess masonry arch bridges under service loads, despite the limitations of this method. Specifically, linear-elastic analysis can be sensitive to material properties, geometry, and support settlements, while also allowing the development of tensile stresses that may be unrealistic for masonry structures. However, even though linear-elastic methods remain appealing for their simplicity, it is rare to evaluate their output against experimental data. In this paper, detailed strain and displacement monitoring data for a masonry arch viaduct are used to evaluate a series of independently developed linear-elastic simulations of this structure. Although uncertainties in input parameters mean the magnitude of modeling results cannot be presumed accurate, the simulated response pattern was found to agree reasonably well with monitoring data in regions of low damage. However, more damaged regions produced a markedly different local response. Comparisons between the simulations revealed useful conclusions regarding common modeling assumptions, namely the importance of modeling backing material, spandrels, and foundation stiffness, to capture their influence on the arch response. © 2019 American Society of Civil Engineers.","Finite-element modeling; Masonry arch; Structural health monitoring","Arches; Masonry bridges; Masonry construction; Masonry materials; Monitoring; Structural health monitoring; Uncertainty analysis; Displacement monitoring; Foundation stiffness; Linear elastic analysis; Linear elastic finite element analysis; Masonry arch bridges; Masonry arches; Masonry structures; Simulated response; Finite element method",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/I019308/1, EP/K000314/1, EP/L010917/1, EP/N021614/1; EPSRC Centre for Doctoral Training in Medical Imaging","This work forms part of a PhD, which is funded through an EPSRC Doctoral Training Partnership (Grant No. EP/M506485/1). Data collection was made possible by the Cambridge Centre for Smart Infrastructure and Construction, through additional EPSRC funding (Grant No. EP/L010917/1). The authors would like to thank Melanie Banes and Giuseppe Narciso for their help in the early stages of this project. Additionally, they are grateful to Network Rail for providing access to the Marsh Lane viaduct and for their continued interest in this research.",,,,,,,,,,"Acikgoz, S., DeJong, M.J., Kechavarzi, C., Soga, K., Dynamic response of a damaged masonry rail viaduct: Measurement and interpretation (2018) Eng. Struct., 168 (AUG), pp. 544-558. , https://doi.org/10.1016/j.engstruct.2018.04.054, a. "" ""; Acikgoz, S., DeJong, M.J., Soga, K., Sensing dynamic displacements in masonry rail bridges using 2D digital image correlation (2018) Struct. Control Health Monit., 25 (8), p. 2187. , https://doi.org/10.1002/stc.2187; Armstrong, D.M., Sibbald, A., Forde, M.C., Integrity assessment of masonry arch bridges using the dynamic stiffness technique (1995) NDT E Int., 28 (6), pp. 367-375. , https://doi.org/10.1016/0963-8695(95)00047-X; Augenti, N., Acconcia, E., Parisi, F., (2012) MADA: MAsonry DAtabase, , http://www.reluis.it/index.php?option=com_mada&Itemid=160, Accessed August 1, 2017; Augusthus-Nelson, L., Swift, G., Smith, C., Gilbert, M., Melbourne, C., (2016) Behaviour of Backfilled Masonry Arch Bridges Subjected to Cyclic Loading, pp. 1039-1048. , In Proc. ARCH'16 Int. Conf. on Arch Bridges, Wrocław, Poland: Wrocław Univ. of Science and Technology; Boothby, T.E., Domalik, D.E., Dalal, V.A., Service load response of masonry arch bridges (1998) J. Struct. Eng., 124 (JAN), pp. 17-23. , https://doi.org/10.1061/(ASCE)0733-9445(1998)124:1(17); Brencich, A., Cassini, G., Pera, D., (2016) Load Bearing Structure of Masonry Bridges, pp. 767-774. , In Proc. ARCH'16 Int. Conf. on Arch Bridges, Wrocław, Poland: Wrocław Univ. of Science and Technology; Brencich, A., Morbiducci, R., Masonry arches: Historical rules and modern mechanics (2007) Int. J. Archit. Heritage, 1 (2), pp. 165-189. , https://doi.org/10.1080/15583050701312926; Costa, C., Arêde, A., Morais, M., Aníbal, A., Detailed FE and de modelling of stone masonry arch bridges for the assessment of load-carrying capacity (2015) Procedia Eng., 114, pp. 854-861. , https://doi.org/10.1016/j.proeng.2015.08.039; DeJong, M.J., Vibert, C., Seismic response of stone masonry spires: Computational and experimental modeling (2012) Eng. Struct., 40 (JUL), pp. 566-574. , https://doi.org/10.1016/j.engstruct.2012.03.001; Domede, N., Sellier, A., Stablon, T., Structural analysis of a multi-span railway masonry bridge combining in situ observations, laboratory tests and damage modelling (2013) Eng. Struct., 56 (NOV), pp. 837-849. , https://doi.org/10.1016/j.engstruct.2013.05.052; (1987) Italian Seismic Design Code (OPCM 3274/03 and Further Modifications), , Eucentre. "" Italian seismic design code (OPCM 3274/03 and further modifications): Structural masonry chapters."" In. Pavia, Italy: Eucentre; Fanning, P.J., Boothby, T.E., Three-dimensional modelling and full-scale testing of stone arch bridges (2001) Comput. Struct., 79 (2930), pp. 2645-2662. , https://doi.org/10.1016/S0045-7949(01)00109-2; Fanning, P.J., Boothby, T.E., Roberts, B.J., Longitudinal and transverse effects in masonry arch assessment (2001) Constr. Build. Mater., 15 (1), pp. 51-60. , https://doi.org/10.1016/S0950-0618(00)00069-6; Forgács, T., Sarhosis, V., Bagi, K., Minimum thickness of semi-circular skewed masonry arches (2017) Eng. Struct., 140 (JUN), pp. 317-336. , https://doi.org/10.1016/j.engstruct.2017.02.036; Gibbons, N., (2014) Modelling and Assessment of Masonry Arch Bridges, , Ph.D. thesis, Dept. of Philosophy, Univ. college Dublin; Gilbert, M., (2017) LimitState:RING-Masonry Arch Bridge Analysis Software|LimitState, , http://www.limitstate.com/ring, Accessed August 26, 2017; Harvey, B., (2017) Archie-M Homepage, , http://www.obvis.com/, Accessed August 26, 2017; Lemos, J.V., Discrete element modeling of masonry structures (2007) Int. J. Archit. Heritage, 1 (2), pp. 190-213. , https://doi.org/10.1080/15583050601176868; McInerney, J., DeJong, M.J., Discrete element modeling of groin vault displacement capacity (2014) Int. J. Archit. Heritage, 9 (8), pp. 1037-1049. , https://doi.org/10.1080/15583058.2014.923953; (2006) The Structural Assessment of Underbridges, , Network Rail. London: Network Rail; (2013) Bridge Detailed Examination Report for Marsh Lane Viaduct (HUL4/48), , Network Rail. York, UK: Network Rail; Wolf, J.P., (1994) Foundation Vibration Analysis Using Simple Physical Models, , 1st ed. Englewood Cliffs, NJ: PTR Prentice Hall; Ye, C., Acikgoz, S., Pendrigh, S., Riley, E., DeJong, M.J., Mapping deformations and inferring movements of masonry arch bridges using point cloud data (2018) Eng. Struct., 173 (OCT), pp. 530-545. , https://doi.org/10.1016/j.engstruct.2018.06.094; Zhang, Y., Macorini, L., Izzuddin, B.A., Numerical investigation of arches in brick-masonry bridges (2017) Struct. Infrastruct. Eng., 2479 (JUL), pp. 1-19. , https://doi.org/10.1080/15732479.2017.1324883","Cocking, S.; Dept. of Engineering, Civil Engineering Bldg., JJ Thompson Ave. 7a, United Kingdom; email: sc740@cam.ac.uk",,,"American Society of Civil Engineers (ASCE)",,,,,10760431,,JAEIE,,"English","J Archit Eng",Article,"Final","",Scopus,2-s2.0-85076116971
"Deng Y., Li A., Feng D.","55218285200;7403291516;55973702300;","Fatigue performance investigation for hangers of suspension bridges based on site-specific vehicle loads",2019,"Structural Health Monitoring","18","3",,"934","948",,9,"10.1177/1475921718786710","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050139691&doi=10.1177%2f1475921718786710&partnerID=40&md5=fd801f064ee30720eefe93af18d6a085","Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, China; Beijing Key Laboratory of Functional Materials for Building Structure and Environment Remediation, Beijing University of Civil Engineering and Architecture, Beijing, China; School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China; Weidlinger Transportation Practice, Thornton Tomasetti, Inc, New York, NY, United States","Deng, Y., Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, China, Beijing Key Laboratory of Functional Materials for Building Structure and Environment Remediation, Beijing University of Civil Engineering and Architecture, Beijing, China, School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China; Li, A., Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, China, Beijing Key Laboratory of Functional Materials for Building Structure and Environment Remediation, Beijing University of Civil Engineering and Architecture, Beijing, China, School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China; Feng, D., Weidlinger Transportation Practice, Thornton Tomasetti, Inc, New York, NY, United States","Hangers or suspenders of a suspension bridge are the primary load-carrying members and are vital to the structural integrity and service life of the bridge. Site-specific vehicle loads monitored by the weigh-in-motion system can assist to obtain the operational cyclic stresses of hangers. Differing from most existing studies, herein, a framework for fatigue performance investigation for hangers of suspension bridges is proposed utilizing the full information of the weigh-in-motion data. This framework includes four steps: (1) generate influence surfaces for hangers, (2) reconstruct vehicular loading flows based on the weigh-in-motion data, (3) calculate time histories of hanger tension forces, and (4) evaluate fatigue damages and predict fatigue lives. Critical issues, such as the loading configuration of trucks, the threshold of the gross vehicle weight, and the time step for stress calculation, have been studied and discussed in detail. Based on 8-month weigh-in-motion data of a prototype suspension bridge, it is shown that the fatigue damage of hangers can be evaluated day by day, and subsequently the fatigue lives can be predicted. The correlation between the fatigue damages and vehicular loads is also investigated in this study. © The Author(s) 2018.","fatigue damage; finite element analysis; hanger; influence surface; structural health monitoring; Suspension bridge; weigh-in-motion","Automobile suspensions; Fatigue damage; Finite element method; Loads (forces); Stress analysis; Structural health monitoring; Suspension bridges; Suspensions (components); Weigh-in-motion (WIM); Fatigue performance; Gross vehicle weight; hanger; Influence surfaces; Loading configuration; Stress calculations; Weigh-in-motion datum; Weigh-in-motion systems; Fatigue of materials",,,,,"National Natural Science Foundation of China, NSFC: 51308073, 51438002; Beijing University of Civil Engineering and Architecture, BUCEA: X18004","This work was supported by the National Natural Science Foundation of China (51438002 and 51308073) and the Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (X18004).",,,,,,,,,,"Li, S., Zhu, S., Xu, Y.L., Long-term condition assessment of suspenders under traffic loads based on structural monitoring system: application to the Tsing Ma Bridge (2012) Struct Control Health Monit, 19, pp. 82-101; Chen, B., Li, X., Xie, X., Fatigue performance assessment of composite arch bridge suspenders based on actual vehicle loads (2015) Shock Vib, 2015, p. 659092; Takena, K., Miki, C., Shimokawa, H., Fatigue resistance of large-diameter cable for cable stayed bridges (1992) J Struct Eng: ASCE, 118 (3), pp. 701-715; Suh, J.I., Chang, S.P., Experimental study on fatigue behaviour of wire ropes (2000) Int J Fatigue, 22, pp. 339-347; Li, S., Xu, Y., Zhu, S., Probabilistic deterioration model of high-strength steel wires and its application to bridge cables (2015) Struct Infrastruct E, 11 (9), pp. 1240-1249; He, J., Zhou, Z., Ou, J., Optic fiber sensor-based smart bridge cable with functionality of self-sensing (2013) Mech Syst Signal Pr, 35, pp. 84-94; Bao, Y., Shi, Z., Beck, J.L., Identification of time-varying cable tension forces based on adaptive sparse time-frequency analysis of cable vibrations (2017) Struct Control Health Monit, 24, p. e1889; Feng, D.M., Scarangello, T., Feng, M.Q., Cable tension force estimate using novel noncontact vision-based sensor (2017) Measurement, 99, pp. 44-52; Guo, T., Frangopol, D.M., Chen, Y., Fatigue reliability assessment of steel bridge details integrating weigh-in-motion data and probabilistic finite element analysis (2012) Comput Struct, 112-113, pp. 245-257; Liu, Y., Zhang, H., Liu, Y., Fatigue reliability assessment for orthotropic steel deck details under traffic flow and temperature loading (2017) Eng Fail Anal, 71, pp. 179-194; Liu, Z., Guo, T., Chai, S., Probabilistic fatigue life prediction of bridge cables based on multiscaling and mesoscopic fracture mechanics (2016) Appl Sci, 6, p. 99; Liu, Z., Guo, T., Huang, L., Fatigue life evaluation on short suspenders of long-span suspension bridge with central clamps (2017) J Bridge Eng: ASCE, 22 (10), p. 04017074; Petrini, F., Bontempi, F., Estimation of fatigue life for long span suspension bridge hangers under wind action and train transit (2011) Struct Infrastruct E, 7 (7-8), pp. 491-507; Deng, Y., Liu, Y., Chen, S., Long-term in-service monitoring and performance assessment of the main cables of long-span suspension bridges (2017) Sensors, 17, p. 1414; Liu, Y., Deng, Y., Cai, C.S., Deflection monitoring and assessment for a suspension bridge using a connected pipe system: a case study in China (2015) Struct Control Health Monit, 22, pp. 1408-1425; (2015) General code for design highway bridges and culverts JTG D60-2015, , Beijing, China, China Communications Press; (2010) LRFD bridge design specifications, , Washington, DC, AASHTO; Ma, L., Han, W.S., Ji, B., Study of vehicle-bridge coupling vibration under actual traffic flow (2012) China J Highw Trans, 25 (6), pp. 80-87. , (,):, –, (in Chinese; Downing, S.D., Socie, D.F., Simplified rainflow cycle counting algorithms (1982) Int J Fatigue, 4, pp. 31-40; Miner, M.A., Cumulative damage in fatigue (1945) J Applied Mechanics, 12, pp. 159-164; Faber, M.H., Engelund, S., Rackwitz, R., Aspects of parallel wire cable reliability (2003) Struct Saf, 25 (2), pp. 201-225; Zeng, Y., Chen, A.R., Tang, H.M., Fatigue assessment of hanger wires of suspension bridges in its operation life based on in-situ traffic flow (2014) J Disaster Prev Mitig Eng, 34 (2), pp. 185-191. , (,):, –, (in Chinese; Hosford, W.F., (2005) Mechanical behavior of materials, , Cambridge, Cambridge University Press","Deng, Y.; Beijing Advanced Innovation Center for Future Urban Design, China; email: dengyang@bucea.edu.cn",,,"SAGE Publications Ltd",,,,,14759217,,,,"English","Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85050139691
"Bud M.A., Moldovan I., Radu L., Nedelcu M., Figueiredo E.","57194503406;26321771600;55365138300;35786491000;35619844900;","Reliability of probabilistic numerical data for training machine learning algorithms to detect damage in bridges",2022,"Structural Control and Health Monitoring","29","7","e2950","","",,8,"10.1002/stc.2950","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125602500&doi=10.1002%2fstc.2950&partnerID=40&md5=c66356eb51cd4d5ca2e880146aeab8f7","Faculty of Civil Engineering, Technical University of Cluj-Napoca, Cluj-Napoca, Romania; Faculty of Engineering, Lusófona University, Lisbon, Portugal; CERIS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal; Departamento de Ciências e Tecnologias, Universidade Autónoma de Lisboa, Lisbon, Portugal","Bud, M.A., Faculty of Civil Engineering, Technical University of Cluj-Napoca, Cluj-Napoca, Romania; Moldovan, I., Faculty of Engineering, Lusófona University, Lisbon, Portugal, CERIS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal; Radu, L., Departamento de Ciências e Tecnologias, Universidade Autónoma de Lisboa, Lisbon, Portugal; Nedelcu, M., Faculty of Civil Engineering, Technical University of Cluj-Napoca, Cluj-Napoca, Romania; Figueiredo, E., Faculty of Engineering, Lusófona University, Lisbon, Portugal, CERIS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal","In structural health monitoring of bridges, machine learning algorithms for damage detection are typically trained using an unsupervised learning strategy, with data gathered from monitoring systems, and assuming the structures are undamaged and functioning under normal operational conditions during a certain period of time. However, the scarcity of information regarding the structural response under seasonal environmental variations and less frequent operational conditions makes the distinction between these undamaged states and damaged ones very challenging and may cause damage detection algorithms to yield false indications. To overcome this limitation, hybrid approaches for the training of machine learning algorithms have recently been proposed. Rather than relying exclusively on monitoring data, hybrid approaches use finite element models of the structure to generate numerical data for less frequent undamaged scenarios. The numerical data are used for the training of machine learning algorithms together with the monitoring data. This paper addresses the reliability of numerical data for the training of machine learning algorithms by quantifying the damage detection performance of an algorithm trained with numerical data only. Monitoring data are used only for the initial calibration of the finite element model, which does not need to be exceedingly detailed, as the probabilistic variation of the uncertain parameters is considered. The damage detection performance is quantified both in terms of quality (number of ill-classified observations) and robustness to sub-optimal choices of the training data and algorithmic parameters. A general procedure for the generation of model-based data for the training of machine learning algorithms to detect damage is given and validated using the well-known Z-24 Bridge benchmark. © 2022 John Wiley & Sons, Ltd.","damage detection; environmental variability; finite element modeling; machine learning; structural health monitoring","Finite element method; Learning algorithms; Machine learning; Structural health monitoring; Uncertainty analysis; Detection performance; Environmental variability; Finite element modelling (FEM); Hybrid approach; Machine learning algorithms; Monitoring system; Numerical data; Operational conditions; Probabilistics; Structural response; Damage detection",,,,,"Fundação para a Ciência e a Tecnologia, FCT: UIDB/04625/2020","The authors thank Computers and Structures Inc. for the license of CSi Bridge used to generate the numerical models of Z‐24 Bridge and the financial support provided by the Fundação para a Ciência e a Tecnologia through project UIDB/04625/2020.",,,,,,,,,,"Figueiredo, E., Moldovan, I., Marques, M.B., (2013) Condition Assessment of Bridges: Past, Present, and Future—A Complementary Approach, , Lisboa, Universidade Catolica Editora; Rytter, A., (1993) Vibrational based inspection of civil engineering structures, , PhD Thesis; Aalborg, Denmark Aalborg University; Figueiredo, E., Park, G., Farrar, C.R., Worden, K., Figueiras, J., Machine learning algorithms for damage detection under operational and environmental variability (2011) Struct Health Monit, 10 (6), pp. 559-572; Santos, A., Figueiredo, E., Costa, J., (2015) Clustering studies for damage detection in bridges: A comparison study, , 10, Int. Workshop on Structural Health Monitoring; Stanford; Figueiredo, E., Cross, E., Linear approaches to modeling nonlinearities in long-term monitoring of bridges (2013) J Civ Struct Health Monit, 3 (3), pp. 187-194; Wursten, E.R.G., Roeck, G.D., Output-only structural health monitoring in changing environmental conditions by means of nonlinear system identification (2014) Struct Health Monit, 13 (1), pp. 82-93; Santos, A., Figueiredo, E., Silva, M.F.M., Sales, C.S., Costa, J.C.W.A., Machine learning algorithms for damage detection: kernel-based approaches (2016) J Sound Vib, 363, pp. 584-599; Mirzaee, A., Abbasnia, R., Shayanfar, M., A comparative study on sensitivity-based damage detection methods in bridges (2015) Shock Vib, pp. 1-19; Giagopoulos, D., Arailopoulos, A., Dertimanis, V., Papadimitriou, C., Chatzi, E., Grompanopoulos, K., Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating (2019) Struct. Health Monit., 18 (4), pp. 1189-1206; Sohn, H., Effects of environmental and operational variability on structural health monitoring (2007) Philos Trans R Soc a, 365 (1851), pp. 539-560; Silva, M., Santos, A., Santos, R., Figueiredo, E., Sales, C., Costa, J.C., Deep principal component analysis: An enhanced approach for structural damage identification (2019) Struct Health Monit, 18 (5-6), pp. 1444-1463; Zhou, S., Song, W., Environmental-effects-embedded model updating method considering environmental impacts (2018) Struct Control Hlth, 25 (3); Barthorpe, R.J., (2010) On model- and data-based approaches to structural health monitoring, , PhD Thesis. Sheffield, UK Univ. of Sheffield; Liu, Y., Zhang, S., Probabilistic baseline of finite element model of bridges under environmental temperature changes (2017) Comput Aided Civ Infrastruct Eng, 32 (7), pp. 581-598; Neves, C., Gonzalez, I., Leander, J., Karoumi, R., Structural health monitoring of bridges: a model-free ANN-based approach to damage detection (2017) J Civil Struct Health Monit, 7 (5), pp. 689-702; Malekzadeh, M., Atia, G., Catbas, F.N., Performance-based structural health monitoring through an innovative hybrid data interpretation framework (2015) J Civ Struct Health Monit, 5 (3), pp. 287-305; Figueiredo, E., Moldovan, I., Santos, A., Campos, P., Costa, J.C.W.A., Finite element-based machine learning approach to detect damage in bridges under operatoinal and environmental variations (2019) J Bridge Eng, 24 (7); Figueiredo, E., Radu, L., Worden, K., Farrar, C.R., A Bayesian approach based on a Markov-chain Monte Carlo method for damage detection under unknown sources of variability (2014) Eng Struct, 80 (1), pp. 1-10; McLachlan, G.J., Peel, D., (2000) Finite Mixture Models, , New York, John Wiley & Sons, Inc; Dempster, A.P., Laird, N.M., Rubin, D.B., Maximum likelihood from incomplete data via the EM algorithm (1977) J R Stat Soc Ser B Stat Method, 39 (1), pp. 1-22; Box, G.E.P., Jenkins, G.M., Reinsel, G.C., (2008) Time Series Analysis: Forecasting and Control, , 4th, ed., Hoboken, NJ, John Wiley & Sons, Inc; Krämer, C., (1999) Brite EuRam Projects SIMCES, Task A1 and A2: long term monitoring and bridge tests. Technical report 168′349/21, , Dübendorf, Switzerland; January; Steenackers, G., Guillaume, P., (2005) Structural health monitoring of the Z-24 bridge in presence of environmental changes using modal analysis, , Vrije Universiteit Brussel; Peeters, B., de Roeck, G., One-year monitoring of the Z24-Bridge: Environmental effects versus damage events (2001) Earthquake Eng Struct Dyn, 30 (2), pp. 149-171; Farrar, C.R., Park, G., Allen, D.W., Todd, M.D., Sensor network paradigms for structural health monitoring (2006) Struct Control Hlth, 13 (1), pp. 210-225; Peeters, B., de Roeck, G., Reference-based stochastic supspace identification for output-only modal analysis (1999) Mech Syst Signal Process, 13 (6), pp. 855-878; Reynders, E., Teughels, A., Roeck, G., Finite element model updating and structural damage identification using OMAX data (2010) Mech Syst Signal Process, 24 (5), pp. 1306-1323; Watson, D.K., Rajapakse, R.K.N.D., Seasonal variation in material properties of a flexible pavement (2000) Can J Civ Eng, 27 (1), pp. 44-54; Hase, M., Oelkers, C., Influence of low temperature behavior of PmB on life cycle, , 7th International RILEM Symposium ATCBM09 on Advanced Testing and Characterization of Bituminous Materials. Rhodes, Greece; May 27-29, 200923-32; Johnson, T.C., Cole, D.M., Chamberlain, E.J., (1978) Influence of freezing and thawing on the resilient properties of a silt soil beneath an asphalt concrete pavement, , Hanover, New Hampshire, U.S. Army Cold Regions Research and Engineering Laboratory, September; Bud, M.A., Nedelcu, M., Radu, L., Moldovan, I., Figueiredo, E., (2019) On the reliability of finite element models for training machine learning algorithms for damage detection in bridges, , 12, Int. Workshop on Structural Health Monitoring; Stanford, CA; Santos, A., Figueiredo, E., Silva, M., Santos, R., Sales, C., Costa, J.C.W.A., Genetic-based EM algorithm to improve the robustness of Gaussian mixture models for damage detection in bridges (2017) Struct Control Hlth, 24 (3); Figueiredo, E., Park, G., Figueiras, J., Farrar, C., Worden, K., Influence of the autoregressive model order on damage detection (2011) Comput Aided Civ Inf Eng, 26 (3), pp. 225-238","Moldovan, I.; Faculty of Engineering, Portugal; email: dragos.moldovan@tecnico.ulisboa.pt",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-85125602500
"Zhu J., Lu Z., Zhang C.","56136041700;57212025740;57212207006;","A marker-free method for structural dynamic displacement measurement based on optical flow",2021,"Structure and Infrastructure Engineering","18","1",,"84","96",,8,"10.1080/15732479.2020.1835999","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093982027&doi=10.1080%2f15732479.2020.1835999&partnerID=40&md5=e757c85432c55ee1f624f755021cb14f","Key Laboratory of Coast Civil Structure Safety (Ministry of Education), School of Civil Engineering, Tianjin University, Tianjin, China; School of Civil Engineering, Tianjin University, Tianjin, China","Zhu, J., Key Laboratory of Coast Civil Structure Safety (Ministry of Education), School of Civil Engineering, Tianjin University, Tianjin, China; Lu, Z., School of Civil Engineering, Tianjin University, Tianjin, China; Zhang, C., School of Civil Engineering, Tianjin University, Tianjin, China","Information on dynamic displacement is an effective indicator for structures condition evaluation and provides a quantised insight into the structural analysis. This paper proposes a low-cost system based on computer vision using a smartphone to measure dynamic displacement, and then identify the dynamic properties of structures. Conventional sensors like linear variable differential transformers (LVDT), GPS, and accelerometer in monitoring systems have limitations of high price, inaccessibility, and accuracy. However, some new technologies eliminate these disadvantages. For example, a smartphone with a high-resolution camera becomes more affordable, which is regarded as appropriate equipment for structural health monitoring (SHM). Based on the optical flow, this method allows users to track points with a specific interval in the chosen region and reduces the displacement drift induced by the Kanade–Lucas–Tomasi (KLT) method. With the method applied, the region of interest is relocated according to pixel motion, and feature points are reselected. The accuracy of the system is verified on a laboratory suspension bridge model, and the results of modal frequency are confirmed with an accelerometer and FEM simulations. © 2020 Informa UK Limited, trading as Taylor & Francis Group.","Computer vision; dynamic displacements; measurements image processing; optical flow; structural health monitoring","Accelerometers; Image segmentation; Smartphones; Structural dynamics; Structural health monitoring; Condition evaluation; Conventional sensors; Dynamic displacements; High resolution camera; Linear variable differential transformer; Monitoring system; Region of interest; Structural health monitoring (SHM); Optical flows",,,,,"National Natural Science Foundation of China, NSFC: 51578370; Tianjin University, TJU; National Key Research and Development Program of China, NKRDPC: 2018YFB1600300, 2018YFB1600301; Tianjin Municipal Transportation Commission Science and Technology Development Plan Project: G2018-29","The authors would like to acknowledge members of the Key Laboratory of Coast Civil Structure Safety (Ministry of Education) and the research group of bridges at Tianjin University for their endless support. This work presented here was supported by the National Key R&D Program of China (2018YFB1600300 and 2018YFB1600301), the National Science Foundation of China (51578370) and the Tianjin Transportation Science and Technology Development Plan Project (G2018-29). Any opinions, findings and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect those of the sponsor.","The authors would like to acknowledge members of the Key Laboratory of Coast Civil Structure Safety (Ministry of Education) and the research group of bridges at Tianjin University for their endless support. This work presented here was supported by the National Key R&D Program of China (2018YFB1600300 and 2018YFB1600301), the National Science Foundation of China (51578370) and the Tianjin Transportation Science and Technology Development Plan Project (G2018-29). Any opinions, findings and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect those of the sponsor.",,,,,,,,,"Adelson, E.H., Anderson, C.H., Bergen, J.R., Burt, P.J., Ogden, J.M., Pyramid methods in image processing (1984) RCA Engineer, 29 (6), pp. 33-41; Aslani, S., Mahdavi-Nasab, H., Optical flow based moving object detection and tracking for traffic surveillance (2013) International Journal of Electrical and Computer Engineering, 7 (9), pp. 1252-1256; Boncelet, C., Image noise models (2009) The essential guide to image processing, , In, (pp Academic Press,Editor Alan C Bovik Publisher Academic Press 1st Edition (June 11 2009; Bouguet, J.-Y., (1999) Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm, , OpenCV Documents; Brownjohn, J.M., Structural health monitoring of civil infrastructure (2007) Philosophical Transactions. 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Infrastructure Eng.",Article,"Final","",Scopus,2-s2.0-85093982027
"Hou N., Sun L., Chen L.","36455887900;7403956279;56427217600;","Cable Reliability Assessments for Cable-Stayed Bridges using Identified Tension Forces and Monitored Loads",2020,"Journal of Bridge Engineering","25","7","05020003","","",,8,"10.1061/(ASCE)BE.1943-5592.0001573","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085252050&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001573&partnerID=40&md5=3ae10e9b07944939f90e0fcd580977b5","Dept. of Bridge Engineering, Tongji Univ., Shanghai, 200092, China; State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai, 200092, China","Hou, N., Dept. of Bridge Engineering, Tongji Univ., Shanghai, 200092, China; Sun, L., State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai, 200092, China; Chen, L., Dept. of Bridge Engineering, Tongji Univ., Shanghai, 200092, China","The reliability of stay cables is critical to the safety of cable-stayed bridges. This paper investigates and compares reliability assessments of stay cables by using identified cable tension forces and monitored bridge loads. One-year monitoring data from a cable-stayed bridge was used to characterize the probability distributions of cable forces and pertinent bridges loads including temperature of the cable, wind load, and vehicle load. The results show that, for the bridge under study, the cable temperature, the wind load, and the vehicle weight obey the Beta distribution, whereas the axle weight obeys the lognormal distribution, indicating deviations from the design codes. Subsequently, two performance functions are proposed to compute the cable reliability index, where one directly uses the monitored cable forces and the other is based on the monitored loads and the finite element method simulation of the bridge. The computed index based on the monitored cable forces and the performance function I is larger than that based on the monitored loads and the performance function II. The reasonings attributed to the differences and the implication of the present findings in structural design and optimization are discussed. © 2020 American Society of Civil Engineers.","Performance function; Probability distributions; Reliability analysis; Stay cables; Structural health monitoring; Structural optimization","Aerodynamic loads; Cable stayed bridges; Monitoring; Probability distributions; Reliability analysis; Robustness (control systems); Structural design; Structural optimization; Wind stress; Beta distributions; Cable reliability; Cable temperatures; Design and optimization; Finite element method simulation; Log-normal distribution; Performance functions; Reliability assessments; Bridge cables",,,,,"51478347; State Key Laboratory for Disaster Reduction in Civil Engineering; National Basic Research Program of China (973 Program): 2017YFC1500605","The research described in this paper was supported by the National Nature Science Foundation of China (Grant No. 51478347), the National Key Research and Development Program of China (Grant No. 2017YFC1500605), and the State key Laboratory of Disaster Reduction in Civil Engineering (Grant No. SLDRCE15-A-02), which is greatly appreciated.",,,,,,,,,,"(2017) AASHTO LRFD Bridge Design Specifications, , AASHTO. 8th ed. 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Liu, Zurich, Switzerland: Trans Tech; Manzana, N., Pandey, M., Van Der Weide, J., Probability distribution of maximum load generated by stochastic hazards modeled asshock, pulse, and alternating renewal processes (2019) J. Risk. Uncertain. Eng. Syst. Part A: Civ. Eng., 5 (1), p. 04018045. , http://doi.org/10.1061/AJRUA6.0000994; Marco, L., Vincenzo, G., Static and dynamic response of elastic suspended cables with thermal effects (2012) Int. J. Solids Struct., 49 (9), pp. 1103-1116. , http://doi.org/10.1016/j.ijsolstr.2012.01.008; Mehrabi, A., In-service evaluation of cable-stayed bridges, overview of available methods, and findings (2006) J. Bridge Eng., 11 (6), pp. 716-724. , http://doi.org/10.1061/(ASCE)1084-0702(2006)11:6(716); Miao, T., Chan, T., Bridge live load models from WIM data (2002) Eng. Struct., 24 (8), pp. 1071-1084. , http://doi.org/10.1016/S0141-0296(02)00034-2; Modares, M., Taha, R., Mohammadi, J., Reliability assessment of structures using interval uncertainty analysis (2014) Proc., 2nd Int. Conf. On Vulnerability and Risk Analysis and Management and the 6th Int. Symp. On Uncertainty, Modeling, and Analysis, pp. 204-214. , In, Liverpool, UK: ASCE; (1999) Unified Standard for Reliability Design of Highway Engineering Structures, , MOT (Ministry of Transport). [In Chinese.] GBT 50283. Beijing: China Communications Publishing House; (2011) Standards for Technical Condition Evaluation of Highway Bridges, , MOT (Ministry of Transport). [In Chinese.] JTGT H21. Beijing: China Communications Publishing House; Nowak, A., Live load model for highway bridges (1993) Struct. Saf., 13 (12), pp. 53-66. , http://doi.org/10.1016/0167-4730(93)90048-6; Nowak, A., Szerszen, M., Bridge load and resistance models (1998) Eng. 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Process., 124, pp. 330-348. , http://doi.org/10.1016/j.ymssp.2019.01.026; Zhou, Y., Sun, L., Effects of environmental and operational actions on the modal frequency variations of a sea-crossing bridge: A periodicity perspective (2019) Mech. Syst. Sig. Process., 131, pp. 505-523. , http://doi.org/10.1016/j.ymssp.2019.05.063; Zhou, Y., Sun, L., Insights into temperature effects on structural deformation of a cable-stayed bridge based on structural health monitoring (2019) Struct. Health Monit., 18 (3), pp. 778-791. , http://doi.org/10.1177/1475921718773954","Sun, L.; State Key Laboratory of Disaster Reduction in Civil Engineering, China; email: lmsun@tongji.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85085252050
"Spada A., Capriotti M., Lanza di Scalea F.","35115805800;56584299300;55665735300;","Global-Local model for guided wave scattering problems with application to defect characterization in built-up composite structures",2020,"International Journal of Solids and Structures","182-183",,,"267","280",,8,"10.1016/j.ijsolstr.2019.08.015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071125116&doi=10.1016%2fj.ijsolstr.2019.08.015&partnerID=40&md5=8220becc289a0b852062b9bd0024b712","Department of Civil, Environmental, Aerospace, Materials Engineering (DICAM), University of Palermo, Viale delle Scienze, Ed. 8, Palermo, PA  90128, Italy; Experimental Mechanics & NDE Laboratory, Department of Structural Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA  92093-0085, United States","Spada, A., Department of Civil, Environmental, Aerospace, Materials Engineering (DICAM), University of Palermo, Viale delle Scienze, Ed. 8, Palermo, PA  90128, Italy; Capriotti, M., Experimental Mechanics & NDE Laboratory, Department of Structural Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA  92093-0085, United States; Lanza di Scalea, F., Experimental Mechanics & NDE Laboratory, Department of Structural Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA  92093-0085, United States","Predicting scattering of elastic guided waves in multi-layered solid plates with geometrical and/or material discontinuities is of great interest to many fields, including ultrasonic-based Non-Destructive Testing (NDT) and health monitoring of critical structural components (SHM). The problem is complicated by the multimode and dispersive behaviour of the guided waves. This paper describes a unified Global-Local (GL) approach that is computationally efficient in cases that can be very complex in terms of geometry and/or material properties. One example of this is a composite built-up structure. The proposed GL procedure discretizes the “local” region with the scattering discontinuity by regular finite elements, and utilizes the efficient Semi-Analytical Finite Element solutions in the “global” region away from the scatterer. The GL formulation that is presented includes the dispersive unforced solutions for each applicable mode, the mode tracking, the scattered spectra (reflection and transmission), and the energy balance calculations. The algorithm is applied to the case of a composite skin-to-stringer assembly used in modern aircraft construction. Various representative defects in this assembly are modelled, and transmission spectra are calculated for both axial or flexural guided wave modes used in excitation. The resulting scattered spectra (which are the broadband transfer functions of the structure) can be useful to either select suitable wave mode-frequency combinations or to identify specific defects in guided-wave NDT or SHM tests of these components. © 2019 Elsevier Ltd","Composite structures; Global-Local method; Guided elastic waves; Non-destructive testing; Scattering; Semi-analytical finite element method; Structural health monitoring","Bridge decks; Composite structures; Defects; Dispersion (waves); Elastic waves; Guided electromagnetic wave propagation; Nondestructive examination; Plates (structural components); Scattering; Structural health monitoring; Structure (composition); Computationally efficient; Defect characterization; Energy balance calculations; Global-local methods; Non destructive testing; Reflection and transmission; Semi-analytical finite element; Semi-analytical finite element methods; Ultrasonic testing",,,,,"Federal Aviation Administration, FAA: E0584038","Part of this work was funded by the Federal Aviation Administration Joint Center of Excellence for Advanced Materials (FAA Cooperative Agreement 12-C-AM-UCSD ). A. Spada was financially supported by the Fulbright Program for the fulfilment of the project E0584038 “Analytical-Numerical models for the simulation of ultrasonic guided wave propagation in composite structures”.",,,,,,,,,,"Ahmad, Z.A.B., Vivar-Perez, J.M., Gabbert, U., Semi-analytical finite element method for modeling of lamb wave propagation (2013) CEAS Aeronaut. J., 4 (1), pp. 21-33; Al-Nassar, Y.N., Datta, S.K., Shah, A.H., Scattering of lamb waves by a normal rectangular strip weldment (1991) Ultrasonics, 29 (2), pp. 125-132; Bartoli, I., Lanza di Scalea, F., Fateh, M., Viola, E., Modeling guided wave propagation with application to the long range defect detection in railroad tracks (2005) NDT&E Int., 38, pp. 325-334; Bartoli, I., Marzani, A., di Scalea, F.L., Viola, E., Modeling wave propagation in damped waveguides of arbitrary cross-section (2006) J. Sound Vib., 295 (3-5), pp. 685-707; Capriotti, M., Kim, H.E., Lanza di Scalea, F., Kim, H., Non-destructive inspection of impact damage in composite aircraft panels by ultrasonic guided waves and statistical processing (2017) Materials (Basel), 10 (6), pp. 1-12; Castaings, M., Le Clezio, E., Hosten, B., Modal decomposition method for modeling the interaction of lamb waves with cracks (2002) J. Acoust. Soc. Am., 112 (6), pp. 2567-2582; Chang, Z., Mal, A.K., A global-local method for wave propagation across a lap joint (1995) ASME Appl. Mech. Div. Publ. AMD, 204, p. 1; Chang, Z., Mal, A., Scattering of lamb waves from a rivet hole with edge cracks (1999) Mech. Mater., 31 (3), pp. 197-204; Datta, S.K., Ju, T.H., Shah, A.H., Scattering of an impact wave by a crack in a composite plate (1992) J. Appl. Mech., 59 (3), pp. 596-603; Datta, S.K., Shah, A.H., Bratton, R.L., Chakraborty, T., Wave propagation in laminated composite plates (1988) J. Acoust. Soc. Am., 83 (6), pp. 2020-2026; Dong, S.B., Goetschel, D.B., Edge effects in laminated composite plates (1982) J. Appl. Mech., 49, pp. 129-135; Galán, J.M., Abascal, R., Numerical simulation of lamb wave scattering in semi‐infinite plates (2002) Int. J. Numer. Methods Eng., 53 (5), pp. 1145-1173; Galán, J.M., Abascal, R., Boundary element solution for the bidimensional scattering of guided waves in laminated plates (2005) Comput. Struct., 83 (10-11), pp. 740-757; Goetschel, D.B., Dong, S.B., Muki, R., A global local finite element analysis of axisymmetric scattering of elastic waves (1982) J. Appl. Mech., 49 (4), pp. 816-820; Guo, N., Cawley, P., The interaction of lamb waves with delaminations in composite laminates (1993) J. Acoust. Soc. Am., 94 (4), pp. 2240-2246; Haider, M.F., Bhuiyan, M.Y., Poddar, B., Lin, B., Giurgiutiu, V., Analytical and experimental investigation of the interaction of lamb waves in a stiffened aluminum plate with a horizontal crack at the root of the stiffener (2018) J. Sound Vib., 431, pp. 212-225; Hayashi, T., Song, W.J., Rose, J.L., Guided wave dispersion curves for a bar with an arbitrary cross-section, a rod and rail example (2003) Ultrasonics, 41 (3), pp. 175-183; Karim, M.R., Awal, M.A., Kundu, T., Elastic wave scattering by cracks and inclusions in plates: in-plane case (1992) Int. J. Solids Struct., 29 (19), pp. 2355-2367; Karim, M.R., Kundu, T., Transient surface response of layered isotropic and anisotropic half-spaces with interface cracks: SH case (1988) Int. J. Fract., 37, pp. 245-262; Karim, M.R., Kundu, T., Desai, C.S., Detection of delamination cracks in layered fiber-reinforced composite plates (1989) J. Press. Vessel Technol., 3, pp. 165-171; Karunasena, W.M., Liew, K.M., Kitipornchai, S., Hybrid analysis of lamb wave reflection by a crack at the fixed edge of a composite plate (1995) Comput. Methods Appl. Mech. Eng., 125 (1-4), pp. 221-233; Loveday, P.W., Long, C.S., Time domain simulation of piezoelectric excitation of guided waves in rails using waveguide finite elements (2007) Sens. Smart Struct. Technol. Civil Mech. Aerosp. Syst., 6529; Mal, A., Chang, Z., A semi‐numerical method for elastic wave scattering calculations (2000) Geophys. J. Int., 143 (2), pp. 328-334; Marzani, A., Viola, E., Bartoli, I., Lanza di Scalea, F., Rizzo, P., A semi-analytical finite element formulation for modeling stress wave propagation in axisymmetric damped waveguides (2008) J. Sound Vib., 318 (3), pp. 488-505; Matt, H., Bartoli, I., Lanza di Scalea, F., Ultrasonic guided wave monitoring of composite wing skin-to-spar bonded joints in aerospace structures (2005) J. Acoust. Soc. Am., 118 (4), pp. 2240-2252; Poddar, B., Giurgiutiu, V., Complex modes expansion with vector projection using power flow to simulate lamb waves scattering from horizontal cracks and disbonds (2016) J. Acoust. Soc. Am., 140 (3), pp. 2123-2133; Poddar, B., Giurgiutiu, V., Scattering of lamb waves from a discontinuity: an improved analytical approach (2016) Wave Motion, 65, pp. 79-91; Rattanawangcharoen, N., Zhuang, W., Shah, A.H., Datta, S.K., Axisymmetric guided waves in jointed laminated cylinders (1997) ASCE J. Eng. Mech., 123 (10), pp. 1020-1026; Ricci, F., Monaco, E., Maio, L., Boffa, N., Mal, A.K., Guided waves in a stiffened composite laminate with a delamination (2016) SHM Int. J., 15 (3), pp. 351-358; Rose, J.L., Ultrasonic Guided Waves in Solid Media (2014), Cambridge university press; Srivastava, A., Lanza di Scalea, F., Quantitative structural health monitoring by ultrasonic guided waves (2010) ASCE J. Eng. Mech., 136 (8), pp. 937-944; Tian, J., Gabbert, U., Berger, H., Su, X., Lamb wave interaction with delaminations in CFRP laminates (2004) Comput. Mater. Contin., 1 (4), pp. 327-336; Zhou, W.J., Ichchou, M.N., Wave scattering by local defect in structural waveguide through wave finite element method (2011) Struct. Health Monit., 10 (4), pp. 335-349","Lanza di Scalea, F.; Experimental Mechanics & NDE Laboratory, 9500 Gilman Drive, United States; email: flanzadiscalea@ucsd.edu",,,"Elsevier Ltd",,,,,00207683,,,,"English","Int. J. Solids Struct.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85071125116
"Li S., Ou J., Wang J., Gao X., Yang C.","57218879558;7202845830;57200333167;57196415453;57205095520;","Level 2 safety evaluation of concrete-filled steel tubular arch bridges incorporating structural health monitoring and inspection information based on China bridge standards",2019,"Structural Control and Health Monitoring","26","3","e2303","","",,8,"10.1002/stc.2303","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058516472&doi=10.1002%2fstc.2303&partnerID=40&md5=c3ad70b8ddbc41af6e0e3572f793c38f","School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China; School of Civil Engineering, Harbin Institute of Technology, Harbin, China; Test and Monitoring Center, CCCC Infrastructure Maintenance Group Co. Ltd., Beijing, China; College of Construction Engineering, Jilin University, Jilin, China; Municipal Design Institute, Lin Tung-Yen & Li Guo-Hao Consultants Shanghai Ltd., Shanghai, China","Li, S., School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China; Ou, J., School of Civil Engineering, Harbin Institute of Technology, Harbin, China; Wang, J., Test and Monitoring Center, CCCC Infrastructure Maintenance Group Co. Ltd., Beijing, China; Gao, X., College of Construction Engineering, Jilin University, Jilin, China; Yang, C., Municipal Design Institute, Lin Tung-Yen & Li Guo-Hao Consultants Shanghai Ltd., Shanghai, China","Concrete-filled steel tubular (CFST) arch bridges have been widely used in mainland China, and their systematic safety evaluation has gained increasing attention from the bridge authorities in recent years. This paper presents the framework and application of Level 2 safety evaluation of CFST arch bridges, incorporating structural health monitoring (SHM) and inspection information. In contrast with the traditional inspection-based qualitative assessment and direct measurement-based quantitative assessment for SHM, Level 2 safety evaluation considers the inevitable resistance deterioration and site-specific loading under normal operation conditions. For such safety evaluations, the most important element is using the well-established finite element modelling for representing the actual conditions as accurately as possible. Thus, in this study, component and global finite element model (FEM) updating techniques are employed to ensure the local and global behaviour of the updated FEM, through which several types of common CFST arch bridge defects are investigated and updated. Measured site-specific live loads experienced by CFST arch bridges are considered and transformed into design loads for comparison. Unfavourable load patterns are applied to the updated FEM for structural reanalysis, to evaluate the component safety status, and gradually increasing unfavourable load patterns are applied to the FEM to perform an ultimate load-bearing analysis to obtain the global safety reservation. Level 2 safety evaluation is conducted by considering modal parameters, displacements, fatigue damage, and component and global safety status. Level 2 safety evaluation is applied to a typical CFST arch bridge, to demonstrate the entire evaluation process and the effectiveness of the proposed safety evaluation framework. © 2018 John Wiley & Sons, Ltd.","CFST arch bridge; Level 2 safety evaluation; structural health monitoring; structural reanalysis; ultimate load-bearing analysis","Arch bridges; Arches; Concretes; Deterioration; Finite element method; Inspection; Modal analysis; Structural dynamics; Cfst arch bridges; Concrete filled steel tubular arch bridges; Concrete-filled steel tubular; Quantitative assessments; Safety evaluations; Structural health monitoring (SHM); Structural reanalysis; Ultimate load bearings; Structural health monitoring",,,,,"710281886032; 2018YFC0705606; National Natural Science Foundation of China, NSFC: 51478149, 51638007, 51678204","Financial support for this study was provided by National Key R&D Program of China (2018YFC0705606), NSFC (Grants 51678204, 51478149, and 51638007), and Guangxi Science Base and Talent Program (Grant 710281886032).","National Key R&D Program of China, Grant/Award Number: 2018YFC0705606; National Natural Science Foundation of China, Grant/Award Numbers: 51478149, 51678204 and 51638007; Guangxi Science Base and Talent Program, Grant/Award Number: 710281886032",,,,,,,,,"Gao, X., (2011) Analysis methods for suspender damage and system reliability of existing concrete filled steel tubular arch bridge, , Doctoral dissertation Harbin Institute of Technology; (2015) Specifications for Design of Highway Concrete-filled Steel Tubular Arch Bridges (JTG/T D62-06-2015), , Beijing, China Communication Press; (2011) Standards for Technical Condition Evaluation of Highway Bridges (JTG/T H21–2011), , Beijing, China Communication Press; (2004) Technical Code of Maintenance for City Bridge (CJJ99-2003), , Beijing, China Architecture& Building Press; (2004) Code for Maintenance of Highway Bridges and Culvers (JTG H11-2004), , Beijing, China Communication Press; (2011) Specification for Inspection and Evaluation of Load-bearing Capacity of Highway Bridges (JTG/T J21–2011), , Beijing, China Communication Press; (2016) Technical Specification for Structural Safety Monitoring Systems of Highway Bridges (JT/T 1037–2016), , Beijing, China Communication Press; Li, H., Li, S.L., Ou, J.P., Li, H.W., Modal identification of bridges under varying environmental conditions: temperature and wind effects (2010) Struct Control Health Monit, 17 (5), pp. 495-512; Yang, Y., Dorn, C., Mancini, T., Blind identification of full-field vibration modes of output-only structures from uniformly-sampled, possibly temporally-aliased (sub-nyquist), video measurements (2017) J Sound Vib, 390, pp. 232-256; Yang, Y., Nagarajaiah, S., Blind identification of damage in time-varying systems using independent component analysis with wavelet transform (2014) Mech Syst Signal Process, 47 (1-2), pp. 3-20; Yang, Y., Nagarajaiah, S., Output-only modal identification with limited sensors using sparse component analysis (2013) J Sound Vib, 332 (19), pp. 4741-4765; Brincker, R., Zhang, L.M., Modal identification of output-only systems using frequency domain decomposition (2001) Smart Mater Struct, 10 (3), pp. 441-445; (2004) Code for design of highway reinforced concrete and prestressed concrete bridges and culverts (JTG D62–2004), , Beijing, China Communication Press; Shakir-Khalil, H., Pushout strength of concrete-filled steel hollow section tubes (1993) Struct Eng, 71, pp. 230-233; Anderson, T.L., Osage, D.A., API 579: a comprehensive fitness-for-service guide (2000) Int J Press Vessel Pip, 77 (14-15), pp. 953-963; (2000) RP579-recommended practice for fitness-for-service, , American Petroleum Institute; Osage, D.A., Janelle, J.L., (2008) API 579-1/ASME FFS-1 2007: a joint API/ASME fitness-for-service standard for pressurized equipment, pp. 777-791. , ASME 2008 Pressure Vessels and Piping Conference American Society of Mechanical Engineers; Li, H., Lan, C., Ju, Y., Li, D., Experimental and numerical study of the fatigue properties of corroded parallel wire cables (2011) J Bridg Eng, 17, pp. 211-220; Li, H., Bao, Y.Q., Li, S.L., Zhang, D.Y., Data science and engineering for structural health monitoring (2015) Eng Mech, 32, pp. 1-7; Ou, J.P., Duan, Z.D., Xiao, Y.Q., (2003) Safety Evaluation of Offshore Platform Structure: Theory, Approach and Practice, , Beijing, Science Press; Friswell, M.I., Mottershead, J.E., Finite element model updating in structural dynamics (1995) Solid Mech Its Appl, p. 38; (2015) General Code for Design of Highway Bridges and Culverts (JTG D60–2015), , Beijing, China Communication Press; Ou, J.P., Liu, X.D., Zhang, X.C., Pan, D.M., Whole stepwise push method of ultimate strength analysis of jacket offshore platform structures and its computer program (in Chinese) (1999) Ocean Eng, 17, pp. 1-10","Li, S.; School of Transportation Science and Engineering, China; email: lishunlong@hit.edu.cn",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-85058516472
"Ataei S., Miri A., Tajalli M.","6506181567;57190337939;57190340794;","Implementing Relative Deflection of Adjacent Blocks in Model Calibration of Masonry Arch Bridges",2018,"Journal of Performance of Constructed Facilities","32","4","04018037","","",,8,"10.1061/(ASCE)CF.1943-5509.0001171","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046162291&doi=10.1061%2f%28ASCE%29CF.1943-5509.0001171&partnerID=40&md5=01430b1eff42fd6d503fe3d115931f05","School of Railway Engineering, Iran Univ. of Science and Technology, Narmak, Tehran, 1684613114, Iran","Ataei, S., School of Railway Engineering, Iran Univ. of Science and Technology, Narmak, Tehran, 1684613114, Iran; Miri, A., School of Railway Engineering, Iran Univ. of Science and Technology, Narmak, Tehran, 1684613114, Iran; Tajalli, M., School of Railway Engineering, Iran Univ. of Science and Technology, Narmak, Tehran, 1684613114, Iran","A new idea for model updating of masonry arch bridges is presented, which employs relative displacement of adjacent masonry blocks (RDAMB). Applicability and accuracy of the proposed method is proved by a case study. The proposed method is of specific interest for bridges with tall piers, or those located in regions with harsh environments, since it is rather difficult to record the vertical deflection of such bridges with common deflection meters. It is proposed, in such cases, to use RDAMB instead of the vertical deflection of midspans and use it for the purpose of model verification. Proposed instrumentation also offers significant cost reduction while preserving data quality. Applicability of RDAMB for calibration of the finite-element model of the bridge is also investigated and compared to common methods. © 2018 American Society of Civil Engineers.","Dynamic load tests; Instrumentation method; Masonry arch bridges; Relative displacement of adjacent masonry blocks; Structural health monitoring","Arches; Cost reduction; Deflection (structures); Dynamic loads; Finite element method; Load testing; Masonry bridges; Masonry construction; Masonry materials; Structural health monitoring; Harsh environment; Instrumentation method; Masonry arch bridges; Masonry Blocks; Model calibration; Model verification; Relative displacement; Vertical deflections; Arch bridges",,,,,"Iran University of Science and Technology, IUST: 94-15388","This work was supported by the Iranian railway organization with the industrial cooperation office of Iran University of science and technology (Grant No. 94-15388).",,,,,,,,,,"Ataei, S., Miri, A., Jahangiri, M., Assessment of load carrying capacity enhancement of an open spandrel masonry arch bridge by dynamic load testing (2017) Int. J. Archit. Heritage, 11 (8), pp. 1086-1100. , https://doi.org/10.1080/15583058.2017.1317882; Ataei, S., Miri, A., Tajalli, M., Dynamic load testing of a railway masonry arch bridge: A case study of Babak bridge (2017) Sci. Iranica, 24 (4), pp. 1834-1842. , https://doi.org/10.24200/sci.2017.4274; Ataei, S., Tajalli, M., Miri, A., Assessment of load carrying capacity and fatigue life expectancy of a monumental masonry arch bridge by field load testing: A case study of Veresk (2016) J. Struct. Eng. Mech., 59 (4), pp. 703-718. , https://doi.org/10.12989/sem.2016.59.4.703; Bayraktar, A., Altunisik, A., Birinci, F., Sevim, B., Turker, T., Finite-element analysis and vibration testing of a two-span masonry arch bridge (2010) J. Perform. Constr. Facil., 24 (1), pp. 46-52. , https://doi.org/10.1061/(ASCE)CF.1943-5509.0000060; Brencich, A., Sabia, D., Experimental identification of a multi-span masonry bridge: The Tanaro bridge (2007) J. Constr. Build. Mater., 22 (10), pp. 2087-2099. , https://doi.org/10.1016/j.conbuildmat.2007.07.031; Caglayan, B.O., Ozakgul, K., Tezer, O., Assessment of a concrete arch bridge using static and dynamic load test (2012) J. Struct. Eng. Mech., 41 (1), pp. 83-94. , https://doi.org/10.12989/sem.2012.41.1.083; Chandra, J.M., Ramaswamy, A., Manohar, C.S., Safety assessment of a masonry arch bridge: Field testing and simulations (2013) J. Bridge Eng., 18 (2), pp. 162-171. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000338; Deng, Y., Phares, B., Automated bridge load rating determination utilizing strain response due to ambient traffic trucks (2016) J. Eng. Struct., 117, pp. 101-117. , https://doi.org/10.1016/j.engstruct.2016.03.004; (1997) The Assessment of Highway Bridges and Structures, 3. , Department of Transport. "" Design manual for roads and bridges of. London: Dept. of Transport; Hong, W., Cao, Y., Wu, Z., Strain-based damage-assessment method for bridges under moving vehicular loads using long-gauge strain sensing (2016) J. Bridge Eng., 21 (10). , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000933, 04016059; Kishen, J.M., Ramaswamy, A., Manohar, C.S., Safety assessment of a masonry arch bridge: Field testing and simulations (2013) J. Bridge Eng., 18 (2), pp. 162-171. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000338; Marefat, M., Ghahremani, E., Ataei, S., Load test of a plain concrete arch railway bridge of 20-m span (2004) J. Constr. Build. Mater., 18 (9), pp. 661-667. , https://doi.org/10.1016/j.conbuildmat.2004.04.025; Oliveira, D., Lourenco, P., Lemos, C., Geometric issues and ultimate load capacity of masonry arch bridges from the northwest Iberian Peninsula (2010) J. Eng. Struct., 32 (12), pp. 3955-3965. , https://doi.org/10.1016/j.engstruct.2010.09.006; Saberi, M., Rahai, A., Sanayei, M., Vogel, R., Bridge fatigue service-life estimation using operational strain measurements (2016) J. Bridge Eng., 21 (5). , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000860, 04016005; Sanayei, M., Khaloo, A., Gul, M., Catbas, F., Automated finite element model updating of a scale bridge model using measured static and modal test data (2015) J. Eng. Struct., 102, pp. 66-79. , https://doi.org/10.1016/j.engstruct.2015.07.029; Sanayei, M., Phelps, J., Sipple, J., Bell, E., Brenner, B., Instrumentation, nondestructive testing, and finite-element model updating for bridge evaluation using strain measurements (2011) J. Bridge Eng., 17 (1), pp. 130-138. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000228; (2014) Report on Material Strength, , Sinab-Gharb Corporation. Tehran, Iran: Engineering consultants; (2004) Assessment, Reliability, and Maintenance of Masonry Arch Bridges, State of the Art Study, , UIC (International Union of Railways). Paris: Research Report of the International Union of Railways; (2011) Recommendations for the Assessment of the Load Carrying Capacity of Existing Masonry and Mass-concrete Arch Bridges, , UIC (International Union of Railways). UIC 778-3. Paris: UIC publication","Ataei, S.; School of Railway Engineering, Iran; email: ataei@iust.ac.ir",,,"American Society of Civil Engineers (ASCE)",,,,,08873828,,JPCFE,,"English","J. Perform. Constr. Facil.",Article,"Final","",Scopus,2-s2.0-85046162291
"Drygala I., Dulinska J., Bednarz L., Jasienko J.","57195316220;54970721200;57188693919;6506897187;","Numerical evaluation of seismic-induced damages in masonry elements of historical arch viaduct",2018,"IOP Conference Series: Materials Science and Engineering","364","1","012006","","",,8,"10.1088/1757-899X/364/1/012006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049377950&doi=10.1088%2f1757-899X%2f364%2f1%2f012006&partnerID=40&md5=b227f94eeb0ffe3e375bada131007f19","Cracow University of Technology, Warszawska 24PL 31-155, Poland; Wroclaw University of Science and Technology, Plac Grunwaldzki 11Pl 50-377, Poland","Drygala, I., Cracow University of Technology, Warszawska 24PL 31-155, Poland; Dulinska, J., Cracow University of Technology, Warszawska 24PL 31-155, Poland; Bednarz, L., Wroclaw University of Science and Technology, Plac Grunwaldzki 11Pl 50-377, Poland; Jasienko, J., Wroclaw University of Science and Technology, Plac Grunwaldzki 11Pl 50-377, Poland","The main purpose of this research was to estimate the seismic response of the historical masonry arch viaduct located in Krakow (Southern Poland) subjected to the representative strong seismic event. For the numerical evaluation a 3D finite element model (FEM) of the masonry viaduct was assembling with the ABAQUS/Standard software program. To represent nonlinear behaviour of the masonry structural elements of the viaduct under the strong seismic event, a Barcelona Model (BM) was assumed as a constitutive material model. Three components of the registered strong seismic shock were applied as kinematic excitation acting in three directions. The results of the numerical simulation showed that evident nonlinear behaviour of the masonry structural elements of the viaduct was observed under the earthquake. The plastic strains as well as the tensile damage (cracking) were obtained in some zones of the walls of the structure after seismic shock. On the basis of the obtained results authors also discussed the methodology for structural health monitoring (SHM) as well as seismic retrofitting which could be dedicated for historical masonry arch viaducts and bridges. © Published under licence by IOP Publishing Ltd.",,"ABAQUS; Arches; Masonry bridges; Masonry construction; Masonry materials; Nonlinear optics; Seismology; Structural health monitoring; 3D finite element model; ABAQUS/STANDARD software; Constitutive materials; Kinematic excitation; Nonlinear behaviours; Seismic retrofitting; Structural elements; Structural health monitoring (SHM); Arch bridges",,,,,,,,,,,,,,,,"Karaton, M., Aksoy, H.S., Sayin, E., Calayir, Y., Nonlinear seismic performance of a 12th century historical masonry bridge under different earthquake levels (2017) Engineering Failure Analysis, 79, pp. 408-421; Santis, S., Felice, G., Seismic analysis of masonry arches (2012) Proc. 15th World Conference on Earthquake Engineering; Kiyono, J., Furukawa, A., Seismic Assessment of Stone Arched Bridges (2012) 15th World Conference on Earthquake Engineering; Drygala, I., Dulinska, J., Ł, B., Jasienko, J., Seismic performance of a historical apartment building using a Barcelona Model (BM) adapted for masonry walls (2017) Key Engineering Materials, 747, pp. 646-652; Wawrzynek, A., Cińcio, A., Numerical verification of the Barcelona Model adapted for brick walls (2006) Proc. of the 7th International Masonry Conference; Lee, J., Fenves, G.L., Plastic-damage model for cyclic loading of concrete structures Journal of Engineering Mechanics, 124, pp. 892-900; Jankowiak, T., Łodygowski, T., Identification of parameters of concrete damage plasticity constitutive model (2005) Foundations of Civil and Environmental Engineering, 6, pp. 53-69; Cińcio, A., (2004) Numerical Analysis of Dynamic Resistance on Semi-seismic Tremors of Low Buildings with Application of Spatial Object Models, , (Library of SUoT) PhD Thesis; Passowicz, A., Norberciak, D., Sielicki, P., (2005), Analiza numeryczna konstrukcji Kosacute;ciolstrok;a Najsacute;wiecedil;tszej Marii Panny na Ostrowie Tumskim w Poznaniu (in Polish) (Library of Poznanacute; University of Technology) Master Thesis; (2008) Praca Statyczna Zabytkowych, Zakrzywionych Konstrukcji Ceglanych Poddanych Zabiegom Naprawy i Wzmacniania, , (in Polish) (Library of Wroclstrok;aw University of Science and Technology) PhD Thesis; Dulińska, J., (2006) Odpowiedź Dynamiczna Budowli Wielopodporowych Na Nierównomierne Wymuszenie Parasejmiczne Pochodzenia Górniczego, , (Wydawnictwo PK) in Polish; (2016) ITACA - The Italian Accelerometric Archive, Version 2.1. [Internet], , http://itaca.mi.ingv.it/, ITACA working group; [cited 2017 February 20]; Ł, B., Jasieńko, J., Rutkowski, M., Nowak, Strengthening and long-term monitoring of the structure of an historical church presbytery (2014) Engineering Structures, 81, pp. 62-75; Barbieri, A., Borri, A., Corradi, M., Di Tommaso, A., Dynamic behaviour of masonry vaults repaired with FRP: Experimental analysis (2002) Proc. 6th International Masonry Conference; Mrozek, M., (2012) Numeryczna Symulacja Wzmacniania Matami CFRP Konstrukcji Murowych Z Ceglstrok;y, , (in Polish) (Library of SUoT) PhD Thesis","Drygala, I.; Cracow University of Technology, Warszawska 24, Poland; email: idrygala@pk.edu.pl",,"","Institute of Physics Publishing","Florence Heri-Tech 2018 - The Future of Heritage Science and Technologies","16 May 2018 through 18 May 2018",,137444,17578981,,,,"English","IOP Conf. Ser. Mater. Sci. Eng.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85049377950
"Whelan M., Salas Zamudio N., Kernicky T.","7103060850;57201472956;56176675300;","Structural identification of a tied arch bridge using parallel genetic algorithms and ambient vibration monitoring with a wireless sensor network",2018,"Journal of Civil Structural Health Monitoring","8","2",,"315","330",,8,"10.1007/s13349-017-0266-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045005591&doi=10.1007%2fs13349-017-0266-z&partnerID=40&md5=21135d698861f7ad65c89821efe06a83","University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC  28223-0001, United States","Whelan, M., University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC  28223-0001, United States; Salas Zamudio, N., University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC  28223-0001, United States; Kernicky, T., University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC  28223-0001, United States","Structural identification has received increased attention as an applied technique for performance-based assessment of large civil structures by providing a means to improve the correlation of simulated responses in numerical models to experimental measurements of actual behavior under in-service conditions. This paper presents the application of structural identification to a large set of modal parameter estimates obtained through ambient vibration monitoring of a tied arch bridge with a wireless sensor network facilitating high-rate, real-time vibration measurement over 48 measurement channels. Model updating of a finite element model of the span is achieved through global optimization of an objective function using an integer-constrained genetic algorithm implemented on a parallel computing cluster to facilitate the use of large population sizes. The influence of the number of modes included in the objective function and the number of uncertain parameters included in the optimization are explored for this real-world application. The results highlight the capability of the genetic algorithm to achieve an exceptionally strong correlation between the calibrated finite element model and the experimentally measured natural frequencies and mode shapes over a large set of modal parameter estimates. However, variations observed in the parameter solutions, identified as the number of uncertain parameters updated and modes included in the objective function are varied, serve to highlight the challenges associated with reliable parameter estimation in structural identification using classical optimization-based approaches. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.","Ambient vibration monitoring; Finite element model updating; Operational modal analysis; Structural health monitoring; Structural identification","Arch bridges; Arches; Cluster computing; Clustering algorithms; Constrained optimization; Finite element method; Genetic algorithms; Global optimization; Integer programming; Modal analysis; Population statistics; Structural analysis; Structural health monitoring; Uncertainty analysis; Vibration analysis; Vibration measurement; Wireless sensor networks; Ambient vibration monitoring; Finite-element model updating; Natural frequencies and modes; Operational modal analysis; Parallel computing clusters; Parallel genetic algorithms; Performance-based assessment; Structural identification; Parameter estimation",,,,,,,,,,,,,,,,"Çatbaş, F., Kijewski-Correa, T., Aktan, A., (2013) Structural identification of constructed systems: approaches, methods, and technologies for effective practice of St-Id, , American Society of Civil Engineers, New York; Aktan, A., Brownjohn, J., Structural identification: opportunities and challenges (2013) J Struct Eng, 139, pp. 1639-1647; Sanayei, M., Phelps, J., Sipple, J., Bell, E., Brenner, B., Intrumentation, nondestructive testing, and finite-element model updating for bridge evaluation using strain measurements (2012) J Bridge Eng, 17 (1), pp. 130-138; Whelan, M.J., Gangone, M.V., Janoyan, K.D., Jha, R., Operational modal analysis of a multi-span skew bridge using real-time wireless sensor networks (2011) J Vib Control, 17 (13), pp. 1952-1963; Sipple, J.D., Sanayei, M., Full-scale bridge finite-element model calibration using measured frequency-response functions (2014) J Bridge Eng, 20 (9), p. 04014103; Lynch, J.P., An overview of wireless structural health monitoring for civil structures (2007) Philos Trans R Soc A, 365 (1851), pp. 345-372; Ntotsios, E., Karakostas, C., Lekidis, V., Panetsos, P., Nikolaou, I., Papadimitriou, C., Salonikos, T., Structural identification of Egnatia Odos bridges based on ambient and earthquake induced vibrations (2009) Bull Earthq Eng, 7 (2), pp. 485-501; Whelan, M.J., Gangone, M.V., Janoyan, K.D., Hoult, N.A., Middleton, C.R., Soga, K., Wireless operational modal analysis of a multi-span prestressed concrete bridge for structural identification (2010) Smart Struct Syst, 6 (5-6), pp. 579-593; Chen, X., Omenzetter, P., Beskhyroun, S., Calibration of the finite element model of a twelve-span prestressed concrete bridge using ambient vibration data (2014) Ewshm-7Th European Workshop on Structural Health Monitoring; Teughels, A., De Roeck, G., Structural damage identification of the highway bridge Z24 by FE model updating (2004) J Sound Vib, 278 (3), pp. 589-610; Moaveni, B., Stavridis, A., Lombaert, G., Conte, J.P., Shing, P.B., Finite-element model updating for assessment of progressive damage in a 3-story infilled RC frame (2012) J Struct Eng, 139 (10), pp. 1665-1674; Kernicky, T.P., Whelan, M.J., Weggel, D.C., Rice, C.D., Structural identification and damage characterization of a masonry infill wall in a full-scale building subjected to internal blast load (2014) J Struct Eng, 141 (1), p. D4014013; Brownjohn, J., De Stefano, A., Xu, Y.-L., Wenzel, H., Aktan, A.E., Vibration-based monitoring of civil infrastructures: challenges and successes (2011) J Civil Struct Health Monit, 1 (3-4), pp. 79-95; Mitchell, M., (1998) An introduction to genetic algorithms, , MIT press, Cambridge; (2014) Global Optimization Toolbox user’s Guide, , Mathworks Inc., Natick; Ribeiro, D., Calçada, R., Delgado, R., Brehm, M., Zabel, V., Finite element model updating of a bowstring-arch railway bridge based on experimental modal parameters (2012) Eng Struct, 40, pp. 413-435; Cantieni, R., Brehm, M., Zabel, V., Rauert, T., Hoffmeister, B., Ambient testing and model updating of a filler beam bridge for high-speed trains (2008) In: Proceedings of 7Th European Conference on Structural Dynamics (EURODYN), pp. 7-9. , Southampton; Mottershead, J.E., Link, M., Friswell, M.I., The sensitivity method in finite element model updating: a tutorial (2011) Mech Syst Signal Process, 25 (7), pp. 2275-2296; Jaishi, B., Ren, W.-X., Structural finite element model updating using ambient vibration test results (2005) J Struct Eng, 131 (4), pp. 617-628; Živanović, S., Pavic, A., Reynolds, P., Finite element modelling and updating of a lively footbridge: the complete process (2007) J Sound Vib, 301 (1), pp. 126-145; Zhou, Y., Prader, J., Weidner, J., Dubbs, N., Moon, F., Aktan, A.E., Structural identification of a deteriorated reinforced concrete bridge (2011) J Bridge Eng, 17 (5), pp. 774-787; Morassi, A., Tonon, S., Dynamic testing for structural identification of a bridge (2008) J Bridge Eng, 13 (6), pp. 573-585; Schlune, H., Plos, M., Gylltoft, K., Improved bridge evaluation through finite element model updating using static and dynamic measurements (2009) Eng Struct, 31, pp. 1477-1485; Caicedo, J., Yun, G., A novel evolutionary algorithm for identifying multiple alternative solutions in model updating (2010) Struct Health Monit, 10 (5), pp. 491-501; Zimmerman, D.C., Yap, K., Hasselman, T., Evolutionary approach for model refinement (1999) Mech Syst Signal Process, 13 (4), pp. 609-625; Perera, R., Fang, S.-E., Ruiz, A., Application of particle swarm optimization and genetic algorithms to multiobjective damage identification inverse problems with modelling errors (2010) Meccanica, 45 (5), pp. 723-734; Perera, R., Ruiz, A., Manzano, C., An evolutionary multiobjective framework for structural damage localization and quantification (2007) Eng Struct, 29 (10), pp. 2540-2550; Marwala, T., (2010) Finite element model updating using computational intelligence techniques, , Springer, New York; Perera, R., Ruiz, A., A multistage FE updating procedure for damage identification in large-scale structures based on multiobjective evolutionary optimization (2008) Mech Syst Signal Process, 22 (4), pp. 970-991; Shabbir, F., Omenzetter, P., Model updating using genetic algorithms with sequential niche technique (2016) Eng Struct, 120, pp. 166-182; Lanczos, C., An iteration method for the solution of the eigenvalue problem of linear differential and integral operators (1950) J Res Natl Bur Stand, 45 (4), pp. 255-282; Schaefer, R., (2007) Foundations of global genetic optimization, , Springer, New York; Levin, R., Lieven, N., Dynamic finite element model updating using simulated annealing and genetic algorithms (1998) Mech Syst Signal Process, 12 (1), pp. 91-120; Kourehli, S., Amiri, G.G., Ghafory-Ashtiany, M., Bagheri, A., Structural damage detection based on incomplete modal data using pattern search algorithm (2012) J Vib Control, 19, pp. 821-833; Shabbir, F., Omenzetter, P., Particle swarm optimization with sequential niche technique for dynamic finite element model updating (2015) Comput Aided Civil Infrastruct Eng, 30 (5), pp. 359-375; Zimmerman, A.T., Lynch, J.P., A parallel simulated annealing architecture for model updating in wireless sensor networks (2009) IEEE Sens J, 9 (11), pp. 1503-1510; Whelan, M.J., Design and application of a wireless sensor network for vibration-based performance assessment of a tied arch bridge (2011) Structural Health monitoring 2011: condition-based maintenance and intelligent structures, pp. 709-716. , Chang F-K, (ed), Lancaster, DEStech Publications Inc; Whelan, M., Janoyan, K., Design of a robust, high-rate wireless sensor network for static and dynamic structural monitoring (2008) J Intell Mater Syst Struct, 20, pp. 849-863; Xia, Y., Chen, B., Weng, S., Ni, Y.-Q., Xu, Y.-L., Temperature effect on vibration properties of civil structures: a literature review and case studies (2012) J Civil Struct Health Monit, 2 (1), pp. 29-46; Van Overschee, P., DeMoor, B., (1996) Subspace identification for linear systems, , Kluwer Academic Press, Dordrecht; Brownjohn, J., Magalhaes, F., Caetano, E., Cunha, A., Ambient vibration re-testing and operational modal analysis of the humber bridge (2010) Eng Struct, 32 (8), pp. 2003-2018; Allemang, R.J., The modal assurance criterion-twenty years of use and abuse (2003) Sound Vib, 37 (8), pp. 14-23; Giraldo, D.F., Song, W., Dyke, S.J., Caicedo, J.M., Modal identification through ambient vibration: comparative study (2009) J Eng Mech, 135 (8), pp. 759-770; Whelan, M., Kernicky, T., Zamudio, N., Structural identification of large finite element models using commodity computing clusters for parallel genetic algorithms (2015) Structural Health Monitoring 2015: System Reliability for Verification and Implementation; Friswell, M., Mottershead, J.E., (1995) Finite element model updating in structural dynamics, , Springer, New York; Janter, T., Sas, P., Uniqueness aspects of model-updating procedures (1990) AIAA J, 28 (3), pp. 538-543; Zárate, B.A., Caicedo, J.M., Finite element model updating: multiple alternatives (2008) Eng Struct, 30 (12), pp. 3724-3730; Jaishi, B., Kim, H.-J., Kim, M.K., Ren, W.-X., Lee, S.-H., Finite element model updating of concrete-filled steel tubular arch bridge under operational condition using modal flexibility (2007) Mech Syst Signal Process, 21 (6), pp. 2406-2426; Friswell, M., Penny, J., Garvey, S., A combined genetic and eigensensitivity algorithm for the location of damage in structures (1998) Comput Struct, 69 (5), pp. 547-556; Hao, H., Xia, Y., Vibration-based damage detection of structures by genetic algorithm (2002) J Comput Civil Eng, 16 (3), pp. 222-229; Goller, B., Beck, J., Schueller, G., Evidence-based identification of weighting factors in bayesian model updating using modal data (2011) J Eng Mech, 138 (5), pp. 430-440; Kernicky, T., Whelan, M., Rauf, U., Al-Shaer, E., Structural identification using a nonlinear constraint satisfaction processor with interval arithmetic and contractor programming (2017) Comput Struct, 188, pp. 1-16","Whelan, M.; University of North Carolina at Charlotte, United States; email: M.Whelan@uncc.edu",,,"Springer Verlag",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85045005591
"You R., Ren L., Yuan C., Song G.","57207773675;8931198600;57188695194;57218184211;","Two-Dimensional Deformation Estimation of Beam-Like Structures Using Inverse Finite-Element Method: Theoretical Study and Experimental Validation",2021,"Journal of Engineering Mechanics","147","5","04021019","","",,7,"10.1061/(ASCE)EM.1943-7889.0001917","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101622426&doi=10.1061%2f%28ASCE%29EM.1943-7889.0001917&partnerID=40&md5=11531023457ba4aeeb1ced08654e9bc6","State Key Laboratory of Coastal and Offshore Engineering, School of Civil and Hydraulic Engineering, Dalian Univ. of Technology, Dalian, 116024, China; Smart Materials and Structures Laboratory, Dept. of Mechanical Engineering, Univ. of Houston, Houston, TX  77004, United States","You, R., State Key Laboratory of Coastal and Offshore Engineering, School of Civil and Hydraulic Engineering, Dalian Univ. of Technology, Dalian, 116024, China; Ren, L., State Key Laboratory of Coastal and Offshore Engineering, School of Civil and Hydraulic Engineering, Dalian Univ. of Technology, Dalian, 116024, China; Yuan, C., State Key Laboratory of Coastal and Offshore Engineering, School of Civil and Hydraulic Engineering, Dalian Univ. of Technology, Dalian, 116024, China; Song, G., Smart Materials and Structures Laboratory, Dept. of Mechanical Engineering, Univ. of Houston, Houston, TX  77004, United States","The real-time estimation of structural deformations using discrete strain data, known as shape sensing, is critical to the health monitoring of structures such as bridges. An innovative methodology called the inverse finite-element method (iFEM) is proposed to solve this issue. In this paper, a novel two-node inverse beam element, iBeam3, is developed for two-dimensional deformation monitoring of beam type structures. The present iFEM formulation is derived based on the least-squares variational principle involving section strains of Euler-Bernoulli beam theory for stretching and bending. The iBeam3 element is able to reconstruct deformed shapes without any prior material and/or loading information because only the strain-displacement relationship is used in the formulation. Static and dynamic validation cases regarding steel beams with different boundary conditions subjected to transverse force are discussed in detail. In the tests, different discretization strategies are used to perform the iFEM analysis, and the effects of sensor positions, number of sensors, and measurement errors are evaluated with respect to iFEM-predicted accuracy. The experimental results demonstrate that the iBeam3 element is accurate, robust, and highly efficient. The present methodology provides promising potential in the real-time shape sensing of civil infrastructures. © 2021 American Society of Civil Engineers.","Euler-Bernoulli beam; Inverse finite-element method; Shape sensing; Structural health monitoring","Continuum mechanics; Deformation; Inverse problems; Strain; Structural health monitoring; Different boundary condition; Euler Bernoulli beam theory; Experimental validations; Health monitoring of structure; Innovative methodologies; Inverse finite element methods; Structural deformation; Variational principles; Finite element method; deformation; estimation method; experimental study; finite element method; health monitoring; inverse analysis; shape; structural component; theoretical study",,,,,,,,,,,,,,,,"Amanzadeh, M., Aminossadati, S.M., Kizil, M.S., Rakic, A.D., Recent developments in fibre optic shape sensing (2018) Measurement, 128 (NOV), pp. 119-137. , https://doi.org/10.1016/j.measurement.2018.06.034; Bang, H.J., Ko, S.W., Jang, M.S., Kim, H.I., (2012) Shape Estimation and Health Monitoring of Wind Turbine Tower Using A FBG Sensor Array, pp. 496-500. , Proc. 2012 IEEE Int. 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Control Health Monit., 20 (5), pp. 649-670. , https://doi.org/10.1002/stc.1501; Yin, X.F., Liu, Y., Song, G., Mo, Y.L., Suppression of bridge vibration induced by moving vehicles using pounding tuned mass dampers (2018) J. Bridge Eng., 23 (7). , https://doi.org/10.1061/(ASCE)BE.1943-5592.0001256, 04018047; You, R.Z., Ren, L., Song, G.B., A novel OFDR-based distributed optical fiber sensing tape: Design, optimization, calibration and application (2020) Smart Mater. Struct., 29 (10), p. 105017. , https://doi.org/10.1088/1361-665X/ab939a; Zhang, J., Tian, Y.D., Yang, C.Q., Wu, B.T., Wu, Z.S., Wu, G., Zhang, X., Zhou, L.M., Vibration and deformation monitoring of a long-span rigid-frame bridge with distributed long-gauge sensors (2017) J. Aerosp. Eng., 30 (2). , https://doi.org/10.1061/(ASCE)AS.1943-5525.0000678, B4016014; Zhao, X.F., Gou, J.H., Song, G., Ou, J.P., Strain monitoring in glass fiber reinforced composites embedded with carbon nanopaper sheet using Fiber Bragg Grating (FBG) sensors (2009) Compos. Pt. B-Eng., 40 (2), pp. 134-140. , https://doi.org/10.1016/j.compositesb.2008.10.007; Zhou, L.Z., Zheng, Y., Song, G., Chen, D.D., Ye, Y.X., Identification of the structural damage mechanism of BFRP bars reinforced concrete beams using smart transducers based on time reversal method (2019) Constr. Build. Mater., 220 (SEP), pp. 615-627. , https://doi.org/10.1016/j.conbuildmat.2019.06.056","Ren, L.; State Key Laboratory of Coastal and Offshore Engineering, China; email: renliang@dlut.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,07339399,,,,"English","J. Eng. Mech.",Article,"Final","",Scopus,2-s2.0-85101622426
"Wang Y.W., Ni Y.Q., Zhang Q.H., Zhang C.","56512491400;7402910024;56152014700;57203259163;","Bayesian approaches for evaluating wind-resistant performance of long-span bridges using structural health monitoring data",2021,"Structural Control and Health Monitoring","28","4","e2699","","",,7,"10.1002/stc.2699","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099274922&doi=10.1002%2fstc.2699&partnerID=40&md5=3ffe25facadee8308f10fd22d1061bf8","Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong","Wang, Y.W., Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; Ni, Y.Q., Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; Zhang, Q.H., Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; Zhang, C., Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong","Reliable estimation of wind-induced displacement responses of long-span bridges is critical to evaluating their wind-resistant performance. In this study, two Bayesian approaches, Bayesian generalized linear model (BGLM) and sparse Bayesian learning (SBL), are proposed for characterizing the wind-induced lateral displacement responses of long-span bridges with structural health monitoring (SHM) data. They are fully model-free data-driven approaches, preferable for reckoning the wind-induced total displacement intended for wind-resistant performance assessment. With the measured displacement responses and wind speeds, a BGLM is developed to characterize the nonlinear relationship between the total displacement response and wind speed, where the Bayesian model class selection (BMCS) criterion is incorporated to determine the optimal model. In the model formulation by SBL, both wind speed and wind direction are treated as explanatory variables to elicit a probabilistic model with sparse structure. The SBL cleverly makes the resulting model to exempt from overfitting and generalizes well on unseen data. The two formulated models are then utilized to forecast the wind-induced displacement responses in extreme typhoon events beyond the monitoring scope, and the predicted displacement responses are contrasted to the finite element analysis results and the design maximum allowable displacement under the serviceability limit state (SLS). The proposed methods are demonstrated using the monitoring data acquired by GPS sensors and anemometers instrumented on a long-span suspension bridge. The results show that the SBL model is superior to the BGLM for wind-induced displacement response prediction and is amenable to SHM-based evaluation of wind-resistant performance under extreme typhoon conditions. © 2021 The Authors. Structural Control and Health Monitoring published by John Wiley & Sons Ltd.","Bayesian generalized linear model; long-span bridge; sparse Bayesian learning; structural health monitoring; wind-resistant performance","Bayesian networks; Hurricanes; Monitoring; Wind; Generalized linear model; Long span suspension bridges; Non-linear relationships; Serviceability limit state; Sparse Bayesian learning (SBL); Structural health monitoring (SHM); Wind resistant performance; Wind-induced displacements; Structural health monitoring",,,,,"K‐BBY1; PolyU 152014/18E; National Natural Science Foundation of China, NSFC: U1934209; Research Grants Council, University Grants Committee, RGC, UGC; Innovation and Technology Commission - Hong Kong","Research Grants Council of the Hong Kong Special Administrative Region, China, Grant/Award Number: PolyU 152014/18E; National Natural Science Foundation of China, Grant/Award Number: U1934209; Innovation and Technology Commission of Hong Kong SAR Government, Grant/Award Number: K‐BBY1 Funding information","The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (grant no. PolyU 152014/18E) and a grant from the National Natural Science Foundation of China (grant no. U1934209). The authors would also like to appreciate the funding support by the Innovation and Technology Commission of Hong Kong SAR Government to the Hong Kong Branch of Chinese National Rail Transit Electrification and Automation Engineering Technology Research Center (grant no. K‐BBY1).","The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (grant no. PolyU 152014/18E) and a grant from the National Natural Science Foundation of China (grant no. U1934209). The authors would also like to appreciate the funding support by the Innovation and Technology Commission of Hong Kong SAR Government to the Hong Kong Branch of Chinese National Rail Transit Electrification and Automation Engineering Technology Research Center (grant no. 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Lam, H.F., Hu, J., Zhang, F.L., Ni, Y.C., Markov chain Monte Carlo-based Bayesian model updating of a sailboat-shaped building using a parallel technique (2019) Eng Struct, 193, pp. 12-27; Wang, Y.W., Ni, Y.Q., Wang, X., Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model (2020) Mech Syst Signal Process, 139, p. 106654; Wang, Y.W., Ni, Y.Q., Bayesian dynamic forecasting of structural strain response using structural health monitoring data (2020) Struct Control Health Monit, 27 (8); Ni, Y.Q., Zhang, Q.H., A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring (2021) Struct Health Monit, , https://doi.org/10.1177/1475921720921772; Gelfand, A.E., Smith, A.F.M., Sampling-based approaches to calculating marginal densities (1990) J Am Stat Assoc, 85 (410), pp. 398-409; Gull, S.F., Bayesian inductive inference and maximum entropy (1998) Maximum-Entropy and Bayesian Methods in Science and Engineering, , Erickson G, Smith CR, eds., Dordrecht, Netherlands, Springer; 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Wong, K.Y., Design of a structural health monitoring system for long-span bridges (2007) Struct Inf Eng, 3 (2), pp. 169-185; Ni, Y.Q., Wong, K.Y., Xia, Y., Health checks through landmark bridges to sky-high structures (2011) Adv Struct Eng, 14 (1), pp. 103-119; (2013) Report of Tropical Cyclones in 2011, , Hong Kong; Hua, X.G., Xu, K., Wang, Y.W., Wen, Q., Chen, Z.Q., Wind-induced responses and dynamic characteristics of a super-tall building under a typhoon event (2020) Smart Struct Syst, 25 (1), pp. 81-96; Xu, Y.L., Xia, Y., (2011) Structural Health Monitoring of Long-Span Suspension Bridges, , New York, USA, CRC Press; Chen, X., Kareem, A., Equivalent static wind loads for buffeting response of bridges (2001) J Struct Eng, 127 (12), pp. 1467-1475; Liu, T.T., Xu, Y.L., Zhang, W.S., Wong, K.Y., Zhou, H.J., Chan, K.W.Y., Buffeting-induced stresses in a long suspension bridge: structural health monitoring oriented stress analysis (2009) Wind Struct, 12 (6), pp. 479-504","Ni, Y.Q.; Department of Civil and Environmental Engineering, Hong Kong; email: ceyqni@polyu.edu.hk
Ni, Y.Q.; National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), Hong Kong; email: ceyqni@polyu.edu.hk",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85099274922
"Civera M., Fragonara L.Z., Surace C.","57191891720;57200215700;56265956500;","A Computer Vision-Based Approach for Non-contact Modal Analysis and Finite Element Model Updating",2021,,"127",,,"481","493",,7,"10.1007/978-3-030-64594-6_47","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101223010&doi=10.1007%2f978-3-030-64594-6_47&partnerID=40&md5=cef5c5b674f8a22289b769f61bf82e67","Department of Mechanical and Aerospace Engineering-DIMEAS, Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin, 10129, Italy; Centre for Autonomous and Cyber-Physical Systems, Cranfield University, Cranfield, Bedford, MK43 0AL, United Kingdom; Department of Structural, Geotechnical and Building Engineering-DISEG, Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin, 10129, Italy","Civera, M., Department of Mechanical and Aerospace Engineering-DIMEAS, Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin, 10129, Italy; Fragonara, L.Z., Centre for Autonomous and Cyber-Physical Systems, Cranfield University, Cranfield, Bedford, MK43 0AL, United Kingdom; Surace, C., Department of Structural, Geotechnical and Building Engineering-DISEG, Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin, 10129, Italy","Computer vision-based techniques for modal analysis and system identification are rapidly becoming of great interest for both academic research and engineering practice in structural engineering. For instance, this is particularly relevant in fields such as bridge or tall building monitoring, where the large size of the structure would require an expensive sensor network, and for the characterisation of very slender, highly-flexible structural components, where physically-attached sensors cannot be deployed without altering the mass and stiffness of the system under investigation. This study concerns the latter case. Here, an algorithm for the full-field, non-contact extraction and processing of useful information from vibrational data is applied. Firstly, video acquisition is used to capture rapidly very spatially- and temporally-dense information regarding the vibrational behaviour of a high-aspect-ratio (HAR) prototype wing, with high image quality and high frame rate. Video processing is then applied to extract displacement time histories from the collected data; in turn, these are used to perform Modal Analysis (MA) and Finite Element Model Updating (FEMU). Results are benchmarked against the ones obtained from a single-point laser Doppler vibrometer (LDV). The study is performed on the beam-like spar of the wing prototype with and without the sensors attached to appreciate the disruptive effects of sensor loading. Promising results were achieved. © 2021, Springer Nature Switzerland AG.","Computer vision; Experimental modal analysis; Model updating; Parameter estimation; System identification; Video processing","Aspect ratio; Computer vision; Data mining; Laser Doppler velocimeters; Modal analysis; Sensor networks; Structural analysis; Structural health monitoring; Tall buildings; Video signal processing; Displacement-time history; Disruptive effects; Engineering practices; Finite-element model updating; High image quality; Laser Doppler vibrometers; Structural component; Vision-based approaches; Finite element method",,,,,,,,,,,,,,,,"Civera, M., Surace, C., Worden, K., (2017) Detection of Cracks in Beams Using Treed Gaussian Processes, pp. 85-97. , , pp. , Springer, Cham; Martucci, D., Civera, M., Surace, C., Worden, K., Novelty detection in a cantilever beam using extreme function theory (2018) J. Phys: Conf. Ser., 1106; Dussart, G., Portapas, V., Pontillo, A., Lone, M., Flight dynamic modelling and simulation of large flexible aircraft (2018) Flight Physics-Models, Techniques and Technologies, , InTech; Pontillo, A., Hayes, D., Dussart, G.X., Lopez Matos, G.E., Carrizales, M.A., Yusuf, S.Y., Lone, M.M., Flexible high aspect ratio wing: Low cost experimental model and computational framework (2018) Proceedings of the 2018 AIAA Atmospheric Flight Mechanics Conference, , American Institute of Aeronautics and Astronautics, Reston, Virginia; Mottershead, J.E., Friswell, M.I., Model updating in structural dynamics: A survey (1993) J. Sound Vib., 167, pp. 347-375; Sehgal, S., Kumar, H., Structural dynamic model updating techniques: A state of the art review (2015) Arch. Comput. Meth. Eng., 23 (3), pp. 515-533; Modak, S.V., Kundra, T.K., Nakra, B.C., Comparative study of model updating methods using simulated experimental data (2002) Comput. Struct., 80, pp. 437-447; Boscato, G., Russo, S., Ceravolo, R., Fragonara, L.Z., Global sensitivity-based model updating for heritage structures (2015) Comput. Civ. Infrastruct. Eng., 30, pp. 620-635; Friswell, M., Mottershead, J.E., (2013) Finite Element Model Updating in Structural Dynamics; Mottershead, J.E., Link, M., Friswell, M.I., The sensitivity method in finite element model updating: A tutorial (2011) Mech. Syst. Signal Process., 25, pp. 2275-2296; Ewins, D.J., Adjustment or updating of models. Sadhana-Acad (2000) Proc. Eng. Sci., 25, pp. 235-245; Hooke, R., Jeeves, T.A., Direct search” solution of numerical and statistical problems (1961) J. ACM, 8, pp. 212-229; Civera, M., Ferraris, M., Ceravolo, R., Surace, C., Betti, R., The Teager-Kaiser energy cepstral coefficients as an effective structural health monitoring tool (2019) Appl. Sci., 9, p. 5064; Civera, M., Zanotti Fragonara, L., Surace, C., Video processing techniques for the contactless investigation of large oscillations Proceedings of the Proceedings of the AIVELA XXVI Meeting, , https://doi.org/10.1088/1742-6596/1249/1/012004; Civera, M., Zanotti Fragonara, L., Surace, C., Nonlinear dynamics of cracked, cantilevered beam-like structures undergoing large deflections (2019) Proceedings of the Metrology for Aerospace; Savitzky, A., Golay, M.J.E., Smoothing and differentiation of data by simplified least squares procedures (1964) Anal. Chem., 36, pp. 1627-1639; Wadhwa, N., Rubinstein, M., Durand, F., Freeman, W.T., Phase-based video motion processing (2013) ACM Trans. Graph., 32, p. 1; Civera, M., Zanotti Fragonara, L., Surace, C., Using video processing for the full-field identification of backbone curves in case of large vibrations (2019) Sensors, 19, p. 2345; Civera, M., Zanotti Fragonara, L., Surace, C., An experimental study of the feasibility of phase-based video magnification for damage detection and localisation in operational deflection shapes (2020) Strain, 56 (1); Civera, M., Calamai, G., Zanotti Fragonara, L., Experimental Modal analysis of structural systems by using the fast relaxed vector fitting method. Accepted for publication (2020) Structural Control and Health Monitoring","Civera, M.; Department of Mechanical and Aerospace Engineering-DIMEAS, Corso Duca degli Abruzzi 24, Italy; email: marco.civera@polito.it","Rizzo P.Milazzo A.",,"Springer Science and Business Media Deutschland GmbH","European Workshop on Structural Health Monitoring, EWSHM 2020","6 July 2020 through 9 July 2020",,254359,23662557,9783030645939,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85101223010
"Zhang J., Au F.T.K., Yang D.","55907824800;7005204072;55768563000;","Finite element model updating of long-span cable-stayed bridge by Kriging surrogate model",2020,"Structural Engineering and Mechanics","74","2",,"157","173",,7,"10.12989/sem.2020.74.2.157","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084181158&doi=10.12989%2fsem.2020.74.2.157&partnerID=40&md5=f91360658242fe8d7e2e3a2493e71e0f","Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui Province, China; Department of Civil Engineering, University of Hong Kong, Pokfulam Road, Hong Kong","Zhang, J., Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui Province, China, Department of Civil Engineering, University of Hong Kong, Pokfulam Road, Hong Kong; Au, F.T.K., Department of Civil Engineering, University of Hong Kong, Pokfulam Road, Hong Kong; Yang, D., Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui Province, China, Department of Civil Engineering, University of Hong Kong, Pokfulam Road, Hong Kong","In the finite element modelling of long-span cable-stayed bridges, there are a lot of uncertainties brought about by the complex structural configuration, material behaviour, boundary conditions, structural connections, etc. In order to reduce the discrepancies between the theoretical finite element model and the actual static and dynamic behaviour, updating is indispensable after establishment of the finite element model to provide a reliable baseline version for further analysis. Traditional sensitivity-based updating methods cannot support updating based on static and dynamic measurement data at the same time. The finite element model is required in every optimization iteration which limits the efficiency greatly. A convenient but accurate Kriging surrogate model for updating of the finite element model of cable-stayed bridge is proposed. First, a simple cable-stayed bridge is used to verify the method and the updating results of Kriging model are compared with those using the response surface model. Results show that Kriging model has higher accuracy than the response surface model. Then the method is utilized to update the model of a long-span cable-stayed bridge in Hong Kong. The natural frequencies are extracted using various methods from the ambient data collected by the Wind and Structural Health Monitoring System installed on the bridge. The maximum deflection records at two specific locations in the load test form the updating objective function. Finally, the fatigue lives of the structure at two cross sections are calculated with the finite element models before and after updating considering the mean stress effect. Results are compared with those calculated from the strain gauge data for verification. © 2020 Techno-Press, Ltd.","Cable-stayed bridge; Fatigue life; Health monitoring; Mean stress effect; Model updating; Surrogate model","Buffeting; Cable stayed bridges; Cables; Interpolation; Iterative methods; Load testing; Strain gages; Structural health monitoring; Surface properties; Uncertainty analysis; Finite element modelling; Finite-element model updating; Kriging surrogate model; Long span cable stayed bridges; Response surface modeling; Static and dynamic behaviours; Structural configurations; Structural health monitoring systems; Finite element method",,,,,"National Natural Science Foundation of China, NSFC: 51608162","The authors gratefully acknowledge the Highways Department of the Hong Kong Government for assistance received in producing this paper as well as permission of its publication. Any opinions expressed or conclusions reached in the paper are entirely of the authors. Financial support from the National Natural Science Foundation of China under Grant No. 51608162 is acknowledged.",,,,,,,,,,"Ahmadian, H., Gladwell, G.M.L., Ismail, F., Parameter selection strategies in finite element model updating (1997) J. Vib. Acoust., 119 (1), pp. 37-45; (2009) Structural Analysis Guide, Canonsburg, , ANSYS Multiphysics 12.0; Arora, V., FE model updating method incorporating damping matrices for structural dynamic modifications (2014) Struct. Eng. Mech., 52 (2), pp. 261-274; Au, F.T.K., Tham, L.G., Lee, P.K.K., Su, C., Han, D.J., Yan, Q.S., Wong, K.Y., Ambient vibration measurements and finite element modelling for the Hong Kong Ting Kau Bridge (2003) Struct. Eng. Mech., 15 (1), pp. 115-134; Au, F.T.K., Lou, P., Li, J., Jiang, R.J., Zhang, J., Leung, C.C.Y., Lee, P.K.K., Chan, H.Y., Simulation of vibrations of Ting Kau Bridge due to vehicular loading from measurements (2011) Struct. Eng. 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Geoenviron., 139 (4), pp. 651-655","Yang, D.; Department of Civil Engineering, China; email: yangdong@hfut.edu.cn",,,"Techno-Press",,,,,12254568,,SEGME,,"English","Struct Eng Mech",Article,"Final","",Scopus,2-s2.0-85084181158
"Górski P., Stankiewicz B., Tatara M.","24080223700;25637967700;57162931600;","Structural evaluation of all-GFRP cable-stayed footbridge after 20 years of service life",2018,"Steel and Composite Structures","29","4",,"527","544",,7,"10.12989/scs.2018.29.4.527","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057291006&doi=10.12989%2fscs.2018.29.4.527&partnerID=40&md5=df15f770cf0e3f44b8c145639c51f188","Department of Roads and Bridges, Faculty of Civil Engineering and Architecture, Opole University of Technology, Katowicka 48 Street, Opole, 45-061, Poland","Górski, P., Department of Roads and Bridges, Faculty of Civil Engineering and Architecture, Opole University of Technology, Katowicka 48 Street, Opole, 45-061, Poland; Stankiewicz, B., Department of Roads and Bridges, Faculty of Civil Engineering and Architecture, Opole University of Technology, Katowicka 48 Street, Opole, 45-061, Poland; Tatara, M., Department of Roads and Bridges, Faculty of Civil Engineering and Architecture, Opole University of Technology, Katowicka 48 Street, Opole, 45-061, Poland","The paper presents the study on a change in modal parameters and structural stiffness of cable-stayed Fiberline Bridge made entirely of Glass Fiber Reinforced Polymer (GFRP) composite used for 20 years in the fjord area of Kolding, Denmark. Due to this specific location the bridge structure was subjected to natural aging in harsh environmental conditions. The flexural properties of the pultruded GFRP profiles acquired from the analyzed footbridge in 1997 and 2012 were determined through three-point bending tests. It was found that the Young's modulus increased by approximately 9%. Moreover, the influence of the temperature on the storage and loss modulus of GFRP material acquired from the Fiberline Bridge was studied by the dynamic mechanical analysis. The good thermal stability in potential real temperatures was found. The natural vibration frequencies and mode shapes of the bridge for its original state were evaluated through the application of the Finite Element (FE) method. The initial FE model was created using the real geometrical and material data obtained from both the design data and flexural test results performed in 1997 for the intact composite GFRP material. Full scale experimental investigations of the free-decay response under human jumping for the experimental state were carried out applying accelerometers. Seven natural frequencies, corresponding mode shapes and damping ratios were identified. The numerical and experimental results were compared. Based on the difference in the fundamental natural frequency it was again confirmed that the structural stiffness of the bridge increased by about 9% after 20 years of service life. Data collected from this study were used to validate the assumed FE model. It can be concluded that the updated FE model accurately reproduces the dynamic behavior of the bridge and can be used as a proper baseline model for the long-term monitoring to evaluate the overall structural response under service loads. The obtained results provided a relevant data for the structural health monitoring of all-GFRP bridge. © 2018 Techno-Press, Ltd.","dynamic characteristics; dynamic mechanical analysis; finite element analysis; free-decay vibration; GFRP bridge structure; structural stiffness","Bending tests; Cables; Digital storage; Dynamic mechanical analysis; Elastic moduli; Fiber reinforced plastics; Footbridges; Modal analysis; Natural frequencies; Stiffness; Structural analysis; Structural health monitoring; Vibration analysis; Bridge structures; Change in modal parameters; Dynamic characteristics; Experimental investigations; Free decay; Glass fiber reinforced polymer; Natural vibration frequencies and mode shapes; Structural stiffness; Finite element method",,,,,,,,,,,,,,,,"Apicella, A., Migliaresi, C., Nicodemo, L., Nicolais, L., Iaccarino, L., Roccotelli, S., Water sorption and mechanical properties of a glass-reinforced polyester resin (1982) Compos., 13 (4), pp. 406-410; Ascione, F., Mancusi, G., Spadea, S., Lamberti, M., Lebon, F., Maurel-Pantel, A., On the flexural behaviour of GFRP beams obtained by bonding simple panels: An experimental investigation (2015) Compos. Struct., 131, pp. 55-65; Baccaroninskas, D., Rimkus, A., Rumšys, D., Meškenas, A., Bielinis, S., Sokolov, A., Merkevicius, T., Structural analysis of GFRP truss bridge model (2017) Procedia Eng., 172, pp. 68-74; Bai, Y., Keller, T., Modal parameter identification for a GFRP pedestrian bridge (2008) Compos. Struct., 82 (1), pp. 90-100; Brincker, R., Zhang, L., Andersen, P., Modal identification from ambient responses using frequency domain decomposition (2000) Proceedings of the 18th International Modal Analysis Conference, , Kissimmee, FL, USA, February; Brownjohn, J.M.W., Bocian, M., Hester, D., Quattrone, A., Hudson, W., Moore, D., Goh, S., Lim, M.S., Footbridge system identification using wireless inertial measurement units for force and response measurements (2016) J. 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Struct., Int. J., 23 (2), pp. 205-215; Wu, H.C., Fu, G., Gibson, R.F., Yan, A., Durability of FRP composite bridge deck materials under freeze-thaw and low temperature conditions (2006) J. Bridge Eng., 11 (4), pp. 443-451","Górski, P.; Department of Roads and Bridges, Katowicka 48 Street, Poland; email: p.gorski@po.opole.pl",,,"Techno Press",,,,,12299367,,,,"English","Steel Compos. Struct.",Article,"Final","",Scopus,2-s2.0-85057291006
"Zhong W., Ding Y.-L., Song Y.-S., Zhao H.-W.","57201157161;55768944900;55494118800;57191694306;","Fatigue behavior evaluation of full-field hangers in a rigid tied arch high-speed railway bridge: Case study",2018,"Journal of Bridge Engineering","23","5","05018003","","",,7,"10.1061/(ASCE)BE.1943-5592.0001235","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043585738&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001235&partnerID=40&md5=701b53d926bfc02ae9ed8486931629e5","Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast Univ, 2 Sipailou Rd, Xuanwu District, Nanjing, 210096, China; Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast Univ., 2 Sipailou Rd., Xuanwu District, Nanjing, 210096, China; Jinling Institute of Technology, 99 Hongjing Avenue, Jiangning District, Nanjing, 211169, China; Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast Univ., 2 Sipailou Rd, Xuanwu District, Nanjing, 210096, China","Zhong, W., Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast Univ, 2 Sipailou Rd, Xuanwu District, Nanjing, 210096, China; Ding, Y.-L., Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast Univ., 2 Sipailou Rd., Xuanwu District, Nanjing, 210096, China; Song, Y.-S., Jinling Institute of Technology, 99 Hongjing Avenue, Jiangning District, Nanjing, 211169, China; Zhao, H.-W., Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast Univ., 2 Sipailou Rd, Xuanwu District, Nanjing, 210096, China","The steel truss arch is an important structural type for high-speed railway bridges with long spans. The fatigue assessment of rigid hangers under long-Term train loads is an important concern. In this study, the Nanjing Dashengguan Bridge, a six-line railway steel arch bridge with three planes of truss arches and the largest span in the world, was taken as a case study. First, a fatigue assessment of one short hanger was carried out based on dynamic strain monitoring data in 2015. The influence of bending behavior, train lane, number of carriages, and train speed on the fatigue performance of the short hanger were investigated. Second, the fatigue damage distribution of full-field hangers induced by a single train was analyzed based on a calibrated finite-element model. The influence of driving direction on the fatigue performance of rigid hangers was discussed. Finally, an engineering approach for fatigue performance assessment of full-field hangers was proposed based on annual train volume. Consequently, it was shown that the bending behavior has considerable influence on the fatigue effects of short hangers. In addition, train lane and driving direction also have a significant influence on the fatigue performance of hangers. The fatigue damage of short hangers in the middle truss arch was the most remarkable, followed by the side truss arch on the downstream side (the Beijing-Shanghai High Speed Railway) and then by the side truss arch on the upstream side (the Shanghai-Wuhan-Chengdu Railway), which should be paid greater attention to in daily maintenance. © 2018 American Society of Civil Engineers.","Fatigue damage; Finite-element model; Rigid hanger; Steel truss arch bridge; Structural health monitoring","Arch bridges; Arches; Finite element method; Railroad bridges; Railroad engineering; Railroad plant and structures; Railroad transportation; Railroads; Steel bridges; Structural health monitoring; Trusses; Vehicle performance; Damage distribution; Fatigue assessments; Fatigue performance; High - speed railways; High-speed railway bridges; Rigid hanger; Steel arch bridges; Steel truss-arch bridge; Fatigue damage",,,,,,,,,,,,,,,,"Design of steel structures, part 1-9: Fatigue (1993) Eurocode 3, , CEN (European Committee for Standardization). 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Bridge Eng., p. 05016005; Sun, Z., Zou, Z., Zhang, Y., Utilization of structural health monitoring in long-span bridges: Case studies (2017) Struct. Control Health Monit., 24 (10), pp. 1-12; Turmo, J., Luco, J.E., Effect of hanger flexibility on dynamic response of suspension bridges (2010) J. Eng. Mech., pp. 1444-1459; Ye, X.W., Ni, Y.Q., Wong, K.Y., Ko, J.M., Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data (2012) Eng. Struct., 45, pp. 166-176; Zhao, C.F., Basic scientific issues on dynamic performance evolution of the high-speed railway infrastructure and its service safety (2014) Sci. Sin., 44 (7), pp. 645-660. , (in Chinese); Zhao, H.W., Ding, Y.L., An, Y.H., Li, A.Q., Transverse dynamic mechanical behavior of hangers in the rigid tied-Arch bridge under train loads (2017) J. Perform. Constr. Facil., p. 04016072; Zhou, Y.E., Beecher, J.B., Guzda, M.K., Cunningham, D.R.I.I., Investigation and retrofit of distortion-induced fatigue cracks in a double-deck cantilever-suspended steel truss bridge (2015) J. Struct. Eng., p. D4014011","Ding, Y.-L.; Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, China; email: civilchina@hotmail.com",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85043585738
"Jamali S., Chan T.H.T., Nguyen A., Thambiratnam D.P.","57201483048;7402687570;57310688400;35583914600;","Modelling techniques for structural evaluation for bridge assessment",2018,"Journal of Civil Structural Health Monitoring","8","2",,"271","283",,7,"10.1007/s13349-018-0269-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045039474&doi=10.1007%2fs13349-018-0269-4&partnerID=40&md5=2bd884fe4742b70c1c1211539ae91aed","Department of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia","Jamali, S., Department of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia; Chan, T.H.T., Department of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia; Nguyen, A., Department of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia; Thambiratnam, D.P., Department of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia","Load assessment of existing bridges in Australia is evaluated mainly using beam line model and the grillage analogy to examine the structural integrity of bridge components due to live loadings. With the majority of existing bridge networks designed for superseded design vehicular loading, the necessity to utilise more rigorous analysis methods to assess the load effects of bridges is indispensable. In this paper, various vehicular loading cases on a grillage model of a box girder bridge and its equivalent finite element model (FE) are considered, and their applicability for bridge assessment using structural health monitoring (SHM) as defined in the new revision of AS 5100.7 is studied. Based on numerical analyses, it was observed that component-level load effects in the two models have notable differences, irrespective of vehicle speed, position and loading. However, when global-level load responses are compared, the discrepancy in analysis outputs drops dramatically. The modelling ratios developed in this paper are practical and will be applicable with any modelling techniques for bridge assessment under vehicular loading on both a global and component-response basis. It was also observed that FE is more efficient in terms of model updating and damage simulation, and hence more appropriate for implementation of SHM techniques. The proposed flowchart suggested for heavy load assessment incorporates detailed and simple modelling approaches aligned with experimental data obtained by SHM techniques, which can be used for periodic and long-term monitoring of bridges. It can enhance the proper determination of bridge condition states, as any conservative estimation of bridge capacity may result in unnecessary load limitations. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.","Bridge assessment; Finite element model; Grillage analogy; Load assessment; Structural health monitoring","Box girder bridges; Bridge components; Finite element method; Steel bridges; Structural health monitoring; Bridge assessment; Grillage analogy; Long term monitoring; Modelling techniques; Rigorous analysis; Structural evaluation; Structural health monitoring (SHM); Vehicular loading; Loading",,,,,"Queensland University of Technology, QUT","Acknowledgements The first author is thankful for full financial support provided by Queensland University of Technology. Also, valuable comments provided by Prof. Eugene O’Brien for grillage modelling is appreciated.",,,,,,,,,,"Shaw, P., Pritchard, R., Heywood, R., Bridge analysis: Are we data managers or engineers? (2014) Proceedings of 9Th Austroads Bridge Conference, , Sydney, New South Wales, Australia; Morrison, S., Moses, J., Benefits and uses of FE modelling in bridge assessment and design (2011) Proceedings of the 8Th Austroads Bridge Conference, , Sydney, New South Wales, Australia; Jamali, S., Chan, T.H.T., Thambiratnam, D.P., Ross Pritchard, Nguyen A (2016) Pre-test finite element modelling of box girder overpass-application for bridge condition assessment Proceedings of the Australasian Structural Engineering Conference (ASEC), , Brisbane, Australia; Jaeger, L.G., Bakht, B., The grillage analogy in bridge analysis (1982) Can J Civ Eng, 9 (2), pp. 224-235; Lu, P., Li, F., Shao, C., Analysis of a T-frame bridge (2012) Math Probl Eng, 2012. , http://dx.doi.org/10.1155/2012/640854; Sadeghi, J., Fathali, M., Grillage analogy applications in analysis of bridge decks (2012) Aust J Civ Eng, 10 (1), pp. 23-36; Yang, M., Zhong, H., Telste, M., Gajan, S., Bridge damage localization through modified curvature method (2016) J Civ Struct Health Monit, 6 (1), pp. 175-188; McElwain, B.A., Laman, J.A., Experimental verification of horizontally curved I-girder bridge behavior (2000) J Bridge Eng, 5 (4), pp. 284-292; Krzmarzick, D.P., Hajjar, J.F., Load rating of curved composite steel I-girder bridges through load testing with heavy trucks (2006) Structures Congress, , https://doi.org/10.1061/40889(201)149; (2013) Tier 1 Heavy Vehicle Bridge Assessment Criteria, , Transport and Main Roads (TMR), Queesnland, Australia; (2017) Bridge design—part 2: Design Loads (AS 5100.2), , SAI Global, Sydney; (2017) Bridge design—part 7: Bridge Assessment (AS 5100.7), , SAI Global, Sydney; Pritchard, R., AS 5100.7 bridge assessment: 2014 revision (2014) Proceedings of the 9Th Austroads Bridge Conference, , Sydney, Australia; Pritchard, R., Revision of Australian Standard AS 5100 part 7: Bridge assessment (2017) Proceedings of the 10Th Austroads Bridge Conference, , Melbourne; Hambly, E.C., (1991) Bridge deck behaviour, , 2, E & FN Spon, London; Qaqish, M., Fadda, E., Akawwi, E., Design of T-beam bridge by finite element method and AASHTO specification (2008) KMITL Sci J, 8 (1), pp. 24-34; Jenkins, D., Bridge deck behaviour revisited (2004) Proceedings of the 5Th Austroads Bridge Conference, , Hobart, Tasmania; Obrien, E.J., Keogh, D., O’Connor, A., (2014) Bridge deck analysis, , 2, CRC Press, Boca Raton, Flordia; Surana, C., Agrawal, R., (1998) Grillage analogy in bridge deck analysis, , Narosa, New Delhi; (2017) Bridge design—part 5: Concrete (AS 5100.5), , SAI Global, Sydney; Reddy, J.N., (2006) Theory and analysis of elastic plates and shells, , 2, CRC Press, Bosa Roca; Moravej, H., Jamali, S., Chan, T.H.T., Nguyen, A., Finite element model updating of civil engineering infrastructures: A review literature (2017) International Conference on Structural Health Monitoring of Intelligent Infrastructure, , Brisbane, Australia; Hambly, E., Pennells, E., Grillage analysis applied to cellular bridge decks (1975) Struct Eng, 53 (7), pp. 267-276; (1976) NAASRA Bridge Design Specification, , 5th edn. National Association of Australian State Road Authorities, Sydney; (2002) Investigating the Development of a Bridge Assessment Tool for Determining Access for High Productivity Freight Vehicles (AP-R398-12), , https://www.onlinepublications.austroads.com.au/items/AP-R398-12, Accessed 1 Jun 2016; Lake, N., Ngo, H., Kotze, R., (2014) Review of AS 5100.7 rating of existing bridges and the bridge assessment group guidelines (AP-R452-14), , https://www.onlinepublications.austroads.com.au/items/AP-R466-14, Accessed 1 Jun 2016; Lake, N., Seskis, J., Ngo, H., Kotze, R., (2014) Review of Axle Spacing Mass Schedules and Future Framework for Assessment of Heavy Vehicle Access Applications (AP-R466-14), , https://www.onlinepublications.austroads.com.au/items/AP-R466-14, Accessed 1 Jun 2016; Pritchard, R., Heywood, R., Shaw, P., Structural assessment of freight bridges in Queensland (2014) Proceedings of the 9Th Austroads Bridge Conference, , Sydney, New South Wales, Australia; Schlune, H., Plos, M., (2008) Bridge assessment and maintenance based on finite element structural models and field measurements, , Chalmers University of Technology, Göteborg","Chan, T.H.T.; Department of Civil Engineering and Built Environment, Australia; email: tommy.chan@qut.edu.au",,,"Springer Verlag",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85045039474
"Zhou S., Song W.","56892975700;55586801400;","Environmental-effects-embedded model updating method considering environmental impacts",2018,"Structural Control and Health Monitoring","25","3","e2116","","",,7,"10.1002/stc.2116","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041656899&doi=10.1002%2fstc.2116&partnerID=40&md5=969878192f9515b8caa96fc015f3ea23","Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL  35487, United States","Zhou, S., Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL  35487, United States; Song, W., Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL  35487, United States","In structural health monitoring, one practical challenge is to separate the change of structural characteristics (e.g., natural frequency and mode shape) due to environmental impacts from those induced by actual damage. Generally, data-driven regression models are applied to remove the environmental impacts before model updating takes place. Model selection and training procedures are required in constructing these regression models, which are often subjective, prone to overfitting issue, and human errors. This paper proposes a novel physics-based Environmental-Effects-Embedded model updating method. By embedding physical mechanisms of environmental impacts into the formulation of the finite element model, the proposed method is capable of considering these impacts during the finite element model updating. A comparative numerical study is performed by applying both the Environmental-Effects-Embedded model updating method and traditional method on a pedestrian bridge model subjected to structural damage, temperature variation, and boundary condition change. Comparison between the proposed and traditional methods has demonstrated that the proposed method can offer more accurate results in localizing and quantifying the structural damage under environmental impacts. Copyright © 2017 John Wiley & Sons, Ltd.","boundary condition; environmental impacts; finite element model updating; modal flexibility residual; structural health monitoring; temperature","Boundary conditions; Finite element method; Footbridges; Numerical methods; Regression analysis; Structural health monitoring; Temperature; Finite-element model updating; Modal flexibility residual; Physical mechanism; Regression model; Structural characteristics; Structural damages; Temperature variation; Training procedures; Environmental impact",,,,,,,,,,,,,,,,"Doebling, S.W., Farrar, C.R., Prime, M.B., (1998) Shock Vib. Dig., 30 (2), p. 91; Cornwell, P., Farrar, C.R., Doebling, S.W., Sohn, H., (1999) Exp. Tech., 23 (6), p. 45; Teughels, A., Maeck, J., De Roeck, G., (2002) Comput. 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Signal Process., 13 (6), p. 855; Parloo, E., Verboven, P., Guillaume, P., Overmeire, M.V., (2002) Mech. Syst. Signal Process., 16 (5), p. 757; Parloo, E., Cauberghe, B., Benedettini, F., Alaggio, R., Guillaume, P., (2005) Mech. Syst. Signal Process., 19 (1), p. 43; Brincker, R., Andersen, P., (2003) Proceedings of the 21st International Modal Analysis Conference (IMAC), , in, Orlando, Florida; Bernal, D., (2004) J. Eng. Mech., ASCE, 130 (9), p. 1083; (2014) MathWorks, Inc.; Qu, Z.Q., (2013) Model order reduction techniques with applications in finite element analysis, p. 56. , Springer Science & Business Media; Yuen, K.-V., Beck, J.L., Katafygiotis, L.S., (2006) Struct. Control Health Monit., 13 (1), p. 91; Pedram, M., Esfandiari, A., Khedmati, M.R., (2016) Struct. Control Health Monit., 23 (11), p. 1314; Farshadi, M., Esfandiari, A., Vahedi, M., (2017) Struct. Control Health Monit., 24 (7); Giraldo, D.F., Song, W., Dyke, S.J., Caicedo, J.M., (2009) J. Eng. Mech., 135 (8), p. 759","Song, W.; Department of Civil, United States; email: wsong@eng.ua.edu",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-85041656899
"Chen G.S., Xiao F., Zatar W., Hulsey J.L.","55615798900;56070134700;6602971374;6602858255;","Characterization of Nonstationary Mode Interaction of Bridge by Considering Deterioration of Bearing",2018,"Advances in Materials Science and Engineering","2018",,"5454387","","",,7,"10.1155/2018/5454387","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048730900&doi=10.1155%2f2018%2f5454387&partnerID=40&md5=e28ee856cdae2670574b4427431d266f","College of Information Technology and Engineering, Marshall University, Huntington, WV, United States; Department of Civil Engineering, Nanjing University of Science and Technology, Nanjing, China; Department of Civil and Environmental Engineering, University of Alaska Fairbanks, Fairbanks, AK, United States","Chen, G.S., College of Information Technology and Engineering, Marshall University, Huntington, WV, United States; Xiao, F., Department of Civil Engineering, Nanjing University of Science and Technology, Nanjing, China; Zatar, W., College of Information Technology and Engineering, Marshall University, Huntington, WV, United States; Hulsey, J.L., Department of Civil and Environmental Engineering, University of Alaska Fairbanks, Fairbanks, AK, United States","As all bridges get deteriorated over time, structural health monitoring of these structures has become very important for the damage identification and maintenance work. Evaluating a bridge's health condition requires the testing of a variety of physical quantities including bridge dynamic responses and the evaluation of the functions of varied bridge subsystems. In this study, both the acceleration of the deck and the dynamic rotational angle of the bearings in a long-span steel girder bridge were measured when the bridge system was excited by passing-by vehicles. The nonstationary dynamical phenomena including vibration mode interactions and coupling are observed in response spectrogram. To elaborate the phenomena, the linear vibration mode properties of the bridge are characterized by finite element analysis and are correlated with the specific modes in test. A theoretical model is presented showing the mechanism of the mode coupling and instability originated from the friction effects in bearing. This study offers some insights into the correlation between complex bridge vibrations and the bearing effects, which lays a foundation for the in situ health monitoring of bridge bearing by using dynamical testing. © 2018 Gang S. Chen, et al.",,"Bridges; Damage detection; Vibration analysis; Damage Identification; Dynamical phenomena; Health monitoring; Linear vibrations; Nonstationary mode; Physical quantities; Steel girder bridge; Theoretical modeling; Structural health monitoring",,,,,,,,,,,,,,,,"Fan, W., Qiao, P., Vibration-based damage identification methods: A review and comparative study (2011) Structural Health Monitoring, 10 (1), pp. 83-111; Bedon, C., Morassi, A., Dynamic testing and parameter identification of a base-isolated bridge (2014) Engineering Structures, 60, pp. 85-99; Wenzel, H., (2008) Health Monitoring of Bridges, , John Wiley & Sons, Hoboken, NJ, USA; Bakht, B., Mufti, A., (2015) Bridges: Analysis, Design, Structural Health Monitoring, and Rehabilitation, , Berlin, Germany, Springer; Pan, Y., Agrawal, A.K., Ghosn, M., Alampalli, S., Seismic fragility of multispan simply supported steel highway bridges in New York State. 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A4014007; Filipov, E.T., Fahnestock, L.A., Steelman, J.S., Hajjar, J.F., LaFave, J.M., Foutch, D.A., Evaluation of quasi-isolated seismic bridge behavior using nonlinear bearing models (2013) Engineering Structures, 49, pp. 168-181; Mosqueda, G., Whittaker, A.S., Fenves, G.L., Characterization and modeling of friction pendulum bearings subjected to multiple components of excitation (2004) Journal of Structural Engineering, 130 (3), pp. 433-442; Fan, X., McCormick, J., Seismic performance evaluation of corroded steel bridge bearings (2012) Proceedings of the 15th World Conference on Earthquake Engineering, Lisbon, Portugal, September; Fan, X., McCormick, J., Characterization of the cyclic behavior of heavily corroded steel bridge bearings, Tenth U.S (2014) Proceedings of National Conference on Earthquake Engineering Frontiers of Earthquake Engineering, Anchorage, AK, USA, July; Mangalathu, S., Jeon, J.S., Padgett, J.E., DesRoches, R., ANCOVA-based grouping of bridge classes for seismic fragility assessment (2016) Engineering Structures, 123, pp. 379-394; Muthukumar, S., DesRoches, R.A., Hertz contact model with non-linear damping for pounding simulation (2006) Earthquake Engineering & Structural Dynamics, 35 (7), pp. 811-828; Lee, J.J., Ho, H.N., Lee, J.H., A vision-based dynamic rotational angle measurement system for large civil structures (2012) Sensors, 12 (6), pp. 7326-7336; Park, Y.S., Agbayani, J.A., Lee, J.H., Lee, J.J., Rotational angle measurement of bridge support using image processing techniques (2016) Journal of Sensors, 2016, 9p; Zhou, J., Li, X., Xia, R., Yang, J., Zhang, H., Health monitoring and evaluation of long-span bridges based on sensing and data analysis: A survey (2017) Sensors, 17 (3), p. 603; Kim, W., Laman, J.A., Seven-year field monitoring of four integral abutment bridges (2011) Journal of Performance of Constructed Facilities, 26 (1), pp. 54-64; Xiao, F., Hulsey, J.L., Balasubramanian, R., Fiber optic health monitoring and temperature behavior of bridge in cold region (2017) Structural Control and Health Monitoring, 24 (11), p. e2020; Imbsen, R.A., Improved bearing design concepts for increased seismic resistance of highway bridges (1981) Proceedings of First World Congress on Joints and Bearings, 1, pp. 509-523. , Mich.: American Concrete Institute, Detroit, Niagara Falls, NY, USA; Roeder, C.W., Stanton, J.F., (1996) Steel Bridge Bearing Selection and Design Guide, , American Iron and Steel Institute, Washington, DC, USA; Mazroi, A., Wang, L.R., Murray, T.M., (1983) Effective Coefficient of Friction of Steel Bridge Bearings, , National Research Council, Washington, DC, USA; Mander, J.B., Kim, D.K., Chen, S.S., Premus, G.J., Response of steel bridge bearings to reversed cyclic loading (1996) Report No. NCEER-96-0014, State University of New York, Buffalo, NY, USA; Chen, G.S., (2014) Handbook of Friction-Vibration Interactions, , Elsevier, London, UK","Xiao, F.; Department of Civil Engineering, China; email: xiaofeng@njust.edu.cn",,,"Hindawi Limited",,,,,16878434,,,,"English","Adv. Mater. Sci. Eng.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85048730900
"Boscato G., Dal Cin A.","35179098100;55982962200;","Experimental and numerical evaluation of structural dynamic behavior of Rialto Bridge in Venice",2017,"Journal of Civil Structural Health Monitoring","7","4",,"557","572",,7,"10.1007/s13349-017-0242-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031490291&doi=10.1007%2fs13349-017-0242-7&partnerID=40&md5=160b3559a15714a208106c88ee72c208","Laboratory of Strength of Materials (LabSCo), IUAV University of Venice, Venice, Italy; Department of Design and Planning in Complex Environments, IUAV University of Venice, Venice, Italy","Boscato, G., Laboratory of Strength of Materials (LabSCo), IUAV University of Venice, Venice, Italy; Dal Cin, A., Department of Design and Planning in Complex Environments, IUAV University of Venice, Venice, Italy","This paper presents part of the diagnostic activities performed for the subsequent major restoration work of the Rialto Bridge in Venice. The document analysis, the visual inspection, and the destructive tests on extracted masonry samples have proved to be the first important steps for an initial structural assessment and for the characterization of the materials. Moreover, the tensile stresses of the tie-rods, which connect the two sides of the shops structures, have been measured by means of dynamic tests. To study the local and the global dynamic behavior of the bridge structure and its overlying shops structures, a dynamic monitoring was performed. The data were acquired through ambient vibrations test to measure the dynamical properties (mode of vibration, frequencies, displacements, and damping ratios) of the historical construction using a modal identification of output-only systems. The main natural vibration sources were pedestrian traffic, wind, and wave-motion of the Grand Canal. Modal identification was carried out through poly-reference least square complex frequency-domain (pLSFC) estimator. Finally, of the variations of the boundary conditions and the structural interaction between the bridge and the shops, structures on the modal shapes are evaluated through a finite element model. The global structural health monitoring was carried out to define the real dynamic behavior of this important bridge. © 2017, Springer-Verlag GmbH Germany.","Experimental dynamic monitoring; Finite element analysis (FEA); Masonry; Modal identification; Rialto Bridge","Frequency domain analysis; Frequency estimation; Masonry materials; Structural dynamics; Structural health monitoring; Experimental dynamics; Global dynamic behavior; Global structural health monitoring; Historical construction; Masonry; Modal identification; Structural assessments; Structural interactions; Finite element method",,,,,,,,,,,,,,,,"Russo, S., Testing and modelling of dynamic out-of-plane behaviour of the historic masonry façade of Palazzo Ducale in Venice, Italy (2013) Eng Struct, 46, pp. 130-139; Boscato, G., Dal Cin, A., Riva, G., Russo, S., Sciarretta, F., Knowledge of the construction technique of the multiple leaf masonry façades of Palazzo Ducale in Venice with ND and MD tests (2014) Adv Mater Res, 919-921, pp. 318-324; Boscato, G., Russo, S., Ceravolo, R., Fragonara, L., Global sensitivity-based model updating for heritage structure (2015) Comput Aided Civ Infrastruct Eng, 30 (8), pp. 620-635; Duan, Y.F., Advanced finite element model of Tsing Ma Bridge for structural health monitoring (2011) Int J Struct Stab Dyn, 11, p. 313; Boscato, G., Dal Cin, A., Di Giulio, G., Russo, S., Vassallo, M., Seismic monitoring by piezoelectric accelerometers of a damaged historical monument in downtown L’Aquila (2014) Ann Geophys, 57 (6), p. 15p; Costa, B., Magalhães, F., Cunha, Á., Figueiras, J., Rehabilitation assessment of a centenary steel bridge based on modal analysis (2013) Eng Struct, 56, pp. 260-272; Boscato, G., Dal Cin, A., Russo, S., Sciarretta, F., SHM of historic damaged churches (2014) Adv Mater Res, 838-841, pp. 2071-2078; Brinker, R., Kirkegaard, P.H., Special issue on operational modal analysis (2010) Mech Syst Signal Process, 24, pp. 1209-1212; Tributsch, A., Adam, C., A multi-step approach for identification of structural modifications based on operational modal analysis (2014) Int J Struct Stab Dyn, 14, p. 1440004; Russo, S., Using experimental dynamic modal analysis in assessing structural integrity in historic buildings (2014) Open Constr Build Technol J, 8, pp. 357-368; Samuels, J.M., Reyer, M., Hurlebaus, S., Lucy, S.H., Woodcock, D.G., Bracci, J.M., Wireless sensor network to monitor an historic structure under rehabilitation (2011) J Civ Struct Health Monit, 1 (3), pp. 69-78; Russo, S., Integrated assessment of monumental structures through ambient vibrations and ND tests: the case of Rialto Bridge (2016) J Cult Heritage, 19 (1), pp. 402-414; ASV, Provveditori sopra la fabbrica del Ponte di Rialto, dis. 11; Francesco Zamberlan, Piloni del ponte di Rialto; Circolare 2 February 2009, n. 617 Table C8A, 2, p. 1. , NTC08; Belluzzi, O., Scienza delle Costruzioni, Zanichelli (ed) (1998) Bologna, Italy, vol, 4, pp. 383-391. , in Italian; Van der Auweraer, H., Guillaume, P., Verboven, P., Vanlanduit, S., Application of a fast-stabilizing frequency domain parameter estimation method (2001) ASME J Dyn Syst Meas Control, 123 (4), pp. 651-658; LMS Test.Lab: Siemens PLM software, , http://www.lmsintl.com; http://www.restauropontedirialto.it/wp-content/uploads/2015/10/ASD-relazione-conclusiva","Dal Cin, A.; Department of Design and Planning in Complex Environments, Italy; email: adalcin@iuav.it",,,"Springer Verlag",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85031490291
"Kalyankar R., Uddin N.","53063664600;7003593965;","Axle detection on prestressed concrete bridge using bridge weigh-in-motion system",2017,"Journal of Civil Structural Health Monitoring","7","2",,"191","205",,7,"10.1007/s13349-017-0210-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019246697&doi=10.1007%2fs13349-017-0210-2&partnerID=40&md5=7e1f4e849e67a20503cf6111d73b8997","Department of Civil Construction and Environmental Engineering, University of Alabama at Birmingham, Birmingham, AL, United States","Kalyankar, R., Department of Civil Construction and Environmental Engineering, University of Alabama at Birmingham, Birmingham, AL, United States; Uddin, N., Department of Civil Construction and Environmental Engineering, University of Alabama at Birmingham, Birmingham, AL, United States","In the bridge weigh-in-motion (B-WIM) system, the free of axle detector (FAD) sensors, attached to the bottom of the slab, are used to obtain vehicle parameters such as velocity, axle numbers, and their distances. The experimental B-WIM test using FAD sensors by previous researchers on prestressed concrete I girder bridge yielded reliable responses; however, the frequency of vehicle parameter detection was questionable. On the prestressed concrete bridges, when the vehicle paths are near girders, the load gets transferred to the girder thus affecting the vehicle axle detection. Therefore, to properly detect the vehicle parameters, an optimized sensor location and number of sensors is required on the slab and near the girders. The previous experimental test, despite being accurate in single vehicle axle detection, was unsuccessful in breaking down the vehicle parameters on wide-span bridges that are subjected to multiple vehicles. Therefore, the fully developed 3 dimensional finite element model (3D FEM) of vehicle–bridge interaction considering different aspects of vehicle including suspension, damping, tire movement, air pressure, mass distribution on the axles, material and geometric behavior of the bridge was developed and verified with the experimental results. After verification the 3D FEM was used for sensor optimization on slab and the girder. Moreover, the optimized sensor locations were analyzed for multi-vehicles–bridge interaction. With the optimization, the sensor placement was made suitable for the B-WIM implementation on US bridges. Once implemented, the B-WIM system can be used for monitoring traffic for structural health and for law enforcement. © 2017, Springer-Verlag Berlin Heidelberg.","3 Dimensional finite element model (3D FEM); Axle detection; Bridge weigh in motion (B-WIM); FEM validation; Multiple vehicles; Sensor optimization","Atmospheric pressure; Axles; Beams and girders; Concrete beams and girders; Concrete bridges; Concretes; Fiber optic sensors; Finite element method; Magnetic levitation vehicles; Prestressed concrete; Seats; Structural health monitoring; Vehicles; 3-D FEM; Experimental test; Mass distribution; Optimized sensors; Sensor optimization; Structural health; Vehicle parameters; Weigh-in-motion systems; Weigh-in-motion (WIM)",,,,,"National Science Foundation, NSF: NSF-CMMI-1100742","The authors are thankful to the National Science Foundation (NSF-CMMI-1100742) for the financial support.",,,,,,,,,,"(2012) Federal Highway Administration (FHWA), , http://www.fhwa.dot.gov/bridge/nbi/defbr11.cfm, US Department of Transportation. Accessed 9 Dec 2012; (2012) Report card for America’s Infrastructure, , http://www.infrastructurereportcard.org/fact-sheet/bridges, Accessed 9 Dec 2012; Moses, F., Weigh-in-motion system using instrumented bridges (1979) ASCE J Transp Eng, 105, pp. 233-249; O’Brien, E.J., Žnidarič, A., Dempsey, A.T., Comparison of two independently developed bridge WIM systems, heavy vehicle systems (1999) Int J Veh Desi, 6, pp. 147-161; Dempsey, A.T., Jacob, A.T., Carracilli, J., The use of instrumented orthotropic bridges for determining vehicle weights, dimensions and parameters. In: Proceedings of the 5th international symposium on heavy vehicles weights and dimensions (1998) Australian road research board, , Brisbane, Australia; Žnidaric, A., Baumgartner W (1998) Bridge weigh-in-motion systems—an overview Pre-proceedings of the 2nd European conference on weigh-in-motion, pp. 139-152. , Lisbon, Portugal; O’Brien, E.J., Žnidaric, A., Weighing-in-motion of axles and vehicles for Europe (WAVE) (2001) Report of work package, , Bridge WIM Systems, Slovenia; Zhao, H., Bridge weigh-in-motion for bridge safety and maintenance (2010) PhD Dissertation, , Department of Civil Construction and Environmental Engineering, the University of Alabama at Birmingham, Birmingham, AL; Zhao, Z., Simulation of bridge weigh-in-motion system integrated with bridge safety (2012) PhD Dissertation, , Department of Civil Construction and Environmental Engineering, the University of Alabama at Birmingham, Birmingham, AL; Žnidarič, A., Lavrič, I., Kalin, J., Nothing-on-the-road axle detection with threshold analysis.ICWIM4, National University of Taiwan, Taipei, Feb (2005) In: Proceedings of the 4th international conference on WIM; Kalin, J., Znidaric, A., Lavric, I., Practical implementation of nothing-on-the-road bridge weigh-in-motion system (2006) In: 9th International symposium on heavy vehicle weights and dimensions; ALDOT drawings and specifications for I 459 T beam bridge; ALDOT 5 Axle truck for calibration specifications; Chatterjee, P., Obrien, E.J., Li, Y.Y., González, A., Wavelet domain analysis for identification of moving loads from bridge measurements (2006) Comput Struct, 84 (28), pp. 1792-1801; Kalyankar, R., Uddin, N., Simulation of bridge responses to heavy vehicles part I—field verification of 3D finite element model using reinforced concrete T beam bridge and ALDOT 5 axle truck (2015) J Eng Struct (Under Review); (2014) LS pre–post online documentation, , http://www.lstc.com/lspp/index.shtml, Accessed 16 Jan 2014; Version 971 (2007) Livermore Software Technology Corporation; (2013) Web article, , http://www.steelconstruction.info/Modelling_and_analysis_of_beam_bridges, Accessed 19 Jan 2013; Barker, R.M., Puckett, J.A., (2013) Design of highway bridges: an LRFD Approach, , John Wiley & Sons, New Jersey; DADiSP Free Student Edition data analysis and visualization software package for scientific and engineering applications; Li, H., Dynamic response of highway bridges subjected to heavy vehicles (2005) PhD Dissertation, , Department of Civil and Environmental Engineering, the Florida State University, Tallahassee, FL; Wekezer, J., Szurgott, P., Kwasniewski, L., Siervogel, J., Investigation of impact factors for permit vehicles. Contract No (2008) BD 543 (Florida DOT) and 020555 (Florida State University), , Florida DOT, Tallahassee, FL; Mack Products (2007) Mack truck, , http://www.macktrucks.com/assets/mack/Datasheets/Chassis%20Sheets/2008C/CJI6030020738_08C.pdf, Web articleAccessed 29 Jan 2011; Mack Power-train (2008) Mack trucks, , http://www.macktrucks.com/assets/mack/specsheets/MP8040408/1001511_425E.pdf, Web articleAccessed 30 May 2012; Mack Products (2007) Mack trucks, , http://www.macktrucks.com/assets/mack/products/MackAdvantageChassis, Web articleAccessed 27 May 2012; Dunlop Truck Tires (2008) Dunlop commercial truck tires, , http://www.dunloptires.com/truck/tires/sp453measures.html, Web articleAccessed 30 May 2012; Dunlop Truck Tires (2008) Dunlop commercial truck tires, , http://www.dunloptires.com/truck/tires/sp160measures.html, Web articleAccessed 30 May 2012; Goodyear Tires (2010) Goodyear commercial tire systems, , http://www.goodyear.com/cfmx/web/truck/line.cfm?prodline=160904, Web articleAccessed on 27 May 2012; Goodyear Tires (2010) Goodyear commercial tire systems, , http://www.goodyear.com/cfmx/web/truck/line.cfm?prodline=160615, Web articleAccessed 27 May 2012; Ambrosio, J.A.C., Nikravesh, P.E., Crashworthiness analysis of a truck (1990) Math Comput Model, 14, pp. 959-964; Dias, J.P., Pereira, M.S., Optimization methods for crashworthiness design using multibody models (2004) Comput Struct, 82, pp. 1371-1380; Schweizerhof, K., Nilsson, L., Hallquist, J.O., Crashworthiness analysis in the automotive industry (1992) Int J Comput Appl Technol, 5, pp. 134-156; Kalyankar, R., Uddin, N., Simulating the effects of surface roughness on reinforced concrete T-beam bridges under single and multiple vehicles (2015) Adv Acoust Vib, 2016, p. 12; Han, W., Wu, J., Cas, C.S., Chen, S., Characteristics and dynamic impact of overloaded extra-heavy trucks on typical highway bridges (2015) J Bridge Eng, 20 (2), p. 05014011","Kalyankar, R.; Department of Civil Construction and Environmental Engineering, United States; email: krahul2807@gmail.com",,,"Springer Verlag",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85019246697
"Limongelli M.P., Tirone M., Surace C.","6508014623;57194518716;56265956500;","Non-destructive monitoring of a prestressed bridge with a data-driven method",2017,"Proceedings of SPIE - The International Society for Optical Engineering","10170",,"1017033","","",,7,"10.1117/12.2258381","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020524179&doi=10.1117%2f12.2258381&partnerID=40&md5=d7e98bc970df586c6bb525461262726d","Department ABC, Politecnico di Milano, Milano, Italy; Geoconseils, L.S.C. Engineering, Luxembourg, Luxembourg; Department DiSEG Politecnico di Torino, Torino, Italy","Limongelli, M.P., Department ABC, Politecnico di Milano, Milano, Italy; Tirone, M., Geoconseils, L.S.C. Engineering, Luxembourg, Luxembourg; Surace, C., Department DiSEG Politecnico di Torino, Torino, Italy","Non-destructive vibration based methods can be used as diagnostic tool to identify damage in structures. Periodic inspections or permanent monitoring networks of sensors can indicate the emergence of possible damage occurring during the structure lifetime. Several methods have been proposed in literature for damage identification purposes. Some of them allow detecting the existence of damage, others provide information about its location as well. Data driven method are able to localize damage based solely on responses recorded on the structure without the need of a Finite Element model. Many of these methods are based on the detection of irregularities in the deformed shape of the structure: modal or operational shapes have been proposed to this purpose by different authors. The reliability of the methods proposed in literature is often verified on numerical models that, by their nature, cannot reproduce all the sources of uncertainties-environmental, operational, experimental-that affect responses recorded of the structure. The availability of data recorded on real structures provides precious material for the check of damage identification methods. In this paper the performance of the Interpolation Method for damage localization is investigated with reference to the real case study of a prestressed concrete road bridge, the S101 Bridge in Austria. The bridge, built in the early 1960, is a typical example of a European highway bridge. Responses to ambient vibration have been recorded both in the undamaged and in several different damage scenarios artificially inflicted to the bridge. Damage was introduced by lowering one of the bridge piers and by cutting prestressing tendons of one beam of the bridge deck. © 2017 SPIE.","Ambient vibrations; Bridge S101; Damage localization; Data driven; Interpolation Method","Biological systems; Finite element method; Highway bridges; Interpolation; Numerical methods; Prestressed concrete; Structural health monitoring; Ambient vibrations; Damage Identification; Damage localization; Data driven; Interpolation method; Non-destructive monitoring; Periodic inspection; Sources of uncertainty; Damage detection",,,,,,,,,,,,,,,,"Domaneschi, M., Limongelli, M.P., Martinelli, L., Vibration based damage localization using MEMS on a suspension bridge model (2013) SMART STRUCT SYST, 12, pp. 679-694; Domaneschi, M., Limongelli, M.P., Martinelli, L., Multi-site damage localization in a suspension bridge via aftershock monitoring (2013) International Journal of Earthquake Engineering, 3, pp. 56-72; Domaneschi, M., Limongelli, M.P., Martinelli, L., Damage detection and localization on a benchmark cable-stayed bridge (2015) Earthquake and Structures, 8 (5), pp. 1113-1126; Dilena, M., Limongelli, M.P., Morassi, A., Damage localization in bridges via FRF interpolation method (2014) Mechanical Systems and Signal Processing, 52-53, pp. 162-180; Limongelli, M.P., Seismic health monitoring of an instrumented multistorey building using the interpolation method (2014) Earthquake Engng. Struct. Dyn., 43, pp. 1581-1602; (2009) Progressive Damage Test S101. Flyover Reibersdorf, , VCE. Vienna Consulting Engineers. Report nr. 08/2308; Siringoringo, D., Nagayama, T., Fujino, Y., Nagayama, T., Dynamic characteristics of an overpass bridge in a full-scale destructive test (2013) Journal of Engineering Mechanics, 139 (6). , June 1, 2013. ©ASCE; Brincker, R., Zhang, L., Andersen, P., Modal identification of output-only systems using frequency domain decomposition (2001) Smart Mater. Struct., 10, pp. 441-445; Peeters, B., De Roeck, G., Stochastic system identification for operational modal analysis: A review (2001) ASME Journal of Dynamic Systems, Measurement, and Control, 123",,"Kundu T.","Fiberguide Industries;Frontiers Media;OZ Optics, Ltd.;Polytec, Inc.;The Society of Photo-Optical Instrumentation Engineers (SPIE)","SPIE","Health Monitoring of Structural and Biological Systems 2017","26 March 2017 through 29 March 2017",,128085,0277786X,9781510608252,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85020524179
"Martini A., Tronci E.M., Feng M.Q., Leung R.Y.","57552485400;57221215820;7201365644;57212555097;","A computer vision-based method for bridge model updating using displacement influence lines",2022,"Engineering Structures","259",,"114129","","",,6,"10.1016/j.engstruct.2022.114129","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127169009&doi=10.1016%2fj.engstruct.2022.114129&partnerID=40&md5=5b6f69de8d6775cbc3775076337be726","Columbia University in the City of New York, Department of Civil Engineering and Mechanics, United States","Martini, A., Columbia University in the City of New York, Department of Civil Engineering and Mechanics, United States; Tronci, E.M., Columbia University in the City of New York, Department of Civil Engineering and Mechanics, United States; Feng, M.Q., Columbia University in the City of New York, Department of Civil Engineering and Mechanics, United States; Leung, R.Y., Columbia University in the City of New York, Department of Civil Engineering and Mechanics, United States","This paper presents a new computer vision-based method that simultaneously provides the moving vehicle's tire loads, the location of the loads on a bridge, and the bridge's response displacements, based on which the bridge's influence lines can be constructed. The method employs computer vision techniques to measure the displacement influence lines of the bridge at different target positions, which is then later used to perform model updating of the finite element models of the monitored structural system. The method is enabled by a novel computer vision-based vehicle weigh-in-motion method which the co-authors recently introduced. A correlation discriminating filter tracker is used to estimate the displacements at target points and the location of single or multiple moving loads, while a low-cost, non-contact weigh-in-motion technique evaluates the magnitude of the moving vehicle loads. The method described in this paper is tested and validated using a laboratory bridge model. The system was loaded with a vehicle with pressurized tires and equipped with a monitoring system consisting of laser displacement sensors, accelerometers, and cameras. Both artificial and natural targets were considered in the experimental tests to track the displacements with the cameras and yielded robust results consistent with the laser displacement measurements. The extracted normalized displacement influence lines were then successfully used to perform model updating of the structure. The laser displacement sensors were used to validate the accuracy of the proposed computer vision-based approach in deriving the displacement measurements, while the accelerometers were used to derive the system's modal properties employed to validate the updated finite element model. As a result, the updated finite element model correctly predicted the bridge's displacements measured during the tests. Furthermore, the modal parameters estimated by the updated finite element model agreed well with those extracted from the experimental modal analysis carried out on the bridge model. The method described in this paper offers a low-cost non-contact monitoring tool that can be efficiently used without disrupting traffic for bridges in model updating analysis or long-term structural health monitoring. © 2022 Elsevier Ltd","Bridge systems; Computer vision; Displacement influence line; Finite element method model; Modal analysis; Model updating; Structural identification; Vehicle weigh-in-motion","Accelerometers; Cameras; Computer vision; Cost benefit analysis; Costs; Displacement measurement; Modal analysis; Motion analysis; Structural health monitoring; Vehicles; Weigh-in-motion (WIM); Bridge model; Bridge systems; Displacement influence line; Finite element method model; Finite element modelling (FEM); Influence lines; Method model; Model updating; Structural identification; Vehicle weigh-in-motion; Finite element method; bridge; computer vision; displacement; finite element method; health monitoring; loading; numerical model; structural response",,,,,,,,,,,,,,,,"Doebling, S.W., Farrar, C.R., Prime, M.B., A summary review of vibration-based damage identification methods (1998) Shock Vib Dig, 30 (2), pp. 91-105; Fan, W., Qiao, P., Vibration-based damage identification methods: A review and comparative study (2011) Struct Health Monit, 10 (1), pp. 83-111; Sun, Z., Nagayama, T., Fujino, Y., Minimizing noise effect in curvature-based damage detection (2016) J Civ Struct Health Monit, 6 (2), pp. 255-264; Zeinali, Y., Story, B.A., Impairment localization and quantification using noisy static deformation influence lines and iterative multi-parameter tikhonov regularization (2018) Mech Syst Signal Process, 109, pp. 399-419; Officials, T., Standard specifications for highway bridges (2002), AASHTO; Kun, A.J., Vamossy, Z., Traffic monitoring with computer vision (2009) 2009 7th International symposium on applied machine intelligence and informatics, pp. 131-134; Huang, M.-C., Yen, S.-H., A real-time and color-based computer vision for traffic monitoring system (2004) 2004 IEEE international conference on multimedia and expo (IEEE Cat. No. 04TH8763), 3, pp. 2119-2122; Štimac Grandić, I., Grandić, D., Bjelanović, A., Comparison of techniques for damage identification based on influence line approach (2011) Mach Technol Mater, 7, pp. 9-13; Zeinali, Y., Story, B., Framework for flexural rigidity estimation in Euler-Bernoulli beams using deformation influence lines (2017) Infrastructures, 2 (4), p. 23; Zhang, S., Liu, Y., Damage detection in beam bridges using quasi-static displacement influence lines (2019) Appl Sci, 9 (9), p. 1805; Wang, C.-Y., Huang, C.-K., Chen, C.-S., Damage assessment of beam by a quasi-static moving vehicular load (2011) Adv Adapt Data Anal, 3 (4), pp. 417-445; Choi, I.-Y., Lee, J.S., Choi, E., Cho, H.-N., Development of elastic damage load theorem for damage detection in a statically determinate beam (2004) Comput Struct, 82 (29-30), pp. 2483-2492; Moses, F., Weigh-in-motion system using instrumented bridges (1979) Transp Eng J ASCE, 105 (3), pp. 233-249; OBrien, E., McCrum, D., Khan, M.A., Bridge health monitoring using accelerometer responses to passing traffic; Žnidarič, A., Kalin, J., Using bridge weigh-in-motion systems to monitor single-span bridge influence lines (2020) J Civ Struct Health Monit, 10 (5), pp. 743-756; Jian, X., Xia, Y., Lozano-Galant, J.A., Sun, L., Traffic sensing methodology combining influence line theory and computer vision techniques for girder bridges (2019) J Sens, 2019, pp. 1-15; Aktan, A.E., Turer, A., Levi, A., Instrumentation, proof-testing and monitoring of three reinforced concrete deck-on-steel girder bridges prior to, during, and after superload: Tech. rep. (1998); Zaurin, R., Catbas, F.N., Integration of computer imaging and sensor data for structural health monitoring of bridges (2010) Smart Mater Struct, 19 (1); Cook, R.D., Young, W.C., Advanced mechanics of materials, Vol. 2 (1999), Prentice Hall Upper Saddle River, NJ; Argyris, J.H., Kelsey, S., Energy theorems and structural analysis (1960), Springer US Boston, MA; Timoshenko, S., History of strength of materials: With a brief account of the history of theory of elasticity and theory of structures (1983), Courier Corporation; Wang, Y., Li, Y., Zheng, J., A camera calibration technique based on OpenCV (2010) The 3rd international conference on information sciences and interaction sciences, pp. 403-406; https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html, Camera Calibration and 3D Reconstruction - OpenCV 2.4.13.7 documentation. URL; Zhang, Z., A flexible new technique for camera calibration (2000) IEEE Trans Pattern Anal Mach Intell, 22 (11), pp. 1330-1334; Zeng, H., Peng, N., Yu, Z., Gu, Z., Liu, H., Zhang, K., Visual tracking using multi-channel correlation filters (2015) 2015 IEEE international conference on digital signal processing (DSP), pp. 211-214. , IEEE Singapore, Singapore; Liu, H., Li, B., Target tracker with masked discriminative correlation filter (2020) IET Image Process, 14 (10), pp. 2227-2234; Lukežič, A., Vojí ř, T., Čehovin Zajc, L., Matas, J., Kristan, M., Discriminative correlation filter tracker with channel and spatial reliability (2018) Int J Comput Vis, 126 (7), pp. 671-688; Feng, M.Q., Leung, R.Y., Eckersley, C.M., Non-contact vehicle weigh-in-motion using computer vision (2020) Measurement, 153; Feng, M.Q., Leung, R.Y., Application of computer vision for estimation of moving vehicle weight (2020) IEEE Sens J, p. 1; Al-Qadi, I., Wang, H., Ouyang, Y., Grimmelsman, K., Purdy, J.E., LTBP program's literature review on weigh-in-motion systems (2016), Federal Highway Administration United States; (2006), pp. 20-40. , Ill-posed problems and regularization. In: Inverse problems for partial differential equations, Vol. 127. New York: Springer-Verlag; Wahba, G., Spline models for observational data (1990), SIAM; Bauer, F., Lukas, M.A., Comparingparameter choice methods for regularization of ill-posed problems (2011) Math Comput Simulation, 81 (9), pp. 1795-1841; Morozov, V.A., On the solution of functional equations by the method of regularization (1966) Doklady akademii nauk, 167, pp. 510-512. , Russian Academy of Sciences; Tikhonov, A.N., Arsenin, V.Y., Solutions of ill-posed problems, New York, 1 30 (1977); Hämarik, U., Tautenhahn, U., On the monotone error rule for parameter choice in iterative and continuous regularization methods (2001) BIT Numer Math, 41 (5), pp. 1029-1038; https://www.filmicpro.com/products/doubletake/, DoubleTake - FiLMiC pro mobile video - multi-camera video, FiLMiC pro mobile video. URL; Liu, Y., Yang, M., You, Z., Video synchronization based on events alignment (2012) Pattern Recognit Lett, 33 (10), pp. 1338-1348; Bradley, D., Atcheson, B., Ihrke, I., Heidrich, W., Synchronization and rolling shutter compensation for consumer video camera arrays (2009) 2009 IEEE computer society conference on computer vision and pattern recognition workshops, pp. 1-8. , IEEE Miami, FL; Fukuda, Y., Feng, M.Q., Narita, Y., Kaneko, S., Tanaka, T., Vision-based displacement sensor for monitoring dynamic response using robust object search algorithm (2013) IEEE Sens J, 13 (12), pp. 4725-4732; Caspi, Y., Irani, M., Spatio-temporal alignment of sequences (2002) IEEE Trans Pattern Anal Mach Intell, 24 (11), pp. 1409-1424; Henriques, J.F., Caseiro, R., Martins, P., Batista, J., Exploiting the circulant structure of tracking-by-detection with kernels (2012) European conference on computer vision, pp. 702-715. , Springer; Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M., Visual object tracking using adaptive correlation filters (2010) 2010 IEEE computer society conference on computer vision and pattern recognition, pp. 2544-2550. , IEEE; Grabner, H., Grabner, M., Bischof, H., Real-time tracking via on-line boosting (2006) Bmvc, 1, p. 6. , Citeseer; Forward-backward error: Automatic detection of tracking failures (2010) 2010 20th International conference on pattern recognition, pp. 2756-2759; Lin, Y.-H., New method for subpixel image matching with rotation invariance by combining the parametric template method and the ring projection transform process (2006) Opt Eng, 45 (6); Tronci, E.M., De Angelis, M., Betti, R., Altomare, V., Semi-automated operational modal analysis methodology to optimize modal parameter estimation (2020) J Optim Theory Appl, 187 (3), pp. 842-854; Borja, R.I., Plasticity: Modeling & computation (2013), Springer Science & Business Media","Martini, A.; Columbia University in the City of New York, United States; email: am4204@columbia.edu",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","",Scopus,2-s2.0-85127169009
"Abedin M., Mehrabi A.B.","57211253861;7005771645;","Health monitoring of steel box girder bridges using non-contact sensors",2021,"Structures","34",,,"4012","4024",,6,"10.1016/j.istruc.2021.10.021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117157572&doi=10.1016%2fj.istruc.2021.10.021&partnerID=40&md5=a368082a80c056e1fbe5a2940c7f4eaf","Department of Civil and Environmental Engineering, Florida International University, 10555 W. Flagler Street, Miami, FL  33174, United States","Abedin, M., Department of Civil and Environmental Engineering, Florida International University, 10555 W. Flagler Street, Miami, FL  33174, United States; Mehrabi, A.B., Department of Civil and Environmental Engineering, Florida International University, 10555 W. Flagler Street, Miami, FL  33174, United States","This study investigates the behavior of simple and continuous span steel box girder bridges after various damage scenarios, including bracing failure and girder fracture, and develops a non-contact bridge health monitoring technique based on the bridge dynamic responses. A series of field tests were conducted to study the feasibility of using non-contact sensors to capture the bridge dynamic responses. Moreover, detailed finite element models of the bridges were developed and validated using the field test and available experimental test results and were used to investigate the bridge failure mechanisms, maximum load-carrying capacity, alternative load paths, and dynamic responses. The bridge dynamic analysis after damage showed that bridge frequencies are sufficiently sensitive for identifying partial or full-depth girder fracture in the simple span bridges. However, these significant damages may cause very small changes in the natural frequencies of continuous span bridges. The results show a significant change in the mode shapes after damage in both simple and continuous span bridges. The mode shapes are sensitive enough to detect damage at the inflicted locations, in most cases with better resolution when compared to the frequency changes. The comparison of the intact and damaged bridge mode shapes indicates that damage at different locations along the bridge has different amplitude changes in the mode shape that could be used to localize the damage. Moreover, the analyses show that either the individual modal sensitivities or combined sensitivities are indicative for most locations throughout the span. The results also indicate a clear pattern of changes in the frequency and mode shape for each damage scenario that can be used to detect the damage type, severity, and location along the bridge. © 2021 Institution of Structural Engineers","Bridge monitoring; Finite element analysis; Fracture critical; Modal analysis; Non-contact sensor; Steel box girder bridge",,,,,,"Florida International University, FIU","The authors gratefully acknowledge the financial support from the Graduate Student Research Support Program (UGS-GSRSP) at Florida International University.",,,,,,,,,,"Lerose, C., The collapse of the Silver Bridge (2001) West Virginia Hist Soc Q, 15, p. 1; Feldman, B.J., The collapse of the I-35W bridge in Minneapolis (2010) Phys Teach, 48 (8), pp. 541-542; Morgese, M., Ansari, F., Domaneschi, M., Cimellaro, G.P., Post-collapse analysis of Morandi's Polcevera viaduct in Genoa Italy (2020) J Civ Struct Heal Monit, 10 (1), pp. 69-85; (2017), AASHTO. 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A Simplified Approach for Designing SRMs in Composite Continuous Twin-Tub Girder Bridges","Abedin, M.; Department of Civil and Environmental Engineering, 10555 W. Flagler Street, United States; email: mabed005@fiu.edu",,,"Elsevier Ltd",,,,,23520124,,,,"English","Structures",Article,"Final","",Scopus,2-s2.0-85117157572
"Pozzer S., Dalla Rosa F., Pravia Z.M.C., Rezazadeh Azar E., Maldague X.","57216935346;57216932332;25640203100;15134798800;7003528304;","Long-term numerical analysis of subsurface delamination detection in concrete slabs via infrared thermography",2021,"Applied Sciences (Switzerland)","11","10","4323","","",,6,"10.3390/app11104323","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106602579&doi=10.3390%2fapp11104323&partnerID=40&md5=df649c778d324af132463b3e6e82f6e2","Department of Electrical and Computer Engineering, Laval University, 1065, Av., de la Médecine, Quebec, QC  G1V 0A6, Canada; Department of Civil Engineering, University of Passo Fundo, BR 285, Passo Fundo, Rio Grande do Sul, 99052-900, Brazil; Department of Civil Engineering, Lakehead University, 955, Oliver Road, Thunder Bay, ON  P7B 5E1, Canada","Pozzer, S., Department of Electrical and Computer Engineering, Laval University, 1065, Av., de la Médecine, Quebec, QC  G1V 0A6, Canada; Dalla Rosa, F., Department of Civil Engineering, University of Passo Fundo, BR 285, Passo Fundo, Rio Grande do Sul, 99052-900, Brazil; Pravia, Z.M.C., Department of Civil Engineering, University of Passo Fundo, BR 285, Passo Fundo, Rio Grande do Sul, 99052-900, Brazil; Rezazadeh Azar, E., Department of Civil Engineering, Lakehead University, 955, Oliver Road, Thunder Bay, ON  P7B 5E1, Canada; Maldague, X., Department of Electrical and Computer Engineering, Laval University, 1065, Av., de la Médecine, Quebec, QC  G1V 0A6, Canada","One of the concerns about the use of passive Infrared Thermography (IRT) for structural health monitoring (SHM) is the determination of a favorable period to conduct the inspections. This paper investigates the use of numerical simulations to find appropriate periods for IRT-based detection of subsurface damages in concrete bridge slabs under passive heating along a 1 year of time span. A model was built using the Finite Element Method (FEM) and calibrated using the results of a set of thermographic field inspections on a concrete slab sample. The results showed that the numerical simulation properly reproduced the experimental thermographic measurements of the concrete structure under passive heating, allowing the analysis to be extended for a longer testing period. The long-term FEM results demonstrated that the months of spring and summer are the most suitable for passive IRT inspections in this study, with around 17% more detections compared to the autumn and winter periods in Brazil. By enhancing the possibility of using FEM beyond the design stage, we demonstrate that this computation tool can provide support to long-term SHM. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Concrete bridges; Delamination; Finite element method; Infrared thermography; Non-destructive test",,,,,,"Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq: 427757/2016-9","Acknowledgments: The authors are grateful for the financial support from Fundação Universidade de Passo Fundo (FUPF).","Funding: This research was partly funded by the National Council for Scientific and Technological Development -CNPq, grant number 427757/2016-9.",,,,,,,,,"Bridge Report. 2020, , https://artbabridgereport.org/, American Road and Transportation Builders Association (ARTBA). 2020. (accessed on 14 October 2020); HAQ, G., Ortega Hortelano, A., Tsakalidis, A., Gkoumas, K., Pekár, F., Balen, M., Grosso, M., Marques Dos Santos, F.L., (2019) Research and Innovation in Bridge. Maintenance, Inspection and Monitoring: A European Perspective Based on the Transport. 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(accessed on 10 October 2020); Gothäll, H., How to Inspect Your Mesh in COMSOL Multiphysics COMSOL 2017, , https://www.comsol.com/blogs/how-to-inspect-your-mesh-in-comsol-multiphysics/, (accessed on 21 September 2020); Urquhart, B., Ghonima, M., Nguyen, D., Kurtz, B., Chow, C.W., Kleissl, J., Chapter 9-Sky-Imaging Systems for Short-Term Forecasting (2013) Solar Energy Forecasting and Resource Assessment, pp. 195-232. , Kleissl, J., Ed.; Academic Press: Boston, MA, USA; (2013) ASTM D4788–03: Standard Test Method for Detecting Delaminations in Bridge Decks Using Infrared Thermography, , American Society for Testing and Materials. American Society for Testing and Materials: West Conshohocken, PA, USA; Al Gharawi, M., Adu-Gyamfi, Y., Washer, G., A framework for automated time-lapse thermography data processing (2019) Constr. Build. Mater, 227, p. 116507. , [CrossRef]; Ibarra-Castanedo, C., Khodayar, F., Klein, M., Sfarra, S., Maldague, X., Helal, H., Tayoubi, M., Barré, J.C., Infrared vision for artwork and cultural heritage NDE studies: Principles and case studies (2017) Insight Non-Destr. Test. Cond. Monit, 59, pp. 243-248. , [CrossRef]; Omar, T., Nehdi, M.L., Condition Assessment of Reinforced Concrete Bridges: Current Practice and Research Challenges (2018) Infrastructures, 3, p. 36. , [CrossRef]","Pozzer, S.; Department of Electrical and Computer Engineering, 1065, Av., de la Médecine, Canada; email: sandra.pozzer.1@ulaval.ca",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85106602579
"Abedin M., Mokhtari S., Mehrabi A.B.","57211253861;57218949968;7005771645;","Bridge damage detection using machine learning algorithms",2021,"Proceedings of SPIE - The International Society for Optical Engineering","11593",,"115932P","","",,6,"10.1117/12.2581125","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107487331&doi=10.1117%2f12.2581125&partnerID=40&md5=60f9360dfa4f7a05ebfbbbd1a0afd2a1","Department of Civil and Environmental Engineering, Florida International University, Miami, FL  33174, United States; Department of Electrical and Computer Engineering, Florida International University, Miami, FL  33174, United States","Abedin, M., Department of Civil and Environmental Engineering, Florida International University, Miami, FL  33174, United States; Mokhtari, S., Department of Electrical and Computer Engineering, Florida International University, Miami, FL  33174, United States; Mehrabi, A.B., Department of Civil and Environmental Engineering, Florida International University, Miami, FL  33174, United States","The application of accelerated bridge construction (ABC) methods is becoming more widespread owing to their many advantages. In this construction method, prefabricated bridge elements are assembled on-site by establishing in-situ joints to minimize on-site construction time. Despite the improved life-cycle performance and cost benefits of ABC bridges, some concerns exist about the degrading environmental effects on the joints and invisible internal damages. In this study, the long-term performance of an ABC bridge that had been in service for more than 50 years was investigated utilizing machine-learning processes. Observation of reflective cracking on the deck surface and leakage through the joints in this bridge indicated some damage to the bridge longitudinal joints. Damages to the joints are not always visible, nor their extent is known. Therefore, a new damage detection approach is proposed that uses the results of a series of load tests as input in machine-learning techniques with the ultimate aim of detecting the location and severity of joint damages with a high level of certainty. The proposed approach uses the bridge responses obtained from a detailed finite element (FE) model under the assumption of various damage scenarios and predicts the potential damages using the training process of machine-learning algorithms and the actual bridge responses. The results show that the supervised learning algorithm successfully estimated the location and amount of damage in the bridge joints. © 2021 SPIE","Bridge performance; Damage detection; Finite element analysis; Live load testing; Machine learning; Precast concrete","Biological systems; Bridges; Damage detection; Load testing; Machine learning; Structural health monitoring; Accelerated bridge constructions; Construction method; Life-cycle performance; Long term performance; Longitudinal joint; Machine learning techniques; On-site construction; Reflective cracking; Learning algorithms",,,,,,,,,,,,,,,,"Jahromi, A. J., Dickinson, M., Valikhani, A., Azizinamini, A., (2018) Assessing structural integrity of closure pours in ABC projects; Adams, T., Mashayekhizadeh, M., Santini-Bell, E., Wosnik, M., Baldwin, K., Fu, T., (2017) Structural response monitoring of a vertical lift truss bridge; Schulz, J. L., Commander, B., Goble, G. G., Frangopol, D. M., Efficient field testing and load rating of short-and medium-span bridges (1995) Struct. Eng. Rev, 3 (7), pp. 181-194; Cai, C. S., Shahawy, M., Understanding capacity rating of bridges from load tests (2003) Pract. Period. Struct. Des. Constr, 8 (4), pp. 209-216; Abedin, M., Mehrabi, A. B., Novel Approaches for Fracture Detection in Steel Girder Bridges (2019) Infrastructures, 4 (3), p. 42; Abedin, M., Farhangdoust, S., Mehrabi, A. B., Fracture detection in steel girder bridges using self-powered wireless sensors (2019) Risk-Based Bridg. Eng. Proc. 10th New York City Bridg. Conf, p. 216. , August 26-27, CRC Press, New York City, USA (2019); Mokhtari, S., Abbaspour, A., Yen, K. K., Sargolzaei, A., A Machine Learning Approach for Anomaly Detection in Industrial Control Systems Based on Measurement Data (2021) Electronics, 10 (4), p. 407; Abbaspour, A., Mokhtari, S., Sargolzaei, A., Yen, K. K., A Survey on Active Fault-Tolerant Control Systems (2020) Electronics, 9 (9), p. 1513; Khaleghi, M., Salimi, J., Farhangi, V., Moradi, M. J., Karakouzian, M., Application of Artificial Neural Network to Predict Load Bearing Capacity and Stiffness of Perforated Masonry Walls (2021) CivilEng, 2 (1), pp. 48-67; Jin, C., Jang, S., Sun, X., Li, J., Christenson, R., Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network (2016) J. Civ. Struct. Heal. Monit, 6 (3), pp. 545-560; Diez, A., Khoa, N. L. D., Alamdari, M. M., Wang, Y., Chen, F., Runcie, P., A clustering approach for structural health monitoring on bridges (2016) J. Civ. Struct. Heal. Monit, 6 (3), pp. 429-445; Mashayekhi, M., Santini-Bell, E., Detection of Damage-induced Fatigue Response Based on Structural Health Monitoring Data of In-service Steel Bridges Using Artificial Neural Network (2019) Struct. Heal. Monit, 2019; Documentation, D. A., (2016) ABAQUS/CAE Doc, , Simulia Provid. RI, USA; Abedin, M., Mehrabi, A. B., Effect of Cross-Frames on Load Distribution of Steel Bridges with Fractured Girder (2020) Infrastructures, 5 (4), p. 32; Ren, W., Sneed, L. H., Yang, Y., He, R., Numerical simulation of prestressed precast concrete bridge deck panels using damage plasticity model (2015) Int. J. Concr. Struct. Mater, 9 (1), pp. 45-54; Hutcheson, G. D., Ordinary least-squares regression (2011) SAGE Dict. Quant. Manag. Res, pp. 224-228. , L. Moutinho GD Hutcheson; Aho, T., Ženko, B., Džzeroski, S., Elomaa, T., Brodley, C., Multi-target regression with rule ensembles (2012) J. Mach. Learn. Res, 13 (8); Awad, M., Khanna, R., Support vector regression (2015) Efficient learning machines], pp. 67-80. , [Springer; Chai, T., Draxler, R. R., Root mean square error (RMSE) or mean absolute error (MAE)?-Arguments against avoiding RMSE in the literature (2014) Geosci. Model Dev, 7 (3), pp. 1247-1250","Abedin, M.; Department of Civil and Environmental Engineering, United States; email: mabed005@fiu.edu","Fromme P.Su Z.","The Society of Photo-Optical Instrumentation Engineers (SPIE)","SPIE","Health Monitoring of Structural and Biological Systems XV 2021","22 March 2021 through 26 March 2021",,169322,0277786X,9781510640153,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85107487331
"Putra S.A., Trilaksono B.R., Riyansyah M., Laila D.S.","56596545500;57194571217;26325879600;6602639929;","Multiagent Architecture for Bridge Capacity Measurement System Using Wireless Sensor Network and Weight in Motion",2021,"IEEE Transactions on Instrumentation and Measurement","70",,"9223734","","",,6,"10.1109/TIM.2020.3031126","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098324434&doi=10.1109%2fTIM.2020.3031126&partnerID=40&md5=81b6bace450b8f6ddaa5ac754fbda352","Enterprise Intelligent System Lab, Cybernetics Research Group, School of Industrial and System Engineering, Telkom University, Bandung, 40257, Indonesia; Institut Teknologi Bandung (ITB), Bandung, 40132, Indonesia; School of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry, CV1 5FB, United Kingdom; Electrical and Electronics Engineering Department, Faculty of Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan, BE1410, Brunei Darussalam","Putra, S.A., Enterprise Intelligent System Lab, Cybernetics Research Group, School of Industrial and System Engineering, Telkom University, Bandung, 40257, Indonesia; Trilaksono, B.R., Institut Teknologi Bandung (ITB), Bandung, 40132, Indonesia; Riyansyah, M., Institut Teknologi Bandung (ITB), Bandung, 40132, Indonesia; Laila, D.S., School of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry, CV1 5FB, United Kingdom, Electrical and Electronics Engineering Department, Faculty of Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan, BE1410, Brunei Darussalam","Wireless sensor network (WSN) has played important roles in various aspects of life including in bridge structural health monitoring system. Due to environmental and operational circumstances, a bridge needs to be monitored to make sure its continuous availability and operational safety. The structural stiffness of a bridge will decrease after some period of time due to structural strength declining and cracking. The change in bending stiffness will change the structural dynamic characteristics of the bridge and its capacity. Therefore, an automatic assessment system for bridge capacity is required. This article proposes the development of data acquisition and processing method for bridge capacity determination using its dynamic response, based on agent-based in-network processing. The use of WSN in bridge capacity monitoring and diagnostic also allows for sending warning messages to the control room. Experiment in the laboratory-scale bridge is performed to test the proposed system performance and the result was well validated with the finite-element analysis (FEA) results with modal assurance criterion value 0.90 that shows the high correlation degree between the FEA and the experiment. In addition, the processing time and energy consumption are compared with previous similar work, which shows the efficiency of the proposed system. © 1963-2012 IEEE.","Bridge capacity measurement; in-network processing; mobile agent; multiagent system; wireless sensor network (WSN)","Computer architecture; Data acquisition; Data handling; Energy utilization; Multi agent systems; Processing; Stiffness; Structural dynamics; Structural health monitoring; Automatic assessment; Bridge structural health monitoring; Dynamic characteristics; In-network processing; Modal assurance criterion; Multiagent architecture; Structural stiffness; Structural strength; Wireless sensor networks",,,,,"Royal Academy of Engineering, RAENG: IAPP1_100018; Ministry of Science,Technology and Research, MoSTR; Institut Teknologi Bandung, ITB","Manuscript received May 17, 2020; revised September 14, 2020; accepted September 27, 2020. Date of publication October 14, 2020; date of current version December 22, 2020. This work was supported in part by the Royal Academy of Engineering–Newton Funds under Award IAPP1_100018 and in part by the Ministry of Research Technology and Ministry of Education and Culture of Indonesia through the World Class University (WCU) 2020 Program managed by the Institut Teknologi Bandung. The Associate Editor coordinating the review process was Chao Tan. (Corresponding author: Seno Adi Putra.) Seno Adi Putra is with the Enterprise Intelligent System Lab, Cybernetics Research Group, School of Industrial and System Engineering, Telkom University, Bandung 40257, Indonesia (e-mail: adiputra@telkomuniversity.ac.id).","This work was supported in part by the Royal Academy of Engineering-Newton Funds under Award IAPP1-100018 and in part by the Ministry of Research Technology and Ministry of Education and Culture of Indonesia through the World Class University (WCU) 2020 Program managed by the Institut Teknologi Bandung.",,,,,,,,,"Rosario, F., Corte, D., Giuseppe, F., Riccardo, C., Giammaria, P.F., Sensing road pavement health status through acoustic signals analysis (2017) Proc. 13th Conf. Ph.D. Res. Microelectron. Electron. (PRIME), pp. 165-168. , Giardini Naxos, Italy, Jun; Islam, A.K.M.A., Li, F., Hamid, H., Jaroo, A., (2014) Bridge Condition Assessment and Load Rating Using Dynamic Response, , Dept. Trnasportation, Youngstown State Univ., Youngstown, OH, USA, Tech. Rep. FHWA/OH-2014/7; Islam, A.A., Li, F., Kolli, P.K., Structural health monitoring of bridges using wireless sensor network (2011) Appl. Mech. Mater., 82, pp. 796-803. , Jul; Chae, M.J., Yoo, H.S., Kim, J.Y., Cho, M.Y., Development of a wireless sensor network system for suspension bridge health monitoring (2012) Autom. Construct., 21, pp. 237-252. , Jan; Araujo, A., Wireless measurement system for structural health monitoring with high time-synchronization accuracy (2012) Ieee Trans. Instrum. Meas., 61 (3), pp. 801-810. , Mar; Bajwa, R., (2013) Wireless Weigh-in-motion: Using Road Vibrations to Estimate Truck Weights, , Ph.D. dissertation, Dept. Elect. Eng. Comput. Sci., UC Berkeley, Berkeley, CA, USA; Mascarenas, D., Flynn, E., Farrar, C., Park, G., Todd, M., A mobile host approach for wireless powering and interrogation of structural health monitoring sensor networks (2009) Ieee Sensors J., 9 (12), pp. 1719-1726. , Dec; Sazonov, E., Li, H., Curry, D., Pillay, P., Self-powered sensors for monitoring of highway bridges (2009) Ieee Sensor J., 9 (11), pp. 1422-1429. , Nov; Lydon, M., Development of a bridge weigh-in-motion sensor: Performance comparison using fiber optic and electric resistance strain sensor systems (2014) Ieee Sensors J., 14 (12), pp. 4284-4296. , Dec; Bhuiyan, M.Z.A., Wang, G., Cao, J., Wu, J., Deploying wireless sensor networks with fault-tolerance for structural health monitoring (2013) Ieee Trans. Comput., 64 (2), pp. 382-395. , Oct; Guo, P., Cao, J., Liu, X., Lossless in-network processing in WSNs for domain-specific monitoring applications (2017) Ieee Trans. Ind. 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Comput., 1 (1), pp. 14-21; Karl, H., Willig, A., (2007) Protocols and Architectures for Wireless Sensor Networks., , Hoboken, NJ, USA: Wiley; Montoya, A., Restrepo, D.C., Ovalle, D.A., Artificial intelligence for wireless sensor networks enhancement (2010) Smart Wireless Sensor Networks, pp. 73-81. , Dec; Wang, X., Chen, M., Kwon, T., Chao, H.C., Multiple mobile agents' itinerary planning in wireless sensor networks: Survey and evaluation (2011) Iet Commun., 5 (12), pp. 1769-1776. , Aug","Putra, S.A.; Enterprise Intelligent System Lab, Indonesia; email: adiputra@telkomuniversity.ac.id",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,00189456,,IEIMA,,"English","IEEE Trans. Instrum. Meas.",Article,"Final","",Scopus,2-s2.0-85098324434
"Omidalizarandi M., Herrmann R., Kargoll B., Marx S., Paffenholz J.-A., Neumann I.","56422420700;56706902700;55619952200;14013879200;26533278600;26325889100;","A validated robust and automatic procedure for vibration analysis of bridge structures using MEMS accelerometers",2020,"Journal of Applied Geodesy","14","3",,"327","354",,6,"10.1515/jag-2020-0010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086926742&doi=10.1515%2fjag-2020-0010&partnerID=40&md5=4d4d05b20e63ae48fc72d743c3fb2da7","Geodetic Institute, Leibniz University Hannover, Hannover, 463570, Germany; Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany; Institut für Geoinformation und Vermessung Dessau, Hochschule Anhalt, Dessau-Roßlau, Germany; Institute of Concrete Construction, Leibniz University Hannover, Hannover, 463570, Germany; Institute of Geotechnical Engineering and Mine Surveying, Clausthal University of Technology, Clausthal-Zellerfeld, Germany","Omidalizarandi, M., Geodetic Institute, Leibniz University Hannover, Hannover, 463570, Germany; Herrmann, R., Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany; Kargoll, B., Institut für Geoinformation und Vermessung Dessau, Hochschule Anhalt, Dessau-Roßlau, Germany; Marx, S., Institute of Concrete Construction, Leibniz University Hannover, Hannover, 463570, Germany; Paffenholz, J.-A., Institute of Geotechnical Engineering and Mine Surveying, Clausthal University of Technology, Clausthal-Zellerfeld, Germany; Neumann, I., Geodetic Institute, Leibniz University Hannover, Hannover, 463570, Germany","Today, short- and long-term structural health monitoring (SHM) of bridge infrastructures and their safe, reliable and cost-effective maintenance has received considerable attention. From a surveying or civil engineer's point of view, vibration-based SHM can be conducted by inspecting the changes in the global dynamic behaviour of a structure, such as natural frequencies (i. e. eigenfrequencies), mode shapes (i. e. eigenforms) and modal damping, which are known as modal parameters. This research work aims to propose a robust and automatic vibration analysis procedure that is so-called robust time domain modal parameter identification (RT-MPI) technique. It is novel in the sense of automatic and reliable identification of initial eigenfrequencies even closely spaced ones as well as robustly and accurately estimating the modal parameters of a bridge structure using low numbers of cost-effective micro-electro-mechanical systems (MEMS) accelerometers. To estimate amplitude, frequency, phase shift and damping ratio coefficients, an observation model consisting of: (1) a damped harmonic oscillation model, (2) an autoregressive model of coloured measurement noise and (3) a stochastic model in the form of the heavy-tailed family of scaled t-distributions is employed and jointly adjusted by means of a generalised expectation maximisation algorithm. Multiple MEMS as part of a geo-sensor network were mounted at different positions of a bridge structure which is precalculated by means of a finite element model (FEM) analysis. At the end, the estimated eigenfrequencies and eigenforms are compared and validated by the estimated parameters obtained from acceleration measurements of high-end accelerometers of type PCB ICP quartz, velocity measurements from a geophone and the FEM analysis. Additionally, the estimated eigenfrequencies and modal damping are compared with a well-known covariance driven stochastic subspace identification approach, which reveals the superiority of our proposed approach. We performed an experiment in two case studies with simulated data and real applications of a footbridge structure and a synthetic bridge. The results show that MEMS accelerometers are suitable for detecting all occurring eigenfrequencies depending on a sampling frequency specified. Moreover, the vibration analysis procedure demonstrates that amplitudes can be estimated in submillimetre range accuracy, frequencies with an accuracy better than 0.1 Hz and damping ratio coefficients with an accuracy better than 0.1 and 0.2 % for modal and system damping, respectively. © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020.","Automatic modal parameters identification; Bridge monitoring; Double integration; EM algorithm; FEM analysis; MEMS accelerometer; Robust parameter estimation; Vibration analysis","Acceleration measurement; Accelerometers; Composite beams and girders; Cost effectiveness; Damping; Electric measuring bridges; Finite element method; MEMS; Modal analysis; Parameter estimation; Polychlorinated biphenyls; Sensor networks; Stochastic models; Stochastic systems; Structural health monitoring; Time domain analysis; Auto regressive models; Automatic procedures; Bridge infrastructure; Expectation-maximisation; Micro electromechanical system (MEMS); Sampling frequencies; Stochastic subspace identification; Structural health monitoring (SHM); Vibration analysis; automation; bridge; electrochemistry; electronic equipment; geophone; health monitoring; maintenance; PCB; quartz; vibration",,,,,,,,,,,,,,,,"Dawson, B., Vibration condition monitoring techniques for rotating machinery (1976) Shock Vib Dig, 8 (12), p. 3; Alvandi, A., Cremona, C., Assessment of vibration-based damage identification techniques (2006) J Sound Vib, 292 (1), pp. 179-202; Wenzel, H., (2009) Health Monitoring of Bridges, , United Kingdom John Wiley and Sons Ltd; Peeters, B., Maeck, J., De Roeck, G., Vibration-based damage detection in civil engineering: Excitation sources and temperature effects (2001) Smart Mater Struct, 10 (3), pp. 518-527; Rohrmann, R.G., Baessler, M., Said, S., Schmid, W., Ruecker, W.F., (2000) Proc. 18th Int. Modal Analytical Conf. IMAC 18, pp. 1-7. , Structural causes of temperature affected modal data of civil structures obtained by long time monitoring San Antonio, Tex; Duff K, Hyzak M. Structural Monitoring with GPS. 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Operational PolyMAX for Estimating the Dynamic Properties of a Stadium Structure during a Football Game. Proc of the IMAC, Vol. 23, Orlando, FL, USA, January 2005; Peeters B, Van der Auweraer H. PolyMAX: a Revolution in Operational Modal Analysis. 1st International Operational Modal Analysis Conference, Copenhagen, Denmark, April 2005; James, G.H., Carne, T.G., Lauffer, J.P., The natural excitation technique (NExT) for modal parameter extraction from operating structures (1995) Modal Anal, 10 (4), pp. 260-277; Van Overschee, P., De Moor, B., Subspace algorithms for the stochastic identification problem (1993) Automatica, 29 (3), pp. 649-660; Hermans, L., Van Der Auweraer, H., Modal testing and analysis of structures under operational conditions: Industrial applications (1999) Mech Sys Signal Process, 13 (2), pp. 193-216; Peeters, B., (2000) System Identification and Damage Detection in Civil Engineering, , PhD thesis Belgium Department of Civil Engineering, K. U. 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Dataset: Reference Vibration Measurement of Mensa Bridge Hannover. DOI:., , http://doi.org/10.25835/0081614","Omidalizarandi, M.; Geodetic Institute, Germany; email: zarandi@gih.uni-hannover.de",,,"De Gruyter",,,,,18629016,,,,"English","J. Appl. Geod.",Article,"Final","",Scopus,2-s2.0-85086926742
"Ramancha M.K., Astroza R., Conte J.P., Restrepo J.I., Todd M.D.","56926718100;55619989200;7101953827;7005927192;7202805915;","Bayesian Nonlinear Finite Element Model Updating of a Full-Scale Bridge-Column Using Sequential Monte Carlo",2020,"Conference Proceedings of the Society for Experimental Mechanics Series",,,,"389","397",,6,"10.1007/978-3-030-47638-0_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120419366&doi=10.1007%2f978-3-030-47638-0_43&partnerID=40&md5=dcf574fd29d39ad39e5436879c08b9b8","Department of Structural Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States; Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile","Ramancha, M.K., Department of Structural Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States; Astroza, R., Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile; Conte, J.P., Department of Structural Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States; Restrepo, J.I., Department of Structural Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States; Todd, M.D., Department of Structural Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States","Digital twin-based approaches for structural health monitoring (SHM) and damage prognosis (DP) are emerging as a powerful framework for intelligent maintenance of civil structures and infrastructure systems. Model updating of nonlinear mechanics-based Finite Element (FE) models using input and output measurement data with advanced Bayesian inference methods is an effective way of constructing a digital twin. In this regard, the nonlinear FE model updating of a full-scale reinforced-concrete bridge column subjected to seismic excitations applied by a large shake table is considered in this paper. This bridge column, designed according to US seismic design provisions, was tested on the NEES@UCSD Large High-Performance Outdoor Shake Table (LHPOST). The column was subjected to a sequence of ten recorded earthquake ground motions and was densely instrumented with an array of 278 sensors consisting of strain gauges, linear and string potentiometers, accelerometers and Global Positioning System (GPS) based displacement sensors to measure local and global responses during testing. This heterogeneous dataset is used to estimate/update the material and damping parameters of the developed mechanics-based distributed plasticity FE model of the bridge column. The sequential Monte Carlo (SMC) method (set of advanced simulation-based Bayesian inference methods) is used herein for the model updating process. The inherent architecture of SMC methods allows for parallel model evaluations, which is ideal for updating computationally expensive models. © 2020, The Society for Experimental Mechanics, Inc.","Bayesian inference; Digital twin; Earthquake; Finite element; Full-scale structural systems; Model updating; Sequential Monte Carlo; Structural health monitoring","Bayesian networks; Earthquakes; Finite element method; Global positioning system; Inference engines; Reinforced concrete; Seismic design; Structural health monitoring; Voltage dividers; Bayesian inference; Bridge columns; Earthquake; Finite element modelling (FEM); Finite-element model updating; Full-scale structural system; Model updating; Non-linear finite element modeling; Sequential Monte Carlo; Structural systems; Monte Carlo methods",,,,,"Engineer Research and Development Center, ERDC: W912HZ-17-2-0024; U.S. Army Corps of Engineers, USACE","Acknowledgements Funding for this work was provided by the U.S. Army Corps of Engineers through the U.S. Army Engineer Research and Development Center Research Cooperative Agreement W912HZ-17-2-0024.",,,,,,,,,,"Kadry, S., Diagnostics and prognostics of engineering systems: Methods and techniques (2012) Diagnostics Prognostics of Engineering Systems: Methods and Techniques, pp. 1-433. , pp., IGI Global, Hershey; Farrar, C.R., Worden, K., An introduction to structural health monitoring (2007) Math Phys Eng Sci, 365, pp. 303-315; Farrar, C.R., Worden, K., (2012) Structural Health Monitoring: A Machine Learning Perspective, , Wiley, Chichester; Astroza, R., Ebrahimian, H., Conte, J.P., Material parameter identification in distributed plasticity FE models of frame-type structures using nonlinear stochastic filtering (2015) J. Eng. Mech. ASCE., 141 (5), pp. 1-17; Astroza, R., Alessandri, A., Conte, J.P., A dual adaptive filtering approach for nonlinear finite element model updating accounting for modeling uncertainty (2019) Mech. Syst. Signal Process., 115, pp. 782-800; Ramancha, M.K., Madarshahian, R., Astroza, R., Conte, J.P., Non-unique estimates in material parameter identification of nonlinear FE models governed by multiaxial material models using unscented kalman filtering (2020) Conference Proceedings of Society of Experimental Mechanics Series, pp. 257-265. , pp., Springer International Publishing, Cham; Ebrahimian, H., Astroza, R., Conte, J.P., de Callafon, R.A., Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation (2017) Mech. Syst. Signal Process., 84, pp. 194-222; Schoettler, D.C., Restrepo, J.I., Guerrini, G., A full-scale, single-column bridge bent tested by shake-table excitation (2015) PEER Rep, 11 (3), pp. 555-565; Minson, S.E., Simons, M., Beck, J.L., Bayesian inversion for finite fault earthquake source models I-theory and algorithm (2013) Geophys. J. Int., 194 (3), pp. 1701-1726; Ching, J., Chen, Y.C., Transitional Markov chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging (2007) J. Eng. Mech., 133 (7), pp. 816-832. , July; Chatfield, C., Model uncertainty, data mining and statistical inference (1995) J. R. Stat. Soc. Ser. A., 158, pp. 419-444; Kemp, F., An introduction to sequential Monte Carlo methods (2003) J R Stat Soc Ser D, 52 (4), pp. 694-695. , December; Taucer, F.F., Spacone, E., Filippou, F.C., A Fiber Beam-Column Element for Seismic Response Analysis of Reinforced Concrete Structures Rep 91/17, , Berkeley, CA; Popovics, S., A numerical approach to the complete stress-strain curve of concrete (1973) Cem. Concr. Res., 3 (5), pp. 583-599; Brown, J., Kunnath, S.K., (2000) Low-Cycle Fatigue Behavior of Longitudinal Reinforcement in Reinforced Concrete Bridge Columns. Multidisci-Plinary, , Buffalo","Ramancha, M.K.; Department of Structural Engineering, United States; email: mramanch@eng.ucsd.edu","Mao Z.",,"Springer","38th IMAC, A Conference and Exposition on Structural Dynamics, 2020","10 February 2020 through 13 February 2020",,245349,21915644,9783030487782,,,"English","Conf. Proc. Soc. Exp. Mech. Ser.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85120419366
"Niu Y., Wang Y., Tang Y.","24465331100;57219364233;57204218764;","Analysis of temperature-induced deformation and stress distribution of long-span concrete truss combination arch bridge based on bridge health monitoring data and finite element simulation",2020,"International Journal of Distributed Sensor Networks","16","10",,"","",,6,"10.1177/1550147720945205","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092415529&doi=10.1177%2f1550147720945205&partnerID=40&md5=56e6f7f22c85841d5afbe185a4f2318a","School of Highway, Chang’an University, Xi’an, China; Qingdao Municipal Engineering Design Research Institute, Qingdao, China; School of Science, Chang’an University, Xi’an, China","Niu, Y., School of Highway, Chang’an University, Xi’an, China; Wang, Y., Qingdao Municipal Engineering Design Research Institute, Qingdao, China; Tang, Y., School of Science, Chang’an University, Xi’an, China","Through decades of operation, deformation fluctuation becomes a central problem affecting the normal operating of concrete truss combination arch bridge. In order to clarify the mechanism of temperature-induced deformation and its impact on structural stress distribution, this article reports on the temperature distribution and its effect on the deformation of concrete truss combination arch bridge based on bridge health monitoring on a proto bridge with 138 m main span. The temperature distribution and deformation characteristics of the bridge structure in deep valley area are studied. Both of the daily and yearly temperature variation and structural deformation are studied based on bridge health monitoring. Using the outcome of monitoring data, three-dimensional solid finite element models are established to analyze the mechanism of temperature-induced deformation of the whole bridge under different temperature fields. The influence of temperature-induced effect is discussed on local damage based on the damage observation of the background bridge. The outcome of comparisons with field observation validates the analysis results. The relevant monitoring and simulation result can be referenced for the design and evaluation of similar bridges. © The Author(s) 2020.","Arch bridge; deflection; finite element method; health monitoring; temperature","Arches; Concretes; Deformation; Finite element method; Health; Monitoring; Stress concentration; Structural health monitoring; Temperature distribution; Trusses; Bridge health monitoring; Deformation and stress; Deformation Characteristics; Design and evaluations; Finite element simulations; Structural deformation; Temperature-induced effects; Three-dimensional solids; Arch bridges",,,,,"51208056; Fundamental Research Funds for the Central Universities: 300102218213, 310821161013","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Science Foundation of the People’s Republic China (51208056) and the Fundamental Research Funds for the Central Universities (310821161013 and 300102218213).",,,,,,,,,,"Chen, T.B., (2001) Combination truss arch bridge, , Beijing, China, China Communications Press; Chen, G.H., Du, R., Local stress analysis of new arch foot joints of a composite truss arch bridge Bridge Construct, 2009 (4), pp. 35-38; He, F., Guo, G.Q., Study on parameter selection and calculation method of composite truss arch bridge (2010) Sino-Foreign Highway, 30 (3), pp. 139-142; Du, B., Characteristic of main diseases of composite truss arch bridge (2014) Sino-Foreign Highway, 34 (5), pp. 126-128; Li, G.B., (2008) Research on reinforcement technology of “energy release method” truss composite arch bridge, , Chongqing Jiaotong University, Chongqing, China, Master thesis; Niu, H., Jia, L., Mu, Y., Contrastive analysis of four reinforcement methods for composite truss arch bridge Bridge Construct, 2009 (1), pp. 78-80; Li, J.S., Discussion on reinforcement method of composite truss arch bridge Heilongjiang Sci Tech Inform, 2010 (14), p. 210; Xiao, W., Zhang, X.S., Tang, F., Load test analysis of a composite truss arch bridge after reinforcement Transp Sci Tech Econ, 2011 (1), pp. 1-4; Du, B., Jia, N., Ding, Z.C., Analysis and discussion on the calculation parameters of temperature variation and concrete shrinkage internal force of truss composite arch bridge system (2007) Guizhou Sci, 5 (25), pp. 223-229; Elbadry, M.M., Ghali, A., Temperature variation in concrete bridge (1983) J Struct Eng, 109 (10), pp. 2355-2374; Roberts, C.L., Breen, J.E., Cawrse, J., Measurements of thermal gradients and their effects on segmental concrete bridge (2002) J Bridge Eng, 7 (3), pp. 166-174; Suzuki, J., Ohba, Y., Uchikawa, Y., Monitoring temperature on a real box-girder bridge and energy budget analysis for basic information on bridge cooling and surface freezing (2007) J Bridge Eng, 12 (1), pp. 45-52; Peiretti, H.C., Parrotta, J.E., Oregui, A.B., Experimental study of thermal actions on a solid slab concrete deck bridge and comparison with Eurocode 1 (2014) J Bridge Eng, 19 (10), p. 04014041; Guo, T., Chen, Z.H., Liu, T., Time-dependent reliability of strengthened PSC box-girder bridge using phased and incremental static analyses (2016) Eng Struct, 117, pp. 358-371; Zhao, H.W., Ding, Y.L., Nagarajaiah, S., Behavior analysis and early warning of girder deflections of a steel-truss arch railway bridge under the effects of temperature and trains: case study (2019) J Bridge Eng, 24 (1), p. 05018013; Xia, Q., Zhang, J., Tian, Y., Experimental study of thermal effects on a long-span suspension bridge (2017) J Bridge Eng, 22 (7), p. 04017034; Cao, Y.H., Yim, J., Zhao, Y., Temperature effects on cable stayed bridge using health monitoring system: a case study (2010) Struct Health Monit, 10 (5), pp. 523-537; Le, V.H., Nishio, M., Time-series analysis of GPS monitoring data from a long-span bridge considering the global deformation due to air temperature changes (2015) J Civil Struct Health Monit, 5, pp. 415-425; Zhao, H.W., Ding, Y.L., Nagarajaiah, S., Longitudinal displacement behavior and girder end reliability of a jointless steel-truss arch railway bridge during Operation (2019) Appl Sci, 9, p. 112222; Moazam, A.M., Hasani, N., Yazdani, M., 3D simulation of railway bridges for estimating fundamental frequency using geometrical and mechanical properties (2017) Adv Comput Design, 2 (4), pp. 257-271; Yazdani, M., Khaji, N., Khodakarami, M.I., Development of a new semi-analytical method in fracture mechanics problems based on the energy release rate (2016) Acta Mechanica, 227, pp. 3529-3547; Code for design of highway reinforced concrete and prestressed concrete bridges and culverts; Yazdani, M., Jahdngiri, V., Marefat, M.S., Seismic performance assessment of plain concrete arch bridges under near-field earthquakes using incremental dynamic analysis (2019) Eng Failure Anal, 106, p. 104170; Jahdngiri, V., Yazdani, M., Seismic reliability and limit state risk evaluation of plain concrete arch bridges Struct Infrastruct Eng, , Epub ahead of print 2020; Standards for technical condition evaluation of highway bridges; Megson, T.H.G., (2005) Structural and stress analysis, , 2nd ed., New York, Elsevier; Liu, H.W., (2004) Mechanics of materials (I), , 4th ed, Beijing, China, Higher Education Press","Niu, Y.; School of Highway, China; email: niuyanwei@chd.edu.cn",,,"SAGE Publications Ltd",,,,,15501329,,,,"English","Int. J. Distrib. Sens. Netw.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85092415529
"Sokolov S.A., Plotnikov D.G., Grachev A.A., Lebedev V.A.","57102766800;57204929103;57203459807;57217410944;","Evaluation of loads applied on engineering structures based on structural health monitoring data",2020,"International Review of Mechanical Engineering","14","2",,"146","150",,6,"10.15866/ireme.v14i2.18269","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085692996&doi=10.15866%2fireme.v14i2.18269&partnerID=40&md5=89bb88c0045f0612a6cabc5a8c711faa","Peter the Great Saint-Petersburg Polytechnic University, Saint-Petersburg, Russian Federation; «Robo-TeC» LLC, Saint-Petersburg, Russian Federation","Sokolov, S.A., Peter the Great Saint-Petersburg Polytechnic University, Saint-Petersburg, Russian Federation; Plotnikov, D.G., Peter the Great Saint-Petersburg Polytechnic University, Saint-Petersburg, Russian Federation; Grachev, A.A., Peter the Great Saint-Petersburg Polytechnic University, Saint-Petersburg, Russian Federation; Lebedev, V.A., «Robo-TeC» LLC, Saint-Petersburg, Russian Federation","The article is devoted to the monitoring methods and reliability prediction of the metal structures of large critical constructions improving. A technology for the in-line processing of the coming data from the measuring complex and the following formation on its basis of a model of processes of operational loading of the structure is proposed. This technology includes the following procedures: (a) conducting of preliminary finite element analysis to obtain the influence coefficients of various loads, (b) processing of current information from sensors for modeling loading processes and (c) the following numerical modeling of the stress-strain state of the structure using the loading model. This combination of hardware monitoring capabilities and numerical analysis significantly increases the monitoring efficiency. The example shows that this technology allows, for a limited number of sensors, obtaining extensive information about the operational load of the structure and changes in its functioning due to the supports subsidence or significant damage. The proposed technology is designed to improve the effectiveness of the monitoring of the technical condition of bridges, towers, large sports facilities. © 2020 Praise Worthy Prize S.r.l.-All rights reserved.","FEA; Loading; Method; Structural Health Monitoring",,,,,,,,,,,,,,,,,"Plotnikov, D.G., Sokolov, S.A., (2015) Methodology for Prediction of Breakdown of Welded Metal Structures of Carrying and Lifting Machines, (12). , Repair, restoration, upgrading. – M; Bely, A.A., Belov, A.A., Osadchy, G.V., Dolinsky, K.Y., (2018) Conception of Monitoring of Artificial Structures of Saint Petersburg., 71. , Roads: Innovation in construction; Bely, A.A., Belov, A.A., Osadchy, G.V., Dolinsky, K.Y., Conception of monitoring of artificial structures of Saint Petersburg (Completion) (2018) Roads: Innovation in Construction, 72; General Concepts. Terms and Definitions.; Osadchy, G.V., Bely, A.A., Efanov, D.V., (2018) Shestovitsky. Structural Health Monitoring of the Sliding Roof of the Sankt Petersburg Arena Stadium. Construction of Unique Buildings and Structures, 6 (69); Bely, A.A., Dolinsky, K.Y., Osadchy, G.V., A system of monitoring of engineering structures in construction of a tunnel under the river Smolenka (Saint Petersburg) (2016) Geotechnics, 2, pp. 18-27; Kazakov, V.D., Lazarev, D.V., (2009) Virtual Devices. Simulation of Measurement Devices and Systems, , Reference book. Ministry of Education and Science of the Russian Federation, Federal Agency for Education, Federal State Educational Institution of Higher Professional Education I.N. Ulyanov Chuvash State University. Cheboksary; Makhutov, N.A., Structural strength, life and technogenic safety (2005) Part 1 Strength and Life Criteria, p. 496. , Novosibirsk: –Nauka; N.A. Makhutov. Structural strength, life and technogenic safety. Part 2 Justification of life and safety. – Novosibirsk: –Nauka. – 2005. –612p; S.A. Sokolov Metal structures of carrying and lifting machines: – SPb: Politekhnika, 2005. – 423 p; Sokolov, S., Analysis of the Fatigue Strength of Welds in Terms of Local Stress (2018) Russian Engineering Research, 38 (3), pp. 151-156. , ISSN 1068-798X; Sokolov, S., Grachev, A., Local Criterion for Strength of Elements of Steelwork (2018) International Review of Mechanical Engineering (IREME), 12 (5), pp. 448-453. , https://doi.org/10.15866/ireme.v12i5.14582; Shlepetinsky, A.Y., Manzhula, K.P., Savelyev, A.G., Trajectory and growth speed of fatigue cracks due to poor weld fusion in a weld joint (2019) Transport Engineering and Technology, p. 46; Efanov, D.V., Sapozhnikov, V.V., Sapozhnikov, V.V., Pivovarov, D.V., Synthesis of Built-in Self-Test Control Circuits Based on the Method of Boolean Complement to Constant-Weight 1-out-of-n Codes (2019) Automatic Control and Computer Sciences, 53 (6), pp. 481-491; Efanov, D.V., Osadchy, G.V., Khóroshev, V.V., Shestovitskiy, D.A., Diagnostics of Audio-Frequency Track Circuits in Continuous Monitoring Systems for Remote Control Devices: Some Aspects (2019) Proceedings of 17Th IEEE East-West Design & Test Symposium (EWDTS`2019), pp. 162-170. , Batumi, Georgia, September 13-16; Efanov, D.V., Osadchy, G.V., Barch, D.V., Belyi, A., Permanent Monitoring Systems of the Contact-Wire of Railroad Catenary: The Main Tasks of Implementation (2019) Proceedings of 17Th IEEE East-West Design & Test Symposium (EWDTS`2019), pp. 484-487. , Batumi, Georgia, September 13-16; Efanov, D., Osadchy, G., Plotnikov, D., Average Number of Orders Calculation Concerning Diagnostic Test of Measuring Controllers During Permanent Monitoring Performance Based on Stationary Model of Queueing System (2018) Proceedings of 16Th IEEE East-West Design & Test Symposium (EWDTS`2018), pp. 660-670. , Kazan, Russia, September 14-17; Efanov, D., Pristensky, D., Osadchy, G., Razvitnov, I., Sedykh, D., Skurlov P. New Technology in Sphere of Diagnostic Information Transfer within Monitoring System of Transportation and Industry (2017) Proceedings of 15Th IEEE East-West Design & Test Symposium (EWDTS`2017), pp. 231-236. , Novi Sad, Serbia, September 29 – October 2",,,,"Praise Worthy Prize",,,,,19708734,,,,"English","Int. Rev. Mech. Eng.",Article,"Final","",Scopus,2-s2.0-85085692996
"Kirillova E.V., Seemann W., Shevtsova M.S.","24402885500;36830773800;26656323900;","The influence of an adhesive layer on the interaction between a piezo-actuator and an elastic 3D-layer and on the excited wave fields",2019,"Materials Physics and Mechanics","42","1",,"40","53",,6,"10.18720/MPM.4212019_5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067463758&doi=10.18720%2fMPM.4212019_5&partnerID=40&md5=722bd6abf392e583bc3ee49e0cf7bb4f","RheinMain University of Applied Sciences, Wiesbaden, Germany; Karlsruhe Institute of Technology, Karlsruhe, Germany","Kirillova, E.V., RheinMain University of Applied Sciences, Wiesbaden, Germany; Seemann, W., Karlsruhe Institute of Technology, Karlsruhe, Germany; Shevtsova, M.S., RheinMain University of Applied Sciences, Wiesbaden, Germany, Karlsruhe Institute of Technology, Karlsruhe, Germany","Piezoceramic transducers are extensively used in nondestructive testing (NDT), structural health monitoring (SHM) and condition monitoring (CM) of various mechanical systems including wind turbines, aircraft structures, bridges and pipeline systems. Piezoelectric transducers are surface bonded on the host structure and are excited to produce structural responses. This article highlights the effect of the adhesive layer between the studied structure and the transducer on the contact characteristics and the structural wave fields. The research also focuses on the efficiency of the both methods used for calculation of the occuring wave fields: finite-element (FE) method and semi-analytical approach based on the Green’s matrix representations and the Fourier transform. © 2019, Peter the Great St. Petersburg Polytechnic University","Anisotropic infinite layer; Finite element model; Fourier transform; Green's matrix; Piezoelectric actuator; Wave excitation","Adhesives; Aircraft manufacture; Airframes; Condition monitoring; D region; Finite element method; Fourier transforms; Nondestructive examination; Piezoelectric actuators; Piezoelectric ceramics; Piezoelectric transducers; Piezoelectricity; Wind turbines; Contact characteristics; Green's matrices; Infinite-layer; Piezoceramic transducer; Semi-analytical approaches; Structural health monitoring (SHM); Structural response; Wave excitation; Structural health monitoring",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 13FH009IX5","Acknowledgements. This research was supported by the Education and Research (BMBF), Grant No. 13FH009IX5. one line indent",,,,,,,,,,"Kalliomäki, K., Condition monitoring, methods and a general purpose monitoring system (1983) IFAC Proceedings Volumes, 16 (21), pp. 295-304; Wechsler, A., Mecrow, B.C., Atkinson, D.J., Bennett, J.W., Benarous, M., Condition monitoring of dc-link capacitors in aerospace drives (2012) IEEE Trans. Ind. Appl., 48 (6), pp. 1866-1874; Jolly, M.R., Prabhakar, A., Sturzu, B., Hollstein, K., Singh, R., Thomas, S., Foote, P., Shaw, A., Review of Non-destructive Testing (NDT) Techniques and their applicability to thick walled composites (2015) Procedia CIRP, 38, pp. 129-136; Giurgiutiu, V., Tuned lamb wave excitation and detection with piezoelectric wafer active sensors for structural health monitoring (2005) Intell. Mater. Syst. Struct., 16, pp. 291-305; Raghavan, A., Cesnik, C.E.S., Finite-dimensional piezoelectric transducer modeling for guided wave based structural health monitoring (2005) Smart Mater. Struct., 14, pp. 1448-1461; Giurgiutiu, V., (2007) Structural Health Monitoring with Piezoelectric Wafer Active Sensors, , Elsevier: Academic Press; Salas, K.I., Cesnik, C.E.S., Guided wave structural health monitoring using CLoVER transducers in composite materials (2010) Smart Mater. Struct., 19, p. 015014; Karmazin, A., Kirillova, E., Seemann, W., Syromyatnikov, P., A study of time harmonic guided Lamb waves and their caustics in composite plates (2013) Ultrasonics, 53, pp. 283-293; Glushkov, E.V., Glushkova, N.V., Evdokimov, A.A., Zhang, C.H., Guided wave Generation in Elastic Layered Substrates with Piezoelectric Coatings and Patches (2015) Physics Procedia, 70, pp. 945-948; Islam, M.M., Huang, H., Effects of adhesive thickness on the Lamb wave pitch-catch signal using bonded piezoelectric wafer transducers (2016) Smart Mater. Struct., 25, p. 085014; Ha, S., Fu-Kuo, C., Adhesive interface layer effects in PZT-induced Lamb wave propagation (2010) Smart Mater. Struct., 19, p. 025006; Nieuwenhuis, J.H., Neumann, J., Greve, D.W., Oppenheim, I.J., Generation and detection of guided waves using PZT wafer transducers. Ultrasonics, ferroelectrics, and frequency control (2005) IEEE Trans. On Ultrasonics, Ferroelectrics, and Frequency Control, 52 (11), pp. 2103-2111; Kirillova, E., Seemann, W., Shevtsova, M., Modeling the interaction of piezoelectric actuators with elastic structures (2017) Advanced Materials Techniques, Physics, Mechanics and Applications. Springer Proceedings in Physics Book Series, 193, pp. 501-510. , Parinov IA, Chang SH., Jani MA. eds SPPHY, Cham: Springer; Ostachowicz, W., Kudela, P., Malinowski, P., Wandowski, T., Damage localisation in plate-like structures based on PZT sensors (2009) Mech. Syst. And Signal Proc., 23, pp. 1805-1829; Giurgiutiu, V., Zagrai, A.N., Characterization of piezoelectric wafer active sensors (2000) J. Intell. Mater. Syst. Struct., 11, pp. 959-976; Crawley, E., Luis, J., Use of piezoelectric actuators as elements of intelligent structures (1987) AIAA J, 25, pp. 1373-1385; Seshu, P., Naganathan, N., Finite-element analysis of strain transfer in an induced strain actuator (1997) Smart Mater. Struct., 6, pp. 76-88; Sirohi, J., Chopra, I., Fundamental understanding of piezoelectric strain sensors (2000) J. Intell. Mater. Syst. Struct., 11, pp. 246-257; Rabinovitch, O., Vinson, J., Adhesive Layer Effects in surface-mounted Piezoelectrics Actuators (2002) J. Intell. Mater. Syst. Struct., 13 (11), pp. 689-704; Han, L., Wang, X.D., Sun, Y., The effect of bonding layer properties on the dynamic behaviour of surface-bonded piezoelectric sensors (2008) Int. J. Solids and Struct., 45, pp. 5599-5612; Bhalla, S., Soh, C., Electromechanical impedance modeling for adhesively bonded piezo-transducers (2004) J. Intell. Mater. Syst. Struct., 15, pp. 955-972; Babeshko, V., Glushkov, E., Zinchenko, Z., (1989) Dynamics of Inhomogeneous Linearly Elastic Media, , Moscow: Nauka; Russian; Xu, P.C., Mal, A., An adaptive integration scheme for irregularly oscillatory functions (1985) Wave Motion, 7, pp. 235-243","Shevtsova, M.S.; RheinMain University of Applied SciencesGermany; email: maria.shevtsova@hs-rm.de",,,"Institute of Problems of Mechanical Engineering",,,,,16052730,,,,"English","Mater. Phys. Mech.",Article,"Final","",Scopus,2-s2.0-85067463758
"Huang H., Wu Z.","55857247400;14632619300;","Monitoring and structural analysis of a rehabilitated box girder bridge based on long-gauge strain sensors",2018,"Structural Health Monitoring","17","3",,"586","597",,6,"10.1177/1475921717707357","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042107436&doi=10.1177%2f1475921717707357&partnerID=40&md5=0b0074fe8208eb8a37b757690899471c","Department of Urban and Civil Engineering, Faculty of Engineering, Ibaraki University, Hitachi, Japan","Huang, H., Department of Urban and Civil Engineering, Faculty of Engineering, Ibaraki University, Hitachi, Japan; Wu, Z., Department of Urban and Civil Engineering, Faculty of Engineering, Ibaraki University, Hitachi, Japan","Structural rehabilitation is playing an increasingly important role in civil engineering owing to issues with aging infrastructure. In this context, a feasible inspection and monitoring system is needed to draw up effective structural rehabilitation projects. This article presents a case study of a real box girder bridge strengthened via external post-tensioning. With the aim of evaluating the strengthening project and the structural behavior changes, a large-scale strain sensing system containing four sensing areas was installed on the bridge before strengthening, and the static and dynamic strain distribution changes were recorded during annual inspections. The text focuses on discussing and comparing the variations of strain distribution across the bridge before and after strengthening, as well as the yearly changes the rehabilitated bridge has undergone. From the measured strain responses, we accurately determined that the rehabilitated bridge had undergone an unexpected reduction in its flexural stiffness as well as a torsion action. Moreover, finite element analysis results of three different damage models are discussed to understand the detailed cause for this. © 2017, © The Author(s) 2017.","Damage identification; fiber Bragg grating; rehabilitated structures; structural analysis; structural health monitoring","Box girder bridges; Bridge decks; Bridges; Damage detection; Fiber Bragg gratings; Finite element method; Steel bridges; Structural analysis; Aging infrastructure; Damage Identification; External post-tensioning; Flexural stiffness; Strain distributions; Strain sensing systems; Structural behaviors; Structural rehabilitation; Structural health monitoring",,,,,,"The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors gratefully acknowledge the financial support from the Priority Research Program of Ibaraki University",,,,,,,,,,"(2015) Annual report on roads maintenance, , Tokyo, Japan, Ministry of Land, Infrastructure, Transport and Tourism; Matthew, K.E., Levinson, D.M., (2011) Fix it first, expand it second, reward it third: a new strategy for America’s highways, , Washington, DC, Brookings Institution, Policy brief no. 2011-03 (The Hamilton Project); Risk management—principles and guidelines; Koo, K.Y., Lee, J.J., Yun, C.B., Damage detection in beam-like structures using deflections obtained by modal flexibility matrices, 56, pp. 483-488. , Proceedings of the advances in science and technology, Hong Kong, Zurich, Trans Tech Publications, In; Tennyson, R., ISIS manual no. 2—guidelines for structural health monitoring, , Toronto, ON, Canada, ISIS Canada; Rücker, W., Hille, F., Rohrmann, R., (2006) F08b: guideline for structural health monitoring, , Berlin, Federal Institute of Materials Research and Testing (BAM), SAMCO; Nguyen, V.H., Schommer, S., Maas, S., Static load testing with temperature compensation for structural health monitoring of bridges (2016) Eng Struct, 127, pp. 700-718; Xin, D., Li, H., Wang, L., Experimental study on static characteristics of the bridge deck section under simultaneous actions of wind and rain (2012) J Wind Eng Ind Aerod, 107, pp. 17-27; Doebling, S.W., Farrar, C.R., Prime, M.B., A review of damage identification methods that examine changes in dynamic properties (1998) Shock Vib Dig, 30, pp. 91-105; Sohn, H., Farrar, C.R., Hemez, F.M., A review of structural health monitoring literature: 1996–2001, , Los Alamos, NM, Los Alamos National Laboratory, Los Alamos National Laboratory Report LA-13976-MS; Li, S.Z., Wu, Z.S., Development of distributed long-gage fiber optic sensing system for structural health monitoring (2007) Struct Health Monit, 6, pp. 133-143; Wu, Z.S., Zhang, H., Yang, C.Q., Development and performance evaluation of non-slippage optical fiber as Brillouin scattering-based distributed sensors (2010) Struct Health Monit, 9, pp. 413-431; Dai, Y.B., Liu, Y.J., Leng, J.S., A novel time-division multiplexing fiber Bragg grating sensor interrogator for structural health monitoring (2009) Opt Laser Eng, 47, pp. 1028-1033; Majumder, M., Gangopadhyay, T.K., Chakraborty, A.K., Fibre Bragg gratings in structural health monitoring—present status and applications (2008) Sensor Actuat A: Phys, 147, pp. 150-164; Zhang, J., Hong, W., Tang, Y.S., Structural health monitoring of a steel stringer bridge with area sensing (2014) Struct Infrastruct E, 10 (8), pp. 1049-1058; Zhang, H., Wu, Z.S., Performance evaluation of BOTDR-based distributed fiber optic sensors for crack monitoring (2008) Struct Health Monit, 7, pp. 143-156; Wu, Z.S., Adewuyi, A.P., Xue, S.T., Identification of damage in reinforced concrete columns under progressive seismic excitation stages (2011) J Earthq Tsunami, 5, pp. 151-165; Adewuyi, A.P., Wu, Z.S., Modal macro-strain flexibility methods for damage localization in flexural structures using long-gage FBG sensors (2011) Struct Control Hlth, 18, pp. 341-360; Li, S.Z., Wu, Z.S., Parametric estimation for RC flexural members based on distributed long-gauge fiber optic sensors (2010) J Struct Eng: ASCE, 136, pp. 144-151; Adewuyi, A.P., Wu, Z.S., Vibration-based damage localization in flexural structures using normalized modal macrostrain techniques from limited measurements (2011) Comput-Aided Civ Inf, 26, pp. 154-172; Zhang, J., Xia, Q., Cheng, Y., Strain flexibility identification of bridges from long-gauge strain measurements Mech Syst Signal Pr, 62, pp. 272-283; Kobayashi, K., Ohira, Y., Toishi, K., Inspection and counter-measures to PC-cable break of Myoukou-Bridge (2011) J Bridge Found Eng, 45 (9), pp. 32-38; Shen, S., Wu, Z.S., Yang, C.Q., An improved conjugated beam method for deformation monitoring with a distributed sensitive fiber optic sensor (2010) Struct Health Monit, 9 (4), pp. 361-378","Wu, Z.; Department of Urban and Civil Engineering, 4-12-1, Nakanarusawa, Japan; email: zswu@mx.ibaraki.ac.jp",,,"SAGE Publications Ltd",,,,,14759217,,,,"English","Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85042107436
"Dang H., Tatipamula M., Nguyen H.X.","57617394600;55900222400;57199967463;","Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning",2022,"IEEE Transactions on Industrial Informatics","18","6",,"3820","3830",,5,"10.1109/TII.2021.3115119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115797070&doi=10.1109%2fTII.2021.3115119&partnerID=40&md5=a431312923e914a6a6c0b148e9a7e83c","Faculty of Building and Industrial Construction, Hanoi University of Civil Engineering, Hanoi, 100000, Viet Nam; Ericsson Silicon Valley, Santa Clara, CA  95054, United States; London Digital Twin Research Centre, Faculty of Science and Technology, Middlesex University, London, NW4 4BT, United Kingdom","Dang, H., Faculty of Building and Industrial Construction, Hanoi University of Civil Engineering, Hanoi, 100000, Viet Nam; Tatipamula, M., Ericsson Silicon Valley, Santa Clara, CA  95054, United States; Nguyen, H.X., London Digital Twin Research Centre, Faculty of Science and Technology, Middlesex University, London, NW4 4BT, United Kingdom","Digital twin (DT) technology has recently gathered pace in the engineering communities as it allows for the convergence of the real structure and its digital counterpart throughout their entire life-cycle. With the rapid development of supporting technologies, including machine learning (ML), 5G/6G, cloud computing, and Internet of Things, DT has been moving progressively from concept to practice. In this article, a DT framework based on cloud computing and deep learning (DL) for structural health monitoring is proposed to efficiently perform real-time monitoring and proactive maintenance. The framework consists of structural components, device measurements, and digital models formed by combining different submodels, including mathematical, finite element, and ML ones. The data interaction among physical structure, digital model, and human interventions are enhanced by using cloud computing infrastructure and a user-friendly web application. The feasibility of the proposed framework is demonstrated via case studies of damage detection of model bridge and real bridge structures using DL algorithms, with high accuracy of 92%. © 2005-2012 IEEE.","Cloud computing; deep learning (DL); digital twin (DT); Internet of Things (IoT); structural health monitoring (SHM)","ABAQUS; Damage detection; Deep learning; E-learning; Engineering education; Finite element method; Internet of things; Learning algorithms; Life cycle; Structural health monitoring; Cloud-based; Cloud-computing; Computational modelling; Deep learning; Digital counterparts; Digital modeling; Engineering community; Real structure; Solid modelling; Cloud computing",,,,,,"This work was supported in part by an Institutional Links under Grant ID 429715093.",,,,,,,,,,"Grieves, M., Digital twin: Manufacturing excellence through virtual factory replication (2014) White Paper, 1, pp. 1-7; Nguyen, H.X., Trestian, R., To, D., Tatipamula, M., Digital twin for 5G and beyond (2021) Ieee Commun. 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TMCE. Las Palmas, pp. 209-217; Tao, F., Zhang, H., Liu, A., Nee, A.Y.C., Digital twin in industry: Stateof-the-art (2019) Ieee Trans. Ind. Informat., 15 (4), pp. 2405-2415. , Apr; Callaham, J.L., Koch, J.V., Brunton, B.W., Kutz, J.N., Brunton, S.L., Learning dominant physical processes with data-driven balance models (2021) Nat. Commun., 12 (1), pp. 1-10; Tran-Ngoc, H., Khatir, S., De Roeck, G., Bui-Tien, T., Nguyen-Ngoc, L., AbdelWahab, M., Model updating for nam O bridge using particle swarm optimization algorithm and genetic algorithm (2018) Sensors, 18 (12), pp. 4131-4150; Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Parizi, R.M., Choo, K.-K.R., Fog data analytics: A taxonomy and process model (2019) J. Netw. Comput. Appl., 128, pp. 90-104; Darwin, D., Dolan, C.W., Nilson, A.H., (2016) Design of Concrete Structures, , New York, NY, USA: McGraw-Hill Education; Kennedy, M.C., O'Hagan, A., Bayesian calibration of computer models (2001) J. Roy. Stat. Soc., Ser. B. (Stat. Methodol.), 63 (3), pp. 425-464; Kaur, K., Container-as-a-service at the edge: Trade-off between energy efficiency and service availability at fog nano data centers (2017) Ieee Wireless Commun., 24 (3), pp. 48-56. , Jun; Mahmoud, M.M., Towards energy-aware fog-enabled cloud of things for healthcare (2018) Comput. Elect. Eng., 67, pp. 58-69; Chaudhary, R., Kumar, N., Zeadally, S., Network service chaining in fog and cloud computing for the 5G environment: Data management and security challenges (2017) Ieee Commun. Mag., 55 (11), pp. 114-122. , Nov; Genez, T.A., Bittencourt, L.F., Madeira, E.R., Workflow scheduling for SAAS/PAAS cloud providers considering two SLA levels (2012) Proc. Ieee Netw. Oper. Manage. Symp., pp. 906-912; Dang, H.V., Tran-Ngoc, H., Nguyen, T.V., Bui-Tien, T., Roeck, G., Nguyen, H.X., Data-driven structural health monitoring using feature fusion and hybrid deep learning (2020) Ieee Trans. Automat. Sci. Eng., 18 (4), pp. 2087-2103. , Oct; Systèmes, D., Abaqus Analysis User's Manual (2007) Simulia Corp, , Providence, RI, USA; Gupta, A., Gurrala, G., Sastry, P., An online power system stability monitoring system using convolutional neural networks (2019) Ieee Trans. Power Syst., 34 (2), pp. 864-872. , Mar; Tai, Y., Qian, K., Huang, X., Zhang, J., Jan, M.A., Yu, Z., Intelligent intraoperative haptic-AR navigation forCOVID-19 lung biopsy using deep hybrid model (2021) Ieee Trans. Ind. Informat., 17 (9), pp. 6519-6527. , Sep; Ince, T., Kiranyaz, S., Eren, L., Askar, M., Gabbouj, M., Real-timemotor fault detection by 1-D convolutional neural networks (2016) Ieee Trans. Ind. Electron., 63 (11), pp. 7067-7075. , Nov; Kiranyaz, S., Ince, T., Gabbouj, M., Real-time patient-specific ECG classification by 1-D convolutional neural networks (2016) Ieee Trans. Biomed. Eng., 63 (3), pp. 664-675. , Mar; He, K., Zhang, X., Ren, S., Sun, J., Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015) Proc. Ieee Int. Conf. Comput. Vis., pp. 1026-1034; Qian, P., Zhang, D., Tian, X., Si, Y., Li, L., A novel wind turbine condition monitoring method based on cloud computing (2019) Renewable Energy, 135, pp. 390-398; Jeong, S., Hou, R., Lynch, J.P., Sohn, H., Law, K.H., A scalable cloud-based cyberinfrastructure platform for bridge monitoring (2019) Struct. Infrastruct. Eng., 15 (1), pp. 82-102","Nguyen, H.X.; London Digital Twin Research Centre, United Kingdom; email: H.Nguyen@mdx.ac.uk",,,"IEEE Computer Society",,,,,15513203,,,,"English","IEEE Trans. Ind. Inf.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85115797070
"Moravej H., Chan T.H.T., Jesus A., Nguyen K.-D.","57188768669;7402687570;56150440500;39262319400;","Computation-Effective Structural Performance Assessment Using Gaussian Process-Based Finite Element Model Updating and Reliability Analysis",2020,"International Journal of Structural Stability and Dynamics","20","10","2042003","","",,5,"10.1142/S0219455420420031","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093357344&doi=10.1142%2fS0219455420420031&partnerID=40&md5=7dcb284c0abc360f8fcea598d60334d3","School of Civil and Environmental Engineering, Faculty of Science and Engineering, Queensland University of Technology (QUT), Brisbane, QLD, Australia; Faculty of Environment and Technology, University of the West of England, Bristol, United Kingdom","Moravej, H., School of Civil and Environmental Engineering, Faculty of Science and Engineering, Queensland University of Technology (QUT), Brisbane, QLD, Australia; Chan, T.H.T., School of Civil and Environmental Engineering, Faculty of Science and Engineering, Queensland University of Technology (QUT), Brisbane, QLD, Australia; Jesus, A., Faculty of Environment and Technology, University of the West of England, Bristol, United Kingdom; Nguyen, K.-D., School of Civil and Environmental Engineering, Faculty of Science and Engineering, Queensland University of Technology (QUT), Brisbane, QLD, Australia","Structural health monitoring data has been widely acknowledged as a significant source for evaluating the performance and health conditions of structures. However, a holistic framework that efficiently incorporates monitored data into structural identification and, in turn, provides a realistic life-cycle performance assessment of structures is yet to be established. There are different sources of uncertainty, such as structural parameters, computer model bias and measurement errors. Neglecting to account for these factors results in unreliable structural identifications, consequent financial losses, and a threat to the safety of structures and human lives. This paper proposes a new framework for structural performance assessment that integrates a comprehensive probabilistic finite element model updating approach, which deals with various structural identification uncertainties and structural reliability analysis. In this framework, Gaussian process surrogate models are replaced with a finite element model and its associate discrepancy function to provide a computationally efficient and all-round uncertainty quantification. Herein, the structural parameters that are most sensitive to measured structural dynamic characteristics are investigated and used to update the numerical model. Sequentially, the updated model is applied to compute the structural capacity with respect to loading demand to evaluate its as-is performance. The proposed framework's feasibility is investigated and validated on a large lab-scale box girder bridge in two different health states, undamaged and damaged, with the latter state representing changes in structural parameters resulted from overloading actions. The results from the box girder bridge indicate a reduced structural performance evidenced by a significant drop in the structural reliability index and an increased probability of failure in the damaged state. The results also demonstrate that the proposed methodology contributes to more reliable judgment about structural safety, which in turn enables more informed maintenance decisions to be made. © 2020 World Scientific Publishing Company.","box girder bridge & modular Bayesian approach; Finite element model updating; Gaussian process; reliability analysis; structural dynamic","Box girder bridges; Finite element method; Gaussian distribution; Gaussian noise (electronic); Life cycle; Losses; Reliability analysis; Steel bridges; Structural analysis; Structural dynamics; Uncertainty analysis; Computationally efficient; Dynamic characteristics; Finite-element model updating; Probabilistic finite elements; Structural identification; Structural reliability analysis; Structural reliability indices; Uncertainty quantifications; Structural health monitoring",,,,,"Australian Research Council, ARC","The research herein is part of Discovery Project DP160101764 funded by the Australian Government through the Australian Research Council (ARC). The authors also would like to thank Dr. Shojaeddin Jamali for his assistance and guidance in preparing the initial FE model of the BGB and experimental data collection.",,,,,,,,,,"Bolton, S., Speculation is cause of tower disasters (2019) Green Left Weekly, p. 3; Hendawi, S., Frangopol, D. M., System reliability and redundancy in structural design and evaluation (1994) Struct. Safety, 16 (1-2), pp. 47-71; Thoft-Christensen, P., Murotsu, Y., (1986) Reliability analysis of structural systems by the-Unzipping method, Application of Structural Systems Reliability Theory, pp. 143-214. , (Springer, Berlin, Heidelberg); Moses, F., System reliability developments in structural engineering (1982) Struct. Safety, 1 (1), pp. 3-13; Moses, F., Rashedi, M. R., The application of system reliability to structural safety (1983) Proc. 4th Int. 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Structural Health Monitoring of Intelligent Infrastructure, International Society for Structural Health Monitoring of Intelligent Infrastructure (ISHMII), pp. 1134-1145; (2011), Structural Vibration Solutions A/S, SVS-ARTeMIS Extractor-Release 5.3, User's Manual, Aalborg; Harris, H. G., Sabnis, G., (1999) Structural Modeling and Experimental Techniques, , (CRC Press); (2017) Bridge Design-Scope and General Principles, , Standards Australia, AS 5100.1-2017; (2010) AASHTO LRFD Bridge Design Specifications: US Customary Units, , American Association of State Highway and Transportation Officials, American Association of State Highway and Transportation Officials","Moravej, H.; School of Civil and Environmental Engineering, Australia; email: h.moravej@qut.edu.au",,,"World Scientific",,,,,02194554,,,,"English","Int. J. Struct. Stab. Dyn.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85093357344
"Yan L., Ren L., He X., Lu S., Guo H., Wu T.","56574585900;57215085291;8901539000;57215084274;57056748500;55476709000;","Strong wind characteristics and buffeting response of a cable-stayed bridge under construction",2020,"Sensors (Switzerland)","20","4","1228","","",,5,"10.3390/s20041228","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079890461&doi=10.3390%2fs20041228&partnerID=40&md5=ddf837fecc727bb309c2b50b1fd5f222","School of Civil Engineering, Central South University, Changsha, 410075, China; Railway Engineering Research Institute, China Academy of Railway Sciences, Beijing, 100081, China; Department of Civil, Structural and Environmental Engineering, University at Buffalo, State University of New York, Buffalo, NY  14126, United States","Yan, L., School of Civil Engineering, Central South University, Changsha, 410075, China; Ren, L., School of Civil Engineering, Central South University, Changsha, 410075, China; He, X., School of Civil Engineering, Central South University, Changsha, 410075, China; Lu, S., School of Civil Engineering, Central South University, Changsha, 410075, China; Guo, H., Railway Engineering Research Institute, China Academy of Railway Sciences, Beijing, 100081, China; Wu, T., Department of Civil, Structural and Environmental Engineering, University at Buffalo, State University of New York, Buffalo, NY  14126, United States","This study carries out a detailed full-scale investigation on the strong wind characteristics at a cable-stayed bridge site and associated buffeting response of the bridge structure during construction, using a field monitoring system. It is found that the wind turbulence parameters during the typhoon and monsoon conditions share a considerable amount of similarity, and they can be described as the input turbulence parameters for the current wind-induced vibration theory. While the longitudinal turbulence integral scales are consistent with those in regional structural codes, the turbulence intensities and gust factors are less than the recommended values. The wind spectra obtained via the field measurements can be well approximated by the von Karman spectra. For the buffeting response of the bridge under strong winds, its vertical acceleration responses at the extreme single-cantilever state are significantly larger than those in the horizontal direction and the increasing tendencies with mean wind velocities are also different from each other. The identified frequencies of the bridge are utilized to validate its finite element model (FEM), and these field-measurement acceleration results are compared with those from the FEM-based numerical buffeting analysis with measured turbulence parameters. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Buffeting response; Cable-stayed bridge; Construction; Field measurement; Wind and structural health monitoring; Wind characteristics; Wireless sensor networks","Buffeting; Cable stayed bridges; Cables; Construction; Structural health monitoring; Turbulence; Wireless sensor networks; Buffeting response; Field measurement; Recommended values; Turbulence intensity; Turbulence parameters; Vertical accelerations; Wind characteristics; Wind induced vibrations; Electric measuring bridges",,,,,"2018YJ048; KLWRTBMC18-03; National Natural Science Foundation of China, NSFC: 51808563, 51925808; Central South University, CSU: 2020CX009; National Key Research and Development Program of China, NKRDPC: 2017YFB1201204","Funding: This research was funded by the National Natural Science Foundation of China (Grant 51808563, 51925808), the Open Research Fund of Key Laboratory of Wind Resistance Technology of Bridges of China (KLWRTBMC18-03), the Foundation of China Academy of Railway Sciences Corporation Limited (2018YJ048), the National Key R & D Program of China (2017YFB1201204), and the Innovation-Driven Project of Central South University (No. 2020CX009). Any opinions and concluding remarks presented in this paper are entirely those of the authors.","This research was funded by the National Natural Science Foundation of China (Grant 51808563, 51925808), the Open Research Fund of Key Laboratory of Wind Resistance Technology of Bridges of China (KLWRTBMC18-03), the Foundation of China Academy of Railway Sciences Corporation Limited (2018YJ048), the National Key R & D Program of China (2017YFB1201204), and the Innovation-Driven Project of Central South University (No. 2020CX009). Any opinions and concluding remarks presented in this paper are entirely those of the authors.",,,,,,,,,"Hui, M.C.H., Larsen, A., Xiang, H.F., Wind turbulence characteristics study at the Stonecutters Bridge site: Part I-Mean wind and turbulence intensities (2009) J. Wind Eng. Ind. 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Sci., 9, p. 5385; Xu, Y.L., Zhu, L.D., Wong, K.Y., Chan, K.W.Y., Field measurement results of Tsing Ma suspension Bridge during Typhoon Victor (2000) Struct. Eng. Mech., 10, pp. 545-559; Yoshizumi, F., Inoue, H., An experimental approach on aerodynamic stability of a cable-stayed cantilever bridge (2002) J. Wind Eng. Ind. Aerodyn., 90, pp. 2099-2111; Macdonald, J.H.G., Evaluation of buffeting predictions of a cable-stayed bridge from full-scale measurements (2003) J. Wind Eng. Ind. Aerodyn., 91, pp. 1465-1483; Cheynet, E., Jakobsen, J.B., Snæbjörnsson, J., Buffeting response of a suspension bridge in complex terrain (2016) Eng. 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Sci., 19, pp. 793-800","He, X.; School of Civil Engineering, China; email: xuhuihe@csu.edu.cn",,,"MDPI AG",,,,,14248220,,,"32102308","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85079890461
"Haidarpour A., Tee K.F.","57188818757;57201346236;","Finite element model updating for structural health monitoring",2020,"SDHM Structural Durability and Health Monitoring","14","1",,"1","17",,5,"10.32604/sdhm.2020.08792","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082750814&doi=10.32604%2fsdhm.2020.08792&partnerID=40&md5=a7c7444c37e753c6d5d2814ace9320e6","School of Engineering, University of Greenwich, Chatham Maritime, Kent, ME4 4TB, United Kingdom","Haidarpour, A., School of Engineering, University of Greenwich, Chatham Maritime, Kent, ME4 4TB, United Kingdom; Tee, K.F., School of Engineering, University of Greenwich, Chatham Maritime, Kent, ME4 4TB, United Kingdom","This paper provides a model updating approach to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration responses. Research in vibration-based damage identification has been rapidly expanding over the last few decades. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause changes in the modal properties which could be obtained by structural health monitoring (SHM). Updating is a process fraught with numerical difficulties. These arise from inaccuracy in the model and imprecision and lack of information in the measurements, mainly taken place in joints and critical points. The motivation for the development of this technology is presented, methods are categorized according to various criteria such as the level of damage detection provided from vibration testing, natural frequency and mode shape readings are then obtained by using modal analysis techniques, which are used for updating structural parameters of the associated finite element model. The experimental studies for the laboratory tested bridge model show that the proposed model updating using ME'scope technique can provide reasonable model updating results. © 2020 Tech Science Press. All rights reserved.","Mode shape; Model updating; Natural frequency; Stiffness; Structural health monitoring; Vibration testing","Damage detection; Damping; Modal analysis; Natural frequencies; Physical properties; Stiffness; Structural health monitoring; Structures (built objects); Vibration analysis; Analysis techniques; Finite-element model updating; Mode shapes; Model updating; Structural health monitoring (SHM); Structural parameter; Vibration based damage identifications; Vibration testing; Finite element method",,,,,,,,,,,,,,,,"Zhang, Y., Kim, C.W., Tee, K.F., Garg, A., Garg, A., Long-term health monitoring for deteriorated bridge structures based on copula theory (2018) Smart Structures and Systems, 21 (2), pp. 171-185; Brownjohn, J.M.W., Xia, P.Q., Dynamic assessment of curved cable-stayed bridge by model updating (2000) Journal of Structural Engineering, 126 (2), pp. 252-260; Tee, K.F., Chapter 6: Optimization of condensed stiffness matrices for structural health monitoring (2019) Optimization of Design for Better Structural Capacity., pp. 150-185. , Belgasmia, M., (eds.), Hershey, PA: IGI Global; Koh, C.G., Quek, S.T., Tee, K.F., Damage identification of structural dynamic system (2002) Proceedings of the 2nd International Conference on Structural Stability and Dynamics, pp. 780-785. , Singapore; Alvandi, A., Cremona, C., Assessment of vibration-based damage identification techniques (2006) Journal of Sound and Vibration, 292 (1-2), pp. 179-202; Pandey, A., Biswas, M., Samman, M., Damage detection from changes in curvature mode shapes (1991) Journal of Sound and Vibration, 145 (2), pp. 321-332; Tee, K.F., Time series analysis for vibration-based structural health monitoring: A review (2018) Structural Durability and Health Monitoring, 12 (3), pp. 129-147; Zou, Y., Tong, L., Steven, G., Vibration-based model-dependent damage (delamination) identification and health monitoring for composite structures - A review (2000) Journal of Sound and Vibration, 230 (2), pp. 357-378; Tee, K.F., (2004) Substructural Identification with Incomplete Measurement for Structural Damage Assessment, , (Ph.D. Thesis). National University of Singapore; Doebling, S.W., Farrar, C.R., Prime, M.B., Shevitz, D.W., (1996) Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review., , Los Alamos National Laboratory, Report No. LA-13070-MS; Farrar, C., Baker, W., Bell, T., Cone, K., Darling, T., (1994) Dynamic Characterization and Damage Detection in the I-40 Bridge over the Rio Grande., , Los Alamos National Laboratory, Report LA-12767-MS; Tee, K.F., Koh, C.G., Quek, S.T., Substructural system identification and damage estimation by OKID/ERA (2004) Proceedings of the 3rd Asian-Pacific Symposium on Structural Reliability and Its Applications, pp. 637-647. , Seoul; Ajay, K., John, S., Herszberg, I., Strain-based structural health monitoring of complex composite structures (2008) Structural Health Monitoring: An International Journal, 7 (3), pp. 203-213; Siegert, D., Multon, S., Toutlemonde, F., Resonant frequencies monitoring of alkali aggregate reaction damaged concrete beams (2005) Experimental Techniques, 29 (6), pp. 37-40; Chen, H.P., Tee, K.F., Ni, Y.Q., Mode shape expansion with consideration of analytical modelling errors and modal measurement uncertainty (2012) Smart Structures and Systems, 10 (4-5), pp. 485-499; Rytter, A., Krawczuk, M., Kirkegaard, P., Experimental and numerical study of damaged cantilever (2000) Journal of Engineering Mechanics, 126 (1), pp. 60-65; Castellini, P., Martarelli, M., Tomasini, E.P., Laser doppler vibrometry: Development of advanced solutions answering to technology's needs (2006) Mechanical Systems and Signal Processing, 20 (6), pp. 1265-1285; Wang, T., Zhang, L., Tee, K.F., Extraction of real modes and physical matrices from modal testing (2011) Earthquake Engineering and Engineering Vibration, 10 (2), pp. 219-227; Mottershead, J.E., Friswell, M.I., Model updating in structural dynamics: A survey (1993) Journal of Sound and Vibration, 167 (3), pp. 347-375; Tee, K.F., Cai, Y., Chen, H.P., Structural damage detection using quantile regression (2013) Journal of Civil Structural Health Monitoring, 3 (1), pp. 19-31; Esfandiari, A., Bakhtiari-Nejad, F., Sanayei, M., Rahai, A., Structural finite element model updating using transfer function data (2010) Computers & Structures, 88 (1-2), pp. 54-64; Natke, H.G., Updating computational models in the frequency domain based on measured data: A survey (1988) Probabilistic Engineering Mechanics, 3 (1), pp. 28-35; Friswell, M.I., Penny, J.E.T., Updating model parameters from frequency domain data via reduced order models (1990) Mechanical Systems and Signal Processing, 4 (5), pp. 377-391; Modak, S.V., Kundra, T.K., Nakra, B.C., Comparative study of model updating methods using simulated experimental data (2002) Computers & Structures, 80 (5-6), pp. 437-447; Klepka, A., Staszewski, W.J., Dimaio, D., Scarpa, F., Tee, K.F., Sensor location analysis in nonlinear acoustics used for damage detection in composite chiral sandwich panels (2013) Advances in Science and Technology, 83, pp. 223-231","Tee, K.F.; School of Engineering, United Kingdom; email: K.F.Tee@gre.ac.uk",,,"Tech Science Press",,,,,19302983,,,,"English","SDHM Struct. Durability Health Monit.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85082750814
"Xiao F., Chen G.S., Hulsey J.L., Zatar W.","56070134700;55615798900;6602858255;6602971374;","Characterization of Nonlinear Dynamics for a Highway Bridge in Alaska",2018,"Journal of Vibration Engineering and Technologies","6","5",,"379","386",,5,"10.1007/s42417-018-0048-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061404424&doi=10.1007%2fs42417-018-0048-x&partnerID=40&md5=a778c1665f04230d9a990dd3d50d47b1","Department of Civil Engineering, Nanjing University of Science and Technology, Nanjing, China; College of IT and Engineering, Marshall University, Huntington, WV, United States; Department of Civil and Environmental Engineering, University of Alaska Fairbanks, Fairbanks, AK, United States","Xiao, F., Department of Civil Engineering, Nanjing University of Science and Technology, Nanjing, China; Chen, G.S., College of IT and Engineering, Marshall University, Huntington, WV, United States; Hulsey, J.L., Department of Civil and Environmental Engineering, University of Alaska Fairbanks, Fairbanks, AK, United States; Zatar, W., College of IT and Engineering, Marshall University, Huntington, WV, United States","Purpose and Methods: This research studies the nonlinear dynamic feature of the Chulitna river bridge in Alaska USA. The acceleration response of the bridge under moving truck loads are evaluated for dynamic invariants. The Fourier spectrum of a free-decay response (after the vehicle passes) exhibits a salient line spectrum, which is correlated to calculated linear modes from a 3-D finite element analysis. The bridge deck is used shell elements and truss members are simulated with beam elements. Results: At some locations on bridge, the Fourier spectrum of the bridge response to vehicle loading exhibits a complicated narrow bandwidth suggesting the possibility of nonlinear characteristics. Chaos dynamics invariants of the response are examined based on the largest Lyapunov exponents, correlation dimension, and K-entropy and used to identify the nonlinear dynamic features. Conclusions: The bridge was found to have following characteristics: (a) modal parameters extracted from free-decay response exhibit linear properties. The free vibrations have relatively small amplitude which is not sensitive to possible structure damages; (b) at some locations, measured responses to moving loads exhibit chaos nonlinear dynamics properties with specific signatures. All of the derived nonlinear dynamics invariants including Lyapunov exponents, correlation dimension, and K-entropy support this observation; and (c) in addition to linear modal parameters, the multiple nonlinear dynamics invariants with damage-sensitive features can be used as additional candidates for the ongoing bridge health monitoring. © 2018, Krishtel eMaging Solutions Private Limited.","Bridge dynamics; Chaos dynamics; Dynamic testing; Safety assessment; Structural health monitoring",,,,,,,,,,,,,,,,,"Oh, B.H., Jung, B.S., Structural damage assessment with data of static and modal tests (1998) J Struct Eng, 124 (8), pp. 956-965; Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R., (2003) A review of structural health monitoring literature: 1996–2001. Report LA-13976-MS, , Los Alamos National Laboratory, Los Alamos; Salawu, O.S., Detection of structural damage through changes in frequency: a review (1997) J Eng Struct, 19 (9), pp. 718-723; Pandey, A.K., Biswas, M., Damage detection in structures using changes in flexibility (1994) J Sound Vib, 169 (1), pp. 3-17; Whalen, T.M., The behaviour of higher order mode shape derivatives in damaged beam-like structures (2008) J Sound Vib, 309 (3-5), pp. 426-464; Zatar, W., Harik, I., Ren, W.X., Zhao, T., Seismic risk assessment of priority bridges along I-24 in Western Kentucky (2008) In: The Sixth National Seismic Conference on Bridges & Highways, , July 27–30, 2008, Charleston, South Carolina; Ren, W.X., Zatar, W., Harik, I., Ambient vibration-based seismic evaluation of a continuous girder bridge (2004) Eng Struct, 26 (5), pp. 631-640; Li, J., Law, S., Hao, H., Improved damage identification in bridge structures subject to moving loads: numerical and experimental studies (2013) Int J Mech Sci, 74 (2013), pp. 99-111; Huffman, J.T., Xiao, F., Chen, G., Hulsey, J.L., Detection of soil-abutment interaction by monitoring bridge response using vehicle excitation (2015) J Civ Struct Health Monit, 5 (4), pp. 389-395; Xiao, F., Hulsey, J.L., Cheng, G.S., Multi-direction bridge model updating using static and dynamic measurement (2015) Appl Phys Res, 7 (1), p. 47; Klepka, A., Straczkiewicz, M., Piezonka, L., Stazewski, W.J., Gelman, L., Aymerich, F., Uhl, T., Triple correlation for detection of damage-related nonlinearities in composite structures (2015) Nonlinear Dyn, 81 (1), pp. 483-486; Lo, K.F., Ni, S.H., Huang, Y.H., Non-destructive test for pile beneath bridge in the time, frequency, and time-frequency domains using transient loading (2010) Nonlinear Dyn, 62 (1), pp. 349-360; Jin, S., Livingston, R.A., Marzougui, D., Lyapunov exponent maps applied to damage detection of aging nonlinear highway infrastructures (2001) Proceeding of SPIE 4337, Health Monitoring and Management of Civil Infrastructure Systems, p. 411; Livingston, R.A., Jin, S., Marzougui, D., Application of nonlinear dynamics analysis to damage detection and health monitoring of highway structures (2001) Proceeding of SPIE—the International Society for Optical Engineering, 4337, pp. 402-410. , Society of Photo-Optical Instrumentation Engineers, Newport Beach; Casciati, F., Casciati, S., Structural health monitoring by Lyapunov exponents of non-linear time series (2006) Struct Control Health Monit, 13, pp. 132-146; Moniz, L., Nichols, J.M., Nichols, C.J., Seaver, M., Trickey, S.T., Todd, M.D., Pecora, L.M., Virgin, L.N., A multivariate, attractor-based approach to structural health monitoring (2005) J Sound Vib, 283, pp. 295-310; Worden, K., Farrar, C.R., The fundamental axioms of structural health monitoring (2007) Proc R Soc Lond Ser A (Mathematical, Physical and Engineering Sciences), 463 (2082), pp. 1639-1664; Kantz, H., Schreiber, T., (1997) Nonlinear time series analysis [M], , Cambridge University Press, Cambridge; Brown, R., Bryant, P., Abarbanel, H.D.I., Computing the Lyapunov exponents of a dynamical system from observed time series (1991) Phys Rev A, 34, pp. 2787-2806; Rossenstein, M.T., Collins, J.J., Deluca, C.J., A practical method for calculating largest Lyapunov exponents from small data sets (1993) Phys D, 65 (1), pp. 117-134; Takens, F., Determining strange attractors in turbulence (1981) Lect Notes Math, 898, pp. 361-381; Cao, L., Practical method for determining the minimum embedding dimension of a scalar time series (1997) Phys D, 110 (1), pp. 43-50; Grassberger, P., Procaccia I, Characterization of strange attractors (1983) Phys Rev Lett A, 50 (5), pp. 346-349; Grassberger, P., Procaccia, I., Estimation of the Kolmogorov entropy from a chaotic signal (1983) Phys Rev A, 28 (4), pp. 2591-2593; Schouten, J.C., Maximum-likelihood estimation of the entropy of an attractor (1994) Phys Rev E, 49 (1), pp. 126-129; Xiao, F., Chen, G.S., Hulsey, J.L., Dolan, J.D., Dong, Y., Ambient loading and modal parameters for the Chulitna river bridge (2016) Adv Struct Eng, 19 (4), pp. 660-670","Chen, G.S.; College of IT and Engineering, United States; email: chenga@marshall.edu",,,"Springer",,,,,25233920,,,,"English","J. Vib. Eng. Technol.",Article,"Final","",Scopus,2-s2.0-85061404424
"Mishra A.K., Mohammed A., Chakraborty S.","55584791703;56038638700;57562701200;","Improved numerical modelling of fiber reinforced plastics I-beam from experimental modal testing and finite element model updating",2018,"International Journal of Acoustics and Vibrations","23","1",,"26","34",,5,"10.20855/ijav.2018.23.11069","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045689975&doi=10.20855%2fijav.2018.23.11069&partnerID=40&md5=522f5134005f003f10d061c292f196b4","Silicon Institute of Technology, Sambalpur, Odisha, India; Research and Development Wing, Indian Register of Shipping, Powai, Mumbai, India; Department of Civil Engineering, Indian Institute of Technology, Kharagpur, 721302, India","Mishra, A.K., Silicon Institute of Technology, Sambalpur, Odisha, India; Mohammed, A., Research and Development Wing, Indian Register of Shipping, Powai, Mumbai, India; Chakraborty, S., Department of Civil Engineering, Indian Institute of Technology, Kharagpur, 721302, India","Fiber reinforced plastics (FRP) is increasingly being used in infrastructural applications like bridges, chemical plants etc., where the environment can limit the expected service life of structures made of conventional materials such as reinforced concrete, steel or timber. Advantages of FRP over conventional constructional materials are its high specific strength and specific stiffness, ease with which it can be moulded to various shapes, corrosion resistance, lower lifecycle cost, durability etc. Estimation of accurate dynamic responses of FRP structures is very important from their operation point of view. Such dynamic responses are functions of material properties, boundary conditions, geometry and applied loading. FRP being an anisotropic and layered composite material, a large number of elastic material property parameters are to be determined. Moreover, its structural fabrication and material fabrication at constituent level being one unified process, the actual existing material property parameters may vary considerably from those specified in established standards or determined from characterisation tests. The present approach attempts at establishing a non- destructive technique based on experimental modal testing and finite element model updating to estimate the elastic material parameters of an ‘I’ beam made of FRP, thereby making the prediction of dynamic responses more accurate. Static load test on the beam and characterisation tests on samples cut from actual structure are conducted to assess the performance of this updating exercise. The current approach can also be used to non- destructively monitor degradations of elastic material properties over time and thus can be used for health monitoring of existing FRP structures. © 2018 International Institute of Acoustics and Vibrations. All rights reserved.",,"Chemical plants; Concrete beams and girders; Corrosion resistance; Dynamic response; Elasticity; Elastomers; Fiber reinforced materials; Fiber reinforced plastics; Load testing; Modal analysis; Nondestructive examination; Plastics applications; Plastics plants; Reinforced concrete; Reinforced plastics; Steel fibers; Structural health monitoring; Structural properties; Constructional material; Elastic material properties; Experimental modal testing; Fiber reinforced plastic(FRP); Finite-element model updating; High specific strength; Material property parameters; Non-destructive technique; Finite element method",,,,,,,,,,,,,,,,"Daniel, I.M., Ishai, O., (2005) Engineering Mechanics of Composite Materials, , Oxford University Press, New York, 2nd ed; Davalos, J.F., Salim, H.A., Qiao, P., Lopez-Anido, R., Barbero, E.J., Analysis and design of pultruded FRP shapes under bending (1995) Compos. Part B-Eng., 27, pp. 295-305; Davalos, J.F., Salim, H.A., Qiao, P., Barbero, E.J., Multi-objective material architecture optimization of pultruded FRP I-beams (1996) Compos. Struct., 35, pp. 271-281; Davalos, J.F., Salim, H.A., Qiao, P., Flexural-torsional buckling of pultruded fiber reinforced plastic composite I-beams: Experimental and analytical evaluations (1997) Compos. Struct., 38, pp. 241-250; Davalos, J.F., Salim, H.A., Qiao, P., Kiger, S.A., Analysis and design of fiber reinforced plastic composite deck and stringer bridges (1997) Compos. Struct., 38, pp. 295-307; Upadhyay, A., Kalyanaraman, V., Simplified analysis of FRP box-girders (2003) Compos. Struct., 59, pp. 217-225; Kumar, P., Chandrashekhara, K., Nanni, A., Structural performance of a FRP bridge deck (2004) Constr. Build. Mater., 18, pp. 35-47; Wael, F.R., Local buckling analysis of pultruded FRP structural shapes subjected to eccentric compression (2010) Thin Wall Struct, 48, pp. 709-717; Esfandiari, A., Bakhtiari-Nejad, F., Sanayei, M., Rahai, A., Structural finite element model updating using transfer function data (2010) Comput. Struct., 88, pp. 54-64; Hollaway, L.C., A review of the present and future util-isation of FRP composites in the civil infrastructure with reference to their important in-service properties (2010) Const. Building Mater., 24 (12), pp. 2419-2445; Zimmermann, K., Zenkert, D., Siemetzki, M., Testing and analysis of ultra thick composites (2010) Compos. Part B-Eng, 41 (4), pp. 326-336; Amir, F., Kim, Y.J., Numerical analysis of pultruded GFRP box girders supporting adhesively bonded concrete deck in flexure (2011) Eng. Struct., 33, pp. 3527-3536; Ascione, L., Giordano, A., Spadea, S., Lateral buckling of pultruded FRP beams (2011) Compos. Part B-Eng, 42, pp. 819-824; Feo, L., Mosallam, A.S., Penna, R., Mechanical behavior of webflange junctions of thin-walled pultruded I-profiles: An experimental and numerical evaluation (2013) Compos. Part B-Eng, 48, pp. 18-39; Napoli, A., Bank, L.C., Brown, V.L., Martinelli, E., Matta, F., Realfonzo, R., Analysis and design of RC structures strengthened with mechanically fastened FRP laminates: A review (2013) Compos. Part B-Eng, 55, pp. 386-399; Gao, Y., Chen, J., Zhang, Z., Fox, D., An advanced FRP floor panel system in buildings (2013) Compos. Struct., 96, pp. 683-690; Cardoso, D.C., Harries, K.A., De Batista, E.M., Compressive local buckling of pultruded GFRP I-sections: Development and numerical/experimental evaluation of an explicit equation (2015) ASCE J. Compos. Const., 19 (2), pp. 1-12; Mottershead, J.E., Friswell, M.I., Model updating in structural dynamics: A survey (1993) J. Sound Vib., 167 (2), pp. 347-375; De Wilde, W.P., Narmon, B., Sol, H., Roovers, M., Determination of the material constants of an anisotropic lamina by free vibration analysis (1984) Proceedings of The International Seminar on Modal Analysis, pp. 44-49. , Orlando, Fla; Sol, H., (1986) Identification of Anisotropic Plate Rigidities Using Free Vibration Data, , PhD dissertation, Vrije Universiteit Brussel, Belgium; Deobald, L.R., Gibson, R.F., Determination of elastic constants of orthotropic plates by a modal analysis/Rayleigh-ritz technique (1988) J. Sound Vib., 124 (2), pp. 269-283; Grdiac, M., Paris, P.A., Direct identification of elastic constants of anisotropic plates by modal analysis: Theoretical and numerical aspects (1996) J. Sound Vib., 195 (3), pp. 401-415; Mota Soares, C.M., Moreira, De Freitas, M., Arajo, A.L., Identification of material properties of composite plate specimens (1993) Compos. Struct., 25, pp. 277-285; Larsson, D., Using modal analysis for estimation of anisotropic material constants (1997) J. Eng. Mech-ASCE, 123 (3), pp. 222-229; Cugnoni, J., Gmr, T., Schorderet, A., Inverse method based on modal analysis for characterizing the constitutive properties of thick composite plates (2007) Comput. Struct., 85, pp. 1310-1320; Collins, J.D., Hart, G.C., Hasselman, T.K., Kennedy, B., Statistical identification of structures (1974) AIAA Journal, 12 (2), pp. 185-190; Chen, J.C., Garba, J.A., Analytical model improvement using modal test results (1980) AIAA Journal, 25 (11), pp. 1494-1499; Dascotte, E., Material identification of composite structures from combined use of finite element analysis and experimental modal analysis (1992) Proceedings of The 10th IMAC, pp. 1274-1280; Mishra, A.K., Chakraborty, S., Determination of material parameters of FRP plates with rotational flexibility at boundaries using experimental modal testing and model updating (2015) Exp. Mech., 55 (5), pp. 803-815; Mishra, A.K., Chakraborty, S., Development of a finite element model updating technique for estimation of constituent level elastic parameters of FRP plates (2015) Appl. Math. Comput., 258, pp. 84-94; (2012) Dynamic Design Solutions NV, , FEMtools v3.5.3; (2008) Sound and Vibration Measurement, , Pulse LabShop v13.1.0.246, Bruel and Kjaer; (2007) ME’scopeVES v4.0.0.96, , Vibrant Technology Inc; (2009) ABAQUS v6.9, , Dassault Systmes Simulia Corp; Ewins, D.J., (2000) Modal Testing: Theory and Practice, , Research Studies Press Ltd., London; (2008) ASTM D3039/D3039M Standard Test Method for Tensile Properties of Polymer Matrix Composite Materials; Jones, R.M., (1999) Mechanics of Composite Materials, , McGraw-Hill, 2nd ed",,,,"International Institute of Acoustics and Vibrations",,,,,10275851,,,,"English","Int. J. Acoust. Vibr.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85045689975
"Nguyen A., Kodikara K.A.T.L., Chan T.H.T., Thambiratnam D.P.","57310688400;57190261200;7402687570;35583914600;","Toward effective structural identification of medium-rise building structures",2018,"Journal of Civil Structural Health Monitoring","8","1",,"63","75",,5,"10.1007/s13349-017-0259-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041524369&doi=10.1007%2fs13349-017-0259-y&partnerID=40&md5=2306ca7e719a92c09ca6ef150924851e","School of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia","Nguyen, A., School of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia; Kodikara, K.A.T.L., School of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia; Chan, T.H.T., School of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia; Thambiratnam, D.P., School of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia","Structural Identification (St-Id) is the process of constructing and calibrating a physics-based model based on the measured static and/or dynamic response of the structure. Over the last two decades, although the St-Id methods have become increasingly popular amongst civil–structural engineering communities, most complete and successful applications are often found with flexible structures such as long-span bridges and towers. Very few comprehensive studies were reported on building structures, especially those with medium-rise characteristics which are often associated with complicated analytical modelling and different degrees of parameter uncertainties. To address this need, this paper presents an in-depth study on St-Id of a benchmark medium-rise building firstly demonstrating the importance of developing appropriate initial analytical models that can be used for the automated model calibration techniques. Then, a novel parametric study-based sensitivity analysis approach is introduced to identify tuning parameters as well as their appropriate ranges to maximise the correlation of the calibrated model whilst preserving the physical relevance of the calibrated model. Modal data of the first few modes measured under ambient vibration conditions are used in this study. Further application of the St-Id process developed herein for structural health monitoring (SHM) of buildings is also discussed. © 2017, Springer-Verlag GmbH Germany, part of Springer Nature.","Automated model calibration; Finite element modelling; Medium rise building; Modal data; Structural health monitoring (SHM); Structural identification (St-Id)","Analytical models; Bridges; Buildings; Finite element method; Flexible structures; Modal analysis; Sensitivity analysis; Structural analysis; Uncertainty analysis; Automated modeling; Finite element modelling; Modal data; Structural health monitoring (SHM); Structural identification; Structural health monitoring",,,,,"Australian Research Council, ARC: DP160101764; Queensland University of Technology, QUT","Acknowledgements The data used in this research are from the PhD research of the second author between 2014 and 2017 at QUT. The study was funded by Queensland University of Technology PhD Scholarships and in part by the Australian Research Council Discovery Project No. DP160101764.",,,,,,,,,,"Aktan, A.E., Brownjohn, J.M.W., Structural identification: opportunities and challenges (2013) J Struct Eng (United States), 139 (10), pp. 1639-1647; Aktan, E., Structural identification: analytical aspects (1998) J struct eng., 124 (7), pp. 817-829; Catbas, F.N., Limitations in structural identification of large constructed structures (2007) J Struct Eng, 133 (8), pp. 1051-1066; Beck, J.L., Jennings, P.C., Structural identification using linear models and earthquake records (1980) Earthquake Eng Struct Dynam, 8 (2), pp. 145-160; Hudson, D.E., (1977) Dynamic tests of full-scale structures., 103 (6), pp. 1141-1157; Yao, J.T.P., Damage assessment and reliability evaluation of existing structures (1979) Eng Struct, 1 (5), pp. 245-251; Aktan, A.E., Structural identification for condition assessment: experimental arts (1997) J struct eng, 123 (12), pp. 1674-1685; Aoki, T., Sabia, D., Structural identification and seismic performance of brick chimneys, Tokoname (2005) Jpn Struct Eng Mech, 21 (5), pp. 553-570; Bonato, P., Ceravolo, R., De Stefano, A., The use of wind excitation in structural identification (1998) J Wind Eng Ind Aerodyn, 74-76, pp. 709-718; Chan, T.H.T., Concurrent multi-scale modeling of civil infrastructures for analyses on structural deteriorating-Part II: model updating and verification (2009) Finite Elem Anal Des, 45 (11), pp. 795-805; Liu, H., Yang, Z., Gaulke, M.S., Structural identification and finite element modeling of a 14-story office building using recorded data (2005) Eng Struct, 27 (3), pp. 463-473; Jaishi, B., Ren, W.-X., Structural finite element model updating using ambient vibration test results (2005) J Struct Eng, 131 (4), pp. 617-628; Zivanovic, S., Pavic, A., Reynolds, P., Finite element modelling and updating of a lively footbridge: the complete process (2007) J Sound Vib, 301 (1-2), pp. 126-145; Brownjohn, J.M.W., Xia, P.-Q., Dynamic assessment of curved cable-stayed bridge by model updating (2000) J struct eng., 126 (2), pp. 252-260; Zhang, Q.W., Chang, T.Y.P., Chang, C.C., Finite-element model updating for the Kap Shui Mun cable-stayed bridge (2001) J Bridge Eng, 6 (4), pp. 285-294; Cismasiu, C., Narciso, A.C., Amarante Dos Santos, F.P., Experimental dynamic characterization and finite-element updating of a footbridge structure (2015) J Perform Constr Facil, 29 (4), p. 04014116; Daniell, W.E., Macdonald, J.H.G., Improved finite element modelling of a cable-stayed bridge through systematic manual tuning (2007) Eng Struct, 29 (3), pp. 358-371; Ding, Y., Li, A., Finite element model updating for the Runyang Cable-stayed Bridge tower using ambient vibration test results (2008) Adv Struct Eng, 11 (3), pp. 323-335; Fei, Q.G., Structural health monitoring oriented finite element model of Tsing Ma bridge tower (2007) Int J Struct Stab Dyn, 7 (4), pp. 647-668; Wu, J.R., Li, Q.S., Finite element model updating for a high-rise structure based on ambient vibration measurements (2004) Eng Struct, 26 (7), pp. 979-990; Ventura, C., (2005) FEM updating of tall buildings using ambient vibration data. In: Proceedings of the sixth European conference on structural dynamics (EURODYN); (2011) QUT Science and Engineering Centre: 5 Star Green Star Rating—Green Star—Education Design v1, , http://www.gbca.org.au/project-profile.asp?projectID=944, Accessed July 2017, Green Building Council Australia; Nguyen, T., Development of a cost-effective and flexible vibration DAQ system for long-term continuous structural health monitoring (2015) Mech Syst Signal Process, 64-65, pp. 313-324; Nguyen, T., SHM through flexible vibration sensing technologies and robust safety evaluation paradigm, PhD Thesis by Publication (2014) Queensland University of Technology; Nguyen, T., Chan, T.H.T., Thambiratnam, D.P., Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance (2014) Struct Health Monit, 13 (4), pp. 473-488; (2015) FEMtools Model Updating User’s Guide (Version 3, p. 8. , Dynamic Design Solutions NV (DDS), Leuven, Belgium; Friswell, M.I., Damage identification using inverse methods (1851) Philos Trans R Soc Lond A, 2007 (365), pp. 393-410; Nguyen, T., Chan, T.H.T., Thambiratnam, D.P., Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance (2014) Struct Health Monit, 13 (4), pp. 461-472","Nguyen, A.; School of Civil Engineering and Built Environment, Australia; email: a68.nguyen@qut.edu.au",,,"Springer Verlag",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85041524369
"Zhou L., Liang C., Chen L., Xia Y.","36496227600;57194522532;55756603800;8673901000;","Numerical simulation method of thermal analysis for bridges without using field measurements",2017,"Procedia Engineering","210",,,"240","245",,5,"10.1016/j.proeng.2017.11.072","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042665203&doi=10.1016%2fj.proeng.2017.11.072&partnerID=40&md5=c19ba0bfef98f9b4368cb91707492b0c","School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China; Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, Hong Kong","Zhou, L., School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China; Liang, C., School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China; Chen, L., School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China; Xia, Y., Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, Hong Kong","Structural temperatures have significantly negative effects on the performances of bridges. In this study, a numerical method of thermal analysis for bridges without using field measurements is proposed and investigated based on a long-span suspension bridge under weather conditions of a sunny day. Firstly, basic theory and methods of bridge thermal analysis are discussed. Secondly, the long-span suspension bridge and the structural health monitoring system are briefly introduced. Then, the finite element model of a typical section of the box girder of the long-span suspension bridge is constructed for transient thermal analysis to calculate the temperature variation and distributions. The thermal boundary conditions are calculated using the meteorological information from the nearby airport rather than the field measurements for thermal analysis. At last, the thermal boundary conditions are applied on the FF model to obtain the structural temperatures using transient thermal analysis. Besides, the conventional method using the bridge field meteorological measurements is also carried out for comparison. All the simulated results of structural temperatures are compared with the field measurements. All of them have good agreements. It is demonstrated that the proposed method is reliable and effective. © 2017 The Authors. Published by Elsevier Ltd.","Bridges; numerical simulation; temperature behavior; transient thermal analysis","Boundary conditions; Box girder bridges; Bridges; Computer simulation; Electric measuring bridges; Numerical methods; Numerical models; Structural analysis; Structural health monitoring; Suspension bridges; Thermoanalysis; Transient analysis; Long span suspension bridges; Meteorological information; Meteorological measurements; Numerical simulation method; Structural health monitoring systems; Temperature behavior; Thermal boundary conditions; Transient thermal analysis; Finite element method",,,,,"Fundamental Research Funds for the Central Universities: 2017ZD020; Science and Technology Planning Project of Guangdong Province: 2014A020218003","This research is supported by the Technology Planning Project of Guangdong Province (Project No. 2014A020218003), and the Fundamental Research Funds for the Central Universities (Grant No. 2017ZD020).",,,,,,,,,,"Priestley, M.J.N., Design temperature gradients for concrete bridges (1976) New Zealand Engineering., 31, pp. 213-219; Priestley, M.J.N., Design of concrete bridges for temperature gradients (1978) ACI Journal Proceedings., 75, pp. 209-217; Kennedy, J., Soliman, M., Temperature distributions in composite bridges (1987) Journal of Structural Engineering, 113, pp. 65-78; Zuk, W., Thermal behavior of composite bridges -insulated and uninsulated (1965) Highway Research Record., 76, pp. 231-253; Emanual, J.H., Hulsey, J.L., Temperature distributions in composite bridges (1978) Journal of Structural Engineering., 104, pp. 65-78; Churchward, A., Yehuda, J.S., Prediction of temperatures in concrete bridges (1981) J. Struct. Div, 107, pp. 2163-2176; Emerson, M., The calculation of the distribution of temperature in bridges (1973) TRRL Report LR561, Department of the Environment, Crowtowne, England; Kehlbeck, F., (1981) Effect of Solar Radiation on Bridge Structure, Translated by Liu, X.F., , Chinese Railway Publishing Company Beijing, China; Hunt, B., Nigel, C., Thermal calculations for bridge design (1975) Journal of Structural Engineering., 101, pp. 1763-1781; Elbadry, M.M., Ghali, A., Temperature variations in concrete bridges (1983) Journal of Structural Engineering., 109, pp. 2355-2374; Xia, Y., Chen, B., Zhou, X.Q., Xu, Y.L., Field monitoring and numerical analysis of Tsing Ma suspension bridge temperature behavior (2013) Structural Control and Health Monitoring., 20, pp. 560-575; Zhou, L.R., Xia, Y., Brownjohn, J.M.W., Koo, K.-Y., Temperature analysis of a long-span suspension bridge based on field monitoring and numerical simulation (2016) Journal of Bridge Engineering, 12; Zhou, L.R., Ou, L.W., Yu, J.P., Temperature numerical analysis of a large rigid-continuous concrete bridge (2015) The 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII 7th), Proceedings of SPIE, , Turin, Italy; Hunt, B., Nigel, C., Thermal calculations for bridge design (1975) Journal of Structural Engineering., 101, pp. 1763-1781; Brownjohn, J.M.W., Dumanoglu, A.A., Severn, R.T., Taylor, C.A., Ambient vibration measurements of the humber suspension bridge and comparison with calculated characteristics (1987) Proc. Inst. Civ. Eng., 83, pp. 561-600","Chen, L.; School of Civil Engineering and Transportation, China; email: chenlan@scut.edu.cn","Kodur V.K.Zhang H.Y.Banthia N.Wu B.","Michigan State University","Elsevier Ltd","6th International Workshop on Performance, Protection & Strengthening of Structures under Extreme Loading, PROTECT 2017","11 December 2017 through 12 December 2017",,139428,18777058,,,,"English","Procedia Eng.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85042665203
"Mohammadi Esfarjani S., Salehi M.","57195198509;57224960889;","Optimization the inner product vector method and its application to structural health monitoring",2017,"Journal of Vibroengineering","19","4",,"2578","2585",,5,"10.21595/jve.2017.18062","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027306521&doi=10.21595%2fjve.2017.18062&partnerID=40&md5=fbcaa64a4203058eab4cbde5eb63e6be","Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran","Mohammadi Esfarjani, S., Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran; Salehi, M., Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran","Fast methods to identification and recognition of structural defects are important issues for the industry. In recent years, a growing interest has been on quick structural inspection to significantly reduce inspection’s cost and time, while minimizing the number of Non-Destructive Testing (NDT). Investigations on damage detection methods based on vibration monitoring have been developed in the last decade. This paper presents a study on Inner Product Vector (IPV) method. The IPV method is a new vibration based damage detection technique. In this method vibration responses are measured before and after damage occurrence. The vibration responses include the time domain acceleration (or displacement or velocity). The IPV method has the potential to significantly reduce costs by minimizing the need for NDT methods. For damage detection via the IPV method, a threshold should be selected for classifying the damaged and undamaged sections of a structure. Proper determination of the threshold value requires selection of Confidence Interval Factor (CIF). In this study, a new algorithm for the IPV method is suggested in which a new optimized model for damage detection is presented. Aforementioned optimized model can provide an accurate value for the CIF. To ascertain the exact CIF, the damage detection method is simulated. An accurate threshold makes the IPV method capable to accurately detect damages. The method has been verified by means of an FEM model as well as an experimental case study. The results show that the optimization process can be used as a reference to ascertain value for the CIF. The IPV method can be used as a Structural Health Monitoring (SHM) method, but it’s necessary to optimize the IPV method for each sample. © JVE INTERNATIONAL LTD.","Cross correlation function; Damage detection; Inner product vector method; Modal dynamic analysis; Non-destructive testing; Structural health monitoring","Bridge decks; Cost reduction; Damage detection; Shape optimization; Structural health monitoring; Structural optimization; Time domain analysis; Confidence interval; Cross-correlation function; Dynamics analysis; Inner product; Inner product vector method; Interval factor; Modal dynamic analyse; Modal dynamics; Non destructive testing; Vector method; Nondestructive examination",,,,,,,,,,,,,,,,"He, K., Zhu, W.D., Structural damage detection using changes in natural frequencies: Theory and applications (2011) Journal of Physics: Conference Series, 305 (1); Maia, N.M.M., Silva, J.M.M., Almas, E.A.M., Damage detection in structures; from mode shape of frequency response function methods (2003) Mechanical Systems and Signal Processing, 17 (3), pp. 489-498; Cattarius, J., Inman, D.J., Time domain analysis for damage detection in smart structures (1997) Mechanical Systems and Signal Processing, 11 (3), pp. 409-423; Kuroiwa, T., Iemura, H., Vibration based damage detection using time series analysis (2008) The 14Th World Conference on Earthquake Engineering, , Beijing, China; Guo, N., Yang, Z., Jia, Y., Wang, L., Model updating using correlation analysis of strain frequency response function (2016) Mechanical Systems and Signal Processing, 70-71, pp. 284-299; Salehi, M., Ziaei-Rad, S., Ghayour, M., Vaziri-Zanjani, M.A., A Structural damage detection technique based on measured frequency response functions (2010) Contemporary Engineering Sciences, 3 (5), pp. 215-226; Zheng, Z.D., Lu, Z.R., Chen, W.H., Liu, J.K., Structural damage identification based on power spectral density sensitivity analysis of dynamic responses (2014) Computers and Structures, 146, pp. 176-184. , https://doi.org/10.1016/j.compstruc.2014.10.011; Zhu, D., Yi, X., Wang, Y., Sensitivity analysis of transmissibility functions for structural damage detection (2011) Proceedings of SPIE 7983, , https://doi.org/10.1117/12.879867; Rizos, D.D., Fassois, S.D., Marioli-Riga, Z.P., Karanika, A.N., Vibration-based skin damage statistical detection and restoration assessment in a stiffened aircraft panel (2008) Mechanical Systems and Signal Processing, 22 (2), pp. 315-337; Zhang, M., Schmidt, R., A comparative study of the correlation function based structural damage detection methods under sinusoidal excitation (2013) The 11Th International Conference on Vibration Problems, , Lisbon Portugal; Zhang, M., Schmidt, R., Sensitivity analysis of an auto-correlation-function-based damage index and its application in structural damage detection (2014) Journal of Sound and Vibration, 333 (26), pp. 7352-7363; Wang, L., Yang, Z., Structural damage detection using inner product vector and low pass filter technique (2012) Applied Mechanics and Materials, 204-208, pp. 2942-2946. , https://doi.org/10.4028/www.scientific.net/AMM.204-208.2942; Wang, L., Yang, Z., Effect of response type and excitation frequency range on the structural damage detection method using correlation functions of vibration responses (2013) Journal of Sound and Vibration, 332 (4), pp. 645-653; Wang, L., Yang, Z.C., Waters, T.P., Structural damage detection using cross correlation functions of vibration response (2010) Journal of Sound and Vibration, 329 (24), pp. 5070-5086; Wang, L., Yang, Z.C., Waters, T.P., Theory of inner product vector and its application to multi-location damage detection (2011) Journal of Physics: Conference Series, 305; Mohammadi Esfarjani, S., Salehi, M., Evaluation of the damage detection capability of inner product vector for LOP and LOSWF defects in V groove weld (2016) Modares Mechanical Engineering, 16 (6), pp. 7-16; Mohammadi Esfarjani, S., Salehi, M., Damage identification in aluminium T3-2024 alloy via cross correlation functions (2016) The 15Th International Conference of Iranian Aerospace, , Tehran, Iran","Mohammadi Esfarjani, S.; Department of Mechanical Engineering, Iran; email: satar.iran@gmail.com",,,"Vibromechanika",,,,,13928716,,,,"English","J. Vibroeng.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85027306521
"Hua X.G., Wen Q., Ni Y.Q., Chen Z.Q.","22940964100;57189657358;7402910024;55611551100;","Assessment of stochastically updated finite element models using reliability indicator",2017,"Mechanical Systems and Signal Processing","82",,,"217","229",,5,"10.1016/j.ymssp.2016.05.020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973889483&doi=10.1016%2fj.ymssp.2016.05.020&partnerID=40&md5=3c0876d9dc692b5813f63c37e614ca0e","Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan University, China; Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong","Hua, X.G., Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan University, China; Wen, Q., Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan University, China; Ni, Y.Q., Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; Chen, Z.Q., Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan University, China","Finite element (FE) model updating techniques have been a viable approach to correcting an initial mathematical model based on test data. Validation of the updated FE models is usually conducted by comparing model predictions with independent test data that have not been used for model updating. This approach of model validation cannot be readily applied in the case of a stochastically updated FE model. In recognizing that structural reliability is a major decision factor throughout the lifecycle of a structure, this study investigates the use of structural reliability as a measure for assessing the quality of stochastically updated FE models. A recently developed perturbation method for stochastic FE model updating is first applied to attain the stochastically updated models by using the measured modal parameters with uncertainty. The reliability index and failure probability for predefined limit states are computed for the initial and the stochastically updated models, respectively, and are compared with those obtained from the ‘true’ model to assess the quality of the two models. Numerical simulation of a truss bridge is provided as an example. The simulated modal parameters involving different uncertainty magnitudes are used to update an initial model of the bridge. It is shown that the reliability index obtained from the updated model is much closer to true reliability index than that obtained from the initial model in the case of small uncertainty magnitude; in the case of large uncertainty magnitude, the reliability index computed from the initial model rather than from the updated model is closer to the true value. The present study confirms the usefulness of measurement-calibrated FE models and at the same time also highlights the importance of the uncertainty reduction in test data for reliable model updating and reliability evaluation. © 2016 Elsevier Ltd","Modal variability; Model validation; Stochastic FE model updating; Structural health monitoring; Structural reliability","Bridge decks; Finite element method; Modal analysis; Perturbation techniques; Reliability; Stochastic systems; Structural health monitoring; Trusses; Uncertainty analysis; FE model updating; Finite-element model updating; Modal variability; Model validation; Reliability Evaluation; Reliability indicators; Structural reliability; Uncertainty reduction; Stochastic models",,,,,"51422806","The authors would like to acknowledge the financial support from the National Science Foundation of China for Excellent Young Scholars (No. 51422806 ).",,,,,,,,,,"Mottershead, J.E., Friswell, M.I., Model updating in structural dynamics: a survey (1993) J. Sound Vib., 67, pp. 347-375; Friswell, M.I., Mottershead, J.E., Finite Element Model Updating in Structural Dynamics (1995), Kluwer Academic Publishers Boston; Link, M., Updating of analytical models – review of numerical procedures and application aspects (2001) Structural Dynamics @ 2000: Current Status and Future Directions, pp. 193-224. , D.J. Ewins D.J. Inman Research Studies Press Hertfordshire; Mottershead, J.E., Link, M., Friswell, M.I., The sensitivity method in finite elemnt model updating: a tutorial (2011) Mech. Syst. Signal Process., 25, pp. 2275-2296; Beck, J.L., Katafygiotis, L.S., Updating models and their uncertainties I: Bayesian statistical framework (1998) J. Eng. Mech. ASCE, 124, pp. 455-461; Yeun, K.V., Beck, J.L., Katafygiotis, L.S., Probabilistic approach for modal identification using non-stationary noisy response measurements only (2002) Earthq. Eng. Struct. Dyn., 32, pp. 1007-1023; Goller, B., Beck, J.L., Schueller, G.I., Evidence-based identification of weighting factors in Bayesian model updating using modal data (2012) J. Eng. Mech. ASCE, 138, pp. 430-440; Liu, P.L., Identification and damage detection of trusses using modal data (1995) J. Struct. Eng. ASCE, 121, pp. 599-608; Papadopoulos, L., Garcia, E., Structural damage identification: a probabilistic approach (1998) AIAA J., 36, pp. 2137-2145; Araki, Y., Hjelmstad, K.D., Optimum sensitivity-based statistical parameters estimation from modal response (2001) AIAA J., 39, pp. 1166-1174; Xia, Y., Hao, H., Brownjohn, J.M.W., Xia, P.Q., Damage identification of structures with uncertain frequency and mode shape data (2002) Earthq. Eng. Struct. Dyn., 31, pp. 1053-1066; Mares, C., Mottershead, J.E., Friswell, M.I., Stochastic model updating part 1: theory and simulated examples (2006) Mech. Syst. Signal Process., 20, pp. 1674-1695; Hua, X.G., Ni, Y.Q., Chen, Z.Q., Ko, J.M., An improved perturbation method for stochastic finite element model updating (2008) Int. J. Numer. Methods Eng., 73, pp. 1845-1864; KHodaparast, H.H., Mottershead, J.E., Friswell, M.I., Perturbation methods for the estimation of parameter variability in stochastic model updating (2008) Mech. Syst. Signal Process., 22, pp. 1751-1773; Gover, Y., Link, M., Stochastic model updating-covariance matrix adjustment from uncertain experimental modal data (2010) Mech. Syst. Signal Process., 24, pp. 696-706; Jacquelin, E., Adhikari, S., Friswell, M.I., A second-moment approach for direct probabilistic mode updating in structural dynamics (2012) Mech. Syst. Signal Process., 29, pp. 262-283; Fang, S.E., Zhang, Q.H., Ren, W.X., Parameter variability estimation using stochastic response surface model updating (2014) Mech. Syst. Signal Process., 49, pp. 249-263; Silva, T.A.N., Maia, N.M.M., Link, M., Mottershead, J.E., Parameter selection and covariance updating (2016) Mech. Syst. Signal Process., 70-71, pp. 269-283; Au, S.K., Connecting Bayesian and frequentist quantification of parameter uncertainty in system identification (2012) Mech. Syst. Signal Process., 29, pp. 328-342; ASME, Guide for Verification and Validation in Computational Solid Mechanics (2006), ASME New York; Hemez, F.M., Doebling, S.W., Anderson, M.C., A brief tutorial on verification and validation (2002) Proceedings of the 22nd International Modal Analysis Conference, Dearborn, pp. 26-36; Aktan, E., Brownjohn, J.M.W., Structural identification: opportunities and challenges (2013) J. Struct. Eng., 139 (10), pp. 1639-1647; Wong, F.S., Yao, J.T.P., Health monitoring and structural reliability as a value chain (2001) Comput. Aided Civ. Infrastruct. Eng., 16, pp. 71-78; Beck, J.L., Au, S.K., Bayesian updating of structural models and reliability using Markov Chain Monte Carlo simulation (2002) J. Eng. Mech., 128, pp. 380-391; Hua, X.G., Structural Health Monitoring and Condition Assessment of Bridge Structures (PhD thesis) (2006), The Hong Kong Polytechnic University Hong Kong; Melcher, R.E., (1999) Structural Reliability Analysis and Prediction, , Second Edition John Wiley Chichester; Yeun, K.V., Beck, J.L., Katafygiotis, L.S., Probabilistic approach for modal identification using non-stationary noisy response measurement only (2002) Earthq. Eng. Struct. Dyn., 31 (4), pp. 1007-1023; Ni, Y.Q., Hua, X.G., Fan, K.Q., Ko, J.M., Correlating modal properties with temperature using long-term monitoring data and support vector machine technique (2005) Eng. Struct., 27 (12), pp. 1762-1773","Hua, X.G.; Wind Engineering Research Center, China; email: cexghua@hotmail.com",,,"Academic Press",,,,,08883270,,MSSPE,,"English","Mech Syst Signal Process",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-84973889483
"Tran-Ngoc H., Khatir S., Le-Xuan T., De Roeck G., Bui-Tien T., Abdel Wahab M.","57204859146;6507792896;57215818468;7007019763;57204859112;7102582536;","Finite element model updating of a multispan bridge with a hybrid metaheuristic search algorithm using experimental data from wireless triaxial sensors",2022,"Engineering with Computers","38",,,"1865","1883",,4,"10.1007/s00366-021-01307-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102517732&doi=10.1007%2fs00366-021-01307-9&partnerID=40&md5=fe4cab84a83845a5f2c4aee30fdf3446","Department of Electrical Energy, Metals, Mechanical Constructions and Systems, Faculty of Engineering and Architecture, Ghent University, Ghent, 9000, Belgium; Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Viet Nam; Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam; Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, Leuven, 3001, Belgium; Division of Computational Mechanics, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam","Tran-Ngoc, H., Department of Electrical Energy, Metals, Mechanical Constructions and Systems, Faculty of Engineering and Architecture, Ghent University, Ghent, 9000, Belgium, Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Viet Nam; Khatir, S., Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam; Le-Xuan, T., Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Viet Nam; De Roeck, G., Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, Leuven, 3001, Belgium; Bui-Tien, T., Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Viet Nam; Abdel Wahab, M., Division of Computational Mechanics, Ton Duc Thang University, Ho Chi Minh City, Viet Nam, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam","The Guadalquivir bridge is a large-scale twin steel truss bridge located in Spain that opened to traffic in 1929. Since the bridge has come into operation for a long time, structural health monitoring (SHM) is strictly necessary to guarantee safety and avoid serious incidents. This paper proposes a novel approach to model updating for the Guadalquivir bridge based on the vibration measurements combined with a hybrid metaheuristic search algorithm. Cuckoo Search (CS) is an evolutionary algorithm derived from global search techniques to look for the best solution. Nevertheless, CS contains some fundamental defects that may reduce its effectiveness in dealing with optimization issues. A main drawback of CS arises in the low convergence level because CS applies fixed values for parameters when looking for the optimal solution. In addition, CS relies a lot on the quality of original populations and does not have the capability to enhance the quality of the next generations. If the position of the original particles is far from the optimal places, it may be challenging to look for the best solution. To remedy the shortcomings of CS, we propose a hybrid metaheuristic algorithm (HGAICS) employing the advantages of both Genetic Algorithm (GA) and Improved Cuckoo Search (ICS) to solve optimization problems. HGAICS contains two outstanding characteristics as follows: (1) GA is employed to create original particles with the best quality based on the capacity of crossover and mutation operators and (2) those particles are then applied to look for the global best derived from the flexible and global search ability of ICS. This paper also presents the application of wireless triaxial sensors (WTSs) taking the place of classical wired systems (CWSs) to the measurements. The use of WTSs increases dramatically the freedom in setting up experimental measurements. The results show that the performance of the proposed hybrid algorithm not only determines uncertain parameters of the Guadalquivir bridge properly, but also is more accurate than GA, CS, and improved CS (ICS). A MATLAB package of the proposed method (HGAICS) is available via GitHub: https://github.com/HoatranCH/HGAICS. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.","Ambient vibration test; Finite element method; Genetic algorithm; Hybrid metaheuristic algorithm; Improved cuckoo search; Large-scale railway bridge; Model updating; Wireless triaxial transducers","Electric measuring bridges; Finite element method; Learning algorithms; Steel bridges; Structural health monitoring; Trusses; Uncertainty analysis; Crossover and mutation; Finite-element model updating; Global search ability; Global search techniques; Hybrid Meta-heuristic; Hybrid metaheuristic algorithms; Optimization problems; Structural health monitoring (SHM); Genetic algorithms",,,,,"Universiteit Gent; Bộ Giáo dục và Ðào tạo: B2020-GHA-02; Vlaamse regering; VLIRUOS: VN2018TEA479A103","The authors acknowledge the financial support of VLIR-UOS TEAM Project, VN2018TEA479A103, ‘Damage assessment tools for Structural Health Monitoring of Vietnamese infrastructures’ funded by the Flemish Government. The authors also acknowledge the assistance of colleagues from the Department of Civil Engineering, KU Leuven, Belgium in carrying out the measurement campaign of the Guadalquivir bridge. Moreover, the first author needs to acknowledge the financial supports from Ministry of Education and Training (MOET) under the project research “B2020-GHA-02” and Bijzonder Onderzoeksfonds (BOF) of Ghent University.",,,,,,,,,,"Li, X., Wen, Z., Su, H., An approach using random forest intelligent algorithm to construct a monitoring model for dam safety (2019) Eng Comput, 2019, pp. 1-18; Li, M., Si, W., Ren, Q., Song, L., Liu, H., An integrated method for evaluating and predicting long-term operation safety of concrete dams considering lag effect (2020) Eng Comput, 2020, pp. 1-15; Hien, T.D., A static analysis of nonuniform column by stochastic finite-element method using weighted integration approach (2020) Transp Commun Sci J, 2020, p. 70; Gillich, G.R., Ntakpe, J.L., Wahab, M.A., Praisach, Z.I., Mimis, M.C., Damage detection in multi-span beams based on the analysis of frequency changes (2017) J Phys Conf Ser, 2017, p. 842; Gillich, G.R., Praisach, Z.I., Detection and quantitative assessment of damages in beam structures using frequency and stiffness changes (2013) Key Eng Mater, 569, pp. 1013-1020; Thein, C.K., Liu, J.S., Numerical modeling of shape and topology optimisation of a piezoelectric cantilever beam in an energy-harvesting sensor (2017) Eng Comput, 33 (1), pp. 137-148; Sun, Z., Wei, M., Zhang, Z., Qu, G., Secure routing protocol based on multi-objective ant-colony-optimization for wireless sensor networks (2019) Appl Soft Comput, 77, pp. 366-375; Hoa, T.N., Khatir, S., De Roeck, G., Long, N.N., Thanh, B.T., Wahab, M.A., An efficient approach for model updating of a large-scale cable-stayed bridge using ambient vibration measurements combined with a hybrid metaheuristic search algorithm (2020) Smart Struct Syst, 25 (4), pp. 487-499; Dang, H.V., Tran-Ngoc, H., Nguyen, T.V., Bui-Tien, T., De Roeck, G., Nguyen, H.X., Data-driven structural health monitoring using feature fusion and hybrid deep learning (2020) IEEE Trans Autom Sci Eng, 2020, p. 5; Tran-Ngoc, H., Khatir, S., Le-Xuan, T., De Roeck, G., Bui-Tien, T., Wahab, M.A., A novel machine-learning based on the global search techniques using vectorized data for damage detection in structures (2020) Int J Eng Sci, 157, p. 103376; Mai, B.T.T., Cuong, N.H., Quang, N.D., Tai, D.H., (2020) Experimental study on flexural and shear behaviour of sandwich panels using glass textile reinforced concrete and autoclaved aerated concrete; Cuong, N.H., Quang, N.D., (2020) Experimental study on flexural behavior of prestressed and non-prestressed textile reinforced concrete plates; Kao, C.Y., Hung, S.L., Detection of structural damage via free vibration responses generated by approximating artificial neural networks (2003) Comput Struct, 81 (28-29), pp. 2631-2644; Ashebo, D.B., Chan, T.H., Yu, L., Evaluation of dynamic loads on a skew box girder continuous bridge. 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"Fernandez-Navamuel A., Zamora-Sánchez D., Omella Á.J., Pardo D., Garcia-Sanchez D., Magalhães F.","57218765955;55818645500;56268814900;56562669900;57278451300;22835635300;","Supervised Deep Learning with Finite Element simulations for damage identification in bridges",2022,"Engineering Structures","257",,"114016","","",,4,"10.1016/j.engstruct.2022.114016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125603042&doi=10.1016%2fj.engstruct.2022.114016&partnerID=40&md5=eebd113a17fb0ca3b3d190bb77d5bdfa","TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico y Tecnológico de Bizkaia, Astondo bidea, Edificio 700, Derio, E- 48160, Spain; Basque Center for Applied Mathematics (BCAM), Bilbao, Spain; University of the Basque Country (UPV/EHU) Leioa, Spain; Ikerbasque (Basque Foundation for Sciences), Bilbao, Spain; CONSTRUCT-ViBest, Faculty of Engineering, University of Porto (FEUP), Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Fernandez-Navamuel, A., TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico y Tecnológico de Bizkaia, Astondo bidea, Edificio 700, Derio, E- 48160, Spain, Basque Center for Applied Mathematics (BCAM), Bilbao, Spain, University of the Basque Country (UPV/EHU) Leioa, Spain; Zamora-Sánchez, D., TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico y Tecnológico de Bizkaia, Astondo bidea, Edificio 700, Derio, E- 48160, Spain; Omella, Á.J., University of the Basque Country (UPV/EHU) Leioa, Spain; Pardo, D., Basque Center for Applied Mathematics (BCAM), Bilbao, Spain, University of the Basque Country (UPV/EHU) Leioa, Spain, Ikerbasque (Basque Foundation for Sciences), Bilbao, Spain; Garcia-Sanchez, D., TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico y Tecnológico de Bizkaia, Astondo bidea, Edificio 700, Derio, E- 48160, Spain; Magalhães, F., CONSTRUCT-ViBest, Faculty of Engineering, University of Porto (FEUP), Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","This work proposes a supervised Deep Learning approach for damage identification in bridge structures. We employ a hybrid methodology that incorporates Finite Element simulations to enrich the training phase of a Deep Neural Network with synthetic damage scenarios. The neural network is based on autoencoders and its particular architecture allows to activate or deactivate nonlinear connections under need. The methodology intends to contribute to the progress towards the applicability of Structural Health Monitoring practices in full-scale bridge structures. The ultimate goal is to estimate the location and severity of damage from measurements of the dynamic response of the structure. The damages we seek to detect correspond to material degradations that affect wide areas of the structure by reducing its stiffness properties. Our method allows a feasible adaptation to large systems with complex parametrizations and structural particularities. We investigate the performance of the proposed method on two full-scale instrumented bridges, obtaining adequate results for the testing datasets even in presence of measurement uncertainty. Besides, the method successfully predicts the damage condition for two real damage scenarios of increasing severity available in one of the bridges. © 2022 The Author(s)","Autoencoders; Damage identification; Deep Learning; Structural Health Monitoring","Damage detection; Deep neural networks; Finite element method; Uncertainty analysis; Auto encoders; Bridge structures; Damage Identification; Damage scenarios; Deep learning; Finite elements simulation; Hybrid methodologies; Learning approach; Neural-networks; Training phasis; Structural health monitoring; bridge; damage mechanics; dynamic response; finite element method; health monitoring; machine learning; structural response",,,,,"KK-2020/00049; KK-2021/00048; 769373; IT1294-19, KK-2021/00095; U.S. Department of Education, ED; Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA: 777778; European Commission, EC; Eusko Jaurlaritza: UIDB/04708/ 2020; Ministerio de Ciencia e Innovación, MICINN: PID2019-108111RB-I00; Ministério da Ciência, Tecnologia e Ensino Superior, MCTES; Horizon 2020; European Regional Development Fund, ERDF: EFA362/19; Fundació Catalana de Trasplantament, FCT; Agencia Estatal de Investigación, AEI: PDC2021-121093-I00, SEV-2017-0718; Innovation and Networks Executive Agency, INEA; Institute of Research and Development in Structures and Construction","This work has received funding from the European’s Union Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project). This paper reflects only the author’s views. The European Commission and INEA are not responsible for any use that may be made of the information contained therein.","This work was financially supported by: Base Funding - UIDB/04708/ 2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções - funded by national funds through the FCT/MCTES (PIDDAC) .","David Pardo has received funding from: the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS); the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program POCTEFA 2014-2020 Project PIXIL ( EFA362/19 ); the Spanish Ministry of Science and Innovation projects with references PID2019-108111RB-I00 (FEDER/AEI) and PDC2021-121093-I00 , the “BCAM Severo Ochoa” accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2018-2021 program, the three Elkartek projects 3KIA (KK-2020/00049), EXPERTIA (KK-2021/00048), and SIGZE (KK-2021/00095), and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education.","This work has received funding from the European's Union Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project). This paper reflects only the author's views. The European Commission and INEA are not responsible for any use that may be made of the information contained therein. Authors would like to acknowledge the Basque Government funding within the ELKARTEK programme (SIGZE project (KK-2021/00095)). This work was financially supported by: Base Funding - UIDB/04708/ 2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Constru??es - funded by national funds through the FCT/MCTES (PIDDAC). David Pardo has received funding from: the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS); the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program POCTEFA 2014-2020 Project PIXIL (EFA362/19); the Spanish Ministry of Science and Innovation projects with references PID2019-108111RB-I00 (FEDER/AEI) and PDC2021-121093-I00, the ?BCAM Severo Ochoa? accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2018-2021 program, the three Elkartek projects 3KIA (KK-2020/00049), EXPERTIA (KK-2021/00048), and SIGZE (KK-2021/00095), and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education.",,,,,,,"Farrar, C., Worden, K., Structural Health Monitoring A Machine Learning Perspective (2013), Wiley; Brownjohn, J.M., de Stefano, A., Xu, Y.L., Wenzel, H., Aktan, A.E., Vibration-based monitoring of civil infrastructure: challenges and successes (2011) J Civ Struct Health Monit, 1 (3-4), pp. 79-95; Friswell, M.I., Damage identification using inverse methods (2007) Phil Trans R Soc A, 365 (1851), pp. 393-410; An, Y., Chatzi, E., Sim, S.-H., Laflamme, S., Blachowski, B., Ou, J., Recent progress and future trends on damage identification methods for bridge structures (2019) Struct. 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Peeters, B., De Roeck, G., One-year monitoring of the z24-bridge: environmental effectsversus damage events (2001) Earthq Eng Struct Dyn, 30 (2), pp. 149-171","Fernandez-Navamuel, A.; TECNALIA, Astondo bidea, Edificio 700, Spain; email: afdeznavamuel001@ikasle.ehu.eus",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85125603042
"Wang X., Wang L., Wang H., Ning Y., Huang K., Wang W.","57206698991;37012933100;56946133500;57470573300;57300187900;57192007966;","Performance Evaluation of a Long-Span Cable-Stayed Bridge Using Non-Destructive Field Loading Tests",2022,"Applied Sciences (Switzerland)","12","5","2367","","",,4,"10.3390/app12052367","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125505542&doi=10.3390%2fapp12052367&partnerID=40&md5=7cacade9505d0f15767b031f44cf20e8","Bridge Engineering Research Institute, Guangxi Transportation Science and Technology Group Co., Ltd., Nanning, 530007, China; School of Civil Engineering, Southeast University, Nanjing, 211189, China; Postdoctoral Workstation, Guangxi Beibu Gulf Investment Group Co., Ltd., Nanning, 530029, China; College of Transportation, Jilin University, Changchun, 130025, China","Wang, X., Bridge Engineering Research Institute, Guangxi Transportation Science and Technology Group Co., Ltd., Nanning, 530007, China; Wang, L., Bridge Engineering Research Institute, Guangxi Transportation Science and Technology Group Co., Ltd., Nanning, 530007, China, School of Civil Engineering, Southeast University, Nanjing, 211189, China; Wang, H., Bridge Engineering Research Institute, Guangxi Transportation Science and Technology Group Co., Ltd., Nanning, 530007, China, Postdoctoral Workstation, Guangxi Beibu Gulf Investment Group Co., Ltd., Nanning, 530029, China; Ning, Y., Bridge Engineering Research Institute, Guangxi Transportation Science and Technology Group Co., Ltd., Nanning, 530007, China; Huang, K., Bridge Engineering Research Institute, Guangxi Transportation Science and Technology Group Co., Ltd., Nanning, 530007, China; Wang, W., College of Transportation, Jilin University, Changchun, 130025, China","As an important part of the transportation network, the reliability of bridge structures is of great significance to people’s personal safety, as well as to the national economy. In order to evaluate the performance of complex bridge structures, their mechanical behavior and fundamental characteristics need to be studied. Structural health monitoring (SHM) has been introduced into bridge engineering, and the structural response assessment, load effects monitoring, and reliability evaluation have been developed based on the collected SHM information. In this study, a performance evaluation method for complex bridge structures based on non-destructive field loading tests is proposed. The cable-stayed bridge in Guangxi with the largest span (Pingnan Xiangsizhou Bridge) was selected as the research object, and loading on the main girder was transferred to the piers and tower through the stay cables, whose structural responses are critical in the process of bridge operation. Therefore, the field loading tests—including deflection and strain testing of the main girder, as well as cable force tests—were also conducted for Pingnan Xiangsizhou Bridge by using non-destructive measurement techniques (multifunctional static strain test system, radar interferometric deformation measurement technology, etc.). Based on the numerically simulated results of a finite element model for Pingnan Xiangsizhou Bridge, reasonable field loading test conditions and loading arrangement were determined. Non-destructive field loading test results showed that the quality of the bridge’s construction is up to standard, due to a good agreement between the calculated and measured frequencies of the bridge. In addition, the calibration coefficients of displacement and strain were less than 1, indicating that Pingnan Xiangsizhou Bridge has satisfactory stiffness and strength. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","Cable-stayed bridge; Finite element method; Non-destructive field loading test; Performance evaluation; Structural health monitoring",,,,,,"RC20180108; RC20190206; 2018-01-04; AD19245152; 2021AC19125; 20210508028RQ; China Postdoctoral Science Foundation: 2021T140262","Funding: This research was funded by the Scientific and Technological Project of the Science and Technology Department of Jilin Province (grant number: 20210508028RQ), the Scientific and Technological Project of the Science and Technology Department of Guangxi Province (grant number: 2021AC19125), the Nanning Excellent Young Scientist Program (grant number: RC20180108), the Nanning Excellent Young Scientist Program and Guangxi Beibu Gulf Economic Zone Major Talent Program (grant number: RC20190206), the Science and Technology Base and Talent Special Project of Guangxi Province (grant number: AD19245152,) and the “Yongjiang plan” of Nanning Leading Talents in Innovation and Entrepreneurship (grant number: 2018-01-04). This research was also supported by the China Postdoctoral Science Foundation (grant number: 2021T140262).",,,,,,,,,,"Zhao, G., Wang, Z., Zhu, S., Hao, J., Wang, J., Experimental study of mitigation of wind-induced vibration in asymmetric cable-stayed bridge using sharp wind fairings (2021) Appl. Sci, 12, p. 242. , [CrossRef]; Wu, Y., Wu, X., Li, J., Xin, H., Sun, Q., Wang, J., Investigation of vortex-induced vibration of a cable-stayed bridge without backstays based on wind tunnel tests (2022) Eng. Struct, 250, p. 113436. , [CrossRef]; Herrera, D., Varela, G., Tolentino, D., Reliability assessment of RC bridges subjected to seismic loadings (2021) Appl. Sci, 12, p. 206. , [CrossRef]; Liu, H., Wang, X., Tan, G., He, X., System reliability evaluation of a bridge structure based on multivariate copulas and the AHP–EW method that considers multiple failure criteria (2020) Appl. 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Wang, W.; College of Transportation, China; email: wangws@jlu.edu.cn",,,"MDPI",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85125505542
"Rager K., Jaworski D., von der Heide C., Kyriazis A., Sinapius M., Constantinou I., Dietzel A.","57208837266;57273302100;57208688426;57204834141;6603273621;56262165400;6603865796;","Space‐filling curve resistor on ultra‐thin polyetherimide foil for strain impervious temperature sensing",2021,"Sensors","21","19","6479","","",,4,"10.3390/s21196479","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115772071&doi=10.3390%2fs21196479&partnerID=40&md5=0bc5fed2430b7165ed58d090876a568e","Institut für Mikrotechnik, Technische Universität Braunschweig, Braunschweig, 38124, Germany; Institut für Mechanik und Adaptronik, Technische Universität Braunschweig, Braunschweig, 38106, Germany","Rager, K., Institut für Mikrotechnik, Technische Universität Braunschweig, Braunschweig, 38124, Germany; Jaworski, D., Institut für Mikrotechnik, Technische Universität Braunschweig, Braunschweig, 38124, Germany; von der Heide, C., Institut für Mikrotechnik, Technische Universität Braunschweig, Braunschweig, 38124, Germany; Kyriazis, A., Institut für Mechanik und Adaptronik, Technische Universität Braunschweig, Braunschweig, 38106, Germany; Sinapius, M., Institut für Mechanik und Adaptronik, Technische Universität Braunschweig, Braunschweig, 38106, Germany; Constantinou, I., Institut für Mikrotechnik, Technische Universität Braunschweig, Braunschweig, 38124, Germany; Dietzel, A., Institut für Mikrotechnik, Technische Universität Braunschweig, Braunschweig, 38124, Germany","Monitoring process parameters in the manufacture of composite structures is key to ensuring product quality and safety. Ideally, this can be done by sensors that are embedded during production and can remain as devices to monitor structural health. Extremely thin foil‐based sensors weaken the finished workpiece very little. Under ideal conditions, the foil substrate bonds with the resin in the autoclaving process, as is the case when polyetherimide is used. Here, we present a temperature sensor as part of an 8 μm thick multi‐sensor node foil for monitoring processing conditions during the production and structural health during the lifetime of a construction. A metallic thin film conductor was shaped in the form of a space‐filling curve to suppress the influences of resistance changes due to strain, which could otherwise interfere with the measurement of the tem-perature. FEM simulations as well as experiments confirm that this type of sensor is completely insensitive to the direction of strain and sufficiently insensitive to the amount of strain, so that me-chanical strains that can occur in the composite curing process practically do not interfere with the temperature measurement. The temperature sensor is combined with a capacitive sensor for curing monitoring based on impedance measurement and a half‐bridge strain gauge sensor element. All three types are made of the same materials and are manufactured together in one process flow. This is the key to cost‐effective distributed sensor arrays that can be embedded during production and remain in the workpiece, thus ensuring not only the quality of the initial product but also the oper-ational reliability during the service life of light‐weight composite constructions. © 2021 by the author. Licensee MDPI, Basel, Switzerland.","Composite material monitoring; Sensor integration; Space‐filling curve; Strain impervious temperature sensor; Ultra‐thin sensor foil","Capacitive sensors; Curing; Filling; Sensor nodes; Strain gages; Strain measurement; Structural health monitoring; Temperature measurement; Temperature sensors; Composite material monitoring; Composites material; Sensor foil; Sensor integration; Space-filling curve; Strain impervious temperature sensor; Structural health; Ultra-thin; Ultra‐thin sensor foil; Workpiece; Strain; polyetherimide; polymer; reproducibility; temperature; temperature sense; Polymers; Reproducibility of Results; Temperature; Thermosensing",,"polyetherimide; Polymers",,,"ZF4433703EB9; Scuola IMT Alti Studi Lucca, IMT; Deutsche Forschungsgemeinschaft, DFG: 397053684; Technische Universität Braunschweig","K. Rager and A. Kyriazis were funded by Deutsche Forschungsgemeinschaft (DFG) under Grant number 397053684, “Eingebettete multifunktionale Sensoren zur Steuerung des Aushärteprozesses von Faserverbunden”. D. Jaworski was funded by Zentrales Innovationsprogramm Mittelstand under Grant number ZF4433703EB9, “Entwicklung elastischer Gitterstrukturen durch additive Fertigung für verschiedene Anwendungen; Simulationsmodelle für mechanische und flu-idische Auslegung von 3D‐gedruckten Gitter‐ und Hohlstrukturen”. We acknowledge support by the German Research Foundation and the Open Access Publication Funds of Technische Universität Braunschweig.","Acknowledgments: We acknowledge support by Jan Niklas Haus from IMT for help with the wir‐ ing and the measurement setup.",,,,,,,,,"Tian, B., Liu, Q., Luo, C., Feng, Y., Wu, W., Multifunctional Ultrastretchable Printed Soft Electronic Devices for Wearable Appli-cations (2020) Adv. Electron. Mater, 6, p. 1900922. , https://doi.org/10.1002/aelm.201900922; Hassan, G., Bae, J., Hassan, A., Ali, S., Hyun Lee, C., Choi, Y., Ink‐jet Printed Stretchable Strain Sensor Based on Graphene/ZnO Composite on Micro‐random Ridged PDMS Substrate (2018) Compos. Part A, 107, pp. 519-528. , https://doi.org/10.1016/j.compo-sitesa.2018.01.031; von der Heide, C., Grein, M., Dietzel, A., Femtosecond laser‐contoured micro‐strain gages (2019) Microelectron. Eng, 214, pp. 81-86. , https://doi.org/10.1016/j.mee.2019.05.002; Estevam Schmiedt, R., Qian, C., Behr, C., Hecht, L., Dietzel, A., Flexible sensors on polymide fabricated by femtosecond laser for integration in fiber reinforced polymers (2018) Flex. Print. Electron, 3, p. 025003. , https://doi.org/10.1088/2058‐8585/aabe45; Koch, E., Dietzel, A., Skin attachable flexible sensor array for respiratory monitoring (2016) Sens. Actuators A Phys, 250, pp. 138-144. , https://doi.org/10.1016/j.sna.2016.09.020; von der Heide, C., Steinmetz, J., Schollerer, M.J., Hühne, C., Sinapius, M., Dietzel, A., Smart Inlays for Simultaneous Crack Sensing and Arrest in Multifunctional Bondlines of Composites (2021) Sensors, 21, p. 3852. , https://doi.org/10.3390/s21113852; Nau, M., (2004) Elektrische Temperaturmessung, , Jumo GmbH & Co. KG: Fulda, Germany, ISBN‐13: 978‐3‐935742‐06‐1; Moser, Y., Gils, M.A.M., Miniaturized Flexible Temperature Sensor (2007) J. Microelectromechanical Syst, 16, pp. 1349-1354. , https://doi.org/10.1109/JMEMS.2007.908437; Schwerter, M., Beutel, T., Leester‐Schädel, M., Büttgenbach, S., Dietzel, A., Flexible hot‐film anemometer arrays on curved structures for active flow control on airplane wings (2014) Microsyst. Technol, 20, pp. 821-829. , https://doi.org/10.1007/s00542‐013‐2054‐y; Kyriazis, A., Kilian, R., Sinapius, M., Rager, K., Dietzel, A., Tensile Strength and Structure of the Interface between a Room‐ Curing Epoxy Resin and Thermoplastic Films for the Purpose of Sensor Integration (2021) Polymers, 13, p. 330. , https://doi.org/10.3390/polym13030330; Yu, C., Wang, Z., Yu, H., Jiang, H., A stretchable temperature sensor based on elastically buckled thin film devices on elastomeric substrates (2009) Appl. Phys. Lett, 95, p. 141912. , https://doi.org/10.1063/1.3243692; Lichtenwalner, D.J., Hydrick, A.E., Kingon, A.I., Flexible thin film temperature and strain sensor array utilizing a novel sensing concept (2007) Sens. Actuators A Phys, 135, pp. 593-597. , https://doi.org/10.1016/j.sna.2006.07.019; Ahn, C.H., Park, H.W., Kim, H.H., Park, S.H., Son, C., Kim, M.C., Lee, J.H., Go, J.S., Direct fabrication of thin film gold resistance temperature detection sensors on a curved surface using a flexible dry film photoresist and their calibration up to 450 °C (2013) J. Micromech. Microeng, 23, p. 065031. , http://dx.doi.org/10.1088/0960‐1317/23/6/065031; Elektronik, Bungard, (2001) SUR‐TIN Chem. Verzinnung, Arbeitsanleitung, , Bungard Elektronik GmbH & Co. KG: Windeck, Germany; Jagadish, H.V., Analysis of the Hilbert curve for representing two‐dimensional space (1997) Inf. Process. Lett, 62, pp. 17-22. , https://doi.org/10.1016/S0020‐0190(97)00014‐8; Keil, S., (2017) Dehnungsmessstreifen, , https://doi.org/10.1007/978‐3‐658‐13612‐3, 2nd ed.; Springer Vieweg: Wiesbaden, Germany; Kyriazis, A., Asali, K., Sinapius, M., Rager, K., Dietzel, A., Adhesion of Multifunctional Substrates for Integrated Cure Monitoring Film Sensors to Carbon Fiber Reinforced Polymers (2020) J. Compos. Sci, 4, p. 138. , https://doi.org/10.3390/jcs4030138; Kyriazis, A., Feder, J., Rager, K., von der Heide, C., Dietzel, A., Sinapius, M., Reducing the weakening effect in fibre reinforced polymers caused by integrated film sensors (2021) J. Compos. Sci, 5, p. 256. , https://doi.org/10.3390/jcs5100256","Rager, K.; Institut für Mikrotechnik, Germany; email: k.rager@tu‐braunschweig.de",,,"MDPI",,,,,14248220,,,"34640795","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85115772071
"Gou H., Liu C., Bao Y., Han B., Pu Q.","25642595400;57200371297;56520828300;57212993251;23098055200;","Construction Monitoring of Self-Anchored Suspension Bridge with Inclined Tower",2021,"Journal of Bridge Engineering","26","10","05021011","","",,4,"10.1061/(ASCE)BE.1943-5592.0001777","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111927604&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001777&partnerID=40&md5=a799619027c94e2426afed2f94025fe2","Dept. of Bridge Engineering, School of Civil Engineering, Key Laboratory of High-Speed Railway Engineering, Southwest Jiaotong Univ., Chengdu, Sichuan, 610031, China; Dept. of Bridge Engineering, School of Civil Engineering, Southwest Jiaotong Univ., Chengdu, Sichuan, 610031, China; Dept. of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ  07030, United States; Southern Sichuan Intercity Railway Co. Ltd, Zigong, Sichuan, 610031, China; School of Civil Engineering, Southwest Jiaotong Univ., Chengdu, Sichuan, 610031, China","Gou, H., Dept. of Bridge Engineering, School of Civil Engineering, Key Laboratory of High-Speed Railway Engineering, Southwest Jiaotong Univ., Chengdu, Sichuan, 610031, China; Liu, C., Dept. of Bridge Engineering, School of Civil Engineering, Southwest Jiaotong Univ., Chengdu, Sichuan, 610031, China; Bao, Y., Dept. of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ  07030, United States; Han, B., Southern Sichuan Intercity Railway Co. Ltd, Zigong, Sichuan, 610031, China; Pu, Q., School of Civil Engineering, Southwest Jiaotong Univ., Chengdu, Sichuan, 610031, China","Inclined single-tower self-anchored suspension bridges feature a high adaptability to geological conditions but involve complicated structural behaviors during construction due to system transformation. The erection of the tower and main cable is accompanied by changes in internal loading and represents a challenge in bridge construction. This paper reports a new construction scheme for the tower and main cable, introduces the characteristics of temporary structures, and presents the monitoring and control method for construction, through a case study of the Yingbin Bridge. The responses of the temporary structures, girders, cables, and suspenders were monitored using a structural health monitoring system and analyzed using a finite-element model. The results show that the proposed construction scheme can satisfy the safety and quality requirements of bridge construction, and the finite-element model can reasonably predict the bridge behaviors in construction. This study is expected to promote the construction of self-anchored suspension bridges with inclined towers. © 2021 American Society of Civil Engineers.","Construction monitoring; Inclined single tower; Self-anchored suspension bridge; Structural behaviors; Vertical rotation construction","Cable stayed bridges; Cables; Finite element method; Suspension bridges; Temporary bridges; Towers; Bridge constructions; Construction monitoring; Geological conditions; Monitoring and control; Quality requirements; Self-anchored suspension bridge; Single-tower self-anchored suspension bridges; Structural health monitoring systems; Structural health monitoring",,,,,"BHSKL20-09-KF; R110120H01128","The research was funded by the Open Projects Foundation (Grant No. BHSKL20-09-KF) of State Key Laboratory for Health and Safety of Bridge Structures; and the Major Bridge Engineering Research Projects of Sichuan Intercity Railway Co. Ltd (Grant No. R110120H01128).",,,,,,,,,,"Brown, P., Kuhendran, K., Marks, J., Peace Bridge, Londonderry: Design and construction (2015) Proc. Inst. Civ. Eng. Bridge Eng., 168 (2), pp. 163-172. , https://doi.org/10.1680/bren.14.00004; (2011) Technical Specification for Highway Bridge and Culvert Construction, , CCCC First Highway Engineering Bureau Company. [In Chinese.] JTG/T F50-2011. Beijing: China Communications Press; (2011) Code for Design of Urban Bridges, , China Building Standard Design and Research Institute. [In Chinese.] CJJ11-2011. Beijing: China Construction Industry Press; Chun, S.B., Choi, J.H., Kim, J.H., Sorok Bridge - Standard for an economical suspension bridge (2012) Struct. Eng. Int., 22 (1), pp. 40-43. , https://doi.org/10.2749/101686612X13216060213239; Dongjoo, K., Hyunyang, S., Jongho, Y., Hwakshin, C., Design and special feature of Lusail Ring Bridge (2012) Iabse Congress Rep., 18 (2), pp. 1882-1889. , https://doi.org/10.2749/222137912805112626; He, P., Wang, C., Bridge tower design of Jiayu Yangtze River Highway Bridge (2018) Bridge Constr., 48 (4), pp. 84-89. , https://doi.org/10.3969/j.issn.1003-4722.2018.04.016, [In Chinese.]; Jung, M.R., Jang, M.J., Attard, M.M., Kim, M.Y., Elastic stability behavior of self-anchored suspension bridges by the deflection theory (2017) Int. J. Struct. Stab. Dyn., 17 (4), p. 1750050. , https://doi.org/10.1142/S021945541750050X; Kamei, M., Maruyama, T., Tanka, H., Konohana Bridge, Japan (2018) Struct. Eng. Int., 2 (1), pp. 4-6. , https://doi.org/10.2749/101686692780616968; Kim, H.K., Lee, M.J., Chang, S.P., Non-linear shape-finding analysis of a self-anchored suspension bridge (2002) Eng. Struct., 24 (12), pp. 1547-1559. , https://doi.org/10.1016/S0141-0296(02)00097-4; Liu, Z., Hebdon, M.H., Correia, J.A.F.O., Carvalho, H., Vilela, P., De Jesus, A.M.P., Calçada, R.A.B., Fatigue life evaluation of critical details of the Hercílio Luz Suspension Bridge (2017) Procedia Struct. Integrity, 5, pp. 1027-1034. , https://doi.org/10.1016/j.prostr.2017.07.063; Ma, X., Nie, J., Fan, J., Longitudinal stiffness of multispan suspension bridges (2016) J. Bridge Eng., 21 (5), p. 06015010. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000878; Mahjoubi, S., Barhemat, R., Bao, Y., Optimal placement of triaxial accelerometers using hypotrochoid spiral optimization algorithm for automated monitoring of high-rise buildings (2020) Autom. Constr., 118, p. 103273. , https://doi.org/10.1016/j.autcon.2020.103273; Niu, D., Zhou, Z., Wu, H., Wang, S., Stress free state control method for system transformation of self-anchored suspension bridge (2014) J. Chongqing Jiaotong Univ., 33 (1), pp. 21-24. , https://doi.org/10.3969/j.issn.1674-0696.2014.01.05, [In Chinese.]; Pan, S., Cui, Y., Zhang, Z., Zhu, W., Behaviour and design of three-tower, self-anchored suspension bridge with a concrete girder (2019) Bridge Eng., 172 (BE3), pp. 190-203. , https://doi.org/10.1680/jbren.18.00023; Shao, X., Hu, J., Deng, L., Cao, J., Conceptual design of superspan partial ground-anchored cable-stayed bridge with crossing stay cables (2014) J. Bridge Eng., 19 (3), p. 06013001. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000534; Sun, Y., Zhu, H.P., Xu, D., A specific rod model based efficient analysis and design of hanger installation for self-anchored suspension bridges with 3D curved cables (2016) Eng. Struct., 110 (2016), pp. 184-208. , https://doi.org/10.1016/j.engstruct.2015.11.040; Wang, Z., Monitoring technology for superstructure construction of Wuhan Yingwuzhou Yangtze River Bridge (2018) Bridge Constr., 48 (1), pp. 100-105. , https://doi.org/10.3969/j.issn.1003-4722.2018.01.018, [In Chinese.]; Wang, Z., Niu, Z., Chen, M., Lifting and swiveling process design with floating crane for the tower of river-sea direct channel bridge (2016) Constr. Technol., 45 (11), pp. 24-27. , https://doi.org/10.7672/sgjs2016110024, [In Chinese.]; Wang, Z., Wu, H.J., Zhou, Z.X., Wang, S.N., Analysis of main cable displacement characteristics of long-span self-anchored suspension bridge (2015) China Civ. Eng. J., 48 (7), pp. 102-111. , https://doi.org/10.15951/j.tmgcxb.2015.07.011, [In Chinese.]; Wang, X., Wang, X., Dong, Y., Wang, C., A novel construction technology for self-anchored suspension bridge considering safety and sustainability performance (2020) Sustainability, 12 (7), p. 2973. , https://doi.org/10.3390/su12072973; Xue, G., Yan, Y., Shen, L., Jin, C., Xian, Z., Summary of superstructure construction scheme of Taizhou Yangtze River Highway Bridge (2009) Bridge Constr., 2009 (4), pp. 59-63. , [In Chinese.]; Zhou, G., Li, A., Li, J., Duan, M., Structural health monitoring and time-dependent effects analysis of self-anchored suspension bridge with extra-wide concrete girder (2018) Appl. Sci., 8 (1), p. 115. , https://doi.org/10.3390/app8010115; Zhuo, W., Liu, Z., Key technology for main cable construction of Nanjing Xiaolongwan self-anchored suspension bridge (2014) Struct. Eng., 30 (5), pp. 192-197. , https://doi.org/10.15935/j.cnki.jggcs.2014.05.029, [In Chinese.]","Gou, H.; Dept. of Bridge Engineering, Chengdu, China; email: gouhongye@home.swjtu.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85111927604
"Hashlamon I., Nikbakht E., Topa A., Elhattab A.","57205484515;56009891000;56051068600;57192432976;","Numerical parametric study on the effectiveness of the contact-point response of a stationary vehicle for bridge health monitoring",2021,"Applied Sciences (Switzerland)","11","15","7028","","",,4,"10.3390/app11157028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111961678&doi=10.3390%2fapp11157028&partnerID=40&md5=4367d7e5f1b74019211343d132f5d3c3","Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia; Institute of Transportation Infrastructure, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia; Department of Maritime Technology, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Terengganu, 21300, Malaysia; Southern Company Structural Engineering, 600 North 18th Street, Birmingham, AL  35203-2206, United States","Hashlamon, I., Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia; Nikbakht, E., Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia, Institute of Transportation Infrastructure, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia; Topa, A., Institute of Transportation Infrastructure, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia, Department of Maritime Technology, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Terengganu, 21300, Malaysia; Elhattab, A., Southern Company Structural Engineering, 600 North 18th Street, Birmingham, AL  35203-2206, United States","Indirect bridge health monitoring is conducted by running an instrumented vehicle over a bridge, where the vehicle serves as a source of excitation and as a signal receiver; however, it is also important to investigate the response of the instrumented vehicle while it is in a stationary position while the bridge is excited by other source of excitation. In this paper, a numerical model of a stationary vehicle parked on a bridge excited by another moving vehicle is developed. Both stationary and moving vehicles are modeled as spring–mass single-degree-of-freedom systems. The bridges are simply supported and are modeled as 1D beam elements. It is known that the stationary vehicle response is different from the true bridge response at the same location. This paper investigates the effectiveness of contact-point response in reflecting the true response of the bridge. The stationary vehicle response is obtained from the numerical model, and its contact-point response is calculated by MATLAB. The contact-point response of the stationary vehicle is investigated under various conditions. These conditions include different vehicle frequencies, damped and undamped conditions, different locations of the stationary vehicle, road roughness effects, different moving vehicle speeds and masses, and a longer span for the bridge. In the time domain, the discrepancy of the stationary vehicle response with the true bridge response is clear, while the contact-point response agrees well with the true bridge response. The contact-point response could detect the first, second, and third modes of frequency clearly, unlike the stationary vehicle response spectra. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Bridge health monitoring; Contact-point response; Fast fourier transform; Finite element method; Indirect method; Moving average filter",,,,,,"Yayasan UTP, YUTP: 015LC0-056","Acknowledgments: This research was supported by the Universiti Teknologi PETRONAS Malaysia and Yayasan UTP (YUTP) under Research Grant (Cost Center 015LC0-056).",,,,,,,,,,"Rytter, A., (1993) Vibration Based Inspection of Civil Engineering Structures, , Aalborg University: Aalborg, Denmark; Fan, W., Qiao, P., Vibration-based damage identification methods: A review and comparative study (2011) Struct. Health Monit, 10, pp. 83-111. , [CrossRef]; Znidaric, A., Pakrashi, V., O’Brien, E.J., A review of road structure data in six European countries (2011) Proc. Inst. Civ. Eng. J. Urban Des. Plan, 164, pp. 225-232; (2013) Annual Report on Road Statistics: Current State of Bridges, , Ministry of Land Infrastructure Transport and Tourism. Ministry of Land Infrastructure Transport and Tourism: Tokyo, Japan; Davis, S.L., Goldberg, D., DeGood, K., Donohue, N., Corless, J., (2013) The Fix We’re in for: The State of Our Nation’s Bridges, , Transportation for America: Washington, DC, USA; An, N., Xia, H., Zhan, J., Identification of beam crack using the dynamic response of a moving spring-mass unit (2010) Interact. Multiscale Mech, 3, pp. 321-331. , [CrossRef]; Huang, M., Li, X., Lei, Y., Gu, J., Structural damage identification based on modal frequency strain energy assurance criterion and flexibility using enhanced Moth-Flame optimization (2020) Structures, 28, pp. 1119-1136. , [CrossRef]; Huang, M., Cheng, X., Lei, Y., Structural damage identification based on substructure method and improved whale optimization algorithm (2021) J. Civ. Struct. Health Monit, 11, pp. 351-380. , [CrossRef]; Huang, M., Cheng, S., Zhang, H., Gul, M., Lu, H., Structural damage identification under temperature variations based on PSO–CS hybrid algorithm (2019) Int. J. Struct. Stab. Dyn, 19, p. 1950139. , [CrossRef]; Huang, M.-S., Gül, M., Zhu, H.-P., Vibration-based structural damage identification under varying temperature effects (2018) J. Aerosp. Eng, 31, p. 04018014. , [CrossRef]; Zhu, X., Law, S.-S., Structural health monitoring based on vehicle-bridge interaction: Accomplishments and challenges (2015) Adv. Struct. Eng, 18, pp. 1999-2015. , [CrossRef]; Yang, Y., Zhu, Y., Wang, L.L., Jia, B.Y., Jin, R., Structural Damage Identification of Bridges from Passing Test Vehicles (2018) Sensors, 18, p. 4035. , [CrossRef]; Yang, Y.B., Yang, J.P., State-of-the-art review on modal identification and damage detection of bridges by moving test vehicles (2018) Int. J. Struct. Stab. Dyn, 18, p. 1850025. , [CrossRef]; González, A., Hester, D., The use of wavelets on the response of a beam to a calibrated vehicle for damage detection (2009) Proceedings of the 7th International Symposium on Nondestructive Testing in Civil Engineering (NDTCE’09), , Nantes, France, 30 June–3 July; Yang, Y.B., Lin, C.W., Yau, J.D., Extracting bridge frequencies from the dynamic response of a passing vehicle (2004) J. 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Mater, 29, pp. 153-163; Urushadze, S., Yau, J.-D., Experimental Verification of Indirect Bridge Frequency Measurement Using a Passing Vehicle (2017) Procedia Eng, 190, pp. 554-559. , [CrossRef]; McGetrick, P., Kim, C.A., A wavelet based drive-by bridge inspection system Proceedings of the 7th International Conference of Bridge Maintenance, Safety and Management, IABMAS, 2014, , https://core.ac.uk/reader/20536260, (accessed on 13 July 2021); Siringoringo, D.M., Fujino, Y., Estimating bridge fundamental frequency from vibration response of instrumented passing vehicle: Analytical and experimental study (2012) Adv. Struct. Eng, 15, pp. 417-433. , [CrossRef]; Yang, Y.B., Zhang, B., Qian, Y., Wu, Y., Contact-point response for modal identification of bridges by a moving test vehicle (2018) Int. J. Struct. Stab. Dyn, 18, p. 1850073. , [CrossRef]; Zhang, B., Qian, Y., Wu, Y., Yang, Y., An effective means for damage detection of bridges using the contact-point response of a moving test vehicle (2018) J. Sound Vib, 419, pp. 158-172. , [CrossRef]; Yang, Y., Zhang, B., Qian, Y., Wu, Y., Further Revelation on Damage Detection by IAS Computed from the Contact-Point Response of a Moving Vehicle (2018) Int. J. Struct. Stab. Dyn, 18, p. 1850137. , [CrossRef]; Li, J., Zhu, X., Law, S.-S., Samali, B., Indirect bridge modal parameters identification with one stationary and one moving sensors and stochastic subspace identification (2019) J. Sound Vib, 446, pp. 1-21. , [CrossRef]; Oshima, Y., Yamamoto, K., Sugiura, K., Stiffness estimation of RC bridges based on vehicle responses (2014) Proceedings of the Conference of Assessment, Durability, Monitoring and Retrofitting of Concrete Structures, , Seoul, Korea, 24–29 August; He, W.-Y., Ren, W.-X., Structural damage detection using a parked vehicle induced frequency variation (2018) Eng. Struct, 170, pp. 34-41. , [CrossRef]; He, W.Y., Ren, W.X., Zuo, X.H., Mass-normalized mode shape identification method for bridge structures using parking vehicle-induced frequency change (2018) Struct. Control Health Monit, 25, p. e2174. , [CrossRef]; Yang, Y., Xu, H., Zhang, B., Xiong, F., Wang, Z., Measuring bridge frequencies by a test vehicle in non-moving and moving states (2020) Eng. Struct, 203, p. 109859. , [CrossRef]; Yang, Y.-B., Yau, J., Yao, Z., Wu, Y., (2004) Vehicle-Bridge Interaction Dynamics: With Applications to High-Speed Railways, , World Scientific Publishing Co. Pte. Ltd.: Singapore; Calvo, J., Diaz, V., Roman, J., Establishing inspection criteria to verify the dynamic behaviour of the vehicle suspension system by a platform vibrating test bench (2005) Int. J. Veh. Des, 38, pp. 290-306. , [CrossRef]; (2016) Mechanical Vibration–Road Surface Profiles–Reporting of Measured Data, , ISO. 8608: ISO: London, UK; Lyons, R.G., (2004) Understanding Digital Signal Processing, , 3/E; Pearson Education India: Bengalore, India; Dubaisi, R., Dusseau, A., Natural Frequencies of Concrete Bridges in the Pacific Northwest (1992) Transp. Res. Rec, 1393, p. 119","Hashlamon, I.; Department of Civil and Environmental Engineering, Malaysia; email: ibrahim_19001027@utp.edu.my
Nikbakht, E.; Department of Civil and Environmental Engineering, Malaysia; email: ehsan.nikbakht@utp.edu.my",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85111961678
"Zhou Y., Xia Y., Fujino Y., Yamaguchi K.","57191652362;8673901000;55504765700;36816576900;","Analytical formulas of thermal deformation of suspension bridges",2021,"Engineering Structures","238",,"112228","","",,4,"10.1016/j.engstruct.2021.112228","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103614223&doi=10.1016%2fj.engstruct.2021.112228&partnerID=40&md5=9018267ba250443c005702aae1ed59df","Beijing Key Laboratory of Urban Underground Space Engineering, University of Science & Technology Beijing, Beijing, 100083, China; Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong; Institute of Advanced Sciences, Yokohama National University, Yokohama, 240-8501, Japan; Honshu-Shikoku Bridge Expressway Co., Ltd., Kobe, 651-0088, Japan","Zhou, Y., Beijing Key Laboratory of Urban Underground Space Engineering, University of Science & Technology Beijing, Beijing, 100083, China, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong; Xia, Y., Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong; Fujino, Y., Institute of Advanced Sciences, Yokohama National University, Yokohama, 240-8501, Japan; Yamaguchi, K., Honshu-Shikoku Bridge Expressway Co., Ltd., Kobe, 651-0088, Japan","Deformation of a long-span suspension bridge is mainly caused by ambient temperature changes. The temperature-induced deformation of a bridge is usually calculated using complex three-dimensional finite element analysis, the mechanism of which is often unclear. In this study, we derive general, succinct analytical formulas of the thermal deformation of three-span suspension bridges. The deformation of different components is unified into a one-dimensional thermal expansion formula (δL=LEθ·δT) by introducing an equivalent length LE. The sag effect of side-span cables is characterized by the modification coefficients, which demonstrate that the neglect of the sag effect overestimates the thermal deformation. Furthermore, the thermal deformation of the main- and side-span cables and towers is found to interact with each other as a result of the cable tension changes with varying temperature. The analytical formulas are validated using eight long-span suspension bridges including the Akashi Kaikyo bridge, the longest main-span suspension bridge in the world. The closed-form solutions herein also apply to the self-anchored suspension bridges. © 2021 Elsevier Ltd","Analytical solution; Sag effect; Structural health monitoring; Suspension bridge; Thermal deformation","Bridge cables; Cable stayed bridges; Structural health monitoring; Suspension bridges; Thermal expansion; Ambient temperature change; Analytical formulas; Health monitoring; Long span suspension bridges; One-dimensional; Sag effect; Structural health; Temperature-induced; Thermal deformation; Three dimensional finite element analysis; Deformation; analytical method; bridge; deformation; finite element method; thermal expansion; Akashi Strait; Honshu; Hyogo; Japan; Kinki",,,,,"XJ2018062; Hong Kong Polytechnic University, PolyU; University of Science and Technology Beijing, USTB; Fundamental Research Funds for the Central Universities: FRF- IDRY-19-030","The authors are grateful for the significant assistance received from the Honshu-Shikoku Bridge Expressway Co., Ltd. This research was supported by the Hong Kong Polytechnic University (Project No. ZE1F), the Hong Kong Scholars Program (Grant No. XJ2018062), and the Interdisciplinary Research Project for Young Teachers of USTB ( Fundamental Research Funds for the Central Universities ) (Grant No. FRF- IDRY-19-030 ).",,,,,,,,,,"Gimsing, N.J., Georgakis, C.T., Cable supported bridges: Concept and design (2012), 3rd ed. John Wiley & Sons Ltd Chichester, United Kingdom; Yanev, B., Suspension bridges: An overview (2016) Inspection, evaluation and maintenance of suspension bridges, pp. 1-50. , S. Alampalli W.J. Moreau CRC Press Boca Raton, FL; Xu, Y.L., Xia, Y., Structural health monitoring of long-span suspension bridges (2011), Spon Press Abingdon, Oxfordshire; Brownjohn, J.M.W., Koo, K., Scullion, A., List, D., Operational deformations in long-span bridges (2015) Struct Infrastruct E, 11 (4), pp. 556-574; Ogihara, K., Design and construction of suspension bridges (2016) Inspection, evaluation and maintenance of suspension bridges, pp. 51-68. , S. Alampalli W.J. Moreau CRC Press Boca Raton, FL; Boller, C., Structural health monitoring—An introduction and definitions (2009) Encyclopedia of structural health monitoring, , C. Boller F.K. Chang Y. Fujino John Wiley & Sons Ltd Chichester, United Kingdom; Fujino, Y., Siringoringo, D.M., Ikeda, Y., Nagayama, T., Mizutani, T., Research and implementations of structural monitoring for bridges and buildings in Japan (2019) Eng-PRC, 5 (6), pp. 1093-1119; Bao, Y., Chen, Z., Wei, S., Xu, Y., Tang, Z., Li, H., The state of the art of data science and engineering in structural health monitoring (2019) Eng-PRC, 5 (2), pp. 234-242; Xu, Y.L., Chen, B., Ng, C.L., Wong, K.Y., Chan, W.Y., Monitoring temperature effect on a long suspension bridge (2010) Struct Control Health Monit, 17 (6), pp. 632-653; Koo, K.Y., Brownjohn, J.M.W., List, D.I., Cole, R., Structural health monitoring of the Tamar suspension bridge (2013) Struct Control Health Monit, 20 (4), pp. 609-625; Kashima, S., Yanaka, Y., Suzuki, S., Mori, K., Monitoring the Akashi Kaikyo Bridge: First experiences (2001) Struct Eng Int, 11 (2), pp. 120-123; Kromanis, R., Kripakaran, P., Predicting thermal response of bridges using regression models derived from measurement histories (2014) Comput Struct, 136, pp. 64-77; Zhou, G., Yi, T., Chen, B., Chen, X., Modeling deformation induced by thermal loading using long-term bridge monitoring data (2018) J Perform Constr Fac, 32 (3), p. 4018011; Xia, Y., Chen, B., Zhou, X., Xu, Y., Field monitoring and numerical analysis of Tsing Ma Suspension Bridge temperature behavior (2013) Struct Control Health Monit, 20 (4), pp. 560-575; Tome, E.S., Pimentel, M., Figueiras, J., Structural response of a concrete cable-stayed bridge under thermal loads (2018) Eng Struct, 176, pp. 652-672; Zhou, L., Xia, Y., Brownjohn, J.M.W., Koo, K.Y., Temperature analysis of a long-span suspension bridge based on field monitoring and numerical simulation (2016) J Bridge Eng, 21 (1), p. 4015027; Timoshenko, S.P., Young, H.D., Theory of structures (1965), 2nd ed. McGraw-Hill Inc New York; Zhou, Y., Xia, Y., Chen, B., Fujino, Y., Analytical solution to temperature-induced deformation of suspension bridges (2020) Mech Syst Signal Pr, 139; Stavridis, L.T., A simplified analysis of the behavior of suspension bridges under live load (2008) Struct Eng Mech, 30 (5), pp. 559-576; Thai, H., Choi, D., Advanced analysis of multi-span suspension bridges (2013) J Constr Steel Res, 90, pp. 29-41; Buonopane, S.G., Billington, D.P., Theory and history of suspension bridge design from 1823 to 1940 (1993) J Struct Eng, 119 (3), pp. 954-977; Roeder, C., Proposed design method for thermal bridge movements (2003) J Bridge Eng, 8 (1), pp. 12-19; (2015), Ministry of Transport of the People's Republic of China. Specifications for Design of Highway Suspension Bridge. JTG/T D65-05—2015. Beijing: China Communications Press;; Kitagawa, M., Technology of the Akashi Kaikyo Bridge (2004) Struct Control Health Monit, 11 (2), pp. 75-90; Ochsendorf, J.A., Billington, D.P., Self-anchored suspension bridges (1999) J Bridge Eng, 4 (3), pp. 151-156; Bernstein, D.S., Matrix mathematics: Theory, facts, and formulas (2009), 2nd ed. Princeton University Press Princeton, NJ","Zhou, Y.; Beijing Key Laboratory of Urban Underground Space Engineering, China; email: zhouyi@ustb.edu.cn",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","",Scopus,2-s2.0-85103614223
"Xiao F., Chen G.S., Zatar W., Hulsey J.L.","56070134700;55615798900;6602971374;6602858255;","Signature extraction from the dynamic responses of a bridge subjected to a moving vehicle using complete ensemble empirical mode decomposition",2021,"Journal of Low Frequency Noise Vibration and Active Control","40","1",,"278","294",,4,"10.1177/1461348419872878","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075053412&doi=10.1177%2f1461348419872878&partnerID=40&md5=7f5a4fee8d7db148a6ebbea79c7546f7","Department of Civil Engineering, Nanjing University of Science and Technology, Nanjing, China; College of Information Technology and Engineering, Marshall University, Huntington, United States; Department of Civil and Environmental Engineering, University of Alaska Fairbanks, Fairbanks, United States","Xiao, F., Department of Civil Engineering, Nanjing University of Science and Technology, Nanjing, China; Chen, G.S., College of Information Technology and Engineering, Marshall University, Huntington, United States; Zatar, W., College of Information Technology and Engineering, Marshall University, Huntington, United States; Hulsey, J.L., Department of Civil and Environmental Engineering, University of Alaska Fairbanks, Fairbanks, United States","Technology that measures bridge responses when a vehicle is crossing over it for structural health monitoring has been under development for approximately a decade. Most of the proposed methods are based on identification of the dynamic characteristics of a bridge such as the natural frequency, the mode shapes, and the damping. Specifically, many time–frequency domain approaches have been used to extract complex spectrum signatures from the complicated vibrations of a bridge due to the interactions of a vehicle with the bridge, which usually involves nonlinear, nonstationary, stochastic, and impact vibrations. In this paper, a method known as complete ensemble empirical mode decomposition with adaptive noise is applied for the first time to analyze the acceleration response of a bridge to a moving vehicle, and the purpose is to extract the spectrum signature of the vehicle–bridge response for structural health monitoring. The time–frequency Hilbert-Huang transform (HHT) spectrum of the decomposed mode from complete ensemble empirical mode decomposition with adaptive noise is presented. The results are well-correlated with finite element analysis. The advantages of the complete ensemble empirical mode decomposition with adaptive noise method are demonstrated in comparing the data from conventional methods, including power spectra, spectrograms, scalograms, and empirical mode decomposition. © The Author(s) 2019.","bridge structural dynamic; Complete ensemble empirical mode decomposition with adaptive noise; structural health monitoring; vehicle–bridge interactions","Frequency domain analysis; Mathematical transformations; Signal processing; Spurious signal noise; Stochastic systems; Structural dynamics; Vehicles; Vibrations (mechanical); Acceleration response; Adaptive noise; Conventional methods; Dynamic characteristics; Empirical Mode Decomposition; Ensemble empirical mode decomposition; Frequency domain approaches; Hilbert Huang transforms; Structural health monitoring",,,,,"510015; University of Alaska Anchorage, UAA; Alaska Department of Transportation and Public Facilities, Alaska DOT&PF; Nanjing University of Science and Technology, NUST: AE89991","The writers wish to acknowledge the support from the Alaska Department of Transportation & Public Facilities, University of Alaska Fairbanks, University of Alaska Anchorage, Alaska Native Science and Engineering Program, Chandler Monitoring System Inc., Micron Optic, Inc.","The writers wish to acknowledge the support from the Alaska Department of Transportation & Public Facilities, University of Alaska Fairbanks, University of Alaska Anchorage, Alaska Native Science and Engineering Program, Chandler Monitoring System Inc., Micron Optic, Inc. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Alaska University Transportation Center Grant No. 510015 and Nanjing University of Science and Technology, Start?up, Grant Number: AE89991.",,,,,,,,,"Zhu, X.Q., Law, S.S., Structural health monitoring based on vehicle-bridge interaction: accomplishments and challenges (2015) Adv Struct Eng, 18, pp. 1999-2015; Cantero, D., Obrien, E.J., The non-stationarity of apparent bridge natural frequencies during vehicle crossing events (2013) FME Trans, 41, pp. 279-284; A discussion on the merits and limitations of using drive-by monitoring to detect localised damage in a bridge (2017) Mech Syst Signal Process, 90, pp. 234-253. , Hester D and Gonzále A; Mahato, S., Vinay Teja, M., Chakraborty, A., Combined wavelet–Hilbert transform-based modal identification of road bridge using vehicular excitation (2017) J Civil Struct Health Monit, 7, pp. 29-44; Malekjafarian, A., Obrien, E.J., On the use of a passing vehicle for the estimation of bridge mode shapes (2017) J Sound Vib, 397, pp. 77-91; Huffman, J.T., Xiao, F., Chen, G., Detection of soil-abutment interaction by monitoring bridge response using vehicle excitation (2015) J Civil Struct Health Monit, 5, pp. 389-395; Hlawatsch, F., Boudreaux-Bartels, G.F., Linear and quadratic time-frequency signal representations (1992) IEEE Signal Process Mag, 9, pp. 21-67; Huang, N.E., Shen, Z., Long, S.R., The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis (1998) Proc R Soc Lond A, 454, pp. 903-995; Smith, S., The local mean decomposition and its application to EEG perception data (2005) J R Soc Interface, 2, pp. 443-454; Rioul, O., Flandrin, P., Time scale energy distributions: a general class extending wavelet transforms (1992) IEEE Trans Signal Process, 40, pp. 1746-1757; Cohen, L., Time-frequency distributions-a review (1989) Proc IEEE, 77, pp. 941-981; Poyil, A.T., Aljahdali, S., Nasimudeen, K.M., Significance of Cohen’s class for time frequency analysis of signals (2013) Int J Comput Appl, , 72(12): 1–12; Baraniuk, R.G., Jones, D.L., Signal-dependent time frequency representation: optimal kernel design (1993) IEEE Trans Signal Process, 41, pp. 1589-1602; Wu, Z., Huang, N.E., Ensemble empirical mode decomposition: a noise-assisted data analysis method (2009) Adv Adapt Data Anal, 1, pp. 1-41; Fang, Y.-M., Feng, H.-L., Li, J., Stress wave signal denoising using ensemble empirical mode decomposition and an instantaneous half period model (2011) Sensors, 11, pp. 7554-7567; He, X., Goubran, R.A., Liu, X.P., (2012), Ensemble empirical mode decomposition and adaptive filtering for ECG signal enhancement. 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Low Freq. Noise Vib. Act. Control",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85075053412
"Luleci F., Li L., Chi J., Reiners D., Cruz-Neira C., Catbas F.N.","57376834500;57209412346;57209421801;6507129285;57203797818;6603396768;","Structural health monitoring of a foot bridge in virtual reality environment",2021,"Procedia Structural Integrity","37","C",,"65","72",,4,"10.1016/j.prostr.2022.01.060","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129462428&doi=10.1016%2fj.prostr.2022.01.060&partnerID=40&md5=8da22f8bee55c076fb4d02209a8b9163","Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL  32816, United States; Department of Computer Science, University of Central Florida, Orlando, FL  32816, United States","Luleci, F., Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL  32816, United States; Li, L., Department of Computer Science, University of Central Florida, Orlando, FL  32816, United States; Chi, J., Department of Computer Science, University of Central Florida, Orlando, FL  32816, United States; Reiners, D., Department of Computer Science, University of Central Florida, Orlando, FL  32816, United States; Cruz-Neira, C., Department of Computer Science, University of Central Florida, Orlando, FL  32816, United States; Catbas, F.N., Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL  32816, United States","Aging civil infrastructure systems require imminent attention before any failure mechanism becomes critical. Structural Health Monitoring (SHM) is employed to track inputs and/or responses of structural systems for decision support. Inspections and structural health monitoring require field visits, and subsequently expert assessment of critical elements at site, which may be both time-consuming and costly. Also, fieldwork including visits and inspections may pose danger, require personal protective equipment and structure closures during the fieldwork. To address some of these issues, a Virtual Reality (VR) collaborative application is developed to bring the structure and SHM data from the field to the office such that many experts from different places can simultaneously “virtually visit” the bridge structure for final assessment. In this work, we present a SHM system in a VR environment that includes the technical and visual information necessary for the engineers to make decisions for a footbridge on the campus of the University of Central Florida. In this VR application, for the visualization stage, UAV (Unmanned Air Vehicle) photogrammetry and LiDAR (Light Detection and Ranging) methods are used to capture the bridge. For the technical assessment stage, Finite Element Analysis (FEA) and Operational Modal Analysis (OMA) from vibration data as part of SHM are analyzed. To better visualize the dynamic response of the structure, the operational behavior from the FEA is reflected on the LiDAR point cloud model for immersive. The multi-user feature allowing teams to collaborate simultaneously is essential for decision-making activities. In conclusion, the proposed VR environment offers the potential to provide beneficial features with further automated and real-time improvements along with the SHM and FEA models. © 2022 The Authors.","LiDAR; SHM; UAV Photogrammetry; VR",,,,,,,,,,,,,,,,,"(2021) Report Card for America's Infrastructure; Branco, F.A., Brito, J., (2004) Handbook of Concrete Bridge Management, , ASCE Press, Reston, Virginia, USA; Dinis, F.M., Guimarães, A.S., Carvalho, B.R., Poças, M.J.P., An immersive Virtual Reality interface for Civil Engineering dissemination amongst pre-university students (2017) 2017 4th Experiment@International Conference (exp.at'17), pp. 157-158. , 2017; Fogarty, J., El-Tawil, S., McCormick, J., Exploring Structural Behavior and Component Detailing in Virtual Reality (2015) ASCE Structures Congress 2015; Hadipriono, F.C., Virtual Reality Applications in Civil Engineering (1996) Proceedings of the ACM Symposium on Virtual Reality Software and Technology (VRST'96), pp. 93-100. , https://doi.org/10.1145/3304181.3304200, Association for Computing Machinery, New York, NY, USA; Jáuregui, D.V., White, K.R., Implementation of Virtual Reality in Routine Bridge Inspection (2003) Transportation Research Record: Journal of the Transportation, 1827 (1), pp. 29-35; Kaminska, D., Sapinski, T., Wiak, S., Tikk, T., Haamer, R.E., Avots, E., Helmi, A., Anbarjafari, G., Virtual Reality and Its Applications in Education: Survey (2019) Information, 10 (10), p. 318. , https://doi.org/10.3390/info10100318; Moseley, K., Luleci, F., Catbas, N., Investigation of Tablet and Terrestrial LiDAR and Photogrammetry Applications to Structural Engineering (2021) Poster presented at the Scholar Symposium - University of Central Florida, , May 2021, Orlando, Florida; Mustapha, G., Hoemsen, R., Spewak, R., Knight, K., Application and Visualization for Advanced Sensor Networks. Case Study: Sensor Installation in Skilled Trades and Technology Centre (2017) 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure Brisbane, , Australia, 5-8 December 2017; Omer, M., Margetts, L., Mosleh, M.H., Cunningham, L.S., Inspection of Concrete Bridge Structures: Case Study Comparing Conventional Techniques with a Virtual Reality Approach (2021) Journal of Bridge Engineering, 26 (10). , October 2021; Omer, M., Hewitt, S., Mosleh, M.H., Margetts, L., Parwaiz, M., Performance Evaluation of Bridges Using Virtual Reality (2018) 6th European Conference on Computational Mechanics 7th European Conference on Computational Fluid Dynamics, , Glasgow, United Kingdom; Omer, M., Margetts, L., Mosleh, M.H., Hewitt, S., Parwaiz, M., Use of Gaming Technology to Bring Bridge Inspection to the Office (2019) Structure and Infrastructure Engineering, 15 (10), pp. 1292-1307; Quinn, G.C., Galeazzi, A., Schneider, F., Gengnagel, C., StructVR Virtual Reality Structures. Creativity in Structural Design (2018) Annual Symposium of the International Association for Shell and Spatial Structures, , 2018 Boston, MA, USA; Rehm, K.C.P.E., (2013) Bridge Inspection: Primary Element Bridge Inspection Continues to Evolve in U.S., , https://www.roadsbridges.com/bridge-inspection-primary-element; Sampaio, A.Z., Virtual Reality Technology Applied in Teaching and Research in Civil Engineering Education (2012) Journal of Information Technology and Application in Education, 1 (4). , December 2012; Sampaio, A.Z., Henriques, P.G., Cruz, C.O., Interactive models used in Civil Engineering education based on virtual reality technology, 2009 (2009) 2nd Conference on Human System Interactions, pp. 170-176; Setareh, M., Bowman, D.A., Kalita, A., Development of a Virtual Reality Structural Analysis (2005) Journal of Architectural Engineering, 11 (4). , December 2005; Thabet, W., Shiratuddin, M.F., Bowman, D., Virtual Reality in Construction: A Review (2002) Engineering Computational Technology, pp. 25-52. , Civil-Comp press, GBR; Wang, P., Wu, P., Wang, J., Chi, H.-L., Wang, X., A Critical Review of the Use of Virtual Reality in Construction Engineering Education and Training (2018) International Journal of Environmental Research and Public Health, 15 (6), p. 1204. , https://doi.org/10.3390/ijerph15061204; Wang, Y., Application of Virtual Reality Technique in the Construction of Modular Teaching Resources (2020) International Journal of Emerging Technologies in Learning (iJET), 15 (10), pp. 126-139. , Kassel, Germany: International Journal of Emerging Technology in Learning; Whisker, V., Yerrapathruni, S., Messner, J., Baratta, A., Using Virtual Reality to Improve Construction Engineering Education Paper presented (2003) 2003 Annual Conference, , Nashville, Tennessee","Catbas, F.N.; Department of Civil, United States; email: catbas@ucf.edu",,,"Elsevier B.V.","4th International Conference on Structural Integrity, ICSI 2021","30 August 2021 through 2 September 2021",,147238,24523216,,,,"English","Proc. Struc. Inte.",Conference Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85129462428
"Sokol M., Venglár M., Lamperová K., Márföldi M.","53985383700;57191739008;57202967108;57205604785;","Performance assessment of a renovated precast concrete bridge using static and dynamic tests",2020,"Applied Sciences (Switzerland)","10","17","5904","","",,4,"10.3390/app10175904","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090237072&doi=10.3390%2fapp10175904&partnerID=40&md5=f10cb763979bea6c937c49ad365afbe3","Faculty of Civil Engineering, Slovak University of Technology, Radlinského 11, Bratislava, SK-810 05, Slovakia","Sokol, M., Faculty of Civil Engineering, Slovak University of Technology, Radlinského 11, Bratislava, SK-810 05, Slovakia; Venglár, M., Faculty of Civil Engineering, Slovak University of Technology, Radlinského 11, Bratislava, SK-810 05, Slovakia; Lamperová, K., Faculty of Civil Engineering, Slovak University of Technology, Radlinského 11, Bratislava, SK-810 05, Slovakia; Márföldi, M., Faculty of Civil Engineering, Slovak University of Technology, Radlinského 11, Bratislava, SK-810 05, Slovakia","The article presents the development of a SHM (Structural Health Monitoring) strategy intended to confirm the improvement of the load-bearing capacity of a bridge over the Ružín Dam using static and dynamic load tests, as well as numerical simulations. The paper comprises measurements of the global response of the bridge to prepare a verified and validated FEM (Finite Element Method) model. A complex measuring system used for the tests consisted of two main parts: an interferometric IBIS-S (Image by Interferometric Survey-Structures) radar and a multichannel vibration and strain data logger. Next, structure-vehicle interactions were modelled, and non-linear numerical dynamic analyses were performed. As a result, the time histories of displacements of the structure from traffic effects were obtained. Their comparison with IBIS-S radar records proves that this method can be effectively used for assessing bridges subjected to common traffic loads. The results (measured accelerations) obtained by local tests in external pre-stressed cables are presented and a convenient method for acquiring the axial force in the cables is proposed. © 2020 by the authors.","Accelerations; Displacements; FEM model; In situ measurements; Non-linear dynamic analysis; Reinforced concrete bridge; Strains",,,,,,"Ministerstvo školstva, vedy, výskumu a športu Slovenskej republiky: 1/0749/19","Funding: This paper was supported by the Grant Agency of the Ministry of Education, Science, Research and Sports of the Slovak Republic VEGA No. 1/0749/19.",,,,,,,,,,"Strauss, A., Frangopol, D.M., Kim, S., Use of monitoring extreme data for the performance prediction of structures: Bayesian updating (2008) Eng. Struct., 30, pp. 3654-3666; Chang, P.C., Flatau, A., Liu, S.C., Review Paper: Health Monitoring of Civil Infrastructure (2003) Struct. 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Sci., 10, p. 4084; Guan, H., Karbhari, V.M., (2008) Vibration-Based Structural Health Monitoring of Highway Bridges, , Dept. of Structural Engineering, University of California: La Jolla, CA, USA; Haardt, P., Holst, R., The Value of Structural Health Monitoring for the reliable Bridge Management Monitoring during life cycle of bridges to establish performance indicators (2017) In Value of Structural Health Monitoring for the reliable Bridge Management, Proceedings of the IABSE WC1 WORKSHOP the Value of Structural Health Monitoring for the reliable Bridge Management, Zagreb, Croatia, 2-3 March 2017, pp. 1-9. , Faculty of Civil Engineering, University of Zagreb: Zagreb, Croatia; Brincker, R., Ventura, C.E., Introduction (2015) In Introduction to Operational Modal Analysis, pp. 1-16. , John Wiley & Sons, Ltd.: Chichester, UK; Thacker, B.H., Doebling, S.W., Hemez, F.M., Anderson, M., Pepin, J., Rodriguez, E., (2004) Concepts of Model Verification and Validation, , Los Alamos National Lab: Los Alamos, NM, USA; Zhang, L., Zhao, H., Obrien, E.J., Shao, X., Tan, C., The influence of vehicle-tire contact force area on vehicle-bridge dynamic interaction (2016) Can. J. Civ. Eng., 43, pp. 769-772; Kanda, V., Reconstruction of the Bridge II/547 020 Ružín., , www.asb.sk/stavebnictvo/inzinierske-stavby/mosty/rekonstrukcia-mosta-ii-547-020-ruzin, (accessed on 12 April 2019); Results of National Traffic Census in Slovak Republic in 2015., , www.ssc.sk/files/documents/dopravne-inzinierstvo/csd_2015/ke/scitanie_vuc_ke_2015.pdf, (accessed on 12 April 2019); (2010) Eurocode 8: Design of Structures for Earthquake Resistance-Part 2: Bridges, , European Committee for Standardization: Brussels, Belgium; Venglar, M., Milan, S., Ároch, R., Budaj, J., Initial Experimental Test of the Port Bridge for Structural Health Monitoring (2016) Appl. Mech. Mater., 837, pp. 135-139; Alani, A.M., Aboutalebi, M., Kilic, G., Use of non-contact sensors (IBIS-S) and finite element methods in the assessment of bridge deck structures (2014) Struct. Concr., 15, pp. 240-247; Gentile, C., Application of Radar Technology to Deflection Measurement and Dynamic Testing of Bridges (2010) Radar Technology, , Kouemou, G., Ed.; IntechOpen: Rijeka, Croatia; Reynders, E., Degrauwe, D., De Roeck, G., Magalhães, F., Caetano, E., Combined Experimental-Operational Modal Testing of Footbridges (2010) J. Eng. Mech., 136, pp. 687-696; Peeters, B., (2000) System Identification and Damage Detection in Civil Engineering, , Ph.D. Thesis, KU Leuven, Leuven, Belgium; Peeters, B., Lau, J., Lanslot, J., van der Auweraer, H., Automatic modal analysis-Myth or reality (2008) Sound Vib., 42, p. 17; Rosso, C., Bonisoli, E., Bruzzone, F., (2017) Could the veering phenomenon be a mechanical design instrument? In Topics in Modal Analysis & Testing, 10, pp. 85-95. , Conference Proceedings of the Society for Experimental Mechanics Series; Mains, M., Blough, J.R., Eds.; Springer International Publishing: Cham, Switzerland; Zhu, B., Han, J., Zhao, J., Tire-Pressure Identification Using Intelligent Tire with Three-Axis Accelerometer (2019) Sensors., 19, p. 2560; Clough, R.W., Penzien, J., (1993) Dynamics of Structures, , McGraw-Hill: New York, NY, USA; Furtmüller, T., Adam, C., Compensation of Temperature Effects in Long-Term Monitoring of a Highway Bridge located in the Austrian Alps (2017) Procedia Eng., 199, pp. 2078-2083","Sokol, M.; Faculty of Civil Engineering, Radlinského 11, Slovakia; email: milan.sokol@stuba.sk",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85090237072
"Ulger T., Okeil A.M., Elshoura A.","57576839700;6602375318;57214107348;","Load Testing and Rating of Cast-in-Place Concrete Box Culverts",2020,"Journal of Performance of Constructed Facilities","34","2","04020008","","",,4,"10.1061/(ASCE)CF.1943-5509.0001401","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078266790&doi=10.1061%2f%28ASCE%29CF.1943-5509.0001401&partnerID=40&md5=53f86ff1e43d8af3ac1371307046e17e","Dept. of Civil Engineering, Zonguldak Bulent Ecevit Univ., Üncivez Mahallesi, Üniversite Cd., Merkez/Zonguldak Merkez/Zonguldak, 67100, Turkey; Dept. of Civil and Enviromental Engineering, Louisiana State Univ., 3255 P.F. Taylor Hall, Baton Rouge, LA  70803, United States","Ulger, T., Dept. of Civil Engineering, Zonguldak Bulent Ecevit Univ., Üncivez Mahallesi, Üniversite Cd., Merkez/Zonguldak Merkez/Zonguldak, 67100, Turkey; Okeil, A.M., Dept. of Civil and Enviromental Engineering, Louisiana State Univ., 3255 P.F. Taylor Hall, Baton Rouge, LA  70803, United States; Elshoura, A., Dept. of Civil and Enviromental Engineering, Louisiana State Univ., 3255 P.F. Taylor Hall, Baton Rouge, LA  70803, United States","One of the largest items in the bridge inventory for many states is culverts, and cast-in-place reinforced concrete box culverts constitute a sizeable portion of the overall culvert inventory. In Louisiana, most of these culverts were constructed using old standard details. Following current load rating procedures for these culverts often yields unacceptable load rating factors, even though their performance is acceptable with no signs of distress. The discrepancy between calculated load rating and observed performance of the culverts is investigated for eight selected culverts by field live load testing and finite element modeling. Each culvert was instrumented using a 48-sensor structural health monitoring system before a loaded truck was driven on the culvert along three different load paths to collect field data. Three-dimensional (3D) finite element (FE) models were calibrated using the recorded field data, and an HL-93 design truck and 10 legal trucks such as Type 3-3 were used to load the calibrated FE models to obtain culvert specific load rating factors. Acceptable rating factors were obtained for all eight culverts. The rating methodology, rating factors, and recommendations for rating culverts are presented in this paper. © 2020 American Society of Civil Engineers.","Cast-in-place; Culvert; Load rating; Load test","Cast in place concrete; Concrete testing; Finite element method; Load testing; Reinforced concrete; Structural health monitoring; Trucks; Cast in place; Concrete box; Current loads; Load paths; Load ratings; Specific loads; Structural health monitoring systems; Threedimensional (3-d); Culverts",,,,,"Louisiana Transportation Research Center, LTRC: 16-3ST","This research is sponsored by the LTRC (Louisiana Transportation Research Center), under Project No. 16-3ST. Additional support from the Department of Civil and Environmental Engineering at Louisiana State University is also acknowledged. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsoring agencies.",,,,,,,,,,"(2002) Standard Specifications for Highway Bridges, , AASHTO. Washington, DC: AASHTO; (2014) LRFD Bridge Design Specifications, , AASHTO. Washington, DC: AASHTO; (2018) Manual for Bridge Evaluation, , AASHTO. Washington, DC: AASHTO; Abdel-Karim, A.M., Tadros, M.K., Benak, J.V., Live load distribution on concrete box culverts (1990) Transp. Res. Rec., 1288 (1), pp. 136-151; Abolmaali, A., Garg, A.K., Effect of wheel live load on shear behavior of precast reinforced concrete box culverts (2008) J. Bridge Eng., 13 (1), pp. 93-99. , https://doi.org/10.1061/(ASCE)1084-0702(2008)13:1(93); Acharya, R., Han, J., Brennan, J.J., Parsons, R.L., Khatri, D.K., Structural response of a low-fill box culvert under static and traffic loading (2016) J. Perform. Constr. Facil., 30 (1). , https://doi.org/10.1061/(ASCE)CF.1943-5509.0000690, 04014184; Acharya, R., Han, J., Parsons, R.L., Numerical analysis of low-fill box culvert under rigid pavement subjected to static traffic loading (2016) Int. J. Geomech., 16 (5). , https://doi.org/10.1061/(ASCE)GM.1943-5622.0000652, 04016016; Chen, B.-G., Zheng, J.-J., Han, J., Experimental study and numerical simulation on concrete box culverts in trenches (2010) J. Perform. Constr. Facil., 24 (3), pp. 223-234. , https://doi.org/10.1061/(ASCE)CF.1943-5509.0000098; (2016) NBI (National Bridge Inventory), , https://www.fhwa.dot.gov/bridge/nbi.cfm, FHWA (Federal Highway Administration). Accessed Febuary 14, 2019; Garg, A.K., Abolmaali, A., Finite-element modeling and analysis of reinforced concrete box culverts (2009) J. Transp. Eng., 135 (3), pp. 121-128. , https://doi.org/10.1061/(ASCE)0733-947X(2009)135:3(121); McGrath, T.J., Liepins, A.A., Beaver, J.L., Live load distribution widths for reinforced concrete box section (2005) Proc. 6th Int. Bridge Engineering Conf, pp. 99-108. , Washington, DC: Transportation Research Board; Okeil, A.M., Ulger, T., Elshoura, A., (2018) Live Load Rating of Cast-in-place Concrete Box Culverts, , Baton Rouge, LA: Louisiana Transportation Research Center; Orton, S.L., Loehr, J.E., Boeckmann, A., Havens, G., Live-load effect in reinforced concrete box culverts under soil fill (2015) J. Bridge Eng., 20 (11). , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000745, 04015003; Petersen, D.L., Nelson, C.R., Li, G., McGrath, T.J., Kitane, Y., (2010) Recommended Design Specifications for Live Load Distribution to Buried Structures, , Washington, DC: Transportation Research Board; Van Til, C.J., McCullough, B.F., Vallerga, B.A., Hicks, R.G., (1972) Evaluation of AASHO Interim Guides for Design of Pavement Structures, , Washington, DC: Highway Research Board; Wood, T., Newhouse, C.D., Jayawickrama, P., Lawson, W.D., (2010) Evaluating Existing Culverts for Load Capacity Allowing for Soil Structure Interaction, , Lubbock, TX: Texas Tech Univ; Wood, T.A., Lawson, W.D., Jayawickrama, P.W., Newhouse, C.D., Evaluation of production models for load rating reinforced concrete box culverts (2015) J. Bridge Eng., 20 (1). , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000638, 04014057; Wood, T.A., Lawson, W.D., Surles, J.G., Jayawickrama, P.W., Seo, H., Improved load rating of reinforced-concrete box culverts using depth-calibrated live-load attenuation (2016) J. Bridge Eng., 21 (12). , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000967, 04016095","Okeil, A.M.; Dept. of Civil and Enviromental Engineering, United States; email: aokeil@lsu.edu",,,"American Society of Civil Engineers (ASCE)",,,,,08873828,,JPCFE,,"English","J. Perform. Constr. Facil.",Article,"Final","",Scopus,2-s2.0-85078266790
"Mashayekhi M., Santini-Bell E.","57204763685;9040150900;","Fatigue assessment of the gusset-less connection using field data and numerical model",2019,"Bridge Structures","15","1-2",,"75","86",,4,"10.3233/BRS-190157","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071678387&doi=10.3233%2fBRS-190157&partnerID=40&md5=18e57030c4f3784c1d2b18c6465d01e6","Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, United States","Mashayekhi, M., Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, United States; Santini-Bell, E., Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, United States","Fatigue assessment of the novel structural components that are not explicitly addressed in the existing bridge design codes require the application of the local fatigue assessment methods. This study presents fatigue assessment of the novel gusset-less connection of the case-study vertical lift truss bridge, the Memorial Bridge, in Portsmouth, NH. The long-term structural health monitoring responses are collected from the instrumented gusset-less connection at the Memorial Bridge to determine the nominal fatigue response using the collected strain responses. In addition, a global multi-scale finite element model of the bridge is created to effectively model the structural components of the bridge. A local sub-structure finite element model of the connection is created to determine the stress concentration factors that are applied for the hot-spot fatigue assessment method. The acquired stress concentration factors under the static and dynamic load test are applied for hot-spot fatigue assessment of the gusset-less connection. © 2019 - IOS Press and the authors. All rights reserved.","gusset-less truss connection; Hot-spot stress fatigue assessment; multi-scale structural modelling; stress concentration factor","Bridges; Dynamic loads; Finite element method; Load testing; Stress concentration; Structural health monitoring; Trusses; Existing bridge; Fatigue assessments; Fatigue response; gusset-less truss connection; Static and dynamic load tests; Stress concentration factors; Structural component; Structural modelling; Fatigue of materials",,,,,,,,,,,,,,,,"Adams, T., Mashayekhizadeh, M., (2017) Santinni-Bell E,Wosnik M, Baldwin K, Fu T, , Structural Response Monitoring of a Vertical Lift Truss Bridge. 96th Annual Meeting. Washington, D. C: Transportation Research Board; Alancer, G., De Jesus, A.M.P., Calcade, R.A.B., Da Silva, J.G.S., Fatigue life evaluation of a composite steel-concrete roadway bridge through the hot-spot stress method considering progressive deterioration (2018) Engineering Structures., 166, pp. 46-61; American Association of State Highway and Transportation Officials, AASHTO. LRFD bridge design specifications. 5. Washington, DC, 2012; Aygul, M., Bokesjo, M., Heshmati, M., Al-Emrani, M., A comparative study of different fatigue failure assessments of welded bridge details (2013) International Journal of Fatigue., 49, pp. 62-72; Chan, T.H.T., Li, Z.X., Ko, J.M., Fatigue analysis and life prediction of bridges with structural health monitoring data-Part II: Application (2001) International Journal of Fatigue., 23 (1), pp. 55-64; Chan, T.H.T., Guo, L., Li, Z.X., Finite element modelling for fatigue stress analysis of large suspension bridges (2003) Journal of Sound and Vibration., 261 (3), pp. 443-464; Doerk, O., Fricke, W., Weissenborn, C., Comparison of different calculation methods for structural stresses at welded joints (2003) International Journal of Fatigue., 25 (5), pp. 359-369; Dong, P., A robust structural stress method for fatigue analysis of offshore/marine structures (2005) ASME Journal Offshore Mechanical Architecture Engineering., 127, pp. 68-74; Dong, P., A structural stress definition and numerical implementation for fatigue analysis of welded joints (2001) International Journal of Fatigue., 23 (10), pp. 865-876; Downing, S.D., Socie, D.F., Simple rainflow counting algorithms (1982) International Journal of Fatigue., 4 (1), pp. 31-40; Eurrocode3. Design of steel structures-Part 1-9:Fatigue. European Standard, May 2005; Fricke, W., (2001) Recommended Hot Spot Analysis Procedure for Structural Details of FPSOs and Ships Based on Round-Robin FE Analyses, , The Eleventh International Offshore and Polar Engineering Conference. Stavanger, Norway: The International Society of Offshore and Polar Engineers; Hobbacher, A.F., (2015) Recommendations for Fatigue Design of Welded Joints and Components, IIW Document IIW-2259-15/ex XIII-2460-13/XV-1440-13, , International Institute of Welding, Springer; Lotsberg, I., (2004) Recommended Methodology for Analysis of Structural Stress for Fatigue Assessment of Plated Structures, , Houston, USA: Proceedings of OMAE specialty symposium on integrity of FPSO system; Maddox, S.J., Hot-spot stress design curves for fatigue assessment of welded structures (2002) International Journal of Offshore and Polar Engineering., 12 (2), pp. 131-141; Mashayekhi, M., Santini-Bell, E., (2018) Three-dimensional Multiscale Finite Element Models for In-service Performance Assessment of Bridges, , Computer-aided civil and infrastructure engineering; Mashayekhizadeh, M., Santini-Bell, E., Adams, T., (2017) Instrumentation and Structural Health Monitoring of A Vertical Lift Bridge, , Jacksonville, Fl: Proceedings of 27th ASNT Research Symposium; NHDOT. Memorial Bridge Project Innovations. New Hampshire Department of Transportation, 2016; Ni, Y.Q., Ye, X.W., Ko, J.M., Monitoring-based fatigue reliability assessment of steel bridges: Analytical model and application (2012) Journal of Structural Engineering., 110 (12), pp. 1563-1573; Niemi, E., (1995) Stress Determination for Fatigue Analysis of Welded Components, IIW Doc XIII-1221-93, , Cambridge, Abington: International Institute of Welding; Niemi, E., Fricke, W., Maddox, S.J., (2016) Structural Hot-Spot Stress Approach to Fatigue Analysis of Welded Components, XIII 2636r3-16 XV-1521r3-16, , Designer's Guide, International Institute of welding; Niemi, E., Fricke, W., Maddox, S.J., (2006) Fatigue Analysis of Welded Components: Designer's Guide to the Structural Hot-spot Stress Approach, , Cambridge, UK. : Woodhead Publishing; Niemi, E., Tanskanen, P., (1999) Hot-Spot Stress Determination ForWelded Edge Gussets, , The International Institute of Welding-IIW Doc. XIII-1781-99; Fricke, H.W.P., Massel, T., (1991) Application of the Local Approach to the Fatigue Strength Assessment of Welded Structures in Ship, , IIW Doc. XIII-1409-91. International Institute of Welding; Radaj, D., (1990) Design and Analysis of Fatigue Resistant Welded Structures, , Cambridge, UK: Abington Publishing; Radaj, D., Sonsino, C.M., (1998) Fatigue Assessment of Welded Joints by Local Approaches, , Woodhead Publishing; Savaidis, G., Vormwald, M., Hot-spot stress evaluation of fatigue in welded structural connections supported by finite element analysis (2000) International Journal of Fatigue., 22 (2), pp. 85-91; Wei, X., Xiao, L., Pei, S., Fatigue assessment and stress analysis of cope-hole details in welded joints of steel truss bridge (2017) International Journal of Fatigue., 100, pp. 136-147","Mashayekhi, M.; Department of Civil and Environmental Engineering, United States; email: mm1182@wildcats.unh.edu",,,"IOS Press",,,,,15732487,,,,"English","Bridge Struct.",Article,"Final","",Scopus,2-s2.0-85071678387
"Heo G., Son B., Kim C., Jeon S., Jeon J.","7006264218;14832094100;55697744100;55849182200;14829050500;","Development of a wireless unified-maintenance system for the structural health monitoring of civil structures",2018,"Sensors (Switzerland)","18","5","1485","","",,4,"10.3390/s18051485","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046742647&doi=10.3390%2fs18051485&partnerID=40&md5=558e9eb45e0553c5aefc397d569f015b","Department of International Civil and Plant Engineering, Konyang University, 121 Daehak-ro, Nonsan, Chungnam  32992, South Korea; Department of Civil Engineering, Chungnam National University, Daejeon, 34134, South Korea","Heo, G., Department of International Civil and Plant Engineering, Konyang University, 121 Daehak-ro, Nonsan, Chungnam  32992, South Korea; Son, B., Department of International Civil and Plant Engineering, Konyang University, 121 Daehak-ro, Nonsan, Chungnam  32992, South Korea; Kim, C., Department of International Civil and Plant Engineering, Konyang University, 121 Daehak-ro, Nonsan, Chungnam  32992, South Korea; Jeon, S., Department of Civil Engineering, Chungnam National University, Daejeon, 34134, South Korea; Jeon, J., Department of International Civil and Plant Engineering, Konyang University, 121 Daehak-ro, Nonsan, Chungnam  32992, South Korea","A disaster preventive structural health monitoring (SHM) system needs to be equipped with the following abilities: First, it should be able to simultaneously measure diverse types of data (e.g., displacement, velocity, acceleration, strain, load, temperature, humidity, etc.) for accurate diagnosis. Second, it also requires standalone power supply to guarantee its immediate response in crisis (e.g., sudden interruption of normal AC power in disaster situations). Furthermore, it should be capable of prompt processing and realtime wireless communication of a huge amount of data. Therefore, this study is aimed at developing a wireless unified-maintenance system (WUMS) that would satisfy all the requirements for a disaster preventive SHM system of civil structures. The WUMS is designed to measure diverse types of structural responses in realtime based on wireless communication, allowing users to selectively use WiFi RF band and finally working in standalone mode by means of the field-programmable gate array (FPGA) technology. To verify its performance, the following tests were performed: (i) A test to see how far communication is possible in open field, (ii) a test on a shaker to see how accurate responses are, (iii) a modal test on a bridge to see how exactly characteristic real-time dynamic responses are of structures. The test results proved that the WUMS was able to secure stable communication far up to nearly 800 m away by acquiring wireless responses in realtime accurately, when compared to the displacement and acceleration responses which were acquired through wired communication. The analysis of dynamic characteristics also showed that the wireless acceleration responses in real-time represented satisfactorily the dynamic properties of structures. Therefore, the WUMS is proved valid as a SHM, and its outstanding performance is also proven. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.","Civil structures; Finite element analysis; Modal characteristics; Modal test; Structural health monitoring; Wireless sensor networks; Wireless unified-maintenance system","Disasters; Field programmable gate arrays (FPGA); Finite element method; Modal analysis; Preventive maintenance; Testing; Wi-Fi; Wireless sensor networks; Wireless telecommunication systems; Civil structure; Dynamic characteristics; Maintenance systems; Modal characteristics; Modal test; Real-time wireless communications; Structural health monitoring (SHM); Wireless communications; Structural health monitoring",,,,,"Ministry of Science, ICT and Future Planning, MSIP: NRF-2016R1A2A1A05005499, NRF-2017R1A2B4001836; National Research Foundation of Korea, NRF; Ministry of Science ICT and Future Planning, MSIP","This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (grant number: NRF-2016R1A2A1A05005499, grant number: NRF-2017R1A2B4001836).","Acknowledgments: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (grant number: NRF-2016R1A2A1A05005499, grant number: NRF-2017R1A2B4001836).",,,,,,,,,"Doebling, S.W., Farrar, C.R., Prime, M.B., A summary review of vibration-based damage identification methods (1998) Shock Vib. Dig, 30, pp. 91-105; Ge, M., Liu, E.M., Structural damage identification using system dynamic properties (2005) Comput. Struct, 83, pp. 2185-2196; Giraldo, D.F., Dyke, S.J., Caicedo, J.M., Damage detection accommodating varying environmental conditions (2006) Struct. Health Monit. Int. J, 5, pp. 155-172; Ko, J.M., Ni, Y.Q., Technology developments in structural health monitoring of large-scale bridges (2005) Eng. Struct, 27, pp. 1715-1725; Caicedo, J.M., Dyke, S.J., Experimental validation of structural health monitoring for flexible bridge structures (2005) Struct. Control Health Monit, 12, pp. 425-443; Farrar, C.R., Worden, K., An introduction to structural health monitoring (2007) Philos. Trans. R. Soc. A Math. Phys. Eng. Sci, 365, pp. 303-315; Na, W.S., Impedance-based Non-Destructive Testing Method Combined with Unmanned Aerial Vehicle for Structural Health Monitoring of Civil Infrastructures (2017) Appl. Sci, 17, p. 15; Cross, E.J., Koo, K.Y., Brownjohn, J.M.W., Worden, K., Long-term monitoring and data analysis of the Tamar Bridge (2013) Mech. Syst. Signal Process, 35, pp. 16-34; Alamdari, M.M., Samali, B., Li, J., Kalhori, H., Mustapha, S., Spectral-based damage identification in structures under ambient vibration (2015) J. Comput. Civ. Eng, p. 30; Dworakowski, Z., Kohut, P., Gallina, A., Holak, K., Uhl, T., Vision-based algorithms for damage detection and localization in structural health monitoring (2016) Struct. Control Health Monit, 23, pp. 35-50; Heo, G., Jeon, J., A Smart Monitoring System Based on Ubiquitous Computing Technique for Infra-structural System: Centering on Identification of Dynamic Characteristics of Self-Anchored Suspension Bridge (2009) KSCE J. Civ. Eng, 13, pp. 333-337; Alamdari, M.M., Rakotoarivelo, T., Khoa, N.L.D., A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge (2017) Mech. Syst. Signal Process, 87, pp. 384-400; Straser, E.G., Kiremidjian, A.S., Meng, T.H., Redlefsen, L., A modular, Wireless Network platform for monitoring structures (1998) Proc. SPIE Int. Soc. Opt. Eng, 3243, pp. 450-456; Spencer, B.F., Ruiz-Sandoval, M.E., Kurata, N., Smart sensing technology: Opportunities and challenges (2004) J. Struct. Control Health Monit, 11, pp. 349-368; Lynch, J.P., Loh, K.J., A summary review of wireless sensors and sensor networks for structural health monitoring (2006) Shock Vib. Dig, 28, pp. 91-126; Rice, J.A., Spencer, B.F., Structural health monitoring sensor development for the Imote2 platform (2008) Proc. SPIE Int. Soc. Opt. Eng, p. 6932; Nagayama, T., Spencer, B.F., Mechitov, K.A., Agha, G.A., Middleware services for structural health monitoring using smart sensors (2009) Smart Struct. Syst, 5, pp. 119-137; Jang, S., Jo, H., Cho, S., Mechitov, K., Rice, J.A., Sim, S.H., Jung, H.J., Agha, G., Structural health monitoring of a cable-stayed bridge using smart sensor technology: Deployment and evaluation (2010) Smart Struct. Syst, 6, pp. 439-459; Nagayama, T., Sim, S.H., Miyamori, Y., Spencer, B.F., Issues in structural health monitoring employing smart sensors (2007) Smart Struct. Syst, 3, pp. 299-320; Heo, G., Jeon, J., A Study on the Data Compression Technology-Based Intelligent Data Acquisition (IDAQ) System for Structural Health Monitoring of Civil Structures (2017) Sensors, 17, p. 1620; Park, J.-W., Sim, S.-H., Jung, H.-J., Spencer, B.F., Development of a Wireless Displacement Measurement System Using Acceleration Responses (2013) Sensors, 13, pp. 8377-8392; (2012) Wireless System Networks | Microstrain, , http://www.microstrain.com/wireless/systems, accessed on 20 April 2018; (2012) STS WiFi Wireless Structural Testing System, , Bridge Diagnostics Inc.: Boulder, CO, USA; (2012) Wireless Measurement Device Selection Guide, , http://sine.ni.com/np/app/main/p/ap/global/lang/en/pg/1/sn/n24:Wireless/fmid/2988/, accessed on 20 April 2018; Ewins, D.J., (2000) Modal Testing: Theory, Practice and Application, , Research Studies Press Ltd.: England, UK, ISBN 0-86380-218-4","Jeon, J.; Department of International Civil and Plant Engineering, 121 Daehak-ro, South Korea; email: jrjeon@konyang.ac.kr",,,"MDPI AG",,,,,14248220,,,"29747403","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85046742647
"Güray E., Birgül R.","57205476940;56473596300;","Determination of favorable time window for infrared inspection by numerical simulation of heat propagation in concrete",2018,"Lecture Notes in Civil Engineering","7",,,"577","591",,4,"10.1007/978-3-319-64349-6_46","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060124532&doi=10.1007%2f978-3-319-64349-6_46&partnerID=40&md5=03086e9d43b747c8cc6d575f10c6d338","Department of Civil Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, 48170, Turkey","Güray, E., Department of Civil Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, 48170, Turkey; Birgül, R., Department of Civil Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, 48170, Turkey","Every bridge is subjected to a thorough inspection process every other year at most. Nondestructive evaluation techniques, especially noncontact methods, are gaining popularity to take part in structural health monitoring of existing bridges for expediting the inspection process. Infrared thermography is one of the noncontact testing methods; it is based on capturing and processing the thermal gradient on a radiant surface which is highly affected by the ambient environmental conditions. The objective of this study is to numerically search for an appropriate time window to carry out infrared inspections. To this end, a numerical model of a bridge deck with certain initial and boundary conditions was used to numerically obtain temperature differentials at any nodes across the model for a period of 24 h. A delamination with a constant thickness was positioned in the concrete deck. The transient solutions of the nonlinear partial differential equation were obtained by utilizing the finite element method. The numerical results point to afternoon as the most favorable time window to conduct infrared inspections; this result coincides with some of the experimental research found in literature. Additionally, it was shown that the existence of water in the defect greatly affected the heat conduction process. © Springer International Publishing AG, part of Springer Nature 2018.","Bridge decks; Infrared inspection; Subsurface defects","Boundary conditions; Bridge decks; Heat conduction; Inspection; Nondestructive examination; Nonlinear equations; Numerical models; Partial differential equations; Structural health monitoring; Environmental conditions; Experimental research; Infrared inspections; Initial and boundary conditions; Non-destructive evaluation techniques; Nonlinear partial differential equations; Subsurface defect; Temperature differential; Concretes",,,,,,,,,,,,,,,,"Gucunski, N., Imani, A., Romero, F., Nazarian, S., Yuan, D., Wiggenhauser, H., Shokouhi, P., Kutrubes, D., (2013) Nondestructive Testing to Identify Concrete Bridge Deck Deterioration, , SHRP 2 Report, S2-R06A-RR-1, Transportation Research Board, Washington, DC; Vaghefi, K., Ahlborn, T.M., Harris, D.K., Brooks, C.N., Combined imaging technologies for concrete bridge deck condition assessment (2015) J Perform Constr Facil, 29 (4); Oh, T., Kee, S., Arndt, R.W., Popovics, J.S., Asce, M., Zhu, J., Comparison of NDT methods for assessment of a concrete bridge deck (2013) J Eng Mech, 139, pp. 305-314; Kaplan, H., (2007) Practical Applications of Infrared Thermal Sensing and Imaging Equipment, , 3rd edn. Society of Photo-Optical Instrumentation Engineers, Bellingham, Washington; Meola, C., Carlomagno, G.M., Giorleo, L., Geometrical limitations to detection of defects in composites by means of infrared thermography (2004) J Nondestruct Eval, 23 (4), pp. 125-132; Washer, G., Fenwick, R., Bolleni, N., Effects of solar loading on infrared imaging of subsurface (2010) J Bridge Eng, 15 (4), pp. 384-390; (2007) Standard Test Method for Detecting Delaminations in Bridge Decks Using Infrared Thermography, , American Society of Testing Materials; Yehia, S., Abudayyeh, O., Nabulsi, S., Abdelqader, I., Detection of common defects in concrete bridge decks using nondestructive evaluation techniques (2007) J Bridge Eng, 12, pp. 215-225; Ahlborn, T.M., Shuchman, R.A., Sutter, L.L., Harris, D.K., Brooks, C.N., Burns, J.W., (2013) Report: Bridge Condition Assessment Using Remote Sensors. United States Department of Transportation Research and Innovative Technology Administration, , Project No: DT0S59-10-H-00001; Starnes, M.A., Carino, N.J., Kausel, E.A., Preliminary thermography studies for quality control of concrete structures strengthened with fiber-reinforced polymer composites (2003) J Mater Civ Eng, 15 (3), pp. 266-273; Minkina, W., Dudzik, S., (2009) Infrared Thermography: Errors and Uncertainties, , Wiley, Chichester; Washer, G., Fenwick, R., Bolleni, N., Harper, J., Effects of environmental variables on infrared imaging of subsurface features of concrete bridges (2009) Transp Res Rec J Transp Res Board, 2108, pp. 107-114; Zhang, J., Gupta, A., Baker, J., Effect of relative humidity on the prediction of natural convection heat transfer coefficients (2007) Heat Transf Eng, 28 (4), pp. 335-342; Matsumoto, M., Mitani, K., Çatbaş, F.N., Bridge assessment methods using image processing and infrared thermography technology: On-site pilot application in Florida (2013) Proceedings of Transportation Research Board; 92Nd Annual Meeting, , Washington, DC; http://www.flir.com, Accessed 1 Dec 2015; Washer, G., Fenwick, R., Bolleni, N., (2009) Development of Hand-Held Thermographic Inspection Technologies, , Organizational Results Research Report, September 2009, OR10-007, Submitted to MoDOT","Güray, E.; Department of Civil Engineering, Turkey; email: ersan.guray@mu.edu.tr",,,"Springer",,,,,23662557,,,,"English","Lect. Notes Civ. Eng.",Book Chapter,"Final","",Scopus,2-s2.0-85060124532
"Sokol M., Venglár M., Ároch R., Kopáčik A., Kyrinovič P., Erdélyi J., Šišmišová Z., Lamperová K.","53985383700;57191739008;57148106900;55311805500;55312095700;55884600500;57202967215;57202967108;","Traffic response pattern of cable-stayed bridge as a comparison tool for SHM",2017,"IABSE Conference, Vancouver 2017: Engineering the Future - Report",,,,"191","197",,4,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050018604&partnerID=40&md5=df8161e270422fdd5547c35586790b02","Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia","Sokol, M., Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia; Venglár, M., Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia; Ároch, R., Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia; Kopáčik, A., Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia; Kyrinovič, P., Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia; Erdélyi, J., Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia; Šišmišová, Z., Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia; Lamperová, K., Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia","A cable-stayed bridge across the Danube in Bratislava, Slovakia with a 303 m length of the main span was monitored and subsequently the data were used for Structural Health Monitoring (SHM). It was necessary to record the traffic in order to be able to incorporate it into the numerical analyses of the dynamic response of the bridge subjected to a well described dynamic load synchronized with the camera records. For the test's purposes a 28 channel National Instrument system has been used. The vibrations - displacements time series of a few control points on the bottom of the bridge deck have been also acquired using an interferometric radar IBIS-S located below the bridge. A precise FEM model was used to compare the measured values - accelerations and displacements with those from the time history analyses. Response spectra have been compared as well. A specific traffic situation (pattern) was selected from the traffic stream that can be used as a comparison tool for future SHM of the bridge. © 2018 Ingenta.","Cable-stayed bridge; FEM model; Interferometric radar; NI measurement system; Structural health monitoring; Time/history analysis; Traffic pattern","Cables; Dynamic loads; Interferometry; Radar; Radar measurement; Structural health monitoring; FEM modeling; Interferometric radars; Measurement system; Time/history analysis; Traffic pattern; Cable stayed bridges",,,,,,,,,,,,,,,,"Collins, J., Mullins, G., Lewis, C., Winters, D., (2014) State of the Practice and Art for Structural Health Monitoring of Bridge Substructures, , Publ. No. FHWA-HRT-09-040. Federal Highway Administration; Farrar, C.R., James, G.H., System identification from ambient vibration measurements on bridges (1997) J of Sound and Vibration, 205 (1), pp. 1-18; Ko, J., Ni, Y., Chan, T., Dynamic monitoring of structural health in cable-supported bridges. Smart structures and materials 1999: Smart systems for bridges, structures, and highways (1999) Proceedings of SPIE, 3, pp. 161-172; Seo, J., Hu, J., Lee, J., Summary review of structural health monitoring applications for highway bridges (2015) J Perform Constr Facil, , ASCE; Wenzel, H., (2009) Health Monitoring of Bridges, , John Wiley & Sons; Tesár, A., Design of the main structure of the new road bridge across the danube in bratislava (1970) Zborník Vedeckých Prác SvF SVŠT, pp. 7-88. , (in Slovak) Bratislava; Venglár, M., Sokol, M., Ároch, R., Budaj, J., Experience with dynamic measurement of the port bridge (2015) Proceedings of the 13th International Conference on New Trends in Statics and Dynamics of Buildings, , 2015 Oct 15-16; Bratislava, Slovakia. Faculty of Civil Engineering STU Bratislava; (2013) NI LabVIEW for CompactRIO Developer's Guide, , National Instruments (Austin, Texas); Ároch, R., Sokol, M., Venglár, M., Structural health monitoring of major danube bridges in bratislava (2016) Proceedings of the 9th International Conference ""Bridges in Danube Basin 2016"", , Žilina, Slovakia. 2016","Ároch, R.; Faculty of Civil Engineering, Slovakia; email: rudolf.aroch@stuba.sk",,"Autodesk;Bentley Systems;COWI;et al.;Parsons;WSP","International Association for Bridge and Structural Engineering (IABSE)","39th IABSE Symposium in Vancouver 2017: Engineering the Future","21 September 2017 through 23 September 2017",,137292,,9783857481536,,,"English","IABSE Conf., Vancouver: Eng. Future - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85050018604
"Fang C., Liu H.-J., Lam H.-F., Adeagbo M.O., Peng H.-Y.","57405096800;56263967000;7202774985;57192834606;56701517600;","Practical model updating of the Ting Kau Bridge through the MCMC-based Bayesian algorithm utilizing measured modal parameters",2022,"Engineering Structures","254",,"113839","","",,3,"10.1016/j.engstruct.2022.113839","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122517440&doi=10.1016%2fj.engstruct.2022.113839&partnerID=40&md5=e998d4347bfec996c72984ca24fad523","School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, China; Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong Special Administrative Region, China","Fang, C., School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, China; Liu, H.-J., School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, China; Lam, H.-F., School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, China, Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong Special Administrative Region, China; Adeagbo, M.O., Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong Special Administrative Region, China; Peng, H.-Y., School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, China","Bayesian model updating framework provides a reliable method for building high-fidelity finite element models (FEMs). To realize the efficient model updating of large-scale civil engineering structures, a practical Bayesian inference framework based on software interaction is proposed. The newly developed framework was applied to update the FEM of a long-span cable-stayed bridge, Ting Kau Bridge in Hong Kong, utilizing measured modal parameters from the literature. The model updating results are found to be highly sensitive to the selection of model classes. Furthermore, the area of the main girder of the bridge deck is a key parameter influencing the lower modes of the cable-stayed bridge. A full-scale vehicular load test is conducted on the Ting Kau Bridge to obtain the displacement influence line through the data recorded by GPS sensors on the bridge. The set of measured influence lines is employed to verify the accuracy of the updated FEM. The results demonstrate that the characteristics of the FEM updated using the proposed Bayesian model updating framework based on measured dynamic properties are consistent with the structural characteristics of the bridge. The proposed framework can facilitate the structural health monitoring of large-scale civil engineering structures. © 2022 Elsevier Ltd","Bayesian model updating; Cable-stayed bridge; Influence line; Markov chain Monte Carlo","Bayesian networks; Cable stayed bridges; Cables; Composite beams and girders; Inference engines; Load testing; Markov processes; Structural health monitoring; Bayesian model updating; Civil engineering structures; Finite element modelling (FEM); Influence lines; Large-scales; Markov chain Monte Carlo; Markov Chain Monte-Carlo; Modal parameters; Model updating; Ting kau bridge; Modal analysis; algorithm; Bayesian analysis; bridge; finite element method; GPS; modeling; sensor; China; Hong Kong",,,,,"2021KTSCX367; National Natural Science Foundation of China, NSFC: 51808174, 51978221; Research Grants Council, University Grants Committee, 研究資助局: R5020-18, RIF 8799008; Science, Technology and Innovation Commission of Shenzhen Municipality: GXWD20201230155427003-20200824100128002","The work described in this paper was supported by the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. R5020-18 (RIF 8799008)], National Natural Science Foundation of China (Project No.: 51808174 and 51978221), Shenzhen Science and Technology Innovation Committee (Project No.: GXWD20201230155427003-20200824100128002), and Characteristic and Innovation Projects of Universities in Guangdong Province (Grant No.: 2021KTSCX367). The authors would like to record their special thanks to the Director of Highways for his kind permission for using the data in the project in publishing the paper. Thanks are also due to the following persons at the Highways Department for design information of the Ting Kau Bridge and kind assistance in the project: Ir. Kenneth W.Y. Chan, the then Chief Highway Engineer/Bridges & Structures; Ir. Stephen P. H. Chung, Senior Structural Engineer; and Ir. Richard T. CHUNG, Structural Engineer of the project. Ir. Ivan Y. S. LAM and Ir. K. L. CHAN, TIML MOM Limited, provided excellent logistics support for the field tests. The authors also want to thank Dr. Joseph C. K. WONG, Mr. C. K. LAI, Mr. Alan K. K. CHAN and Dr. J. HU for their contributions in the field test, and valuable discussions during the model updating process.",,,,,,,,,,"Zhang, B., Ding, X., Werner, C., Tan, K., Zhang, B., Jiang, M.I., Dynamic displacement monitoring of long-span bridges with a microwave radar interferometer (2018) ISPRS J Photogramm Remote Sens, 138, pp. 252-264; Wong, K.-Y., Instrumentation and health monitoring of cable-supported bridges (2004) Structural Control and Health Monitoring, 11 (2), pp. 91-124; Ko, J.M., Ni, Y.Q., Zhou, H.F., Wang, J.Y., Zhou, X.T., Investigation concerning structural health monitoring of an instrumented cable-stayed bridge (2009) Struct Infrastruct Eng, 5 (6), pp. 497-513; Ni, Y.Q., Wang, Y.W., Zhang, C., A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data (2020) Eng Struct, 212, p. 110520; Wong, K.-Y., Design of a structural health monitoring system for long-span bridges (2007) Struct Infrastruct Eng, 3 (2), pp. 169-185; Kuok, S.-C., Yuen, K.-V., Investigation of modal identification and modal identifiability of a cable-stayed bridge with Bayesian framework (2016) Smart Struct Syst, 17 (3), pp. 445-470; Simoen, E., De Roeck, G., Lombaert, G., Dealing with uncertainty in model updating for damage assessment: A review (2015) Mech Syst Sig Process, 56-57, pp. 123-149; Fang, S.-E., Ren, W.-X., Perera, R., A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation (2012) Mech Syst Sig Process, 33, pp. 83-96; Bartilson, D.T., Jang, J., Smyth, A.W., Sensitivity-based singular value decomposition parametrization and optimal regularization in finite element model updating (2020) Struct Control Health Monit, 27 (6), p. N/a; Lam, H.-F., Hu, J., Zhang, F.-L., Ni, Y.-C., Markov chain Monte Carlo-based Bayesian model updating of a sailboat-shaped building using a parallel technique (2019) Eng Struct, 193, pp. 12-27; Yang, J., Lam, H.F., Hu, J., Ambient vibration test, modal identification and structural model updating following bayesian framework (2015) Int J Struct Stab Dyn, 15 (7), p. 1540024; Zhu, T., Tian, W., Weng, S., Ge, H., Xia, Y., Wang, C., Sensitivity-based finite element model updating using dynamic condensation approach (2018) Int J Struct Stab Dyn, 18 (8), p. 1840004; Weng, S., Tian, W., Zhu, H., Xia, Y., Gao, F., Zhang, Y., Dynamic condensation approach to calculation of structural responses and response sensitivities (2017) Mech Syst Sig Process, 88, pp. 302-317; Tian, W., Weng, S., Xia, Y., Kron's substructuring method to the calculation of structural responses and response sensitivities of nonlinear systems (2021) J Sound Vib, 502, p. 116101; Asadollahi, P., Huang, Y., Li, J., Bayesian finite element model updating and assessment of cable-stayed bridges using wireless sensor data (2018) Sensors (Basel, Switzerland), 18 (9), p. 3057; Yang, J.-H., Lam, H.-F., Beck, J.L., Bayes-mode-ID: A Bayesian modal-component-sampling method for operational modal analysis (2019) Eng Struct, 189, pp. 222-240; Wang, J., Liu, X.-Z., Ni, Y.-Q., A bayesian probabilistic approach for acoustic emission-based rail condition assessment (2018) Comput-Aided Civ Infrastruct Eng, 33 (1), pp. 21-34; Cantero-Chinchilla, S., Chiachío, J., Chiachío, M., Chronopoulos, D., Jones, A., A robust Bayesian methodology for damage localization in plate-like structures using ultrasonic guided-waves (2019) Mech Syst Sig Process, 122, pp. 192-205; Zhang, Y.-M., Wang, H., Mao, J.-X., Xu, Z.-D., Zhang, Y.-F., Probabilistic framework with bayesian optimization for predicting typhoon-induced dynamic responses of a long-span bridge (2021) J Struct Eng, 147 (1), p. 04020297; Lam, H.F., Peng, H.Y., Au, S.K., Development of a practical algorithm for Bayesian model updating of a coupled slab system utilizing field test data (2014) Eng Struct, 79, pp. 182-194; Wan, H.-P., Ren, W.-X., A residual-based Gaussian process model framework for finite element model updating (2015) Comput Struct, 156, pp. 149-159; Beck, J.L., (1989), pp. 1395-402. , Statistical system identification of structures. 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ASCE; Beck, J.L., Au, S.-K., Bayesian updating of structural models and reliability using markov chain Monte Carlo simulation (2002) J Eng Mech, 128 (4), pp. 380-391; Ching, J., Chen, Y.-C., Transitional markov chain Monte Carlo method for bayesian model updating, model class selection, and model averaging (2007) J Eng Mech, 133 (7), pp. 816-832; Lam, H.-F., Yang, J., Au, S.-K., Bayesian model updating of a coupled-slab system using field test data utilizing an enhanced Markov chain Monte Carlo simulation algorithm (2015) Eng Struct, 102, pp. 144-155; Adeagbo, M.O., Lam, H.-F., Ni, Y.Q., A Bayesian methodology for detection of railway ballast damage using the modified Ludwik nonlinear model (2021) Eng Struct, 236, p. 112047; Hu, J., Yang, J.-H., Operational modal analysis and bayesian model updating of a coupled building (2019) Int J Struct Stab Dyn, 19 (1), p. 1940012; Lam, H.-F., Hu, J., Yang, J.-H., Bayesian operational modal analysis and Markov chain Monte Carlo-based model updating of a factory building (2017) Eng Struct, 132, pp. 314-336; Jang, J., Smyth, A., Bayesian model updating of a full-scale finite element model with sensitivity–based clustering (2017) Struct Control Health Monit, 24 (11), p. e2004; Cheung, S.H., Beck, J.L., Bayesian model updating using hybrid monte carlo simulation with application to structural dynamic models with many uncertain parameters (2009) J Eng Mech, 135 (4), pp. 243-255; Mao, J., Wang, H., Li, J., Bayesian finite element model updating of a long-span suspension bridge utilizing hybrid monte carlo simulation and kriging predictor (2020) KSCE J Civ Eng, 24 (2), pp. 569-579; Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E., Equation of state calculations by fast computing machines (1953) J Chem Phys, 21 (6), pp. 1087-1092; Hastings, W.K., Monte Carlo sampling methods using Markov chains and their applications (1970) Biometrika, 57 (1), pp. 97-109; Wan, H.-P., Ren, W.-X., Stochastic model updating utilizing Bayesian approach and Gaussian process model (2016) Mech Syst Sig Process, 70-71, pp. 245-268; Yuan, Z., Liang, P., Silva, T., Yu, K., Mottershead, J.E., Parameter selection for model updating with global sensitivity analysis (2019) Mech Syst Sig Process, 115, pp. 483-496; Wan, H.-P., Ren, W.-X., Parameter Selection in finite-element-model updating by global sensitivity analysis using gaussian process metamodel (2015) J Struct Eng (New York, N.Y.), 141 (6), p. 04014164; Jang, J., Smyth, A.W., Model updating of a full-scale FE model with nonlinear constraint equations and sensitivity-based cluster analysis for updating parameters (2017) Mech Syst Sig Process, 83, pp. 337-355; Jensen, H.A., Esse, C., Araya, V., Papadimitriou, C., Implementation of an adaptive meta-model for Bayesian finite element model updating in time domain (2017) Reliab Eng Syst Saf, 160, pp. 174-190; Liu, Y., Li, Y., Wang, D., Zhang, S., Model updating of complex structures using the combination of component mode synthesis and kriging predictor (2014) The Scientific World, 2014, pp. 1-13; Jin, S.-S., Jung, H.-J., Sequential surrogate modeling for efficient finite element model updating (2016) Comput Struct, 168, pp. 30-45; Zhang, J., Taflanidis, A.A., Accelerating MCMC via Kriging-based adaptive independent proposals and delayed rejection (2019) Comput Methods Appl Mech Eng, 355, pp. 1124-1147; Song, K., Zhang, Y., Yu, X., Song, B., A new sequential surrogate method for reliability analysis and its applications in engineering (2019) IEEE Access, 7, pp. 60555-60571; (2003), ABAQUS/CAE User's Manual: Version 6.4, Pawtucket, ABAQUS, RI; Ni, Y.Q., Wang, Y.W., Xia, Y.X., Investigation of mode identifiability of a cable-stayed bridge: Comparison from ambient vibration responses and from typhoon-induced dynamic responses (2015) Smart Struct Syst, 15 (2), pp. 447-468; Au, F.T.K., Tham, L.G., Lee, P.K.K., Su, C., Han, D.J., Yan, Q.S., Ambient Vibration measurements and finite element modelling for the Hong Kong Ting Kau Bridge (2003) Struct Eng Mech, 15 (1), pp. 115-134; Ewins, D.J., Model validation: Correlation for updating (2000) Model validation: Correlation for updating. Sadhana (Bangalore), 25 (3), pp. 221-234; Katafygiotis, L.S., Lam, H.-F., Papadimitriou, C., Treatment of unidentifiability in structural model updating (2000) Adv Struct Eng, 3 (1), pp. 19-39; Katafygiotis, L.S., Lam, H.-F., Tangential-projection algorithm for manifold representation in unidentifiable model updating problems (2002) Earthquake Eng Struct Dyn, 31 (4), pp. 791-812","Peng, H.-Y.; School of Civil and Environmental Engineering, China; email: penghuayi@hit.edu.cn",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85122517440
"Svendsen B.T., Petersen Ø.W., Frøseth G.T., Rønnquist A.","57208452088;56865516700;57188970692;25653400800;","Improved finite element model updating of a full-scale steel bridge using sensitivity analysis",2022,"Structure and Infrastructure Engineering","19","3",,"315","331",,3,"10.1080/15732479.2021.1944227","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110803385&doi=10.1080%2f15732479.2021.1944227&partnerID=40&md5=c327759cd4a67125c9185e8333d41671","Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway","Svendsen, B.T., Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway; Petersen, Ø.W., Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway; Frøseth, G.T., Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway; Rønnquist, A., Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway","There are many uncertainties related to existing bridges that are approaching or have exceeded their original design life. Lifetime extension analysis of bridges should be based on validated numerical models that can be effectively established. This paper presents a new procedure to obtain an optimal solution from sensitivity-based model updating with respect to an improvement in the modal properties, such as the natural frequencies and mode shapes, based on realistic parameter values. The procedure combines variations in the ratios of overdetermined systems with different definitions of local parameter bounds in a structured approach using a sensitivity analysis. The feasibility of the procedure is demonstrated in an experimental case study. Model updating is performed on a full-scale steel bridge using the natural frequencies and modal assurance criterion (MAC) numbers, where the numerical model is established by considering general uncertainties and model simplifications to reduce the model complexity. From the optimal solution for the case study considered, an improvement in modal parameters is obtained with highly reliable parameter values. The proposed procedure can be applied to similar case studies, irrespective of the structure under consideration and the corresponding parameterisation to be made, to effectively obtain a validated numerical model. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","Experimental study; finite element model updating; lifetime extension; parameter bounds; sensitivity method; steel bridge; structural health monitoring","Finite element method; Modal analysis; Natural frequencies; Optimal systems; Sensitivity analysis; Steel bridges; Structural health monitoring; Case-studies; Existing bridge; Experimental study; Finite-element model updating; Lifetime extension; Model updating; Optimal solutions; Parameter bound; Sensitivity methods; Uncertainty; Numerical models",,,,,,"The Hell Bridge Test Arena is financially supported by Bane NOR and the Norwegian Railway Directorate.",,,,,,,,,,"Alkayem, N.F., Cao, M., Zhang, Y., Bayat, M., Su, Z., Structural damage detection using finite element model updating with evolutionary algorithms: A survey (2018) Neural Computing & Applications, 30 (2), pp. 389-411; Allemang, R.J., The modal assurance criterion - Twenty years of use and abuse (2003) Sound and Vibration, 37 (8), pp. 14-21; Allemang, R.J., Brown, D.L., A correlation coefficient for modal vector analysis (1982) Proceedings of the 1St International Modal Analysis Conference & Exhibit, pp. 110-116; Asgari, B., Osman, S.A., Adnan, A., Sensitivity analysis of the influence of structural parameters on dynamic behaviour of highly redundant cable-stayed bridges (2013) Advances in Civil Engineering, 2013, pp. 1-11; Bakir, P.G., Reynders, E., De Roeck, G., Sensitivity-based finite element model updating using constrained optimization with a trust region algorithm (2007) Journal of Sound and Vibration, 305 (1-2), pp. 211-225; Barthorpe, R.J., (2010) On Model- and Data-Based Approaches to Structural Health Monitoring; Benedettini, F., Gentile, C., Operational modal testing and FE model tuning of a cable-stayed bridge (2011) Engineering Structures, 33 (6), pp. 2063-2073; Brincker, R., Zhang, L., Andersen, P., Modal identification of output-only systems using frequency domain decomposition (2001) Smart Materials and Structures, 10 (3), pp. 441-445; Brownjohn, J.M.W., Xia, P.Q., Dynamic assessment of curved cable-stayed bridge by model updating (2000) Journal of Structural Engineering, 126 (2), pp. 252-260; Brownjohn, J.M.W., Xia, P.Q., Hao, H., Xia, Y., Civil structure condition assessment by FE model updating: Methodology and case studies (2001) Finite Elements in Analysis and Design, 37 (10), pp. 761-775; (2014) Abaqus/CAE 6.14 - Documentation, , https://www.3ds.com/, Providence, RI: Author,. Retrieved from; Deng, L., Cai, C.S., Bridge model updating using response surface method and genetic algorithm (2010) Journal of Bridge Engineering, 15 (5), pp. 553-564; Ding, Y., Li, A., Finite element model updating for the Runyang Cable-stayed Bridge tower using ambient vibration test results (2008) Advances in Structural Engineering, 11 (3), pp. 323-335; Doebling, S.W., Farrar, C.R., Prime, M.B., A summary review of vibration-based damage identification methods (1998) The Shock and Vibration Digest, 30 (2), pp. 91-105; Farrar, C.R., Worden, K., An introduction to structural health monitoring (2007) Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 365 (1851), pp. 303-315; Farrar, C.R., Worden, K., (2012) Structural Health Monitoring: A Machine Learning Perspective, , Wiley; Feng, D., Feng, M.Q., Model updating of railway bridge using in situ dynamic displacement measurement under trainloads (2015) Journal of Bridge Engineering, 20 (12), p. 04015019; Friswell, M.I., Mottershead, J.E., (1995) Finite element model updating in structural dynamics, 38. , Dordrecht, Springer Netherlands; Frøseth, G.T., Rönnquist, A., Øiseth, O., Operational modal analysis and model updating of riveted steel bridge (2016) Dynamics of Civil Structures, 2, pp. 229-235. , P. Shamim, J. 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I: Modeling and influence line analysis (2015) Journal of Bridge Engineering, 20 (10), p. 04014112; Zordan, T., Briseghella, B., Liu, T., Finite element model updating of a tied-arch bridge using Douglas-Reid method and Rosenbrock optimization algorithm (2014) Journal of Traffic and Transportation Engineering (English Edition), 1 (4), pp. 280-292","Svendsen, B.T.; Department of Structural Engineering, Norway; email: bjorn.t.svendsen@ntnu.no",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85110803385
"Biliszczuk J., Hawryszków P., Teichgraeber M.","6505849416;36101176300;57202970467;","Shm system and a fem model-based force analysis assessment in stay cables",2021,"Sensors","21","6","1927","1","27",,3,"10.3390/s21061927","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102110251&doi=10.3390%2fs21061927&partnerID=40&md5=8136e10d981c1959c5fa1dcaba6e8a8f","Faculty of Civil Engineering, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, Wrocław, 50-370, Poland","Biliszczuk, J., Faculty of Civil Engineering, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, Wrocław, 50-370, Poland; Hawryszków, P., Faculty of Civil Engineering, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, Wrocław, 50-370, Poland; Teichgraeber, M., Faculty of Civil Engineering, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, Wrocław, 50-370, Poland","The Rędziński Bridge in Wrocław is the biggest Polish concrete cable-stayed bridge. It is equipped with a large structural health monitoring (SHM) system which has been collecting the measured data since the bridge opening in the year 2011. This paper presents a comparison between the measured data and the finite element method (FEM) calculations, while taking into account 7 years of data collection and analyses. The first part of this paper concerns the SHM application. In the next part, which contains comparisons between forces in cables and temperature changes throughout the structure, the measured data are presented. The third part includes SHM-based calculations and simulations with a complex FEM model to check the measured data and to estimate future measurements. The last part contains a durability assessment calculation for the cable stays. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Bridges; Durability; FEM; Maintenance; Monitoring; SHM; Stay cables","Cables; Data acquisition; Structural health monitoring; Concrete cable-stayed bridges; Data collection; Durability assessment; FEM modeling; Force analysis; Stay cable; Structural health monitoring (SHM); Temperature changes; Cable stayed bridges; article; finite element analysis; human",,,,,"Narodowa Agencja Wymiany Akademickiej, NAWA: PPN/BIL/2018/1/ 00235/U/00001","AuthIotriCs owntorirbthu tiaodnds:inCgo ntcheapttutahleiz aatnioanly, Ps.iHs .oafndSHMM.T. ;dmaetathofrdoomlogtyh, eM f.Tir.;ssto1ft0w yareea,rMs .oT.f; vthalei- bridge’s existence shows that it was designed correctly. Continuous observation of data from the monitoring system ensures the safety of the structure and its users, despite minor accidents such as a vehicle fire. Furthermore, the road administration (General Directorate for National Roads and Motorways) receives annual reports based on meas- manuscript. urements from the SHM system supported by a constant FEM analysis. Funding: The research project “Dynamic monitoring of bridge structures”, mentioned in Section 9, is co-financed by the Polish National Agency for Academic Exchange (grant number: PPN/BIL/2018/1/ 00235/U/00001).",,,,,,,,,,"Wenzel, H., (2009) Health Monitoring of Bridges, , John Wiley & Sons Ltd.: Chichester, UK; Maljaars, J., Vrouwenvelder, T., Fatigue failure analysis of stay cables with initial defects: Ewijk bridge case study (2014) Struct. Saf, 51, pp. 47-56. , [CrossRef]; Tokujama, S., Ishihara, S., Taniyama, S., (1995) Full Scale Fatigue Tests for Stay Cable Systems, , ETH Zürich: Zürich, Switzerland, [CrossRef]; Winkler, J., Georgias, C., Fischer, G., Wood, S., Ghannoum, W., Structural response of a multi-strand stay cable to cyclic bending load (2015) Struct. Eng. Int, 2, pp. 141-150. , [CrossRef]; Winkler, J., (2014) Parallel Monostrand Stay Cable Bending Fatigue, , Ph.D. Thesis, Technical University of Denmark, Copenhagen, Denmark; Biliszczuk, J., Hawryszków, P., Teichgraeber, M., Structural health monitoring system of a concrete cable-stayed bridge (2018) Archit. Civ. Eng. Environ, 11, pp. 69-77. , [CrossRef]; Biliszczuk, J., Barcik, W., Onysyk, J., Toczkiewicz, R., Tukendorf, A., Tukendorf, K., Rędziński Bridge in Wrocław—The largest concrete cable-stayed bridge in Poland (2014) Struct. Eng. Int, 24, pp. 285-292. , [CrossRef]; Hawryszków, P., Hildebrand, M., Installation of the largest stay cable system in Poland—the Rędziński bridge in Wrocław Proceedings of the 18th IABSE Congress On Innovative Infrastructures—Toward Human Urbanism, , Seoul, Korea, 19–21 September 2012. [CrossRef]; Hawryszków, P., Investigation of tension forces in a stay cable systems of a road bridge using vibration methods Proceedings of the EVACES’15, 6th International Conference on Experimental Vibration Analysis for Civil Engineering Structures, MATEC Web of Conferences, , Zurich, Switzerland, 19–21 October 2015. [CrossRef]; Bień, J., Kużawa, M., Kamiński, T., Validation of numerical models of concrete box bridges based on load test results (2015) Arch. Civ. Mech. Eng, 15, pp. 1046-1060. , [CrossRef]; Hildebrand, M., Nowak, Ł., Measurement of temperature distribution within steel box girder of Vistula River bridge in Central Europe (2020) Balt. J. Road Bridge Eng, 15, pp. 71-95. , [CrossRef]; Kang, F., Li, J., Zhao, S., Wang, Y., Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation (2019) Eng. Struct, 180, pp. 642-653. , [CrossRef]; Kang, F., Li, J., Dai, J., Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms (2019) Adv. Eng. Softw, 131, pp. 60-76. , [CrossRef]; Caetano, E., (2007) Cable Vibrations in Cable-Stayed Bridges, , 9th ed.; IABSE International Association for Bridge and Structural Engineering: Switzerland, Zürich; Gimsing, N.J., Georgakis, C.T., (2011) Cable Supported Bridges: Concept and Design, , John Wiley & Sons, Ltd.: Chichester, UK; Greco, F., Lonetti, P., Pascuzzo, A., Dynamic Analysis of Cable-Stayed Bridges Affected by Accidental Failure Mechanisms under Moving Loads (2013) Math. Probl. Eng, 2013, p. 302706. , [CrossRef]; Mankar, A., Bayane, I., Sørensen, J.D., Brühwiler, E., Probabilistic reliability framework for assessment of concrete fatigue of existing RC bridge deck slabs using data from monitoring (2019) Eng. Struct, p. 201. , [CrossRef]; Szala, G., Application of two-parameter fatigue characteristics in fatigue persistence calculations of structural components under conditions of a broad spectrum of loads (2016) Pol. Marit. Res, 4, pp. 138-145. , [CrossRef]; Heywood, R.B., (1962) Designing Against Fatigue, , Chapman Hall: London, UK; Schijve, J., (2009) Fatigue of Structures and Materials, , Springer: Dordrecht, The Netherlands; Panontin, T., Sheppard, S., (1999) Fatigue and Fracture Mechanics, 29. , (Eds) ASTM International: West Conshohocken, PA, USA, [CrossRef]; Serensen, S.V., Kogayev, V.P., Shnejderovich, R.M., (1975) Permissible Loading and Strength Calculations of Machine Components, , Maschinos-troenie: Mosow, Russia; Winkelmann, K., Żyliński, K., Górski, J., Probabilistic analysis of settlements under a pile foundation of a road bridge pylon (2020) Soils Found, 61, pp. 80-94. , [CrossRef]; Jang, M., Lee, Y., Won, D., Kang, Y.-J., Kim, S., Static Behaviors of a Long-span Cable-Stayed Bridge with a Floating Tower under Dead Loads (2020) J. Mar. Sci. Eng, 8, p. 816. , [CrossRef]; Seeram, M., Manohar, Y., Two-Dimensional Analysis of Cable Stayed Bridge under Wave Loading (2018) J. Inst. Eng. India Ser. A, 99, pp. 351-357. , [CrossRef]; Bayane, I., Brühwiler, E., Structural condition assessment of reinforced-concrete bridges based on acoustic emission and strain measurements (2020) J. Civ. Struct. Health Monit, 10, pp. 1037-1055. , [CrossRef]; Song, G., Wang, C., Wang, B., Structural Health Monitoring (SHM) of Civil Structures (2017) Appl. Sci, 7, p. 789. , [CrossRef]; Svensson, H., (2012) Cable-Stayed Bridges, 40 Years of Experience Worldwide, , Ernst und Sohn: Berlin, Germany; Chakraborty, J., Katunin, A., Klikowicz, P., Salamak, M., Embedded ultrasonic transmission sensors and signal processing techniques for structural change detection in the Gliwice bridge (2019) Procedia Struct. Integr, 17, pp. 387-394. , [CrossRef]; Wang, X., Chakraborty, J., Niederleithinger, E., Noise Reduction for Improvement of Ultrasonic Monitoring Using Coda Wave Interferometry on a Real Bridge (2021) J. Nondestruct. Eval, 40, p. 14. , [CrossRef]; Sheer, J., (2010) Failed Bridges: Case Studies, Causes and Consequences, , Ernst und Sohn: Berlin, Germany; Hui, L., Shunlong, L., Jinping, O., Hongwei, L., Reliability assessment of cable-stayed bridges based on structural health monitoring techniques (2012) Struct. Infrastruct. Eng, 8, pp. 829-845","Hawryszków, P.; Faculty of Civil Engineering, Wybrzeże Wyspiańskiego 27, Poland; email: pawel.hawryszkow@pwr.edu.pl
Teichgraeber, M.; Faculty of Civil Engineering, Wybrzeże Wyspiańskiego 27, Poland; email: marco.teichgraeber@pwr.edu.pl",,,"MDPI AG",,,,,14248220,,,"33801788","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85102110251
"Zhou Z., Wegner L.D., Sparling B.F.","8833227200;13805490300;15037238700;","Data quality indicators for vibration-based damage detection and localization",2021,"Engineering Structures","230",,"111703","","",,3,"10.1016/j.engstruct.2020.111703","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098705073&doi=10.1016%2fj.engstruct.2020.111703&partnerID=40&md5=33b1d773c5b9f0ac3ad21330cbaf6091","Department of Civil, Geological, and Environmental Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK  S7N 5A9, Canada","Zhou, Z., Department of Civil, Geological, and Environmental Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK  S7N 5A9, Canada; Wegner, L.D., Department of Civil, Geological, and Environmental Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK  S7N 5A9, Canada; Sparling, B.F., Department of Civil, Geological, and Environmental Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK  S7N 5A9, Canada","The effective application of vibration-based damage detection (VBDD) methods as a structural health monitoring approach depends largely on the accurate measurement of modal properties, particularly the mode shapes. However, the modal properties of bridges and other civil engineering structures are commonly measured using an output-only approach and ambient excitation sources, which can lead to considerable variability in the measurements and therefore a lack of confidence in the reliability of the VBDD results. This paper proposes two data quality indicators that can be used to assess the quality of a calculated VBDD parameter in terms of its potential to confidently identify and characterize damage, based on the consistency of the parameter when obtained from different sets of vibration test data. The performance of the indicators is demonstrated by application to a simple-span slab-on-girder bridge deck and comparison to the probabilities of successfully locating nine damage cases, as calculated using transient dynamic finite element analyses under simulated randomly varying loads. The data quality indicators were found to correlate well with the probability of successful localization, and were able to reflect the varying probabilities associated with several factors, including the number of repeated random trials used to estimate the mode shapes, the distance from the damage to the nearest sensor, the proximity of the damage to simple supports, and the severity of the damage. The proposed parameters were therefore shown to be capable of determining whether the available data are of sufficient quality to confidently apply VBDD methods. © 2020 Elsevier Ltd","Bridge deck; Data quality; Random excitation; Transient dynamic analysis; Vibration-based damage detection","Abutments (bridge); Bridge decks; Probability; Proximity indicators; Structural health monitoring; Accurate measurement; Ambient excitation; Civil engineering structures; Lack of confidences; Slab-on-girder bridge; Transient dynamics; Vibration test datum; Vibration-based damage detection; Damage detection; damage mechanics; data quality; detection method; dynamic analysis; finite element method; structural response; vibration",,,,,"AUTO21 Network of Centres of Excellence","This work was supported by the ISIS Canada Network of Centres of Excellence.",,,,,,,,,,"Doebling, S.W., Farrar, C.R., Prime, M.B., A summary review of vibration-based damage identification methods (1998) Shock Vib Dig, 30, pp. 91-105; Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R., A review of structural health monitoring literature : 1996–2001; Los Alamos National Laboratory Report, LA-13976-MS (2003), https://doi.org/LA-13976-MS, Los Alamos NM; Raj, B., Jayakumar, T., Thavasimuthu, M., Practical Non-Destructive Testing (2002), 2nd ed. Alpha Science International Ltd. Pangbourne, UK; Fox, C.H.J., The location of defects in structures: a comparison of the use of natural frequency and mode shape data (1992) Proc. 10th Int. Modal Anal. Conf., San Diego, CA, pp. 522-528; Pandey, A.K., Biswas, M., Samman, M.M., Damage detection from changes in curvature mode shapes (1991) J Sound Vib, 145, pp. 321-332; Zhou, Z., Wegner, L.D., Sparling, B.F., Vibration-based detection of small-scale damage on a bridge deck (2007) J Struct Eng; Wegner, L.D., Zhou, Z., Sparling, B.F., Structural health monitoring of precast concrete box girders using selected vibration-based damage detection methods. Adv (2010) Civ Eng; Brownjohn, J.M.W., de Stefano, A., Xu, Y.L., Wenzel, H., Aktan, A.E., Vibration-based monitoring of civil infrastructure: Challenges and successes (2011) J Civ Struct Heal Monit, 1, pp. 79-95; Peeters, B., De Roeck, G., Stochastic system identification for operational modal analysis: a review (2001) J Dyn Syst Meas Control, 123, pp. 659-667; Farrar, C.R., Cornwell, P.J., Doebling, S.W., Prime, M.B., Structural health monitoring studies of the Alamosa Canyon and I-40 Bridge; Los Alamos National Laboratory Report, LA-13635-MS (2000), Los Alamos NM; Peeters, B., Maeck, J., De Roeck, G., Vibration-based damage detection in civil engineering: excitation sources and temperature effects (2001) Smart Mater Struct, 10, pp. 518-527; Green, M.F., Cebon, D., Dynamic response of highway bridges to heavy vehicle loads: Theory and experimental validation (1994) J Sound Vib, 170, pp. 51-78; Zhang, Z., Error study of bridge tests for the purpose of structure identification (1994) Proc. 12th Int. Modal Anal. Conf., Honolulu, HI, pp. 433-441; Brownjohn, J.M.W., Moyo, P., Omenzetter, P., Lu, Y., Assessment of highway bridge upgrading by dynamic testing and finite-element model updating (2003) J Bridg Eng, 8, pp. 162-172; Alwash, M., Sparling, B.F., Wegner, L.D., Influence of excitation on dynamic system identification for a multi-span reinforced concrete bridge. Adv (2009) Civ Eng; Farrar, C.R., Duffey, T.A., Cornwell, P.J., Doebling, S.W., Excitation methods for bridge structures (1999) Proc. 17th Int. Modal Anal. Conf., Kissimmee, FL, pp. 1063-1068; Zhou, Z., Sparling, B.F., Wegner, L.D., (2005), 5767. , Damage detection on a steel-free bridge deck using random vibration. In: Shull PJ, Gyekenyesi AL, Mufti AA, editors. Nondestruct. Eval. Heal. Monit. Aerosp. Mater. Compos. Civ. Infrastruct. IV, Proc. SPIE - Int. Soc. Opt. Eng., Bellingham, WA: SPIE p. 108–19; Stubbs, N., Kim, Y.I., Farrar, C.R., Field verification of a nondestructive damage localization and severity estimation algorithm (1995) Proc. 13th Int. Modal Anal. Conf., Nashville, TN, pp. 210-218; Pandey, A.K., Biswas, M., Damage detection in structures using changes in flexibility (1994) J Sound Vib, 169, pp. 3-17; Zhang, Z., Aktan, A.E., (1995), pp. 1520-9. , The damage indices for constructed facilities. Proc. 13th Int. Modal Anal. Conf., Nashville, TN p; Salawu, O.S., Williams, C., (1994), pp. 933-9. , Damage location using vibration mode shapes. Proc. 12th Int. Modal Anal. Conf., Honolulu, HI p; Siddique, A.B., Sparling, B.F., Wegner, L.D., Assessment of vibration-based damage detection for an integral abutment bridge. Can (2007) J Civ Eng; Allemang, R.J., Brown, D.L.A., Correlation coefficient for modal vector analysis (1982) Proc. Int. Modal Anal. 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Mech Syst Signal Process, 120, pp. 180-202; Worden, K., Sohn, H., Farrar, C.R., Novelty detection in a changing environment: Regression and inter polation approaches (2002) J Sound Vib, 258, pp. 741-761; Yan, A.M., Kerschen, G., De Boe, P., Golinval, J.C., Structural damage diagnosis under varying environmental conditions - Part I: A linear analysis (2005) Mech Syst Signal Process, 19, pp. 847-864","Wegner, L.D.; Department of Civil, 57 Campus Drive, Canada; email: leon.wegner@usask.ca",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","",Scopus,2-s2.0-85098705073
"Guidio B., Jeong C.","57219626179;25958114500;","On the feasibility of simultaneous identification of a material property of a Timoshenko beam and a moving vibration source",2021,"Engineering Structures","227",,"111346","","",,3,"10.1016/j.engstruct.2020.111346","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094317521&doi=10.1016%2fj.engstruct.2020.111346&partnerID=40&md5=2fd0ea79e00361ceb504571e7614d57e","School of Engineering and Technology, Central Michigan University, Mount Pleasant, MI  48859, United States","Guidio, B., School of Engineering and Technology, Central Michigan University, Mount Pleasant, MI  48859, United States; Jeong, C., School of Engineering and Technology, Central Michigan University, Mount Pleasant, MI  48859, United States","This paper presents a computational study for investigating the feasibility of simultaneous identification of a material property of a Timoshenko continuous beam and a moving vibration source on the beam by using the data of measured vibrations on it. This work employs the finite element method to solve the wave equations of a Timoshenko beam subject to a moving vibrational source. It uses the Genetic Algorithm (GA) as an inversion solver to identify the values of targeted control parameters that characterize a material property of the beam and a moving vibration source on it. The numerical results show that, first, the presented inversion method can detect the characteristics of a moving wave source as well as the spatial variation of the elastic modulus of a Timoshenko-beam continuous bridge model, which is set to be piece wisely homogeneous in this work. Second, the GA-based joint inversion is effective even when the moving vibrational source's moving velocity is not constant over time. Third, the detrimental effect of noise in measurement data on the accuracy of the inversion becomes more significant as the number of control parameters increases. By using the presented method, engineers can take advantage of vehicle-induced ambient vibrations on bridges measured by modern sensors for the sake of passive wave source-based structural health monitoring (SHM). © 2020 Elsevier Ltd","Finite element method (FEM); Genetic algorithm (GA); Joint inversion; Passive wave source-based structural health monitoring (SHM); Timoshenko beam; Vehicle-induced ambient vibrations","Genetic algorithms; Numerical methods; Parameter estimation; Particle beams; Structural health monitoring; Wave equations; Ambient vibrations; Computational studies; Continuous bridges; Control parameters; Simultaneous identification; Spatial variations; Structural health monitoring (SHM); Vibrational sources; Vibrations (mechanical); continuum mechanics; data inversion; feasibility study; finite element method; genetic algorithm; source identification; structural analysis; vibration",,,,,"National Science Foundation, NSF: 1855406, 2044887, CMMI-1855406, CMMI-2044887","This material is based upon work supported by the National Science Foundation under Award No. CMMI-1855406 and CMMI-2044887. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors are very grateful to Dr. Pranav Karve for providing the numerical data of the wave response from his research code for the verification of the presented FEM wave solver. The authors are also grateful to the reviewers, who provided the authors with constructive feedback.","This document is the results of the research project funded by the National Science Foundation.",,,,,,,,,"Akcelik, V., Biros, G., Ghattas, O., (2002), Parallel multiscale Gauss-Newton-Krylov methods for inverse wave propagation. 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"Thedy J., Liao K.-W., Tseng C.-C., Liu C.-M.","57220782882;56001920600;57220783438;57220780369;","Bridge health monitoring via displacement reconstruction-based nb-iot technology",2020,"Applied Sciences (Switzerland)","10","24","8878","1","26",,3,"10.3390/app10248878","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097609322&doi=10.3390%2fapp10248878&partnerID=40&md5=3510abd02cf13317516cb372c1bb95cf","Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan; Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 106, Taiwan; Divison Chief, Public Works Department, Taipei City Government, Taipei, 110, Taiwan; New Construction Office, Public Works Department, Taipei City Government, Taipei, 110, Taiwan","Thedy, J., Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan; Liao, K.-W., Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 106, Taiwan; Tseng, C.-C., Divison Chief, Public Works Department, Taipei City Government, Taipei, 110, Taiwan; Liu, C.-M., New Construction Office, Public Works Department, Taipei City Government, Taipei, 110, Taiwan","An aged bridge’s performance is affected by degradation and becomes one of the major concerns in maintenance. A preliminary, simple and workable procedure of bridge damage detection is required to minimize maintenance costs. In the past, frequency is one of the most common indicators to detect damage occurrence. Recent research found that using frequency as a health indicator still has room to improve. Alternatively, dynamic displacement is used as an indicator in the current study. These dynamic displacements are reconstructed based on measured acceleration records from micro electro mechanical system (MEMS) sensors. The Newmark-beta method with Windows is proposed to acquire the reconstructed displacements of considered bridges. To demonstrate the accuracy and applicability of the proposed approach, three different experiments are carried out; (i) A small scale bridge with the implementation of MEMS acceleration sensors; (ii) a numerical complex finite element method (FEM) bridge model; (iii) an actual bridge with the implementation of MEMS acceleration sensors and narrow bandwidth Internet of things (NB-IoT) technology. The first experiment shows that the proposed method can successfully identify the difference between damaged/undamaged bridges and determine damage location. The second experiment indicates that the proposed method is able to identify the difference between stiffened/unstiffened bridges. The last experiment shows the applicability of the proposed method on an actual bridge health monitoring project. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Bridge health monitoring; Displacement reconstruction; MEMS acceleration sensor; NB-IoT",,,,,,"Ministry of Science and Technology, Taiwan, MOST: 109-2622-E-011-015-CC2; Taipei City Government, TCG","Funding: This research was funded by the Ministry of Science and Technology of Taiwan under grant number 109-2622-E-011-015-CC2 and by the Public Works Department, Taipei City Government. The supports are gratefully acknowledged.",,,,,,,,,,"Rytter, A., (1993) Vibrational Based Inspection of Civil Engineering Structures, , Ph.D. Thesis, Department of Building Technology and Structural Engineering, Aalborg University, Aalborg, Denmark; Schommer, S., Nguyen, V.H., Maas, S., Zürbes, A., Model updating for structural health monitoring using static and dynamic measurements (2017) Procedia Eng, 199, pp. 2146-2153. , [CrossRef]; Yu, S., Ou, J., Structural Health Monitoring and Model Updating of Aizhai Suspension Bridge (2017) J. Aerosp. Eng, 30. , [CrossRef]; Lee, Y.-J., Cho, S., SHM-Based Probabilistic Fatigue Life Prediction for Bridges Based on FE Model Updating (2016) Sensors, 16, p. 317. , [CrossRef]; Zong, Z., Lin, X., Niu, J., Finite element model validation of bridge based on structural health monitoring—Part I: Response surface-based finite element model updating (2015) J. Traffic Transp. Eng, 2, pp. 258-278. , [CrossRef]; Farrar, C.R., Jauregui, A.D., Comparative study of damage identification algorithms applied to a bridge: I. Experiment (1998) Smart Mater. Struct, 7, pp. 704-719. , [CrossRef]; Mehrjoo, M., Khaji, N., Moharrami, H., Bahreininejad, A., Damage detection of truss bridge joints using Artificial Neural Networks (2008) Expert Syst. Appl, 35, pp. 1122-1131. , [CrossRef]; Neves, A.C., González, I., Leander, J., Karoumi, R., Structural health monitoring of bridges: A model-free ANN-based approach to damage detection (2017) J. Civ. Struct. Health Monit, 7, pp. 689-702. , [CrossRef]; Li, Z.H., Au, F.T.K., Damage Detection of a Continuous Bridge from Response of a Moving Vehicle (2014) Shock Vib, 2014, pp. 1-7. , [CrossRef]; Li, H.-N., Li, D.-S., Song, G.-B., Recent applications of fiber optic sensors to health monitoring in civil engineering (2004) Eng. Struct, 26, pp. 1647-1657. , [CrossRef]; Wong, K.-Y., Instrumentation and health monitoring of cable-supported bridges (2004) Struct. Control Health Monit, 11, pp. 91-124. , [CrossRef]; Huseynov, F., Kim, C., Obrien, E., Brownjohn, J., Hester, D., Chang, K., Bridge damage detection using rotation measurements—Experimental validation (2020) Mech. Syst. Signal Process, 135, p. 106380. , [CrossRef]; Zhang, Q.W., Statistical damage identification for bridges using ambient vibration data (2007) Comput. Struct, 85, pp. 476-485. , [CrossRef]; Liao, A.S., Kiremidjian, R., Loh, C.-H., Structural damage detection and localization with unknown post-damage feature distribution using sequential change-point detection method (2019) J. Aerosp. Eng, 32, p. 04018149. , [CrossRef]; Zhang, S., Liu, Y., Damage Detection in Beam Bridges Using Quasi-Static Displacement Influence Lines (2019) Appl. Sci, 9, p. 1805. , [CrossRef]; Zeinali, Y., Story, B.A., Impairment localization and quantification using noisy static deformation influence lines and Iterative Multi-parameter Tikhonov Regularization (2018) Mech. Syst. Signal Process, 109, pp. 399-419. , [CrossRef]; Zeinali, Y., Story, B.A., Framework for Flexural Rigidity Estimation in Euler-Bernoulli Beams Using Deformation Influence Lines (2017) Infrastructures, 2, p. 23. , [CrossRef]; Mei, Q., Gül, M., Boay, M., Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis (2019) Mech. Syst. Signal Process, 119, pp. 523-546. , [CrossRef]; Tennyson, R.C., Mufti, A.A., Rizkalla, S.H., Tadros, G., Benmokrane, B., Structural health monitoring of innovative bridges in Canada with fiber optic sensors (2001) Smart Mater. Struct, 10, pp. 560-573. , [CrossRef]; Yi, T., Li, H., Gu, M., Recent research and applications of GPS based technology for bridge health monitoring (2010) Sci. China Ser. E Technol. Sci, 53, pp. 2597-2610. , [CrossRef]; Cantero, D., McGetrick, P., Kim, C., Obrien, E., Experimental monitoring of bridge frequency evolution during the passage of vehicles with different suspension properties (2019) Eng. Struct, 187, pp. 209-219. , [CrossRef]; Lee, H.S., Hong, Y.H., Park, H.W., Design of an FIR filter for the displacement reconstruction using measured acceleration in low-frequency dominant structures (2010) Int. J. Numer. 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"Hou R., Lynch J.P., Ettouney M.M., Jansson P.O.","57190580657;57199678735;7004428697;57189321099;","Partial Composite-Action and Durability Assessment of Slab-on-Girder Highway Bridge Decks in Negative Bending Using Long-Term Structural Monitoring Data",2020,"Journal of Engineering Mechanics","146","4","04020010","","",,3,"10.1061/(ASCE)EM.1943-7889.0001725","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078658703&doi=10.1061%2f%28ASCE%29EM.1943-7889.0001725&partnerID=40&md5=903739748d4705d7c93c0b99971f2994","Dept. of Civil and Environmental Engineering, Univ. of Michigan, Ann Arbor, MI  48019, United States; Mohammed M. Ettouney Limited Liability Company, 6050 Blvd. East, West New York, NJ  07093, United States; Michigan Dept. of Transportation, Lansing, MI  48909, United States","Hou, R., Dept. of Civil and Environmental Engineering, Univ. of Michigan, Ann Arbor, MI  48019, United States; Lynch, J.P., Dept. of Civil and Environmental Engineering, Univ. of Michigan, Ann Arbor, MI  48019, United States; Ettouney, M.M., Mohammed M. Ettouney Limited Liability Company, 6050 Blvd. East, West New York, NJ  07093, United States; Jansson, P.O., Michigan Dept. of Transportation, Lansing, MI  48909, United States","This paper uses long-term bridge monitoring data to quantitatively assess the composite action exhibited in slab-on-girder highway bridges and investigates the potential relationship between composite action and deck deterioration over negative bending regions. A three-span highway bridge in Michigan is instrumented with a structural monitoring system to observe the flexural response of the spans to vehicular loads. The monitoring system is designed to offer data for quantitative assessment of the degree of composite action in composite and noncomposite sections of the bridge spans using the position of neutral axis and the magnitude of slip strain as key response parameters correlated to composite action. It is shown that unintended nonlinear partial composite action exists in negative bending regions of the bridge. A calibrated analytical model and a finite-element model are developed based on empirical observation allowing tensile strains in the deck to be estimated under load. Estimated surface strains are compared with those with the design assumption of no composite action at the slab-girder interface. It is concluded that the observed partial composite action results in higher tensile strains in the deck which is a likely culprit to accelerated deck deterioration. © 2020 American Society of Civil Engineers.","Composite action; Deck deterioration; Durability; Neutral axis; Slip strain; Structural health monitoring","Bridges; Concrete bridges; Deterioration; Durability; Monitoring; Structural health monitoring; Tensile strain; Composite action; Durability assessment; Long-term structural monitoring; Neutral axis; Quantitative assessments; Response parameters; Slip strains; Structural Monitoring Systems; Highway bridges; bridge; data; durability; finite element method; loading; strain; structural analysis; structural response; Michigan; United States",,,,,"National Science Foundation, NSF: ECCS-1446330, ECCS-1446521","This work was supported by the National Institute of Standards and Technology (NIST) Technology Innovation Program (Cooperative Agreement 70NANB9H9008) and the National Science Foundation (NSF) (Grants ECCS-1446521 and ECCS-1446330). Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NIST and NSF. Additional in-kind support was also provided by the Michigan DOT (MDOT) including MDOT personnel providing on-site installation assistance; this assistance is gratefully acknowledged.",,,,,,,,,,"(2017) AASHTO LRFD Bridge Design Specifications, , AASHTO. 8th ed. Washington, DC: AASHTO; Agdas, D., Rice, J.A., Martinez, J.R., Lasa, I.R., Comparison of visual inspection and structural-health monitoring as bridge condition assessment methods (2016) J. Perform. Constr. Facil., 30 (3). , https://doi.org/10.1061/(ASCE)CF.1943-5509.0000802, 4015049; An, L., Cederwall, K., Push-out tests on studs in high strength and normal strength concrete (1996) J. Constr. Steel Res., 36 (1), pp. 15-29. , https://doi.org/10.1016/0143-974X(94)00036-H; (2017) 2017 Infrastructure Report Card, , ASCE. 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Lansing, MI: MDOT; Mosavi, A.A., Sedarat, H., O'Connor, S.M., Emami-Naeini, A., Jacob, V., Krimotat, A., Lynch, J., Finite element model updating of a skewed highway bridge using a multi-variable sensitivity-based optimization approach (2012) Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security, p. 834727. , Bellingham, WA: International Society for Optics and Photonics; Newmark, N.M., Siess, C.P., Viest, I.M., Tests and analysis of composite beams with incomplete interaction (1951) Proc. Society of Experimental Stress Analysis, 9, pp. 75-92. , Bethel, CT: Society for Experimental Mechanics; O'Connor, S.M., Zhang, Y., Lynch, J.P., Ettouney, M.M., Jansson, P.O., Long-term performance assessment of the Telegraph Road Bridge using a permanent wireless monitoring system and automated statistical process control analytics (2016) Struct. Infrastruct. Eng., 13 (5), pp. 604-624. , https://doi.org/10.1080/15732479.2016.1171883; Oehlers, D.J., Bradford, M.A., (1995) Composite Steel and Concrete Structural Members - Fundamental Behaviour, , New York: Elsevier; Ollgaard, J.G., Shear strength of stud connectors in lightweight and normal-weight concrete (1971) AISC Eng. J., 8, pp. 55-64; Queiroz, F.D., Vellasco, P., Nethercot, D.A., Finite element modelling of composite beams with full and partial shear connection (2007) J. Constr. Steel Res., 63 (4), pp. 505-521. , https://doi.org/10.1016/j.jcsr.2006.06.003; Ramey, G.E., Wolff, A.R., Wright, R.L., Structural design actions to mitigate bridge deck cracking (1997) Pract. Periodical Struct. Des. Constr., 2 (3), pp. 118-124. , https://doi.org/10.1061/(ASCE)1084-0680(1997)2:3(118); Ryu, H.-K., Chang, S.-P., Kim, Y.-J., Kim, B.-S., Crack control of a steel and concrete composite plate girder with prefabricated slabs under hogging moments (2005) Eng. Struct., 27 (11), pp. 1613-1624. , https://doi.org/10.1016/j.engstruct.2005.05.015; Šahinagić-Isović, M., Šahinagić-Isović, M., Markovski, G., Ćećez, M., Shrinkage strain of concrete - Causes and types (2012) Gracrossed D Signevinar, 64 (9), pp. 727-734; Sigurdardottir, D.H., Glisic, B., Neutral axis as damage sensitive feature (2013) Smart Mater. Struct., 22 (7), p. 75030. , https://doi.org/10.1088/0964-1726/22/7/075030; Spacone, E., El-Tawil, S., Nonlinear analysis of steel-concrete composite structures: State of the art (2004) J. Struct. Eng., 130 (2), pp. 159-168. , https://doi.org/10.1061/(ASCE)0733-9445(2004)130:2(159); Stewart, M.G., Rosowsky, D.V., Structural safety and serviceability of concrete bridges subject to corrosion (1998) J. Infrastruct. Syst., 4 (4), pp. 146-155. , https://doi.org/10.1061/(ASCE)1076-0342(1998)4:4(146); Swartz, R.A., Jung, D., Lynch, J.P., Wang, Y., Shi, D., Flynn, M.P., Design of a wireless sensor for scalable distributed in-network computation in a structural health monitoring system (2005) Proc. 5th Int. Workshop on Structural Health Monitoring, pp. 12-14. , Stanford, CA: DEStech Publications; Turmo, J., Lozano-Galant, J.A., Mirambell, E., Xu, D., Modeling composite beams with partial interaction (2015) J. Constr. Steel Res., 114, pp. 380-393. , https://doi.org/10.1016/j.jcsr.2015.07.007, NOV; Vu, K.A.T., Stewart, M.G., Structural reliability of concrete bridges including improved chloride-induced corrosion models (2000) Struct. Saf., 22 (4), pp. 313-333. , https://doi.org/10.1016/S0167-4730(00)00018-7; Williams, C.K.I., Prediction with Gaussian processes: From linear regression to linear prediction and beyond (1998) Learn. Graphical Models, 89, pp. 599-621. , https://doi.org/10.1007/978-94-011-5014-9_23; Wu, Y.F., Oehlers, D.J., Griffith, M.C., Partial-interaction analysis of composite beam/column members (2002) Mech. Struct. Mach., 30 (3), pp. 309-332. , https://doi.org/10.1081/SME-120004420; Zhang, Y., Kurata, M., Lynch, J.P., Long-term modal analysis of wireless structural monitoring data from a suspension bridge under varying environmental and operational conditions: System design and automated modal analysis (2017) J. Eng. Mech., 143 (4). , https://doi.org/10.1061/(ASCE)EM.1943-7889.0001198, 4016124; Zona, A., Ranzi, G., Finite element models for nonlinear analysis of steel - Concrete composite beams with partial interaction in combined bending and shear (2011) Finite Elem. Anal. Des., 47 (2), pp. 98-118. , https://doi.org/10.1016/j.finel.2010.09.006","Lynch, J.P.; Dept. of Civil and Environmental Engineering, United States; email: jerlynch@umich.edu",,,"American Society of Civil Engineers (ASCE)",,,,,07339399,,,,"English","J. Eng. Mech.",Article,"Final","",Scopus,2-s2.0-85078658703
"Park J.W., Yoon J.-H., Yoon G.-H., Lim Y.M.","57196408777;57204815722;7103257903;7402565188;","Bi-axial Seismic Behaviour of a Bridge Structure with a Shape Optimized Metallic Damper",2018,"IOP Conference Series: Materials Science and Engineering","431","12","122008","","",,3,"10.1088/1757-899X/431/12/122008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057308390&doi=10.1088%2f1757-899X%2f431%2f12%2f122008&partnerID=40&md5=61589a8303519a5401e7c82dccd1535a","Department of Civil and Environmental Engineering, Yonsei Univ.03722, South Korea; Department of Convergence Defense, Hanyang Univ.04763, South Korea; Department of Mechanical Engineering, Hanyang Univ.04763, South Korea","Park, J.W., Department of Civil and Environmental Engineering, Yonsei Univ.03722, South Korea; Yoon, J.-H., Department of Convergence Defense, Hanyang Univ.04763, South Korea; Yoon, G.-H., Department of Mechanical Engineering, Hanyang Univ.04763, South Korea; Lim, Y.M., Department of Civil and Environmental Engineering, Yonsei Univ.03722, South Korea","This study performs a structural analysis to determine seismic behaviour of bridge structures with optimally designed metallic damper absorbing earthquake energy. Since the metallic damper utilizes the plastic deformation of steel to reduce vibration of the structures, this optimized metallic damper can be more economic, reliable and sustainable than the conventional bridge dampers such as friction or viscous dampers. The considered earthquake loads applied to the structure is assumed to work on two-dimensional directions. Also, the shape optimization of metallic damper is purposed to perform ideally under those bi-axial earthquake excitations. The optimizing process is based on the calculation of dissipated energy by the damper through finite element analysis of ABAQUS and the SQP algorithm by MATLAB. The MATALB algorithm controls the alteration of the damper's shape to maximize the amount of energy dissipation under the constraint of total mass of the damper. To evaluate the effect of the developed metallic damper using this optimized shape, this study applied the damper into a three-span bridge model and conducted earthquake analysis through SAP2000. © Published under licence by IOP Publishing Ltd.",,"ABAQUS; Bridges; Concretes; Earthquake engineering; Earthquakes; Energy dissipation; Metals; Shape optimization; Structural health monitoring; Bridge structures; Dissipated energy; Earthquake analysis; Earthquake excitation; Earthquake load; Seismic behaviour; SQP algorithm; Viscous dampers; Structural analysis",,,,,"Ministry of Science, ICT and Future Planning, MSIP: NRF-2014M3C1A6038855; National Research Foundation of Korea, NRF","This research was supported by the EDucation-research Integration through Simulation On the Net (EDISON) Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2014M3C1A6038855).",,,,,,,,,,"Kim, E., Field Survey about Seismic Retrofit of Existing Building in San Francisco (2013) J. Korea Inst. Struct. Maint. Insp., 17, pp. 68-73; Choi, K.-M., Jung, H.-J., Cho, S.-W., Lee, I.-W., Application of smart passive damping system using MR damper to highway bridge structure (2007) Journal of Mechanical Science and Technology, 21 (6), pp. 870-874; Makris, N., Zhang, J., Seismic response analysis of a highway overcrossing equipped with elastomeric bearings and fluid dampers (2004) Journal of Structural Engineering, 130 (6), pp. 830-845; Kelly, J.M., The role of damping in seismic isolation (1999) Earthquake Engineering & Structural Dynamics, 28 (1), pp. 3-20; Nakashima, M., Saburi, K., Tsuji, B., Energy input and dissipation behaviour of structures with hysteretic dampers (1996) Earthquake Engineering & Structural Dynamics, 25 (5), pp. 483-496; Deng, K., Pan, P., Sun, J., Liu, J., Xue, Y., Shape optimization design of steel shear panel dampers (2014) Journal of Constructional Steel Research, 99, pp. 187-193; Yoon, J.-H., Park, J.W., Lim, Y.M., Yoon, G.H., Shape Optimization of Uniaxial Vibrating Metal Damper (2017) Journal of Computational Structural Engineering Institute of Korea, 30 (4), pp. 313-318; Nagai, S., Kaneko, T., Kanda, T., Maruta, M., Structural capacity of reinforced PVA-ECC dampers (2004) 6th International RILEM Symposium on Fibre Reinforced Concretes, pp. 1227-1236; Ohsaki, M., Nakajima, T., Optimization of link member of eccentrically braced frames for maximum energy dissipation (2012) Journal of Constructional Steel Research, 75, pp. 38-44; Deng, K., Pan, P., Su, Y., Xue, Y., Shape optimization of U-shaped damper for improving its bi-directional performance under cyclic loading (2015) Engineering Structures, 93, pp. 27-35; Lee, C.-H., Ju, Y.K., Min, J.-K., Lho, S.-H., Kim, S.-D., Non-uniform steel strip dampers subjected to cyclic loadings (2015) Engineering Structures, 99, pp. 192-204; Pan, P., Yan, H., Wang, T., Xu, P., Xie, Q., Development of steel dampers for bridges to allow large displacement through a vertical free mechanism (2014) Earthquake Engineering and Engineering Vibration, 13 (3), pp. 375-388","Lim, Y.M.; Department of Civil and Environmental Engineering, South Korea; email: yunmook@yonsei.ac.kr",,"API-MDC Group of Companies;Seri Pajam Development Sdn Bhd","Institute of Physics Publishing","14th International Conference on Concrete Engineering and Technology, CONCET 2018","8 August 2018 through 9 August 2018",,142035,17578981,,,,"English","IOP Conf. Ser. Mater. Sci. Eng.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85057308390
"Shatilov Y.Y., Lyapin A.A.","57190969512;7006517295;","Vibration-based damage detection techniques for health monitoring of construction of a multi-storey building",2018,"Materials Science Forum","931 MSF",,,"178","183",,3,"10.4028/www.scientific.net/MSF.931.178","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055944822&doi=10.4028%2fwww.scientific.net%2fMSF.931.178&partnerID=40&md5=19aa82ecd65ab0f2c3ac0363e1557846","Don State Technical University, 1 Gagarin square, Rostov-on-Don, 344000, Russian Federation","Shatilov, Y.Y., Don State Technical University, 1 Gagarin square, Rostov-on-Don, 344000, Russian Federation; Lyapin, A.A., Don State Technical University, 1 Gagarin square, Rostov-on-Don, 344000, Russian Federation","Conducting surveys of multi-storey buildings is a laborious task, because large volumes of visual and instrumental research should be carried out. Reduction of labor costs with an increase in the reliability of information about the state of damage and technical condition is an actual scientific and practical task. One of the ways to solve it is to use non-destructive vibration diagnostic methods. The purpose of carrying out diagnostics with the use of vibration based damage detection methods is to search for damages in structural elements that can cause the deviation of the dynamic parameters of a structure from calculated ones. Determination of the dynamic parameters of the structure, in particular natural frequencies and mode shapes of mechanical systems, is one of the most important tasks that allows obtaining integral information about the state of a structure. This article presents the results of calculations for the localization of slabs defects in a multi-storey building with a transverse crack, span L = 4.5 (m), height H = 0.2 (m), with prestressed reinforcement d = 0.05 (m). Vibration based Damage Index method was used to localize the defect. During the study, reliable localization values of the defect area of the slab were obtained, this indicates that the vibration method for determining the damage index with a sufficient degree of accuracy allowed predicting the site of damage to the structure. © 2018 Trans Tech Publications, Switzerland.","Damage detection; Defect; Finite-element method (FEM) reinforcement; Mode shape; Mode shapes; Natural frequency; Numerical experiment; Slabs; Structural engineering; Structural health monitoring; Structural vibration; Vibration based damage detection","Abutments (bridge); Defects; Memory architecture; Natural frequencies; Numerical methods; Reinforcement; Structural design; Structural dynamics; Structural health monitoring; Wages; Mode shapes; Numerical experiments; Slabs; Structural vibrations; Vibration-based damage detection; Damage detection",,,,,"Russian Foundation for Basic Research, RFBR: 18-01-00715-a","The research was supported financially by Russian Foundation for Basic Research (Project No. 18-01-00715-a).",,,,,,,,,,"Zhou, Z., (2008) Vibration-Based Damage Detection of Bridge Superstructures, , VDM Verlag, Germany; Salawu, O.S., Bridge assessment using forced-vibration testing (1995) Am. Soc. Civ. Eng, p. 32; Salawu, O.S., Williams, C., Damage location using vibration mode shapes (1994) Proceedings of the 12Th International Modal Analysis Conference, p. 933. , Society of Experimental Mechanics; Salawu, O.S., Detection of structural damage through changes in frequency: A review (1997) Eng. Struct, 19 (9), p. 718; Bakhary, N., Hao, H., Deeks, A.J., Damage Detection Using Artificial Neural Network with Consideration of Uncertainties (2007) Engineering Structures, 29 (11), pp. 2806-2815; Benitez, M.F., Li, J., Static and Dynamic Evaluation of A Timber Bridge (Cattai Ck. Bridge NSW, Australia) (2002) Proceedings of the 7Th World Conference on Timber, p. 26; Choi, F.C., Li, J., Samali, B., Crews, K., Application of Modal-based Damage Detection Method to Locate and Evaluate Damage in Timber Beams (2007) Journal of Wood Science, 53 (5), pp. 394-400; Marwala, T., Damage Identification Using Committee of Neural Networks (2002) Journal of Engineering Mechanics, 126 (11), pp. 43-50; Hajela, P., Soeiro, F.J., Recent developments in damage detection based on system identification methods (1990) Structural Optimization, 1, pp. 1-10; Casas, J.R., Aparicio, A.C., Structural damage identification from dynamic-test data (1994) Journal of Structural Engineering, 8, pp. 2437-2449; Peeters, B., Maeck, J., De Roeck, G., Vibration-based damage detection in civil engineering: Excitation sources and temperature effects (2001) Smart Materials and Structures, 3, pp. 518-527","Shatilov, Y.Y.; Don State Technical University, 1 Gagarin square, Russian Federation; email: art-web@mail.ru","Yazyev B.Litvinov S.Chepurnenko A.Lapina A.Akay O.",,"Trans Tech Publications Ltd","International Conference on Construction and Architecture: Theory and Practice of Industry Development, CATPID 2018","8 October 2018 through 12 October 2018",,219729,02555476,9783035713671,MSFOE,,"English","Mater. Sci. Forum",Conference Paper,"Final","",Scopus,2-s2.0-85055944822
"Wattana K., Nishio M.","57189519417;22951434200;","Application of a regression model for predicting traffic volume from dynamic monitoring data to the bridge safety evaluation",2017,"Journal of Civil Structural Health Monitoring","7","4",,"429","443",,3,"10.1007/s13349-017-0234-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031505010&doi=10.1007%2fs13349-017-0234-7&partnerID=40&md5=5b63b5a0736a791a8f778c3b57a007cf","Department of Rural Roads, Anusawaree 9, Bangkhen, Bangkok, 10220, Thailand; Department of Civil Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama, 240-8901, Japan","Wattana, K., Department of Rural Roads, Anusawaree 9, Bangkhen, Bangkok, 10220, Thailand; Nishio, M., Department of Civil Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama, 240-8901, Japan","This paper presents the verification and application of estimated traffic volume obtained from a statistical model. The model was constructed from dynamic responses acquired by a structural health monitoring (SHM) system installed on an in-service cable-stayed bridge. The SHM system consists of accelerometers, temperature sensors, and a traffic-counting system based on installed cameras on the bridge. The model performance was firstly assessed, and it was concluded that the constructed regression model for estimating the number of equivalent trucks from the dynamic responses was applicable. However, it was also recognized that the traffic conditions such as the number of passing vehicles and the speed of traffic flow slightly affected the estimation accuracies. Then, the relationships between the traffic volume and the adopted dynamic responses in the statistical model were verified by using the finite element (FE) model in order to apply in the bridge safety evaluation. The analytical results from the FE model revealed that the adopted responses obtained from the analytical data affected by only the traffic volume variability corresponded with those from the measurement, i.e., the adopted responses were sensitive to the traffic volume. Therefore, they were valid for use in the estimation of traffic volume. After that, the reliability indices of the target bridge were calculated by using the estimated traffic volume as the operational load effects with comparison of those calculated from the designed load in the design standard. The bridge reliability based on the estimated traffic volume could provide the actual safety conditions under the operational load, and it contributed more to decision making in bridge maintenance than those based on the design load. © 2017, Springer-Verlag GmbH Germany.","Dynamic monitoring data; Regression model; Reliability index; Traffic response analysis; Traffic volume estimation","Behavioral research; Bridges; Cable stayed bridges; Decision making; Dynamic response; Monitoring; Regression analysis; Reliability; Reliability analysis; Structural health monitoring; Dynamic monitoring; Regression model; Reliability Index; Response analysis; Traffic volumes; Finite element method",,,,,"Japan Society for the Promotion of Science, JSPS: 17H04934",,,,,,,,,,,"(1990) Guide specifications for fatigue evaluation of existing steel bridges, , American Association of State Highway and Transportation Officials, Washington, DC; Fu, (2003) Effect of truck weight on bridge network costs report, , National Cooperative Highway Research Program (NCHRP), Transportation Research Board, Washington, DC; Matsui, S., Muti, K., Rating of lifetime of a damaged RC slab and replacement by steel plate-concrete composite deck (1992) Technol Rep Osaka Univ, 42 (2115), p. 329; Faraz, S., Helmi, K., Algohi, B., Bakht, B., Mufti, A., Sources of errors in fatigue assessment of steel bridges using BWIM (2017) J Civ Struct Health Monit, 7 (3), pp. 291-302; Myra Lydon, S.E., Taylor, D., Robinson, A.M., Brien, E.J.O., Recent developments in bridge weigh in motion (B-WIM) (2016) J Civ Struct Health Monit, 6 (1), pp. 69-81; (2010) The manual for bridge evaluation, , American Association of State Highway and Transportation Officials, Washington, DC; Miyamoto, A., A new damage detection method for bridge condition assessment in structural health monitoring (2013) J Civ Struct Health Monit, 3 (4), pp. 269-284; Li, H., Ou, J., The state of the art in structural health monitoring of cable-stayed bridges (2016) J Civ Struct Health Monit, 6 (1), pp. 43-67; Celebi, M., Eeri, M., Real-time seismic monitoring of the New Cape Girardeau bridge and preliminary analyses of recorded data: an overview (2006) Earthq Spectra, 22 (3), pp. 609-630; Wong, K.Y., Instrumentation and health monitoring of cable-supported bridges (2004) Struct Control Health Monit, 11 (2), pp. 91-124; Frangopol, M.D., Strauss, A., Kim, S., Bridge reliability assessment based on monitoring (2008) J Bridge Eng, 13 (3), pp. 258-270; Liu, M., Frangopol, M.D., Kim, S., Bridge safety evaluation based on monitored live load effects (2009) J Bridge Eng, 14 (4), pp. 257-269; Zhang, Q., Fan, L.C., Yuan, W.C., Traffic-induced variability in dynamic properties of cable-stayed bridge (2002) Earthq Eng Struct Dyn, 31, pp. 2015-2021; Kim, C., Jung, D., Kim, N., Kwon, S., Feng, M., Effect of vehicle weight on natural frequencies of bridges measured from traffic-induced vibration (2003) Earthq Eng, 2 (1), pp. 109-115; Cross, E.J., Brownjohn, J.M.W., Worden, K., Long-term monitoring and data analysis of the Tamar Bridge (2013) Mech Syst Signal Process, 35, pp. 16-34; Wattana, K., Nishio, M., Analysis of multivariate SHM data of a cable-stayed bridge considering operational and environmental effects (2015) Proceedings of the 7th international conference on Structural Health Monitoring of Intelligent Infrastructure, Torino, , In:, Italy; Wattana, K., Nishio, M., Traffic volume estimation in a cable-stayed bridge using dynamic responses acquired in the structural health monitoring (2017) Struct Control Health Monit, 24, p. 4; Deng, L., Yu, Y., Zou, Q., Cai, C.S., State-of-the-art review of dynamic impact factors of highway bridges (2015) J Bridge Eng, 20 (5); Thoft-Christiansen, P., Murotsu, Y., (1986) Application of structural systems reliability theory, , Springer, Berlin; Cornell, C.A., Bounds on the reliability of structural systems (1967) J Struct Div ASCE, 93 (ST1), pp. 171-200; Hasofer, A.M., Lind, N.C., An exact and invariant first order reliability format (1974) J Struct Mech Div ASCE, 100 (1), pp. 111-121; Nowak, A.S., Szerszen, M.M., Calibration of design code for buildings (ACI 318): part 1—statistical models for resistance (2003) Struct J, 100 (3), pp. 377-382; Nowak, A.S., (1999) Calibration of LRFD bridge design code. NCHRP Report 368, Transportation Research Board, Washington, DC, USA; Lutomirska, M., (2009) Live load model for long span bridge, , Doctoral Dissertation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA; (2012) AASHTO LRFD bridge design specifications, , American Association of State Highway and Transportation Officials, Washington, DC","Wattana, K.; Department of Rural Roads, Anusawaree 9, Bangkhen, Thailand; email: wattanakaiwan@gmail.com",,,"Springer Verlag",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85031505010
"Chen L., Liang C., Huang Z., Zhou L.","55756603800;57194522532;57198607254;36496227600;","Numerical simulation on the temperature behavior of the main cable for suspension bridge",2017,"Proceedings of SPIE - The International Society for Optical Engineering","10170",,"1017038","","",,3,"10.1117/12.2259941","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020553707&doi=10.1117%2f12.2259941&partnerID=40&md5=89bf0bf52d674bae36865f803a45c32e","School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China","Chen, L., School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China; Liang, C., School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China; Huang, Z., School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China; Zhou, L., School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China","The main cables are key complements of the suspension bridge. The temperature properties of main cables have significant effects on the structural responses of entire bridge. This paper presents a numerical method for temperature analysis of main cable for suspension bridge. The finite element (FE) model of main cable section is developed as homogeneous material using Plan elements. The material parameters for thermal analysis are determined based on equivalent principle. The third type thermal boundary conditions of a sunny day are calculated and then applied on the FE model for transient thermal analysis. The numerical results are compared with the experimental measurements of a full scale main cable for validation of the thermal analysis method. The results present good agreement with respect to the measurements. The temperature variations exactly explicate the changes of environmental conditions such as solar radiation and ambient temperature of daily. This FE model-based thermal analysis can provide a high effective and precision method for analysis of temperature and induced structural responses of main cables and suspension bridge. © 2017 SPIE.","Main cable; Numerical simulation; Suspension bridge; Temperature behaviour; Transient thermal analysis","Biological systems; Bridge cables; Cables; Computer simulation; Numerical methods; Numerical models; Structural analysis; Structural health monitoring; Suspension bridges; Temperature; Thermoanalysis; Environmental conditions; Homogeneous materials; Main cable; Temperature behavior; Temperature properties; Temperature variation; Thermal boundary conditions; Transient thermal analysis; Finite element method",,,,,,,,,,,,,,,,"Zhang, Y.S., Luo, H., Wang, Z.H., The analysis of temperature influence on empty shape of suspension bridge (2005) Journal of Chongqing Jiaotong University, 24 (6), pp. 21-24; Shen, H., Chen, C.S., Yan, D.H., Iteration calculation for initial configuration of main cable in variable temperature field (2006) Journal of Changsha Communications University, 22 (3), pp. 40-43; Xu, Y.L., Chen, B., Ng, C.L., Monitoring temperature effect on a long suspension bridge (2010) Structural Control and Health Monitoring, 17 (6), pp. 632-653; Xia, Y., Chen, B., Zhou, X., Field monitoring and numerical analysis of Tsing Ma Suspension Bridge temperature behavior (2013) Structural Control and Health Monitoring, 20 (4), pp. 560-575; Westgate, R., Koo, K.Y., Brownjohn, J., Effect of solar radiation on suspension bridge performance (2014) Journal of Bridge Engineering, 20 (5), p. 4014077; Jung, J.W., Moon, D.J., Jung, J.W., A correlation analysis regarding the temperature effect for a suspension bridge (2015) Proc of the 33rd IMAC, pp. 99-106; Zhou, L.R., Xia, Y., Brownjohn, J.M.W., Temperature analysis of a long-span suspension bridge based on field monitoring and numerical simulation (2015) Journal of Bridge Engineering, 21 (1), p. 4015027; Harada, Y., Hasegawa, S., Study on the thermal response of suspension bridge cables (1976) Proceedings of the Japan Society of Civil Engineers, pp. 17-27; Wang, Y., Liu, M., Simulation method of main cable temperature for suspension bridge (2015) 5th International Conference on Civil Engineering and Transportation, pp. 651-656; Yang, S.M., Tao, W.Q., (2006) Heat Transfer, , the fourth edition, Higher Education Press, Beijing; Peng, Y.S., (2007) Studies on Theory of Solar Radiation Thermal Effects on Concrete Bridges with Application, , Southwest Jiaotong University Doctor Degree Dissertation, Chengdu; Yu, D.M., (2010) Research on Main Cable Temperature Field and Temperature Effect of Suspension Bridges, , Southwest Jiaotong University Master Degree Dissertation, Chengdu, China","Zhou, L.; School of Civil Engineering and Transportation, China; email: hitsgszhou@gmail.com","Kundu T.","Fiberguide Industries;Frontiers Media;OZ Optics, Ltd.;Polytec, Inc.;The Society of Photo-Optical Instrumentation Engineers (SPIE)","SPIE","Health Monitoring of Structural and Biological Systems 2017","26 March 2017 through 29 March 2017",,128085,0277786X,9781510608252,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85020553707
"Shafighfard T., Mieloszyk M.","55907681900;26422422200;","Experimental and numerical study of the additively manufactured carbon fibre reinforced polymers including fibre Bragg grating sensors",2022,"Composite Structures","299",,"116027","","",,2,"10.1016/j.compstruct.2022.116027","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135876533&doi=10.1016%2fj.compstruct.2022.116027&partnerID=40&md5=0d22571fa90c04c672652c8c70092f4d","Institute of Fluid Flow Machinery, Polish Academy of Sciences, Fiszera 14 Str., Gdansk, 80-231, Poland","Shafighfard, T., Institute of Fluid Flow Machinery, Polish Academy of Sciences, Fiszera 14 Str., Gdansk, 80-231, Poland; Mieloszyk, M., Institute of Fluid Flow Machinery, Polish Academy of Sciences, Fiszera 14 Str., Gdansk, 80-231, Poland","Fibre Bragg grating (FBG) sensors have been known as one of the smart localized and globalized Structural Health Monitoring (SHM) devices for various structural applications specifically those utilize composite materials. In recent decades the use of advanced composite materials green manufacturing methods has increased the requirement to provide effective means of performing experimental investigations to support analytical and numerical analyses. The main objective of this article is to analyse the performance of additively manufactured (AM) Carbon Fibre Reinforced Polymer (CFRP) sample with FBG sensors under the influence of environmental factors (temperature, relative humidity). The sample was manufactured using continuous carbon fibre applying Fused Deposition Modelling (FDM) technique. During the AM process one FBG sensor was embedded in the middle of the sample, while the second FBG sensor was attached to the finished sample surface. The environmental tests were conducted to investigate the durability of AM elements and influences of embedded FBG sensors on the composite specimen. Additionally, the behaviour of composite materials under environmental loading was modelled using the Finite Element Method (FEM) through an Abaqus software. It allows to achieve a more complex picture of embedded fibre optic influences on AM composite material durability. © 2022 Elsevier Ltd","Additive manufacturing; Carbon Fibre Reinforced Polymer; Fibre Bragg Grating sensor; Thermal strain","3D printers; ABAQUS; Bridge decks; Carbon fiber reinforced plastics; Carbon fibers; Durability; Electric sensing devices; Environmental testing; Fiber Bragg gratings; Numerical methods; Structural health monitoring; Advanced composite materials; Carbon fibre reinforced polymer; Composites material; Experimental and numerical studies; Fiber Bragg Grating Sensors; Green manufacturing; Health monitoring devices; Localised; Structural applications; Thermal strain; Additives",,,,,"Narodowe Centrum Nauki, NCN: 2019/01/Y/ST8/00075","This research was supported by the project ’Additive manufactured composite smart structures with embedded fibre Bragg grating sensors (AMCSS)’ funded by the National Science Centre, Poland under M-ERA.NET 2 Call 2019, grant agreement 2019/01/Y/ST8/00075 . ABAQUS calculations were carried out at the Academic Computer Centre in Gdańsk (Poland).","This research was supported by the project ’Additive manufactured composite smart structures with embedded fibre Bragg grating sensors (AMCSS)’ funded by the National Science Centre, Poland under M-ERA.NET 2 Call 2019, grant agreement 2019/01/Y/ST8/00075. ABAQUS calculations were carried out at the Academic Computer Centre in Gdańsk (Poland).",,,,,,,,,"Gebremichael, Y., Li, W., Boyle, W., Meggitt, B., Grattan, K., McKinley, B., Integration and assessment of fibre Bragg grating sensors in an all-fibre reinforced polymer composite road bridge (2005) Sensors Actuators A, 118 (1), pp. 78-85; Mieloszyk, M., Majewska, K., Ostachowicz, W., Application of embedded fibre Bragg grating sensors for structural health monitoring of complex composite structures for marine applications (2021) Mar Struct, 76; Cucinotta, F., Guglielmino, E., Sfravara, F., Life cycle assessment in yacht industry: A case study of comparison between hand lay-up and vacuum infusion (2017) J Cleaner Prod, 142, pp. 3822-3833; Liu, Z., Liu, X., Zhu, S.-P., Zhu, P., Liu, W., Correia, J.A., Reliability assessment of measurement accuracy for FBG sensors used in structural tests of the wind turbine blades based on strain transfer laws (2020) Eng Fail Anal, 112; Wu, B., Wu, G., Yang, C., Parametric study of a rapid bridge assessment method using distributed macro-strain influence envelope line (2019) Mech Syst Signal Process, 120, pp. 642-663; 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Li, X., Embedded sensors in layered manufacturing (2001), [Ph.D. thesis] Citeseer; Kousiatza, C., Tzetzis, D., Karalekas, D., In-situ characterization of 3D printed continuous fiber reinforced composites: A methodological study using fiber Bragg grating sensors (2019) Compos Sci Technol, 174, pp. 134-141; Zubel, M.G., Sugden, K., Sáez-Rodríguez, D., Nielsen, K., Bang, O., 3D printed sensing patches with embedded polymer optical fibre bragg gratings (2016) Sixth european workshop on optical fibre sensors, 9916, p. 99162E. , International Society for Optics and Photonics; Wang, M., Liu, L., Ren, Y., Xu, G., Wang, Y., Investigation of heated nozzle temperature in ABS specimens fabricated based on fiber bragg grating during fused deposition modeling process (2020) Integr Ferroelectr, 208 (1), pp. 177-180; Kousiatza, C., Karalekas, D., Experimental study of fabrication-induced residual strains and distortions in polymeric square plates built using fused deposition modeling process. Mater Des Process Commun e149; Mekid, S., Daraghma, H., Manufacturing processes of sensorial materials: Sensors placement and experimental validation (2019) IOP Conf Ser: Mater Sci Eng, 538 (1); Hong, C., Bao, C., Fei, J., Zhang, Y., Wang, X., Application of FBG technology in additive manufacturing: monitoring real-time internal temperature of products (2020) IEEE Sens J; Kinet, D., Mégret, P., Goossen, K.W., Qiu, L., Heider, D., Caucheteur, C., Fiber Bragg grating sensors toward structural health monitoring in composite materials: Challenges and solutions (2014) Sensors, 14 (4), pp. 7394-7419; Sorensen, L., Botsis, J., Gmür, T., Cugnoni, J., Delamination detection and characterisation of bridging tractions using long FBG optical sensors (2007) Composites A, 38 (10), pp. 2087-2096; Udd, E., Spillman, W.B., Jr., Fiber optic sensors: an introduction for engineers and scientists (2011), John Wiley & Sons Hoboken, New Jersey; Mieloszyk, M., Andrearczyk, A., Majewska, K., Jurek, M., Ostachowicz, W., Polymeric structure with embedded fiber Bragg grating sensor manufactured using multi-jet printing method (2020) Measurement, 166; ABAQUS/standard user's manual, version 6.14 (2017), Dassault Systèmes Simulia Corp United States; MATLAB, M., Version 9.7 (R2019b) (2019), The MathWorks Inc. Natick, Massachusetts","Mieloszyk, M.; Institute of Fluid Flow Machinery, Fiszera 14 Str., Poland; email: mmieloszyk@imp.gda.pl",,,"Elsevier Ltd",,,,,02638223,,COMSE,,"English","Compos. Struct.",Article,"Final","",Scopus,2-s2.0-85135876533
"Yin T.","55277579100;","A Practical Bayesian Framework for Structural Model Updating and Prediction",2022,"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering","8","1","04021073","","",,2,"10.1061/AJRUA6.0001196","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118124443&doi=10.1061%2fAJRUA6.0001196&partnerID=40&md5=5b015616f63e82beb3c0ac61be24d388","School of Civil Engineering, Wuhan Univ., Wuhan, 430072, China","Yin, T., School of Civil Engineering, Wuhan Univ., Wuhan, 430072, China","Due to the influence of various uncertain factors, there will inevitably be certain errors between the prediction of finite-element (FE) model and observed data for a target structure. It is thus necessary to calibrate the initial FE model using the measured data to ensure the accuracy of the numerical model for the purpose of structural system identification and health monitoring. Although structural FE model updating methods have been extensively studied in the past few decades, the research based on deterministic methods in the current literature still occupies a large proportion, which cannot account for the uncertain effects during the model updating. The noise robustness of both the updating procedure and the generalization capability of the updated model are expected to be poor. The model updating based on the Bayesian theorem can quantify the uncertainty of model identification results, but it is computationally expensive for the Bayesian inference of regularization hyperparameters since the Hessian matrix is generally required to be evaluated repeatedly especially for a huge amount of uncertain model parameters. Also, effective prediction based on the refined FE model is still lacking in the literature, which is essential for judging and evaluating the quality of the updated model. This paper proposes a practical framework for structural FE model updating and prediction based on the Bayesian regularization with incomplete modal data. The structural model parameters and regularization hyperparameters are identified alternatively in an adaptive manner, and the Gauss-Newton method is used to approximate the true Hessian within the framework of the nonlinear least-squares algorithm. This is expected to improve the efficiency and robustness of model updating and prediction for handling large-scale FE models possessing a large number of uncertain model parameters. The proposed methodology is validated through the model updating and prediction conducted on a real-life pedestrian bridge based on field-testing data. © 2021 American Society of Civil Engineers.","Bayesian regularization; Gauss-Newton method; Model prediction; Nonlinear least-squares algorithm; Structural model updating","Bayesian networks; Footbridges; Inference engines; Least squares approximations; Modal analysis; Newton-Raphson method; Parameter estimation; Structural health monitoring; Uncertainty analysis; Bayesian regularization; Finite element modelling (FEM); Finite-element model updating; Gauss-Newton's method; Model prediction; Model updating; Non-linear least squares algorithms; Structural finite elements; Structural model updating; Updated model; Forecasting",,,,,"National Natural Science Foundation of China, NSFC: 51778506; Institute of Engineering Mechanics, China Earthquake Administration, IEM, CEA: 2019EEEVL0401","The author gratefully acknowledges the financial support provided by the National Natural Science Foundation of China (Grant No. 51778506) and the Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Grant No. 2019EEEVL0401). The author would also like to thank the editor and the anonymous reviewers for their constructive comments and valuable suggestions to improve the quality of the article.",,,,,,,,,,"Alkayem, N.F., Cao, M.S., Zhang, Y.F., Bayat, M., Su, Z.Q., Structural damage detection using finite element model updating with evolutionary algorithms: A survey (2018) Neural Comput. Appl., 30 (2), pp. 389-411. , https://doi.org/10.1007/s00521-017-3284-1; Au, S.K., Connecting Bayesian and frequentist quantification of parameter uncertainty in system identification (2012) Mech. Syst. Signal Process., 29 (MAY), pp. 328-342. , https://doi.org/10.1016/j.ymssp.2012.01.010; Bansal, S., Cheung, S.H., Stochastic simulation algorithm for robust reliability updating of structural dynamic systems based on incomplete modal data (2017) ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A: Civ. Eng., 3 (4), p. 04017008. , https://doi.org/10.1061/AJRUA6.0000911; Bartilson, D.T., Jang, J., Smyth, A.W., Finite element model updating using objective-consistent sensitivity-based parameter clustering and Bayesian regularization (2019) Mech. Syst. Signal Process., 114 (JAN), pp. 328-345. , https://doi.org/10.1016/j.ymssp.2018.05.024; Beck, J.L., Katafygiotis, L.S., Updating models and their uncertainties I: Bayesian statistical framework (1998) J. Eng. Mech., 124 (4), pp. 455-461. , https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4(455); Behmanesh, I., Moaveni, B., Lombaert, G., Papadimitriou, C., Hierarchical Bayesian model updating for structural identification (2015) Mech. Syst. Signal Process., 64-65 (DEC), pp. 360-376. , https://doi.org/10.1016/j.ymssp.2015.03.026; Ben-Haim, Y., Cogan, S., Sanseigne, L., Usability of mathematical models in mechanical decision processes (1998) Mech. Syst. 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Signal Process., 127 (JUL), pp. 306-327. , https://doi.org/10.1016/j.ymssp.2019.03.009; Yu, L., Yin, T., Damage identification in frame structures based on FE model updating (2010) J. Vib. Acoust., 132 (5), p. 051007. , https://doi.org/10.1115/1.4002125; Yuen, K.V., Beck, J.L., Katafygiotis, L.S., Efficient model updating and health monitoring methodology using incomplete modal data without mode matching (2006) Struct. Control Health Monit., 13 (1), pp. 91-107. , https://doi.org/10.1002/stc.144; Yuen, K.V., Kuok, S.C., Dong, L., Self-calibrating Bayesian real-time system identification (2019) Comput.-Aided Civ. Infrastruct. Eng., 34 (9), pp. 806-821. , https://doi.org/10.1111/mice.12441; Yuen, K.V., Ortiz, G.A., Multiresolution Bayesian nonparametric general regression for structural model updating (2017) Struct. Control Health Monit., 25 (2), p. 2077. , https://doi.org/10.1002/stc.2077; Zhang, F.L., Ni, Y.C., Lam, H.F., Bayesian structural model updating using ambient vibration data collected by multiple setups (2017) Struct. Control Health Monit., 24 (12), p. 2023. , https://doi.org/10.1002/stc.2023","Yin, T.; School of Civil Engineering, China; email: tyin@whu.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,23767642,,,,"English","ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A. Civ. Eng.",Article,"Final","",Scopus,2-s2.0-85118124443
"Ghahari F., Malekghaini N., Ebrahimian H., Taciroglu E.","57444742200;57445607700;57112070500;6602889035;","Bridge Digital Twinning Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements",2022,"Sensors","22","3","1278","","",,2,"10.3390/s22031278","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124304449&doi=10.3390%2fs22031278&partnerID=40&md5=a94857cf5d412b0ac40c919eebdda28a","Department of Civil & Environmental Engineering, University of California, Los Angeles, CA  90095, United States; Department of Civil & Environmental Engineering, University of Nevada, Reno, NV  89557, United States","Ghahari, F., Department of Civil & Environmental Engineering, University of California, Los Angeles, CA  90095, United States; Malekghaini, N., Department of Civil & Environmental Engineering, University of Nevada, Reno, NV  89557, United States; Ebrahimian, H., Department of Civil & Environmental Engineering, University of Nevada, Reno, NV  89557, United States; Taciroglu, E., Department of Civil & Environmental Engineering, University of California, Los Angeles, CA  90095, United States","Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emer-gency response management and recovery. This can be facilitated using digital technologies to re-move the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agree-ment between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The described process is a practice of digital twinning and the updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","Bayesian inference; Damage diagnosis; Digital twin; Finite element model updating; Foundation input motion; Rapid post-earthquake assessment; Soil-structure interaction; Structural health monitoring","Bayesian networks; Damage detection; Decision making; Earthquakes; Finite element method; Inference engines; Soils; Structural health monitoring; Bayesian inference; Bayesian model updating; Damage diagnosis; Finite element modelling (FEM); Finite-element model updating; Foundation input motion; Model updating techniques; Rapid post-earthquake assessment; Soil-structure interaction; Uncertainty; Soil structure interactions; acceleration; adult; article; California; digital twin; earthquake; finite element analysis; human; motion; soil structure; uncertainty",,,,,"1014-963; California Department of Transportation, CT: 65A0450","The work presented in this manuscript was funded, in part, by the California Geological Survey (Contract No. 1014-963) and by the California Department of Transportation (Grant No. 65A0450). Any opinions, findings, conclusions, or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of the sponsors.",,,,,,,,,,"Karoumi, R., Some modeling aspects in the nonlinear finite element analysis of cable supported bridges (1999) Comput. Struct, 71, pp. 397-412. , https://doi.org/10.1016/S0045-7949(98)00244-2; Shamsabadi, A., Rollins, K.M., Kapuskar, M., Nonlinear soil–abutment–bridge structure interaction for seismic performance-based design (2007) J. Geotech. Geoenvironmental Eng, 133, pp. 707-720. , https://doi.org/10.1061/(ASCE)1090-0241(2007)133:6(707); Johnson, N., Saiidi, M.S., Sanders, D., Nonlinear earthquake response modeling of a large-scale two-span concrete bridge (2009) J. Bridg. 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Eng, 7, pp. 213-227. , https://doi.org/10.1016/S0267-7261(88)80005-8; Ebrahimian, H., Astroza, R., Conte, J.P., de Callafon, R.A., Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation (2017) Mech. Syst. Signal Process, 84, pp. 194-222. , https://doi.org/10.1016/j.ymssp.2016.02.002; Nabiyan, M.S., Khoshnoudian, F., Moaveni, B., Ebrahimian, H., Mechanics-based model updating for identification and virtual sensing of an offshore wind turbine using sparse measurements (2021) Struct. Control Health Monit, 28, p. e2647. , https://doi.org/10.1002/stc.2647","Ebrahimian, H.; Department of Civil & Environmental Engineering, United States; email: hebrahimian@unr.edu",,,"MDPI",,,,,14248220,,,"35162022","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85124304449
"Vagnoli M., Remenyte-Prescott R., Andrews J.","56798645300;24175194400;7403360345;","A Bayesian Belief Network method for bridge deterioration detection",2021,"Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability","235","3",,"338","355",,2,"10.1177/1748006X20979225","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097628319&doi=10.1177%2f1748006X20979225&partnerID=40&md5=6769c9e141650e46405922ccd9b0c35b","Resilience Engineering Research Group, University of Nottingham, Nottingham, United Kingdom","Vagnoli, M., Resilience Engineering Research Group, University of Nottingham, Nottingham, United Kingdom; Remenyte-Prescott, R., Resilience Engineering Research Group, University of Nottingham, Nottingham, United Kingdom; Andrews, J., Resilience Engineering Research Group, University of Nottingham, Nottingham, United Kingdom","Bridges are one of the most important assets of transportation networks. A closure of a bridge can increase the vulnerability of the geographic area served by such networks, as it reduces the number of available routes. Condition monitoring and deterioration detection methods can be used to monitor the health state of a bridge and enable detection of early signs of deterioration. In this paper, a novel Bayesian Belief Network (BBN) methodology for bridge deterioration detection is proposed. A method to build a BBN structure and to define the Conditional Probability Tables (CPTs) is presented first. Then evidence of the bridge behaviour (such as bridge displacement or acceleration due to traffic) is used as an input to the BBN model, the probability of the health state of whole bridge and its elements is updated and the levels of deterioration are detected. The methodology is illustrated using a Finite Element Model (FEM) of a steel truss bridge, and for an in-field post-tensioned concrete bridge. © IMechE 2020.","Bayesian Belief Network; bridge deterioration; detection and diagnostics; structural health monitoring","Condition monitoring; Deterioration; Steel bridges; Trusses; Bridge deterioration; Bridge displacement; Conditional probability tables; Detection methods; Geographic areas; Post-tensioned concrete; Steel truss bridge; Transportation network; Bayesian networks",,,,,"Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA: 642453","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 642453.",,,,,,,,,,"(2012) EU transport in figures (statistical pocketbook), , Brussels, European Commission; 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Dowling, J., Obrien, E.J., González, A., Adaptation of cross entropy optimisation to a dynamic bridge WIM calibration problem (2012) Eng Struct, 44, pp. 13-22; Zhao, X., Liu, H., Yu, Y., Bridge displacement monitoring method based on laser projection-sensing technology (2015) Sensors, 15 (4), pp. 8444-8463; Ni, Y.Q., Xia, H.W., Wong, K.Y., In-service condition assessment of bridge deck using long-term monitoring data of strain response (2012) J Bridge Eng, 17 (6), pp. 876-885; Siringoringo, D.M., Fujino, Y., Nagayama, T., Dynamic characteristics of an overpass bridge in a full-scale destructive test (2013) J Eng Mech, 139 (6), pp. 691-701","Remenyte-Prescott, R.; Resilience Engineering Research Group, United Kingdom; email: r.remenyte-prescott@nottingham.ac.uk",,,"SAGE Publications Ltd",,,,,1748006X,,,,"English","Proc. Inst. Mech. Eng. Part O J. Risk Reliab.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85097628319
"Savino P., Gherlone M., Tondolo F., Greco R.","57211552185;6505966217;23668913100;7103137160;","Shape-sensing of beam elements undergoing material nonlinearities",2021,"Sensors (Switzerland)","21","2","528","1","17",,2,"10.3390/s21020528","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099359729&doi=10.3390%2fs21020528&partnerID=40&md5=374b8679c6600f52a98f027feb12bf5f","Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Department of Civil Engineering, Environmental, Territory, Building and Chemical, Politecnico di Bari, Via Edoardo Orabona 4, Bari, 70125, Italy","Savino, P., Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Gherlone, M., Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Tondolo, F., Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Greco, R., Department of Civil Engineering, Environmental, Territory, Building and Chemical, Politecnico di Bari, Via Edoardo Orabona 4, Bari, 70125, Italy","The use of in situ strain measurements to reconstruct the deformed shape of structures is a key technology for real-time monitoring. A particularly promising, versatile and computationally efficient method is the inverse finite element method (iFEM), which can be used to reconstruct the displacement field of beam elements, plate and shell structures from some discrete strain measure-ments. The iFEM does not require the knowledge of the material properties. Nevertheless, it has always been applied to structures with linear material constitutive behavior. In the present work, advances are proposed to use the method also for concrete structures in civil engineering field such as bridges normally characterized by material nonlinearities due to the behavior of both steel and concrete. The effectiveness of iFEM, for simply supported reinforced concrete beam and continuous beams with load conditions that determine the yielding of reinforcing steel, is studied. In order to assess the influence on displacements and strains reconstructions, different measurement stations and mesh configurations are considered. Hybrid procedures employing iFEM analysis supported by bending moment-curvature relationship are proposed in case of lack of input data in plastic zones. The reliability of the results obtained is tested and commented on to highlight the effectiveness of the approach. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Displacements; IFEM; Nonlinearities; Strain monitoring; Structural health monitoring","Bridges; Concrete construction; Inverse problems; Reinforced concrete; Strain; Computationally efficient; Constitutive behaviors; Inverse finite element methods; Material non-linearity; Moment-curvature relationship; Plate and shell structures; Real time monitoring; Reinforced concrete beams; Concrete beams and girders",,,,,,,,,,,,,,,,"Lynch, J.P., Loh, K.J., A summary review of wireless sensors and sensor networks for structural health monitoring (2006) Shock Vib. Dig, 38, pp. 91-128. , [CrossRef]; Tondolo, F., Cesetti, A., Matta, E., Quattrone, A., Sabia, D., Smart reinforcement steel bars with low-cost MEMS sensors for the structural health monitoring of RC structures (2018) Constr. Build. Mater, 173, pp. 740-753. , [CrossRef]; Tondolo, F., Matta, E., Quattrone, A., Sabia, D., Experimental test on an RC beam equipped with embedded barometric pressure sensors for strains measurement (2019) Smart Mater. Struct, 28, p. 055040. , [CrossRef]; Ko, W.L., Richards, W.L., Fleischer, V.T., (2009) Applications of the Ko Displacement Theory to the Deformed Shape Predictions of the Doubly-Tapered Ikhana Wing, p. 214652. , https://ntrs.nasa.gov/citations/20090040594, (accessed on 12 January 2021); Kang, L.H., Kim, D.K., Han, J.H., Estimation of dynamic structural displacements using fiber Bragg grating strain sensors (2007) J. Sound Vib, 305, pp. 534-542. , [CrossRef]; Bruno, R., Toomarian, N., Salama, M., Shape estimation from incomplete measurements: A neural-net approach (1994) Smart Mater. Struct, 3, pp. 92-97. , [CrossRef]; Tessler, A., Spangler, J.L., (2003) A Variational Principal for Reconstruction of Elastic Deformation of Shear Deformable Plates and Shells, p. 212445. , NASA Technical Memorandum, Langley Research Center: Hampton, VA, USA; Tessler, A., Spangler, J.L., A least-squares variational method for full-field reconstruction of elastic deformations in shear-deformable plates and shells (2005) Comput. Methods Appl. Mech. Eng, 194, pp. 327-339. , [CrossRef]; Kefal, A., Oterkus, E., Tessler, A., Spangler, J.L., A quadrilateral inverse-shell element with drilling degrees of freedom for shape sensing and structural health monitoring (2016) Eng. Sci. Technol. Int. J, 19, pp. 1299-1313. , [CrossRef]; Kefal, A., An efficient curved inverse-shell element for shape sensing and structural health monitoring of cylindrical marine structures (2019) Ocean Eng, 188, p. 106262. , [CrossRef]; Kefal, A., Oterkus, E., Isogeometric iFEM Analysis of Thin Shell Structures (2020) Sensors, 20, p. 2685. , [CrossRef] [PubMed]; Zhao, F., Xu, L., Bao, H., Du, J., Shape sensing of variable cross-section beam using the inverse finite element method and isogeometric analysis (2020) Measurement, 158, p. 107656. , [CrossRef]; Gherlone, M., (2008) Beam Inverse Finite Element Formulation. LAQ Report 2008, , Politecnico di Torino: Torino, Italy; Gherlone, M., Cerracchio, P., Mattone, M., Di Sciuva, M., Tessler, A., Shape sensing of 3D frame structures using an inverse finite element method (2012) Int. J. Solid Struct, 49, pp. 3100-3112. , [CrossRef]; Savino, P., Gherlone, M., Tondolo, F., Shape sensing with inverse Finite Element Method for slender structures (2019) Struct. Eng. Mech, 72, pp. 217-227; Savino, P., Tondolo, F., Gherlone, M., Tessler, A., Application of Inverse Finite Element Method to Shape Sensing of Curved Beams (2020) Sensors, 20, p. 7012. , [CrossRef] [PubMed]; Tessler, A., Roy, R., Esposito, M., Surace, C., Gherlone, M., Shape sensing of plate and shell structures undergoing large displacements using the inverse Finite Element Method (2018) Shock Vib, 2018, p. 8076085. , [CrossRef]; Quach, C.C., Vazquez, S.L., Tessler, A., Moore, J.P., Cooper, E.G., Spangler, J.L., Structural anomaly detection using fiber optic sensors and inverse finite element method (2005) Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit, , San Francisco, CA, USA, 15–18 August; Vazquez, S.L., Tessler, A., Quach, C.C., Cooper, E.G., Parks, J., Spangler, J.L., (2005) Structural Health Monitoring Using High-Density Fiber Optic Strain Sensor and Inverse Finite Element Methods, , NASA Technical Memorandum, Langley Research Center: Hampton, VA, USA; Cerracchio, P., Gherlone, M., Tessler, A., Real-time displacement monitoring of a composite stiffened panel subjected to mechanical and thermal loads (2015) Meccanica, 50, pp. 2487-2496. , [CrossRef]; Kefal, A., Oterkus, E., Structural health monitoring of marine structures by using inverse finite element method (2015) Proceedings of the 5th International Conference on Marine Structures (MARSTRUCT), , Southampton, UK, 25–27 March; Kefal, A., Oterkus, E., Displacement and stress monitoring of a chemical tanker based on inverse finite element method (2016) Ocean Eng, 112, pp. 33-46. , [CrossRef]; Kefal, A., Oterkus, E., Displacement and stress monitoring of a Panamax containership using inverse finite element method (2016) Ocean Eng, 119, pp. 16-29. , [CrossRef]; Li, M., Kefal, A., Oterkus, E., Oterkus, S., Structural health monitoring of an offshore wind turbine tower using iFEM methodology (2020) Ocean Eng, 204, p. 107291. , [CrossRef]; Gherlone, M., Cerracchio, P., Mattone, M., Shape sensing methods: Review and experimental comparison on a wing-shaped plate (2018) Prog. Aerosp. Sci, 99, pp. 14-26. , [CrossRef]; Gherlone, M., Cerracchio, P., Mattone, M., Di Sciuva, M., Tessler, A., An inverse finite element method for beam shape sensing: Theoretical framework and experimental validation (2014) Smart Mater. Struct, 23, p. 045027. , [CrossRef]; Zhao, Y., Bao, H., Duan, X., Fang, H., The application research of inverse Finite Element Method for frame deformation estimation (2017) Int. J. Aerosp. Eng, 2017, p. 1326309. , [CrossRef]; Esposito, M., Gherlone, M., Composite wing box deformed-shape reconstruction based on measured strains: Optimization and comparison of existing approaches (2020) Aerosp. Sci. Technol, 99, p. 105758. , [CrossRef]","Savino, P.; Department of Structural, Corso Duca degli Abruzzi 24, Italy; email: pierclaudio.savino@polito.it",,,"MDPI AG",,,,,14248220,,,"33450965","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85099359729
"Byun N., Lee J., Lee K., Kang Y.-J.","57212105063;57195677690;56729284500;7402784706;","Estimation of structural deformed configuration for bridges using multi-response measurement data",2021,"Applied Sciences (Switzerland)","11","9","4000","","",,2,"10.3390/app11094000","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105716392&doi=10.3390%2fapp11094000&partnerID=40&md5=c489ebd0500765db426de358df46a5a7","School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 02841, South Korea; Future and Fusion Laboratory of Architectural, Civil and Environmental Engineering, Korea University, Seoul, 02841, South Korea; Department of Urban Infrastructure Research, Seoul Institute of Technology, Seoul, 03909, South Korea","Byun, N., School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 02841, South Korea; Lee, J., Future and Fusion Laboratory of Architectural, Civil and Environmental Engineering, Korea University, Seoul, 02841, South Korea; Lee, K., Department of Urban Infrastructure Research, Seoul Institute of Technology, Seoul, 03909, South Korea; Kang, Y.-J., School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 02841, South Korea","The structural deformed shape (SDS) is considered an important factor for evaluating structural conditions owing to its direct relationship with structural stiffness. Recently, an SDS estimation method based on displacement data from a limited number of data points was developed. Although the method showed good performance with a sufficient number of measured data points, application of the SDS estimation method for on-site structures has been quite limited because collecting sufficient displacement data measured from a Global Navigation Satellite System (GNSS) can be quite expensive. Thus, the development of an affordable SDS estimation method with a certain level of accuracy is essential for field application of the SDS estimation technique. This paper proposes an improved SDS estimation method using displacement data combined with additional slope and strain data that can improve the accuracy of the SDS estimation method and reduce the required number of GNSSs. The estimation algorithm was established based on shape superposition with various combined response data (displacement, slope, and strain) and the least-squares method. The proposed SDS estimation method was verified using a finite element method model. In the validation process, three important issues that may affect the estimation accuracy were analyzed: effect of shape function type, sensor placement method, and effectiveness of using multi-response data. Then, the improved SDS estimation method developed in this study was compared with existing SDS estimation methods from the literature. Consequently, it was found that the proposed method can reduce the number of displacement data required to estimate rational SDS by using additional slope and strain data. It is expected that cost-effective structural health monitoring (SHM) can be established using the proposed estimation method. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Estimation method; Multi-response data; SDS; SHM",,,,,,"National Research Foundation of Korea, NRF: 2020R1A2C201445012","Funding: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MIST) (No. 2020R1A2C201445012).",,,,,,,,,,"Chae, M., Yoo, H., Kim, J., Cho, M., Development of a wireless sensor network system for suspension bridge health monitoring (2012) Autom. Constr, 21, pp. 237-252. , [CrossRef]; Zhou, G.-D., Yi, T.-H., Recent Developments on Wireless Sensor Networks Technology for Bridge Health Monitoring (2013) Math. Probl. Eng, 2013, pp. 1-33. , [CrossRef]; Chen, B., Wang, X., Sun, D., Xie, X., Integrated System of Structural Health Monitoring and Intelligent Management for a Cable-Stayed Bridge (2014) Sci. World J, 2014, pp. 1-12. , [CrossRef] [PubMed]; Marchewka, A., Ziółkowski, P., Aguilar-Vidal, V., Framework for Structural Health Monitoring of Steel Bridges by Computer Vision (2020) Sensors, 20, p. 700. , [CrossRef] [PubMed]; Rashidi, M., Mohammadi, M., Kivi, S.S., Abdolvand, M., Truong-Hong, L., Samali, B., A Decade of Modern Bridge Monitoring Using Terrestrial Laser Scanning: Review and Future Directions (2020) Remote Sens, 12, p. 3796. , [CrossRef]; Park, K.-T., Kim, S.-H., Park, H.-S., Lee, K.-W., The determination of bridge displacement using measured acceleration (2005) Eng. Struct, 27, pp. 371-378. , [CrossRef]; Lee, H.S., Hong, Y.H., Park, H.W., Design of an FIR filter for the displacement reconstruction using measured acceleration in low-frequency dominant structures (2009) Int. J. Numer. Methods Eng, 82, pp. 403-434. , [CrossRef]; Park, J.-W., Sim, S.-H., Jung, H.-J., Spencer, B.F., Development of a Wireless Displacement Measurement System Using Acceleration Responses (2013) Sensors, 13, pp. 8377-8392. , [CrossRef]; Cho, S., Sim, S.-H., Park, J.-W., Lee, J., Extension of indirect displacement estimation method using acceleration and strain to various types of beam structures (2014) Smart Struct. Syst, 14, pp. 699-718. , [CrossRef]; Cho, S., Yun, C.-B., Sim, S.-H., Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model (2015) Smart Struct. Syst, 15, pp. 645-663. , [CrossRef]; Hou, X., Yang, X., Huang, Q., Using Inclinometers to Measure Bridge Deflection (2005) J. Bridg. Eng, 10, pp. 564-569. , [CrossRef]; Foss, G.C., Haugse, E.D., Using modal test results to develop strain to displacement transformation (1995) Proceedings of the 13th International Modal Analysis Conference, pp. 112-118. , Nashville, TN, USA, 13–16 February; Shin, S., Lee, S.-U., Kim, Y., Kim, N.-S., Estimation of bridge displacement responses using FBG sensors and theoretical mode shapes (2012) Struct. Eng. Mech, 42, pp. 229-245. , [CrossRef]; Kang, L.-H., Kim, D.-K., Han, J.-H., Estimation of dynamic structural displacements using fiber Bragg grating strain sensors (2007) J. Sound Vib, 305, pp. 534-542. , [CrossRef]; Rapp, S., Kang, L.-H., Han, J.-H., Mueller, U.C., Baier, H., Displacement field estimation for a two-dimensional structure using fiber Bragg grating sensors (2009) Smart Mater. Struct, 18, p. 025006. , [CrossRef]; Cho, S., Park, J.-W., Palanisamy, R.P., Sim, S.-H., Reference-Free Displacement Estimation of Bridges Using Kalman Filter-Based Multimetric Data Fusion (2016) J. Sens, 2016, pp. 1-9. , [CrossRef]; Li, L., Zhong, B.-S., Li, W.-Q., Sun, W., Zhu, X.-J., Structural shape reconstruction of fiber Bragg grating flexible plate based on strain modes using finite element method (2017) J. Intell. Mater. Syst. Struct, 29, pp. 463-478. , [CrossRef]; Deng, H., Zhang, H., Wang, J., Zhang, J., Ma, M., Zhong, X., Modal learning displacement-strain transformation (2019) Rev. Sci. Instrum, 90, p. 075113. , [CrossRef] [PubMed]; Kliewer, K., Glisic, B., A Comparison of Strain-Based Methods for the Evaluation of the Relative Displacement of Beam-Like Structures (2019) Front. Built Environ, 5, p. 118. , [CrossRef]; Choi, J., Lee, K., Kang, Y., Evaluation of quasi-static responses using displacement data from a limited number of points on a structure (2017) Int. J. Steel Struct, 17, pp. 1211-1224. , [CrossRef]; Choi, J., Lee, K., Kang, Y., Quasi-static responses estimation of a cable-stayed bridge from displacement data at a limited number of points (2017) Int. J. Steel Struct, 17, pp. 789-800. , [CrossRef]; Datta, B.N., (2010) Numerical Linear Algebra and Applications, , 2nd ed.; John Wiley &Sons: Hoboken, NJ, USA; Meo, M., Zumpano, G., On the optimal sensor placement techniques for a bridge structure (2005) Eng. Struct, 27, pp. 1488-1497. , [CrossRef]; Worden, K., Burrows, A., Optimal sensor placement for fault detection (2001) Eng. Struct, 23, pp. 885-901. , [CrossRef]; Sunca, F., Okur, F.Y., Altunişik, A.C., Kahya, V., Optimal Sensor Placement for Laminated Composite and Steel Cantilever Beams by the Effective Independence Method (2021) Struct. Eng. Int, 31, pp. 85-92. , [CrossRef]; Kammer, D., Sensor Placement for On-Orbit Modal Identification and Correlation of Large Space Structures (1991) J. Guid. Control Dyn, 14, pp. 251-259. , [CrossRef]; Papadopoulos, M., Garcia, E., Sensor placement methodologies for dynamic testing (1998) AIAA J, 36, pp. 256-263. , [CrossRef]","Kang, Y.-J.; School of Civil, South Korea; email: yjkang@korea.ac.kr",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85105716392
"Mustafa S., Sekiya H., Miki C.","56730098100;57130223000;35616512100;","Determining the location of sensors for seismic damage detection in steel girder bridges with elastomeric bearings",2020,"JVC/Journal of Vibration and Control","26","19-20",,"1779","1790",,2,"10.1177/1077546320905176","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079128948&doi=10.1177%2f1077546320905176&partnerID=40&md5=6c449fb3bb4e15451177dec72f950fe2","Advanced Research Laboratories, Tokyo City University, Japan; Department of Urban and Civil Engineering, Tokyo City University, Japan; Tokyo City University, Japan","Mustafa, S., Advanced Research Laboratories, Tokyo City University, Japan; Sekiya, H., Department of Urban and Civil Engineering, Tokyo City University, Japan; Miki, C., Tokyo City University, Japan","In planning a bridge health monitoring with minimum sensors for long-term monitoring, it is necessary to accurately predict the bridge behavior including its nonlinearity and identify the damaged bridge components when a strong earthquake strikes. This article presents a methodology for the selection of sensors and their arrangement for detecting seismic damage in an in-service steel plate girder bridge system. In this study, a detailed span-based model was developed for the finite element simulation including the effect of the rubber bearing and piers, and the damage control by the side blocks. The finite element dynamic simulation was carried out with input earthquake acceleration to investigate the seismic behavior and grasp the damageable parts during an earthquake. Based on the results of finite element dynamic simulation, a fault tree analysis was carried out to reveal more about the bridge behavior, the failure modes, and the occurrence of damage. It was found that the side block, the bearing stiffener, and the horizontal bracing on the fixed side of the bridge are most important to be monitored for the evaluation of soundness of a plate girder bridge immediately after an earthquake. Finally, a sensor arrangement for the bridge was proposed based on the analysis results. © The Author(s) 2020.","earthquake damage; fault tree analysis; Plate girder; rubber bearing; seismic response analysis; sensor placement","Bearings (structural); Bridge components; Damage detection; Fault tree analysis; Faulting; Finite element method; Nonmetallic bearings; Plate girder bridges; Rubber; Seismic response; Structural analysis; Earthquake damages; Plate girder; Rubber bearing; Seismic response analysis; Sensor placement; Earthquakes",,,,,,,,,,,,,,,,"Aye, M.N., Kasai, A., Shigeishi, M., An investigation of damage mechanism induced by earthquake in a plate girder bridge based on seismic response analysis: case study of Tawarayama bridge under the 2016 Kumamoto earthquake (2018) Advances in Civil Engineering, 2018, pp. 1-19; Bruneau, M., Performance of steel bridges during the 1995 Hyogoken-Nanbu (Kobe, Japan) earthquake-a North American perspective (1998) Engineering Structures, 20 (12), pp. 1063-1078; Cho, S., Yun, C.B., Lynch, J.P., Smart wireless sensor technology for structural health monitoring of civil structures (2008) Steel Structures, 8, pp. 267-275; Christensen, R.M., Observations on the definition of yield stress (2008) Acta Mechanica, 196 (3-4), pp. 239-244; Filipov, E.T., Revell, J.R., Fahnestock, L.A., Seismic performance of highway bridges with fusing bearing components for quasi-isolation (2013) Earthquake Engineering & Structural Dynamics, 42 (9), pp. 1375-1394; Hsu, Y.T., Fu, C.C., Seismic effect on highway bridges in Chi Chi earthquake (2004) Journal of Performance of Constructed Facilities, 18 (1), pp. 47-53; Ishihara, K., Matsumura, M., Yoshida, M., Knock-off effect of steel side block as displacement restrainers on dynamic response of isolated bridge structure (2011) Procedia Engineering, 14, pp. 2341-2349; Itani, A.M., Bruneau, M., Carden, L., Seismic behavior of steel girder bridge superstructures (2004) Journal of Bridge Engineering, 9 (3), pp. 243-249; (2018) Bridges Yearbook Database, , www.jasbc.or.jp/kyoryodb/index.cgi, (accessed 3 December 2018; (2004) Handbook of Road Bridge Bearing, , Tokyo, Japan, Japan Road Association; (2012) Specifications for Highway Bridges Part 5 Seismic Design, , Tokyo, Japan, Japan Road Association; Jara, J.M., Raya, G., Olmos, B.A., Applicability of equivalent linearization methods to irregular isolated bridges (2017) Engineering Structures, 141, pp. 495-511; Kawashima, K., Unjoh, S., The damage of highway bridges in the 1995 Hyogo-ken Nanbu earthquake and its impact on Japanese seismic design (1997) Journal of Earthquake Engineering, 1 (3), pp. 505-541; Koto, Y., Konishi, T., Sekiya, H., Monitoring local damage due to fatigue in plate girder bridge (2019) Journal of Sound and Vibration, 438, pp. 238-250; Kozak, D.L., LaFave, J.M., Fahnestock, L.A., Seismic modeling of integral abutment bridges in Illinois (2018) Engineering Structures, 165, pp. 170-183; Matsumoto, K., Bridge monitoring system for Tokyo Gate Bridge (2012) Bridge Foundation Engineering, 46 (9), pp. 37-40; Matusevich, A.E., Massa, J.C., Mancini, R.A., Computation and uncertainty evaluation of offset yield strength (2013) Journal of Testing and Evaluation, 41 (2), pp. 217-230; Monzon, E.V., Buckle, I.G., Itani, A.M., Seismic performance and response of seismically isolated curved steel I-girder bridge (2016) Journal of Structural Engineering, 142 (12), p. 04016121; Murakoshi, J., Takahashi, M., Yoshioka, T., A study on application of FEM analysis to the design of steel girder bridge (2004) Steel Construction Engineering, 11 (43), pp. 131-145; Mustafa, S., Matsumoto, Y., Yamaguchi, H., Vibration-based health monitoring of an existing truss bridge using energy-based damping evaluation (2018) Journal of Bridge Engineering, 23 (1), p. 04017114; Nazmy, A.S., Seismic analysis and design evaluation of continuous plate-girder bridges: a case study (2003) International Journal of Structural Stability and Dynamics, 3 (1), pp. 91-106; Overschee, P.V., Moor, B.D., Subspace algorithms for the stochastic identification problem (1993) Automatica, 29 (3), pp. 649-660; Ren, W.-X., Zatar, W., Harik, I.E., Ambient vibration-based seismic evaluation of a continuous girder bridge (2004) Engineering Structures, 26 (5), pp. 631-640; Siringoringo, D.M., Fujino, Y., System identification applied to long-span cable-supported bridges using seismic records (2008) Earthquake Engineering & Structural Dynamics, 37 (3), pp. 361-386; Takahashi, Y., Hoshikuma, J.-I., Damage to road bridges induced by ground motion in the 2011 Great East Japan earthquake (2013) Journal of JSCE, 1 (1), pp. 398-410; Usami, T., Lu, Z., Ge, H., Seismic performance evaluation of steel arch bridges against major earthquakes. Part 1: dynamic analysis approach (2004) Earthquake Engineering & Structural Dynamics, 33 (14), pp. 1337-1354","Mustafa, S.; Advanced Research Laboratories, Japan; email: samim@tcu.ac.jp",,,"SAGE Publications Inc.",,,,,10775463,,JVCOF,,"English","JVC/J Vib Control",Article,"Final","",Scopus,2-s2.0-85079128948
"Zhuang M., Miao C., Chen R.","56957884900;56416640100;57207860111;","Fatigue performance analysis and evaluation for steel box girder based on structural health monitoring system",2020,"SDHM Structural Durability and Health Monitoring","14","1",,"51","79",,2,"10.32604/sdhm.2020.07663","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082773619&doi=10.32604%2fsdhm.2020.07663&partnerID=40&md5=73f4429c8a03e194b45c0467d3b7e0f5","Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, Southeast University, Nanjing, 210096, China; School of Civil Engineering, Southeast University, Nanjing, 210096, China; School of Civil, Environmental & Mining Engineering, University of Adelaide, Adelaide, SA  5005, Australia","Zhuang, M., Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, Southeast University, Nanjing, 210096, China, School of Civil Engineering, Southeast University, Nanjing, 210096, China, School of Civil, Environmental & Mining Engineering, University of Adelaide, Adelaide, SA  5005, Australia; Miao, C., Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, Southeast University, Nanjing, 210096, China, School of Civil Engineering, Southeast University, Nanjing, 210096, China; Chen, R., Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, Southeast University, Nanjing, 210096, China, School of Civil Engineering, Southeast University, Nanjing, 210096, China","Taizhou Yangtze River Bridge as a long-span suspension bridge, the finite element model (FEM) of it is established using the ANSYS Software. The beam4 element is used to simulate the main beam to establish the ""spine beam"" model of the Taizhou Yangtze River Bridge. The calculated low-order vibration mode frequency of the FEM is in good agreement with the completion test results. The model can simulate the overall dynamic response of the bridge. Based on the vehicle load survey, the Monte Carlo method is applied to simulate the traffic load flow. Then the overall dynamic response analysis of FEM is carried out. Taking the bending moment of the main beam as the control index, the fatigue sensitive section in the steel box girder of FEM is analyzed. Based on the strain time history data of steel box girder recorded by the structural health monitoring system (SHM), the true stress response of steel box girder under vehicle load is extracted. Taking the cumulative fatigue damage increment as the evaluation index, the fatigue performance evaluation of the steel box girders is conducted based on the collected health monitoring data. The fatigue effect of the beam section near the steel tower, especially the first section of the middle tower, is the key section of the fatigue analysis by health morning system, which is consistent with the calculation results of FEM. © 2020 Tech Science Press. All rights reserved.","Fatigue; Monte Carlo method; Steel box girder; Stress response; Structural health monitoring system","Beams and girders; Box girder bridges; Dynamic response; Electric load flow; Monitoring; Monte Carlo methods; Steel structures; Structural health monitoring; Cumulative fatigue damage; Dynamic response analysis; Fatigue performance; Long span suspension bridges; Steel box girders; Stress response; Structural health monitoring systems; Yangtze river bridge; Fatigue of materials",,,,,"2017YFC0806001; National Natural Science Foundation of China, NSFC: 51778135; China Scholarship Council, CSC; Natural Science Foundation of Jiangsu Province: BK20160207; Aeronautical Science Foundation of China: 20130969010; Fundamental Research Funds for the Central Universities","Funding Statement: This research has been supported by the National Natural Science Foundation of China (Grant No. 51778135), the National Key R&D Program Foundation of China (Grant No. 2017YFC0806001), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20160207), and Aeronautical Science Foundation of China (Grant No. 20130969010), the Fundamental Research Funds for the Central Universities and Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX18_0113 and KYLX16_0253). This research also has been supported by China Scholarship Council.",,,,,,,,,,"Wu, C., Yuan, Y., Jiang, X., Fatigue behavior assessment method of the orthotropic steel deck for a selfanchored suspension railway bridge (2016) Procedia Engineering, 161, pp. 91-96; Yang, M.Y., Kainuma, S., Jeong, Y.S., Structural behavior of orthotropic steel decks with artificial cracks in longitudinal ribs (2018) Journal of Constructional Steel Research, 141, pp. 132-144; Deng, Y., Li, A.Q., Feng, D.M., Fatigue reliability assessment for orthotropic steel decks based on long term strain monitoring (2018) Sensors, 18 (1), p. 181; Farrar, C.R., James, G.H., System identification from ambient vibration measurements on bridges (1997) Journal of Sound and Vibration, 205 (1), pp. 1-18; Seo, J., Hu, J., Lee, J., Summary review of structural health monitoring applications for highway bridges (2016) Journal of Performance of Constructed Facilities, 30 (4), p. 4015072; Wenzel, H., (2009) Health Monitoring of Bridges, , New York: John Wiley & Sons, Inc; Sousa, H., Félix, C., Bento, J., Figueiras, J., Design and implementation of a monitoring system applied to a long-span prestressed concrete bridge (2011) Structural Concrete, 12 (2), pp. 82-93; Sousa, H., Cavadas, F., Henriques, A., Bento, J., Figueiras, J., Bridge deflection evaluation using strain and rotation measurements (2013) Smart Structures and Systems, 11 (4), pp. 365-386; Sousa, H., Costa, B., Henriques, A., Bento, J., Figueiras, J., Assessment of traffic load events and structural effects on road bridges based on strain measurements (2016) Journal of Civil Engineering and Management, 22 (4), pp. 457-469; Hodgson, I., (2007) Personal Discussion for the Acquisition of the Real Data from the Monitoring of the I-39 Northbound Bridge over the Wisconsin River, , Ian Hodgson, Senior Research Engineer. Department of Civil and Environmental Engineering. ATLSS Center. Lehigh University 117 ATLSS Dr. Bethlehem; Frangopol, D.M., Strauss, A., Kim, S., Use of monitoring extreme data for the performance prediction of structures: General approach (2008) Engineering Structures, 30 (12), pp. 3644-3653; Kainuma, S., Yang, M., Jeong, Y.S., Inokuchi, S., Kawabata, A., Experiment on fatigue behavior of rib-to-deck weld root in orthotropic steel decks (2016) Journal of Constructional Steel Research, 119, pp. 113-122; Zhang, Q.H., Bu, Y.Z., Qiao, L.I., Review on fatigue problems of orthotropic steel bridge deck (2017) China Journal of Highway & Transport, 30 (3), pp. 14-30. , 39; Connor, R., Fisher, J., Gatti, W., Gopalaratnam, V., Kozy, B., (2012) Manual for Design, Construction, and Maintenance of Orthotropic Steel Deck Bridges., , Report No. FHWA-IF-12-027, Washington, DC: Federal Highway Administration; Wang, G., Ding, Y., Song, Y., Wei, Z., Influence of temperature action on the fatigue effect of steel deck with pavement (2016) Engineering Mechanics, 33 (5), pp. 115-123; Heng, J., Zheng, K., Gou, C., Zhang, Y., Bao, Y., Fatigue performance of rib-to-deck joints in orthotropic steel decks with thickened edge u-ribs (2017) Journal of Bridge Engineering, 22 (9), p. 04017059; Fu, Z., Ji, B., Zhang, C., Wang, Q., Fatigue performance of roof and U-rib weld of orthotropic steel bridge deck with different penetration rates (2017) Journal of Bridge Engineering, 22 (6), p. 04017016; Fu, Z., Ji, B., Zhang, C., Li, D., Experimental study on the fatigue performance of roof and U-rib welds of orthotropic steel bridge decks (2018) KSCE Journal of Civil Engineering, 22 (1), pp. 270-278; Lu, N.W., Liu, Y., Deng, Y., Fatigue reliability evaluation of orthotropic steel bridge decks based on sitespecific weigh-in-motion measurements (2019) International Journal of Steel Structures, 19 (1), pp. 181-192; Alamdar, M.M., Rakotoarivelo, T., Khoa, N.L.D., A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge (2017) Mechanical Systems and Signal Processing, 87, pp. 384-400; Rubinstein, R.Y., Kroese, D.P., (1981) Simulation and the Monte Carlo Method. Wiley Series in Probability and Mathematical Statistics., , New York: John Wiley & Sons, Inc; Min, Z.H., Sun, Z.H., Dan, D.H., Analysis of wind-induced response and dynamic properties of cablestayed bridge under typhoon (2009) Journal of Tongji University (Natural Science), 37 (9), pp. 1139-1145. , 1173","Miao, C.; Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, China; email: chqmiao@seu.edu.cn",,,"Tech Science Press",,,,,19302983,,,,"English","SDHM Struct. Durability Health Monit.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85082773619
"Chaudhary M.T.A.","7006732722;","Sensitivity of modal parameters of multi-span bridges to ssi and pier column inelasticity and its implications for fem model updating",2020,"Latin American Journal of Solids and Structures","17","2","e254","","",,2,"10.1590/1679-78255895","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081132663&doi=10.1590%2f1679-78255895&partnerID=40&md5=6ed2af9e833b6a007e61f3e413f6a2b0","Civil Engineering Department, Kuwait University, Kuwait","Chaudhary, M.T.A., Civil Engineering Department, Kuwait University, Kuwait","Modal parameters, determined through forced vibration testing, ambient vibrations or seismic excitations, are central to the structural health monitoring process for bridges. These parameters are used to obtain high-fidelity numerical models through FEM model updating by fine-tuning mass, stiffness and boundary conditions and matching the numerical and observed modal parameters. This study investigated sensitivity of modal parameters to changes in boundary conditions (soil-structure interaction effect) and pier column inelasticity (stiffness effect) through more than 450 non-linear dynamic time-history analysis of an ordinary multi-span bridge. The bridge system was founded on shallow foundations in five rock profiles and on pile foundations in five soil profiles and was subjected to 21 seismic ground motions of varying intensity (0.036 to 0.61g). Modal frequencies showed sensitivity to the SSI and pier column inelasticity effects for low and higher levels of seismic excitations respectively. Mode shapes, on the contrary, were insensitive to SSI as well as pier column inelasticity for all levels of seismic excitations. © 2020, Brazilian Association of Computational Mechanics. All rights reserved.","FEM model updating; Modal parameters; Multi-span bridge; Pier inelasticity; Reinforced concrete; Soil-structure interaction","Boundary conditions; Composite beams and girders; Piers; Piles; Reinforced concrete; Seismology; Soil structure interactions; Soils; Stiffness; Structural health monitoring; Structural panels; FEM modeling; Forced vibration testing; Inelasticity effects; Modal parameters; Multi-span bridges; Non-linear dynamics; Seismic ground motions; Soil-Structure Interaction effects; Modal analysis",,,,,"Kuwait University, KU: EV01/16","This work was supported by Kuwait University, Research Grant No. EV01/16.",,,,,,,,,,"(2017) AASHTO LRFD Bridge Design Specifications, , 8th edition, American Association of State Highway and Transportation Officials, Washington, DC; Alampalli, S., Effects of testing, analysis, damage, and environment on modal parameters (2000) Mechanical Systems and Signal Processing, 14 (1), pp. 63-74; Allemang, R.J., Brown, D.L., A correlation coefficient for modal vector analysis (1982) Proceedings of the 1St International Modal Analysis Conference, 1, pp. 110-116; Arici, Y., Mosalam, K.M., System identification and modeling of bridge systems for assessing current design procedures (2000) In Proceedings of SMIP2000 Seminar, pp. 77-95. , September; Quantification of Building Seismic Performance Factors. 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(2017) 16Th World Conference on Earthquake Engineering, , Santiago, Chile, Paper No. 0157; Chaudhary, M.T.A., Effect of soil-foundation-structure interaction and pier column inelasticity on seismic response of bridges supported on shallow foundations (2017) Australian Journal of Structural Engineering, 17 (1), pp. 67-86; Chaudhary, M.T.A., (2018) Investigation of Parameters Influencing Soil-Structure Interaction in Bridges under Seismic Loading, , Research Project Report # EV01/16, Kuwait; Chaudhary, M.T.A., Abe, M., Fujino, Y., Identification of soil–structure interaction effect in base-isolated bridges from earthquake records (2001) Soil Dynamics and Earthquake Engineering, 21 (8), pp. 713-725; Chaudhary, M.T.A., Abe, M., Fujino, Y., Role of structural details in altering the expected seismic response of base-isolated bridges (2002) Mechanical Systems and Signal Processing, 16 (2-3), pp. 413-428; Chen, X.C., Lai, Y.M., Seismic response of bridge piers on elasto-plastic Winkler foundation allowed to uplift (2003) Journal of Sound and Vibration, 266 (5), pp. 957-965; Chen, X., Omenzetter, P., Beskhyroun, S., Calibration of the finite element model of a twelve-span prestressed concrete bridge using ambient vibration data (2014) Proceedings of the 7Th European Workshop on Structural Health Monitoring, , July, Nantes, France; Ciampoli, M., Pinto, P.E., Effects of soil-structure interaction on inelastic seismic response of bridge piers (1995) Journal of Structural Engineering, 121 (5), pp. 806-814; Collins, M.P., Mitchell, D., (1991) Prestressed Concrete Structures, 766. , Prentice Hall, New Jersey; Costa, C., Ribeiro, D., Jorge, P., Silva, R., Arêde, A., Calçada, R., Calibration of the numerical model of a stone masonry railway bridge based on experimentally identified modal parameters (2016) Engineering Structures, 123, pp. 354-371; (2019) Sap2000-Linear and Nonlinear Static and Dynamic Analysis and Design of Three-Dimensional Structures: Basic Analysis Reference Manual, , Computers and Structures, Inc., Berkeley, California; Dilena, M., Morassi, A., Dynamic testing of a damaged bridge (2011) Mechanical Systems and Signal Processing, 25 (5), pp. 1485-1507; Dobry, R., Gazetas, G., Simple method for dynamic stiffness and damping of floating pile groups (1988) Geotechnique, 38 (4), pp. 557-574; Ewins, D.J., (1984) Modal Testing: Theory and Practice, , Research Studies Press, Somerset, UK; Fan, W., Qiao, P., Vibration-based damage identification methods: A review and comparative study (2011) Structural Health Monitoring, 10 (1), pp. 83-111; Faraonis, P., Sextos, A., Papadimitriou, C., Chatzi, E., Panetsos, P., Implications of subsoil-foundation modelling on the dynamic characteristics of a monitored bridge (2019) Structure and Infrastructure Engineering, 15 (2), pp. 180-192; Fraino, M., Ventura, C.E., Liam Finn, W.D., Taiebat, M., Seismic soil-structure interaction effects in instrumented bridges (2012) Proceedings of the 15Th World Conference on Earthquake Engineering, pp. 1-10. , September, In, (pp.,). 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Part 2: Parametric study (2003) Earthquake Engineering & Structural Dynamics, 32 (4), pp. 629-652; Spyrakos, C.C., Assessment of SSI on the longitudinal seismic response of short span bridges (1990) Engineering Structures, 12 (1), pp. 60-66; Taciroglu, E., Shamsabadi, A., Abazarsa, F., Nigbor, R.L., Ghahari, S.F., (2014) Comparative Study of Model Predictions and Data from Caltrans/Csmip Bridge Instrumentation Program: A Case Study on the Eureka-Samoa Channel Bridge, Report No. CA14-2418, UCLA-SGEL Report No. 2014-01, , University of California, LA; Teughels, A., de Roeck, G., Structural damage identification of the highway bridge Z24 by FE model updating (2004) Journal of Sound and Vibration, 278 (3), pp. 589-610; Thorenfeldt, E., Tomaszewicz, A., Jensen, J.J., Mechanical properties of high-strength concrete and application in design (1987) Proceedings of the Symposium “Utilization of High Strength Concrete”, pp. 149-159. , Stavanger, Norway; Vecchio, F.J., Collins, M.P., The modified compression field theory for reinforced concrete elements subjected to shear (1986) ACI Journal, 83 (2), pp. 219-231; Vlassis, A.G., Spyrakos, C.C., Seismically isolated bridge piers on shallow soil stratum with soil–structure interaction (2001) Computers & Structures, 79 (32), pp. 2847-2861; Wang, J.Y., Ko, J.M., Ni, Y.Q., Modal sensitivity analysis of Tsing Ma Bridge for structural damage detection (2000) Proceedings of Nondestructive Evaluation of Highways, 3995, pp. 300-311. , Utilities, and Pipelines IV, Newport Beach, CA, SPIE-The International Society for Optics and Photonics; Wolf, J.P., (1985) Soil-Structure Interaction, , Prentice Hall Inc., Englewood Cliffs, New Jersey; Xia, Y., Chen, B., Weng, S., Ni, Y.Q., Xu, Y.L., Temperature effect on vibration properties of civil structures: A literature review and case studies (2012) Journal of Civil Structural Health Monitoring, 2 (1), pp. 29-46; Zhang, J., Tang, Y., (2006) Evaluating Radiation Damping of Shallow Foundations on Nonlinear Soil Medium for Soil-Structure Interaction Analysis of Bridges, , US-Japan Bridge Engineering Workshop, Seattle, WA; Zhang, Z., Aktan, A.E., The damage indices for the constructed facilities (1995) Proceedings of 13th International Modal Analysis Conference, 13, pp. 1520-1529. , February, Nashville, TN; Zheng, Y., Chen, B., Chen, W., Elasto-plastic seismic response of RC continuous bridge with foundation-pier dynamic interaction (2015) Advances in Structural Engineering, 18 (6), pp. 817-836","Chaudhary, M.T.A.; Civil Engineering Department, Kuwait; email: mtariqch@hotmail.com",,,"Brazilian Association of Computational Mechanics",,,,,16797817,,,,"English","Lat. Am. J. Solids Struct.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85081132663
"Sinsamutpadung N., Sasaki E.","57203550970;25923083100;","Strain-based Evaluation of Bridge Monitoring using Numerical Model Analysis",2019,"IOP Conference Series: Materials Science and Engineering","639","1","012023","","",,2,"10.1088/1757-899X/639/1/012023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075978612&doi=10.1088%2f1757-899X%2f639%2f1%2f012023&partnerID=40&md5=32230dbc73a920c031e2b3733dc4e116","Department of Civil Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, 1 Chalongkrung Road, Ladkrabang, Bangkok, 10520, Thailand; Department of Civil Engineering, Tokyo Institute of Technology, 2-12-1-M1-23 Ookayama, Meguro-ku, Tokyo, 152-8552, Japan","Sinsamutpadung, N., Department of Civil Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, 1 Chalongkrung Road, Ladkrabang, Bangkok, 10520, Thailand; Sasaki, E., Department of Civil Engineering, Tokyo Institute of Technology, 2-12-1-M1-23 Ookayama, Meguro-ku, Tokyo, 152-8552, Japan","According to visual inspection on the bridge site, the target bridge has major cracks in RC deck in the center span. This certain type of damage could affect structural performance and should be monitored. The structural health monitoring system was set up on the target bridge and the strain-based evaluation could be used to quantitatively determine bridge condition. The FE model of the bridge was modeled with various types of RC cracking pattern to represent the severity of bridge damage. The relationship between damage level and strain measurement is generated, As a result, Bridge monitoring curve based on strain index has been established. © Published under licence by IOP Publishing Ltd.","Finite element model; RC deck bridge; Structural Health Monitoring","Condensed matter physics; Engineering; Finite element method; Industrial engineering; Materials science; Bridge damage; Bridge monitoring; Cracking patterns; Deck bridges; Numerical modeling analysis; Structural health monitoring systems; Structural performance; Visual inspection; Structural health monitoring",,,,,"Council for Science, Technology and Innovation, CSTI","The research work shown in this paper was supported by Council for Science, Technology and Innovation, Cross-ministerial Strategic Innovation Promotion Program (SIP), Infrastructure Maintenance, Renovation, and Management” (funding agency: MLIT) and it has been conducted under the collaborative study with Omron Social Solutions, Co., Ltd.",,,,,,,,,,"http://www.mlit.go.jp/road/sisaku/yobohozen/yobohozen.html; (1974) Manual for Maintenance Inpsection of Bridges, , AASHTO; Shinae, J., Hongki, J., Kirill, M., Jennifer, A.R., Sung-Han, S., Hyung-Jo, J.M., Chung-Bang, Y., Gul, A., Structural health monitroing of a cable-stayed bridge using smart senson technology: Deployment and evaluation (2010) Smart Structures and Systems, 6 (5-6), pp. 439-459; Sasaki, E., Minesawa, G.V., Shimozato, T., Arizumi, Y., Nakamine, S., A wireless SHM system solutions for a long span interisland bridge in Oki-nawa (2013) The 12th Japan-Korea Joint Symposium on Steel Bridges; Sasaki, E., Tuttipongsawat, P., Sinsamutpadung, N., Nishida, H., Takase, K., Development of a remote monitoring system with wireless power-saving sensons for analyzing bridge conditions (2018) 6th International Symposium on Life-Cycle Civil Engineering; Sasaki, E., Tuttipongsawat, P., Sinsamutpadung, N., Nishida, H., Takase, K., Condition Evaluation of a Highway Bridge with RC Deck Using Monitoring Data Obtained by Wireless sensors (2018) 1st International Conference on Concrete and Steel Technology, Engineering & Design (CASTED2018); (2014) ABAQUS Analysis User's Manual, Version 6.14, , ABAQUS Inc",,,,"Institute of Physics Publishing","5th International Conference on Engineering, Applied Sciences and Technology, ICEAST 2019","2 July 2019 through 5 July 2019",,155263,17578981,,,,"English","IOP Conf. Ser. Mater. Sci. Eng.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85075978612
"Raeisi F., Mufti A., Algohi B., Thomson D.J.","57190256834;7005756171;57193548544;13310318900;","Placement of distributed crack sensor on I-shaped steel girders of medium-span bridges, using available field data",2019,"Structural Control and Health Monitoring","26","10","e2432","","",,2,"10.1002/stc.2432","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070681683&doi=10.1002%2fstc.2432&partnerID=40&md5=8774bd6c0284cc42a529e51e8a5c4c6c","Department of Civil Engineering, University of Manitoba, Winnipeg, MB, Canada; Department of Electrical Engineering, University of Manitoba, Winnipeg, MB, Canada","Raeisi, F., Department of Civil Engineering, University of Manitoba, Winnipeg, MB, Canada; Mufti, A., Department of Civil Engineering, University of Manitoba, Winnipeg, MB, Canada; Algohi, B., Department of Civil Engineering, University of Manitoba, Winnipeg, MB, Canada; Thomson, D.J., Department of Electrical Engineering, University of Manitoba, Winnipeg, MB, Canada","It is critical to detect cracks in steel girders of bridges before they have the potential to compromise the integrity of the structure. Both distributed binary sensors and distributed fiber optic sensors are capable of detecting cracks that are wider than 0.2 mm in steel girders. The objective of this paper is to report the optimum placement of these sensors on the girder to detect smallest possible length of the crack. In this work, the optimized placement of crack sensors was studied using FEM of two typical medium-span simply supported steel girder bridges (Girder A, 30-m–long span, and Girder B, 22-m–long span). Using loads estimated from field monitoring data and FEM, a map of crack opening along the length of the crack was calculated for stable crack lengths. Using these maps and given the detectable crack opening of 0.2 mm, the optimum place to position a distributed crack sensor to detect the smallest crack length was determined. For Girder A, the sensor should be placed at 150 to 250 mm above flange at midspan and at one third from the support, and for the rest of the length of the girder, it should be placed at 200–300 mm above the bottom flange. For Girder B, the optimum placement for installation of binary sensor is estimated to be at 150 to 220 mm above the tension flange. The proposed method of calculation of placement can be used for installation of distributed sensors on other types of bridges. © 2019 John Wiley & Sons, Ltd.","binary crack sensor; crack detection; placement of distributed sensors; steel girder bridges; structural health monitoring","Fiber optic sensors; Flanges; Plate girder bridges; Steel beams and girders; Steel fibers; Stress intensity factors; Structural health monitoring; Crack sensors; Distributed fiber optic sensor; Distributed sensor; Field monitoring data; Method of calculation; Optimized placements; Optimum placement; Steel girder bridge; Crack detection",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Foundation for Innovation, CFI","The authors wish to express their gratitude and appreciation for the supports received from the following organizations: Natural Science and Engineering Research Council of Canada, Canada Foundation for Innovation, Research Manitoba, Canadian Microelectronic Corporation, and Structural Monitoring Technologies.",,,,,,,,,,"(2017) Infrastructure Report Card, , ” ASCE; 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Ni, Y.Q., Xia, H., Ye, X., Neutral-axis position based damage detection of bridge deck using strain measurement: numerical and experimental verifications (2012) Proceedings of the 6th European Workshop on Structural Health Monitoring, pp. 1-7. , Dresden,Germany; Carden, E.P., Fanning, P., Vibration based condition monitoring: a review (2004) Struct Heal Monit An Int J, 3 (4), pp. 355-377. , https://doi.org/10.1177/1475921704047500; Farrar, C.R., Darling, T.W., Migliori, A., Baker, W.E., Microwave interferometers for non-contact vibration measurments on large structures (1999) Mech Syst Signal Process, 13 (2), pp. 241-253. , https://doi.org/10.1006/mssp.1998.1216; Zhang, B., Wang, S., Li, X., Zhang, X., Yang, G., Crack width monitoring of concrete structures based on smart film (2014) Smart Mater Struct, 23 (4), p. 045031. , https://doi.org/10.1088/0964-1726/23/4/045031; Zhou, Z., Zhang, B., Xia, K., Li, X., Smart film for crack monitoring of concrete bridges (2015) Struct Heal Monit, 10 (3), pp. 275-289. , https://doi.org/10.1177/1475921710373288; Glisic, B., Inaudi, D., Development of method for in-service crack detection based on distributed fiber optic sensors (2012) Struct Heal Monit An Int J, 11 (2), pp. 161-171. , https://doi.org/10.1177/1475921711414233; Raeisi, F., Mufti, A., Thomson, D., Mustapha, G., Binary crack sensor for steel girder bridges: installation procedure in field (2018) 10th International Conference on Short and Medium Span Bridges, , Quebec,Canada; Ostachowicz, W., Soman, R., Malinowski, P., Optimization of sensor placement for structural health monitoring: a review (2019) Struct Heal Monit, 18 (3), pp. 963-988. , https://doi.org/10.1177/1475921719825601; Kaveh, A., Eslamlou, A.D., An efficient two-stage method for optimal sensor placement using graph-theoretical partitioning and evolutionary algorithms (2019) Struct Control Health Monit, 26 (4), pp. 1-17. , https://doi.org/10.1002/stc.2325; Argyris, C., Chowdhury, S., Zabel, V., Papadimitriou, C., Bayesian optimal sensor placement for crack identification in structures using strain measurements (2018) Struct Control Health Monit, 25 (5), pp. 1-21. , https://doi.org/10.1002/stc.2137; Bertola, N., Papadopoulou, M., Vernay, D., Smith, I., Optimal multi-type sensor placement for structural identification by static-load testing (2017) Sensors, 17 (12), p. 2904. , https://doi.org/10.3390/s17122904; Chang, M., Pakzad, S.N., Optimal sensor placement for modal identification of bridge systems considering number of sensing nodes (2014) J Bridg Eng, 19 (6). , https://doi.org/10.1061/(asce)be.1943-5592.0000594; Rolfe, S., Barsom, J., (1977) Fracture and Fatigue Control in Structures: Applications of Fracture Mechanics; Mendes, S., (2014) Elastic bending moment and shear force limit states of steel bridge plate girders considering fatigue crack growth; Sun, C.-T., Jin, Z., (2011) Fracture Mechanics, , Elsevier, Academic Press; Kaufmann, E.J., Connor, R.J., Fisher, J.W., (2004) Failure Analysis of the US 422 Girder Fracture. ATLSS Report No. 04-21; Fisher, J.W., Yuceoglu, U., (1981) A survey of localized cracking in steel bridges; (2014) ABAQUS 6.14 Analysis User's Guide Volume IV: Elements, IV. , Providence, RI, USA., Dassault Systèmes Simulia Corp; User, A.A., (2014) ABAQUS 6.14 Analysis User's Guide Volume II: Analysis, II. , Providence, RI, USA., Dassault Systèmes Simulia Corp; Raeisi, F., Mufti, A., Thomson, D., Mustapha, G., Crack detection in steel girders using a binary sensor (2015) 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, , Turin, Italy; Raeisi, F., Mufti, A., Thomson, D.J., A finite-element model and experimental investigation of the influence of pre-straining of wire on the sensitivity of binary crack sensors (2018) J Civ Struct Heal Monit, 8 (4), pp. 673-687. , https://doi.org/10.1007/s13349-018-0290-7; Cardini, A.J., DeWolf, J.T., Long-term structural health monitoring of a multi-girder steel composite bridge using strain data (2009) Struct Heal Monit An Int J, 8 (1), pp. 47-58. , https://doi.org/10.1177/1475921708094789; Yao, Y., Glisic, B., Detection of steel fatigue cracks with strain sensing sheets based on large area electronics (2015) Sensors, 15 (4), pp. 8088-8108. , https://doi.org/10.3390/s150408088; Glišić, B., (2013) Distributed fiber optic sensing technologies and applications—an overview, , ” in ACI Special Publication; Glišić, B., Posenato, D., Inaudi, D., Integrity monitoring of old steel bridge using fiber optic distributed sensors based on Brillouin scattering (2007) Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2007, 6531, p. 65310P. , SPIE, 10.1117/12.716055","Raeisi, F.; Department of Civil Engineering, Canada; email: raeisif@myumanitoba.ca",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-85070681683
"Schommer S., Kebig T., Nguyen V.-H., Zürbes A., Maas S.","57112350900;57205531324;57196466317;25029398100;35311569000;","Modeling of a prestressed concrete bridge with 3D finite elements for structural health monitoring using model updating techniques",2018,"Proceedings of ISMA 2018 - International Conference on Noise and Vibration Engineering and USD 2018 - International Conference on Uncertainty in Structural Dynamics",,,,"1607","1620",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060381049&partnerID=40&md5=0254daea423d88e2eb3513763349080b","University of Luxembourg, Faculty of Science, Technology and Communication, 6, rue Richard Coudenhove-Kalergi, Luxembourg, L-1359, Luxembourg; Bingen University of Applied Sciences, 2 Berlinstraße 109, Bingen am Rhein, D-55411, Germany","Schommer, S., University of Luxembourg, Faculty of Science, Technology and Communication, 6, rue Richard Coudenhove-Kalergi, Luxembourg, L-1359, Luxembourg; Kebig, T., University of Luxembourg, Faculty of Science, Technology and Communication, 6, rue Richard Coudenhove-Kalergi, Luxembourg, L-1359, Luxembourg; Nguyen, V.-H., University of Luxembourg, Faculty of Science, Technology and Communication, 6, rue Richard Coudenhove-Kalergi, Luxembourg, L-1359, Luxembourg; Zürbes, A., Bingen University of Applied Sciences, 2 Berlinstraße 109, Bingen am Rhein, D-55411, Germany; Maas, S., University of Luxembourg, Faculty of Science, Technology and Communication, 6, rue Richard Coudenhove-Kalergi, Luxembourg, L-1359, Luxembourg","This paper presents a linear finite element model for a prestressed concrete beam, which was part of a real bridge. Static and dynamic tests were carried out and compared to the numerical simulation responses. A solid finite element model was created including the prestressed concrete beam, permanent dead load, two additional live loads and a shaker. A well planned finite element model is very important for later detection and localization of damage. Therefore, a mapped mesh was used to define so-called 'slices', which enables describing stiffness changes, e.g. damage. The model validation was performed by comparing simulated results to measured responses in the healthy state of the beam. After validation of the reference model, it is possible to modify the bending stiffness along the longitudinal axis of the beam by modifying Young's moduli of different slices to adapt for the effect of damage. © Proceedings of ISMA 2018 - International Conference on Noise and Vibration Engineering and USD 2018 - International Conference on Uncertainty in Structural Dynamics. All rights reserved.",,"Concrete beams and girders; Damage detection; Elastic moduli; Prestressed beams and girders; Prestressed concrete; Stiffness; Structural dynamics; Structural health monitoring; 3-D finite elements; Bending stiffness; Detection and localization; Linear finite element model; Model updating techniques; Prestressed concrete beams; Reference modeling; Static and dynamic tests; Finite element method",,,,,,,,,,,,,,,,"Link, M., Updating of analytical models review of numerical procedures and application aspects (1999) Proc., Structural Dynamics Forum SD2000, pp. 193-223. , Research Studies Press, Baldock; Abdel Wahab, M.M., De Roeck, G., Peeters, B., Parameterization of damage in reinforced concrete structures using model updating (1999) Journal of Sound and Vibration, 228 (4), pp. 717-730. , Elsevier; Teughels, A., Maeck, J., De Roeck, G., Damage assessment by FE model updating using damage functions (2002) Computers & Structures, 80 (25), pp. 1869-1879. , Elsevier; Teughels, A., De Roeck, G., Structural damage identification of the highway bridge Z24 by FE model updating (2004) Journal of Sound and Vibration, 278 (3), pp. 589-610. , Elsevier; Nguyen, V.-H., Schommer, S., Maas, S., Zürbes, A., Static load testing with temperature compensation for structural health monitoring of bridges (2016) Engineering Structures, 127, pp. 700-718. , Elsevier; Schommer, S., (2017) Damage Detection in Prestressed Concrete Bridges Based on Static Load Testing, Sagging and Modal Parameters, Using Measurements and Model Updating, , PhD dissertation, University of Luxembourg, Luxembourg; Schommer, S., Nguyen, V.-H., Kebig, T., Zürbes, A., Maas, S., A Gaussian bell curve-based damage function together with a sliced finite element meshing for damage assessment using model updating (2018) Mechanical Systems and Signal Processing, Under Review",,"Moens D.Desmet W.Pluymers B.Rottiers W.",,"KU Leuven - Departement Werktuigkunde","28th International Conference on Noise and Vibration Engineering, ISMA 2018 and 7th International Conference on Uncertainty in Structural Dynamics, USD 2018","17 September 2018 through 19 September 2018",,143592,,9789073802995,,,"English","Proc. ISMA - Int. Conf. Noise Vib. Eng. USD - Int. Conf. Uncertain. Struct. Dyn.",Conference Paper,"Final","",Scopus,2-s2.0-85060381049
"Vemuganti S., Ozdagli A., Liu B., Bajric A., Moreu F., Brake M.R.W., Troyer K.","57191909596;56418699100;36844235900;56707492600;16024768400;8449417300;36899236700;","Sensing and rating of vehicle–railroad bridge collision",2017,"Conference Proceedings of the Society for Experimental Mechanics Series","2 Part F2",,,"227","234",,2,"10.1007/978-3-319-54777-0_28","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034211229&doi=10.1007%2f978-3-319-54777-0_28&partnerID=40&md5=c199e7addf563be2d9f1cfdcf7990f1b","Department of Civil Engineering, University of New Mexico, Albuquerque, NM, United States; Department of Disaster Prevention Engineering, Institute of Disaster Prevention, Beijing, China; Department of Mechanical Engineering, Technical University of Denmark, Lyngby, Denmark; Department of Mechanical Engineering, William Marsh Rice University, Houston, TX, United States; Component Science and Mechanics Department, Sandia National Laboratories, Albuquerque, NM, United States","Vemuganti, S., Department of Civil Engineering, University of New Mexico, Albuquerque, NM, United States; Ozdagli, A., Department of Civil Engineering, University of New Mexico, Albuquerque, NM, United States; Liu, B., Department of Disaster Prevention Engineering, Institute of Disaster Prevention, Beijing, China; Bajric, A., Department of Mechanical Engineering, Technical University of Denmark, Lyngby, Denmark; Moreu, F., Department of Civil Engineering, University of New Mexico, Albuquerque, NM, United States; Brake, M.R.W., Department of Mechanical Engineering, William Marsh Rice University, Houston, TX, United States; Troyer, K., Component Science and Mechanics Department, Sandia National Laboratories, Albuquerque, NM, United States","Overhead collisions of trucks with low-clearance railway bridges cause more than half of the railway traffic interruptions over bridges in the United States. Railroad owners are required to characterize the damage caused by such events and assess the safety of subsequent train crossings. However, damage characterization is currently visual (subjective) and becomes difficult in remote locations where collisions are not reported and inspections are not performed following the impact. To mitigate these shortcomings, this paper presents a new impact definition and rating strategy for automatically and remotely quantify damage. This research proposes an impact rating strategy based on the information that best describes the consequences of vehicle-railway bridge collisions. A series of representative impacts were simulated using numerical finite element models of a steel railway bridge. Railway owners provided information about the bridge and impact characterization based on railway industry experience. The resulting nonlinear dynamic responses were evaluated with the proposed rating strategy to assess the effect of these impacts. In addition, a neural network methodology was implemented on a simplified numerical model to identify spatial characteristics of the impact damage. © The Society for Experimental Mechanics, Inc. 2017.","Finite element model; Impact detection; Neural networks; Railway bridges; Structural health monitoring","Dynamics; Finite element method; Neural networks; Railroad bridges; Railroads; Rating; Structural dynamics; Structural health monitoring; Transportation; Damage characterization; Impact detection; Network methodologies; Railway bridges; Railway industry; Railway traffic; Spatial characteristics; Steel railway bridge; Steel bridges",,,,,,,,,,,,,,,,"Otter, D., Joy, R., Jones, M., Maal, L., Need for bridge monitoring systems to counter railroad bridge service interruptions (2012) Transp. Res. Rec. J. Transp. Res. Board., 2313 (1), pp. 134-143; Joy, R., Jones, M.C., Otter, D., Maal, L., (2013) Characterization of Railroad Bridge Service Interruptions, , Railroad Bridges (No. DOT/FRA/ORD-13/05); Bischoff, R., Meyer, J., Enochsson, O., Feltrin, G., Elfgren, L., Event-based strain monitoring on a railway bridge with a wireless sensor network (2009) Proceedings of the 4Th International Conference on Structural Health Monitoring of Intelligent Infrastructure, pp. 74-82. , Zurich; Staszewski, W.J., Mahzan, S., Traynor, R., Health monitoring of aerospace composite structures–Active and passive approach (2009) Compos. Sci. Technol., 69 (11), pp. 1678-1685; Farrar, C.R., Worden, K., An introduction to structural health monitoring (2007) Philos. Trans. R. Soc. Lond. a Math. Phys. Eng. Sci., 365 (1851), pp. 303-315; Sohn, H., Effects of environmental and operational variability on structural health monitoring (2007) Philos. Trans. R. Soc. Lon. a Math. Phys. Eng. Sci., 365 (1851), pp. 539-560; Moreu, F., Spencer, B.F., (2015) Framework for Consequence-Based Management and Safety of Railroad Bridge Infrastructure Using Wireless Smart Sensors (WSS), , Newmark Structural Engineering Laboratory. University of Illinois at Urbana-Champaign, Champaign; Yun, H., Nayeri, R., Tasbihgoo, F., Wahbeh, M., Caffrey, J., Wolfe, R., Nigbor, R., Sheng, L.H., Monitoring the collision of a Cargo Ship with the Vincent Thomas Bridge (2008) Struct. Control. Health Monit., 15 (2), pp. 183-206; Coverley, P.T., Staszewski, W.J., Impact damage location in composite structures using optimized sensor triangulation procedure (2003) Smart Mater. Struct., 12 (5), pp. 795-803; Sun, Z., Chang, C.C., Structural damage assessment based on wavelet packet trans-form (2002) J. Struct. Eng., 128 (10), pp. 1354-1361; Taha, M.M.R., Noureldin, A., Lucero, J.L., Baca, T.J., Wavelet transform for structural health monitoring: A compendium of uses and features (2006) Struct. Health Monit., 5 (3), pp. 267-295; Song, G., Olmi, C., Gu, H., An overheight vehicle–bridge collision monitoring system using piezoelectric transducers (2007) Smart Mater. Struct., 16 (2), pp. 462-468; Sharma, H., Hurlebaus, S., Overheight collision protection measures for bridges (2012) Structures Congress 2012, pp. 790-797. , ASCE; Kurt, E.G., Varma, A.H., Hong, S., FEM Simulation for INDOT Temporary Concrete Anchored Barrier. Joint Transportation (2012) Research Program; Buth, C.E., Williams, W.F., Brackin, M.S., Lord, D., Geedipally, S.R., Abu-Odeh, A.Y., (2010) Analysis of Large Truck Collisions with Bridge Piers: Phase 1. Report of Guidelines for Designing Bridge Piers and Abutments for Vehicle Collisions, , No. FHWA/TX-10/9-4973-1; Consolazio, G.R., McVay, M.C., Cowan, D.R., Davidson, M.T., Getter, D.J., (2008) Development of Improved Bridge Design Provisions for Barge Impact Loading, , No. UF Project 00051117; Luperi, F.J., Pinto, F., Structural behavior of barges in high-energy collisions against bridge piers (2016) J. Bridg. Eng., 21 (2); (2016) ANSYS Autodyn User’s Manual, , Cecil Township, PA; (2015) ASTM A572-15 Standard Specification for High-Strength Low-Alloy Columbium—Vanadium Structural Steel; Jones, N., (1997) Structural Impact, , Cambridge University Press, Cambridge; Wu, X., Ghaboussi, J., Garrett, J.H., Use of neural networks in detection of structural damage (2010) Br. J. Surg, 81 (11), pp. 578-581; Ondra, V., Sever, I.A., Schwingshackl, C.W., A method for detection and characterization of structural non-linearities using the Hilbert transform and neural networks (2016) Mech. Syst. Signal Process., 83 (2017), pp. 210-227","Vemuganti, S.; Department of Civil Engineering, United States; email: shreya.vemuganti@gmail.com","Pakzad S.Caicedo J.","","Springer New York LLC","35th IMAC Conference and Exposition on Structural Dynamics, 2017","30 January 2016 through 2 February 2016",,191609,21915644,9783319547763,,,"English","Conf. Proc. Soc. Exp. Mech. Ser.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85034211229
"Ursos M.E., Tingatinga E.A., Longalong R.E.","57195837630;23486824400;57191839745;","A finite element based method for estimating natural frequencies of locally damaged homogeneous beams",2017,"Procedia Engineering","199",,,"404","410",,2,"10.1016/j.proeng.2017.09.131","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029910390&doi=10.1016%2fj.proeng.2017.09.131&partnerID=40&md5=5051ad159cb7c65f1df431af8e4904a8","University of the Philippines Diliman, Quezon City, 1101, Philippines","Ursos, M.E., University of the Philippines Diliman, Quezon City, 1101, Philippines; Tingatinga, E.A., University of the Philippines Diliman, Quezon City, 1101, Philippines; Longalong, R.E., University of the Philippines Diliman, Quezon City, 1101, Philippines","Vibration-based damage detection from frequency changes requires the calculation of natural frequencies from assumed damage scenarios and conduct a comparison to the actual frequency of the structure. Analytical solutions in obtaining the natural frequency of homogeneous beams are currently limited to beams with uniform cross-sectional area. Changes in cross-sectional area might occur due to damage within the length of the beam. Finite element modeling and analysis is required in these instances, but may not be efficient in terms of computational effort. For the assumed damaged scenarios, there are unlimited number of possible damage combinations for which the natural frequency will be obtained. There is a need for an analytical alternative as a substitute to the finite element method to calculate these frequencies. This study presents an analytical method to estimate the natural frequencies of locally damaged homogeneous beams based on statistical data obtained from finite element modeling and analysis. The method proposes a multiplier function in terms of the extent of area reduction, length, and location of damage in order to estimate the damaged frequency. The function was derived using curve-fitting techniques of data obtained from finite element modeling and analysis of typical beams with assumed damage cases. Examples show that the method is a good alternative to finite element analysis in estimating the natural frequencies of locally damaged homogeneous beams. The method can be used for vibration-based structural health monitoring to predict the damage state of beams given the change in frequency without the computational burden of finite element modeling and analysis. © 2017 The Authors. Published by Elsevier Ltd.","beam; corrosion; cross-sectional area reduction; finite element method; natural frequency; vibration based damage detection","Abutments (bridge); Corrosion; Curve fitting; Damage detection; Natural frequencies; Structural analysis; Structural dynamics; Structural health monitoring; Vibration analysis; beam; Computational burden; Computational effort; Cross sectional area; Curve fitting technique; Multiplier functions; Vibration-based damage detection; Vibration-based structural health monitoring; Finite element method",,,,,,,,,,,,,,,,"Valliappan, S., Chee, C.K., Aging Degradation of Mechanical Structures (2008) Mechanics of Materials and Structures, , http://msp.org/jomms/2008/3-10/jomms-v3-n10-p09-p.pdf, MSP. [Accessed: 21st December 2016]; (2016) NDT Resource Center, , https://www.nde-ed.org/EducationResources/CommunityCollege/Materials/Physical_Chemical/Corrosion.htm, Corrosion. [Online]. [Accessed: 21st Dec 2016]; Hart, D., Rutherford, S., Wickham, A., Structural reliability analysis of stiffened panels (1986) Trans. RINA, 128, pp. 293-310; Guedes Soares, C., Uncertainty modelling in plate buckling (1988) Struct. Saf., 5, pp. 17-34; Shi, W., In-service assessment of ship structures: Effects of seneral corrosion on ultimate strength (1993) Trans. RINA, 135, pp. 77-91; (2005) CAE - Finite Element Method, , https://ocw.mit.edu/courses/aeronautics-and-astronautics/16-810-engineering-design-and-rapid-prototyping-january-iap-2005/lecture-notes/l5.pdf, Massachusetts Institute of Technology [Accessed: 14th December 2016]; (2016) Types of Metal Deterioration, , http://www.academia.edu/5315138/UNIT-IV_Types_of_Metal_Deterioration, Academia [Accessed 21st December 2016]; Farrar, C.R., Doebling, S.W., (2004) An Overview of Modal-based Damage Identification Methods, , http://mesl.ucsd.edu/gupta/SHM/Reading/damas_overview.pdf, University of California, Microelectronic embedded systems laboratory. [Accessed 22nd June 2016]; Stubbs, N., Osegueda, R., Global non-destructive damage evaluation in solids (1990) Modal Analysis: The International Journal of Analytical and Experimental Modal Analysis., 5 (2), pp. 67-79; Stubbs, N., Osegueda, R., Global damage detection in solids-experimental verification (1990) Modal Analysis: The International Journal of Analytical and Experimental Modal Analysis., 5 (2), pp. 81-97; Akademia Górniczo-Hutnicza (2016) The Finite Element Method, , http://www.kkiem.agh.edu.pl/dydakt/fem/fem.htm, [Accessed 14th December 2016]; Ramsey, K.A., (1983) Experimental Modal Analysis, Structural Modifications and FEM Analysis on A Desktop Computer, , http://www.systemplus.co.jp/support/data/techpaper/mescope/tech/25.pdf, [Accessed: 15th December 2016]; Rieger, N.F., The relationship between finite element analysis and modal analysis (2016) STI Technologies, , http://www.sti-tech.com/dl/feapaper.pdf, [Accessed: 15th December 2016]; Chopra, A.K., (2000) Dynamics of Structures. 2nd Edition, , Prentice Hall; Reddy, J.N., An introduction to the finite element method (2006) McGraw Hill Series in Mechanical Engineering, , 3rd edition MHI; (2016) Least Squares Fitting, , http://mathworld.wolfram.com/LeastSquaresFitting.html, Wolfram [Accessed: 4th November 2016]","Ursos, M.E.; University of the Philippines DilimanPhilippines; email: michael_edward.ursos@upd.edu.ph","Romeo F.Gattulli V.Vestroni F.","","Elsevier Ltd","10th International Conference on Structural Dynamics, EURODYN 2017","10 September 2017 through 13 September 2017",,130585,18777058,,,,"English","Procedia Eng.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85029910390
"Ebrahimian H., Astroza R., Conte J.P., Papadimitriou C.","57112070500;55619989200;7101953827;7103065916;","A Nonlinear Model Inversion Method for Joint System Parameter, Noise, and Input Identification of Civil Structures",2017,"Procedia Engineering","199",,,"924","929",,2,"10.1016/j.proeng.2017.09.240","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029900936&doi=10.1016%2fj.proeng.2017.09.240&partnerID=40&md5=e74dc76494036423a53da4edde53236a","Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA  91125, United States; Faculty of Engineering and Applied Sciences, University of Los Andes, Santiago, Chile; Department of Structural Engineering, University of California San Diego, San Diego, CA  92093, United States; Department of Mechanical Engineering, University of Thessaly, Volos, Greece","Ebrahimian, H., Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA  91125, United States; Astroza, R., Faculty of Engineering and Applied Sciences, University of Los Andes, Santiago, Chile; Conte, J.P., Department of Structural Engineering, University of California San Diego, San Diego, CA  92093, United States; Papadimitriou, C., Department of Mechanical Engineering, University of Thessaly, Volos, Greece","This paper presents a framework for nonlinear system identification of civil structures using sparsely measured dynamic output response of the structure. Using a sequential maximum likelihood estimation (MLE) approach, the unknown FE model parameters, the measurement noise variances, and the input ground acceleration time histories are estimated jointly. This approach requires the computation of FE response sensitivities with respect to the unknown FE model parameters (i.e., FE parameter sensitivities) as well as the FE response sensitivities with respect to the values of the input ground acceleration at every time step (i.e., FE input sensitivities). The FE parameter and input sensitivities are computed using the direct differentiation method (DDM). The presented output-only nonlinear FE model updating method is validated using the numerically simulated seismic response of a realistic three-dimensional five-story reinforced concrete building structure. The simulated building responses to a horizontal bi-directional seismic excitation is contaminated with artificial measurement noise and used to estimate the unknown FE model parameters characterizing the nonlinear material constitutive laws of the reinforced concrete, as well as the root mean square of the measurement noise at each measurement channel, and the full time history of the seismic base acceleration. The method presented in this paper provides a powerful framework for structural system and damage identification of civil structures, when the input excitations are not measured, are partially measured, or the measured input excitations are erroneous. © 2017 The Authors. Published by Elsevier Ltd.","Bayesian Inference; Damage Identification; Input Estimation; Joint Input; Model Updating; Nonlinear Finite Element Model; Structural Health Monitoring; System Identification","Bayesian networks; Concrete bridges; Concrete buildings; Concretes; Damage detection; Identification (control systems); Inference engines; Maximum likelihood; Maximum likelihood estimation; Nonlinear systems; Parameter estimation; Reinforced concrete; Religious buildings; Seismic response; Seismology; Spurious signal noise; Structural analysis; Structural dynamics; Structural health monitoring; Structures (built objects); Bayesian inference; Damage Identification; Input estimation; Model updating; Non-linear finite element model; Finite element method",,,,,,,,,,,,,,,,"Chopra, A.K., (2012) Dynamics of Structures: Theory and Applications to Earthquake Engineering, , Prentice-Hall, Englewood Cliffs, 4th Ed; Ebrahimian, H., Astroza, R., Conte, J.P., De Callafon, R.A., Nonlinear Finite Element Model Updating for Damage Identification of Civil Structures using Batch Bayesian Estimation (2016) Mechanical Systems and Signal Processing, , DOI: 10.1016/j.ymssp.2016.02.002; Beck, J.L., Bayesian System Identification based on Probability Logic (2010) Structural Control and Health Monitoring, 17 (7), pp. 825-847; Vidal, C.A., Lee, H.-S., Haber, R.B., The Consistent Tangent Operator for Design Sensitivity Analysis of History-Dependent Response (1991) Computing Systems in Engineering, 2 (5-6), pp. 509-523; Kleiber, M., Antunez, H., Hien, T.D., Kowalczyk, P., (1997) Parameter Sensitivity in Nonlinear Mechanics: Theory and Finite Element Computations, , John Wiley & Sons New York; Zhang, Y., Der Kiureghian, A., Dynamic Response Sensitivity of Inelastic Structures (1993) Computer Methods in Applied Mechanics and Engineering, 108 (1-2), pp. 23-36; Conte, J.P., Vijalapura, P.K., Meghella, M., Consistent Finite-Element Response Sensitivity Analysis (2003) ASCE Journal of Engineering Mechanics, 129 (12), pp. 1380-1393; Ebrahimian, H., Astroza, R., Conte, J.P., Papadimitriou, C., Bayesian Optimal Estimation for Output-Only Nonlinear System and Damage Identification of Civil Structures (2017) Structural Control and Health Monitoring, , in review; Open System for Earthquake Engineering Simulation, , http://opensees.berkeley.edu/, OpenSees [Accessed 08 2015]; CESMD - A Cooperative Effort, , http://strongmotioncenter.org/, Center for Engineering Strong Motion Data [Accessed 08 2015]; (2012) The MathWorks Inc., , MATLAB Natick, Massachusetts, United States; Byrd, R.H., Hribar, M.E., Nocedal, J., An Interior Point Algorithm for Large-scale Nonlinear Programming (1999) SIAM Journal on Optimization, 9 (4), pp. 877-900","Conte, J.P.; Department of Structural Engineering, United States; email: jpconte@ucsd.edu","Romeo F.Gattulli V.Vestroni F.","","Elsevier Ltd","10th International Conference on Structural Dynamics, EURODYN 2017","10 September 2017 through 13 September 2017",,130585,18777058,,,,"English","Procedia Eng.",Conference Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85029900936
"Parker D.H.","55452983400;","Nondestructive testing and monitoring of stiff large-scale structures by measuring 3D coordinates of cardinal points using electronic distance measurements in a trilateration architecture",2017,"Proceedings of SPIE - The International Society for Optical Engineering","10169",,"1016918","","",,2,"10.1117/12.2260254","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021841536&doi=10.1117%2f12.2260254&partnerID=40&md5=3469c15459cd43c2087e3e9d5e84c803","Parker Intellectual Property Enterprises, LLC, 3919 Deepwoods Road, Earlysville, VA  22936, United States","Parker, D.H., Parker Intellectual Property Enterprises, LLC, 3919 Deepwoods Road, Earlysville, VA  22936, United States","By using three, or more, electronic distance measurement (EDM) instruments, such as commercially available laser trackers, in an unconventional trilateration architecture, 3-D coordinates of specialized retroreflector targets attached to cardinal points on a structure can be measured with absolute uncertainty of less than one part-permillion. For example, 3-D coordinates of a structure within a 100 meter cube can be measured within a volume of a 0.1 mm cube (the thickness of a sheet of paper). Relative dynamic movements, such as vibrations at 30 Hz, are typically measured 10 times better, i.e., within a 0.01 mm cube. Measurements of such accuracy open new areas for nondestructive testing and finite element model confirmation of stiff, large-scale structures, such as: buildings, bridges, cranes, boilers, tank cars, nuclear power plant containment buildings, post-tensioned concrete, and the like by measuring the response to applied loads, changes over the life of the structure, or changes following an accident, fire, earthquake, modification, etc. The sensitivity of these measurements makes it possible to measure parameters such as: linearity, hysteresis, creep, symmetry, damping coefficient, and the like. For example, cracks exhibit a highly non-linear response when strains are reversed from compression to tension. Due to the measurements being 3-D, unexpected movements, such as transverse motion produced by an axial load, could give an indication of an anomaly-such as an asymmetric crack or materials property in a beam, delamination of concrete, or other asymmetry due to failures. Details of the specialized retroreflector are included. © 2017 SPIE.","Deflection; Electronic Distance Measurement; Laser Tracker; Nondestructive Testing; Patent; Structural Health Monitoring; Total Station; Vibration","Accidents; Characterization; Concrete testing; Concretes; Cracks; Creep testing; Deflection (structures); Disasters; Distance measurement; Failure (mechanical); Finite element method; Geometry; Materials handling; Nuclear fuels; Nuclear power plants; Structural health monitoring; Surveying; Uncertainty analysis; Electronic distance measurement; Laser tracker; Patent; Total station; Vibration; Nondestructive examination",,,,,,,,,,,,,,,,"Burnside, C.D., (1991) Electromagnetic Distance Measurement, , BSP Professional Books, third ed; Dukes, J.N., Gordon, G.B., A two-hundred-foot yardstick with graduations every microinch (1970) Hewlett-Packard Journal, pp. 1-8. , Aug; Rudé, A.F., Wayne, K.J., A new tool for old measurements-and new ones too (1970) Hewlett-Packard Journal, p. 9. , Aug; Burgwald, G.M., Kruger, W.P., An instant-on laser for length measurement (1970) Hewlett-Packard Journal, pp. 12-14. , Aug; Meier, D., Dave Meier's HP Laser Interferometer Evolution Page., , http://www.n4mw.com/hp5526/hple.htm; Bagley, A.S., Cutler, L.S., Rando, J.F., (1969) Interferometric System, , US Patent; Bullock, M.L., Warren, R.E., Electronic total station speeds survey operations (1976) Hewlett-Packard Journal, pp. 2-12. , Apr; Epstein, J.S., (1978) Surveying Instrument and Method, , US Patent; Greenleaf, A.H., Watson, J.T., (1984) Self Calibrating Contour Measuring System Using Fringe Counting Interferometer, , US Patent; Merry, J.B., Brown, L.B., (1986) Interferometer System for Controlling Non-rectilinear Movement of An Object, , US Patent; Lau, K.C., Hocken, R.J., (1987) Three and Five Axis Laser Tracking Systems, , US Patent; Brown, L.B., Wells, D.N., Merry, B., (1988) Tracking Laser Interferometer, , US Patent; Meier, D., (1998) Electro-optical Measuring Device for Absolute Distances, , US Patent; Bridges, R.E., Brown, L.B., West, J.K., Ackerson, D.S., (2010) Laser-based Coordinate Measuring Device and Laser-based Method for Measuring Coordinates, , US Patent; Rüeger, J.M., (1990) Electronic Distance Measurement, , Springer-Verlag, third ed; Bell, B., (1992) Proceedings of the Workshop on the Use and Calibration of the Kern ME5000 Mekometer, , Stanford Linear Accelerator Center June. Prepared for the Department of Energy under contract number DE-AC03-76F00515; Parker, D.H., Payne, J.M., (2016) Methods for Measuring and Modeling the Structural Health of Pressure Vessels Based on Electronic Distance Measurements, , US Patent; Estler, W.T., Edmundson, K., Peggs, G., Parker, D.H., Large-scale metrology-an update (2002) Annals of the CIRP, 51 (2), pp. 587-609. , Keynote Paper; Peggs, G.N., Maropoulos, P.G., Hughes, E.B., Forbes, A.B., Robson, S., Ziebart, M., Muralikrishnan, B., Recent developments in large-scale dimensional metrology (2009) Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, , June; Franceschini, F., Galetto, M., Maisano, D., Mastrogiacomo, L., Large-scale dimensional metrology (LSDM): From tapes and theodolites to multi-sensor systems (2014) International Journal of Precision Engineering and Manufacturing, 15, pp. 1739-1758. , Aug; Schmitt, R.H., Peterek, M., Morse, E., Knapp, W., Galetto, M., Härtig, F., Goch, G., Estler, W.T., Advances in large-scale metrology-review and future trends (2016) CIRP Annals Manufacturing Technology, 65, pp. 643-665; Muralikrishnan, B., Phillips, S., Sawyer, D., Laser trackers for large-scale dimensional metrology: A review (2016) Precision Engineering, 44, pp. 13-28; Franceschini, F., Galetto, M., Maisano, D., Mastrogiacomo, L., Combining multiple large volume metrology systems: Competitive versus cooperative data fusion (2016) Precision Engineering, 43, pp. 514-524; Peggs, G.N., Virtual technologies for advanced manufacturing and metrology (2003) International Journal of Computer Integrated Manufacturing, 16 (7-8), pp. 485-490; Hughes, E.B., Wilson, A., Peggs, G.N., Design of a high-accuracy CMM based on multilateration techniques (2000) Ann. CIRP, 49 (1), pp. 391-394; Brinker, R.C., Minnick, R., (1995) The Surveying Handbook, , Chapman & Hall, second ed; Parker, D.H., Payne, J.M., Metrology system for the Green Bank Telescope (1999) Proceedings ASPE 1999 Annual Meeting, pp. 21-24. , American Society for Precision Engineering; Sandwith, S., Predmore, R., Real-time 5-micron uncertainty with laser tracking interferometer systems using weighted trilateration (2001) 2001 Boeing Large-Scale Metrology Conference, , St. Louis, MO; Camboulives, M., Lartigue, C., Bourdet, P., Salgado, J., Calibration of a 3D working space by multilateration (2016) Precision Engineering, 44, pp. 163-170; Atcheson, P.D., (1989) Hemispherical Retroreflector, , US Patent; Zürcher, W., Loser, R., Kyle, S.A., Improved reflector for interferometric tracking in three dimensions (1995) Optical Engineering, 34, pp. 2740-2743. , September; Oakley, J.P., (2009) Retroreflector, , US Patent Application Publication; Parker, D.H., (2014) Methods for Modeling Amplitude Modulated Light Through Dispersive Optical Systems and Electronic Distance Measurement Instruments, , US Patent; Parker, D.H., Multidirectional retroreflector assembly with a common virtual reflection point using fourmirror retroreflectors (2005) Precision Engineering, 29, pp. 361-366; Parker, D.H., (2010) Multidirectional Retroreflectors, , US Patent; Brown, L.B., (1996) Probing Retroreflector and Methods of Measuring Surfaces Therewith, , United States Patent; Bridges, R.E., Brown, L.B., Ackerson, D.S., (1999) Retroreflector for Use with Tooling Ball, , United States Patent; Parker, D.H., Methods for correcting the group index of refraction at the ppm level for outdoor electronic distance measurement (2001) Proceedings ASPE 2001 Annual Meeting], 86-87, American Society for Precision Engineering, , Full presentation is available from The National Radio Astronomy Observatory (NRAO) Library, GBT Archive L0680; Pollinger, F., JRP SIB60 metrology for long distance surveying- A concise survey on major project results (2016) Proceedings of 3rd Joint International Symposium on Deformation Monitoring, , Vienna, Austria], The International Federation of Surveyors (FIG) Working Group 6. 1, Deformation Measurement and Analysis and The International Association of Geodesy (IAG) Sub-Commission 4. 2, Applications of Geodesy in Engineering, FIG; Andreas, E.L., Selected Papers on Turbulence in a refractive medium (1990) SPIE Milestone Series MS 25, SPIE; Kleppe, J.A., (1989) Engineering Applications of Acoustics, , Artech House, first ed; Jones, T.E., (2016) From Vision to Mission, ASNT 1941 to 2016, , ASNT; Aktan, A.E., Catbas, F.N., Grimmelsman, K.A., Pervizpour, M., (2003) Development of A Model Health Monitoring Guide for Major Bridges, , Tech. Rep. Contract/Order DTFH61-01-P-00347, Drexel Intelligent Infrastructure and Transportation Safety Institute . Report Submitted to Federal Highway Administration Research and Development; Ettouney, M.M., Alampalli, S., (2012) Infrastructure Health in Civil Engineering: Volume 1, Theory and Components, , CRC; Ettouney, M.M., Alampalli, S., (2012) Infrastructure Health in Civil Engineering: Volume 2, Applications and Management, , CRC; Fuchs, P.A., (2009) Instrument to Aid in Steel Bridge Fabrication, Final Report of Highway IDEA Project 127, , Transportation Research Board Mar; Fuchs, P.A., (2012) Bridge Retrofit Laser System, Final Report for Highway IDEA Project 153, , Transportation Research Board Sept; Fuchs, P.A., (2014) Apparatus and Method for Bridge Assembly, , US Patent Application Publication; Moreu, F., LaFave, J.M., Spencer, B.F., New regulations on railroad bridge safety: Opportunities and challenges for railroad bridge monitoring (2012) Proceedings of SPIE, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2012, 8345, pp. 834540-834541. , through 11; Cosser, E., Roberts, G.W., Meng, X., Dodson, A.H., Measuring the dynamic deformation of bridges using a total station (2003) Proceedings of 11th FIG Symposium on Deformation Measurements, Santorini, Greece, , The International Federation of Surveyors (FIG), FIG; Merkle, W., Myers, J.J., Use of the total station for serviceability monitoring of bridges (2004) Proceedings of 16th WCNDT 2004-World Conference on NDT, , Montreal, Canada; Matta, F., Bastianini, F., Galati, N., Casadei, P., Nanni, A., (2005) Situ Load Testing of Bridge A6358 Osage Beach, MO, , Tech. Rep. UTC R124-3, University Transportation Center Program at The University of Missouri-Rolla Jan; Galati, N., Casadei, P., Nanni, A., (2005) Situ Load Testing of Bridge A6101 Lexington, MO, , Tech. Rep. UTC R124-1, University Transportation Center Program at The University of Missouri-Rolla Jan; Hernandez, E., Galati, N., Nanni, A., (2005) Assessment of Bridge Technologies Through Field Testing: In-situ Load Testing of Bridges B-20-133 and B-20-134, , Fond du Lac, WI Tech. Rep. UTC R133A, University Transportation Center Program at The University of Missouri-Rolla Dec; Hernandez, E., Galati, N., Nanni, A., (2005) Assessment of Bridge Technologies Through Field Testing: In-situ Load Testing of Bridges B-20-148 and B-20-149, , Fond du Lac, WI Tech. Rep. UTC R133B, University Transportation Center Program at The University of Missouri-Rolla Dec; Galati, N., Casadei, P., Nanni, A., (2005) Situ Load Testing of Bridge A6102 Lexington, , MO Tech. Rep. UTC R124-2, University Transportation Center Program at The University of Missouri-Rolla Jan; James, H., Automatic deformation monitoring (2006) The American Surveyor, , March/April; Gikas, V., Daskalakis, S., Full scale validation of Tracking Total Stations using a long stroke electrodynamic shaker (2006) Proceedings of 23th International FIG Congress, , Munich, Germany; Palazzo, D., Friedmann, R., Nadal, C., Filho, M.S., Veiga, L., Faggion, P., Dynamic monitoring of structures using robotic total station (2006) Proceedings of 23th International FIG Congress, Munich, Germany, , Oct; Umemoto, S., Hara, T., Kubota, K., Miyamoto, N., Fujino, Y., Okamoto, T., Verification of highaccuracy and contact measurement system using FSF laser optical coordinate (2007) Proceedings of the 3rd International Conference on Structural Health Monitoring of Intelligent Infrastructure, , Vancover, British Columbia, Canada], Nov; Kopácik, A., Kyrinovic, P., Lipták, I., Erdély, J., Automated monitoring of the Danube Bridge Apollo in Bratislava (2011) Proceedings of the FIG Working Week 2011, Bridging the Gap between Cultures, Marrakech, Morocco, , May; Attanayake, U., Tang, P., Servi, A., Aktan, H., Non-contact bridge deflection measurement: Application of laser technology (2011) Proceedings of the International Conference Structural Engineering Construction and Management, ICSEM 2011, , Peradeniya, Sri Lanka; Psimoulis, P., Stiros, S., Using robotic theodolites (RTS) in structural health monitoring of short-span railway bridges (2011) Proceedings of 1st Joint International Symposium on Deformation Monitoring, Hong Kong, China], the International Federation of Surveyors (FIG) Working Group 6. 1, , Deformation Measurement and Analysis and The International Association of Geodesy (IAG) Sub-Commission 4. 2, Applications of Geodesy in Engineering, FIG; Barazzetti, L., Giussani, A., Roncoroni, F., Previtali, M., Monitoring structure movement with laser tracking technology (2013) Proceedings of SPIE, Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection, 8791, pp. 879106-879111. , through 12 SPIE; Psimoulis, P.A., Peppa, I., Bonenberg, L., Ince, S., Meng, X., Combination of GPS and RTS measurements for the monitoring of semi-static and dynamic motion of pedestrian bridge (2016) Proceedings of 3rd Joint International Symposium on Deformation Monitoring, Vienna, Austria], the International Federation of Surveyors (FIG) Working Group 6. 1, , Deformation Measurement and Analysis and The International Association of Geodesy (IAG) Sub-Commission 4. 2, Applications of Geodesy in Engineering, FIG; Marendíc, A., Paar, R., Grgac, I., Damjanovíc, D., Monitoring of oscillations and frequency analysis of the railway bridge sava using robotic total station (2016) Proceedings of 3rd Joint International Symposium on Deformation Monitoring, Vienna, Austria, , The International Federation of Surveyors (FIG) Working Group 6. 1, Deformation Measurement and Analysis and The International Association of Geodesy (IAG) Sub-Commission 4. 2, Applications of Geodesy in Engineering, FIG; Hall, R., Goldman, M., Parker, D.H., Payne, J.M., Measurement program for the Green Bank Telescope (1998) Proceedings of SPIE, 3357, pp. 265-276; Payne, J., Parker, D., Bradley, R., Rangefinder with fast multiple range capability (1992) Rev. Sci. Instrum., 63, pp. 3311-3316. , June; Payne, J., Parker, D., Bradley, R., Rangefinder with fast multiple range capability (1995) Selected Papers on Laser Distance Measurements, SPIE Milestone Series MS 115, , Bosch, T. and Lescure, M., eds., 257-262, SPIE Optical Engineering Press. reprint of Review of Scientific Instruments article; Payne, J.M., Parker, D.H., Bradley, R.F., (1995) Optical Electronic Distance Measurement Apparatus with Movable Mirror, , United States Patent; Petticrew, A.L., (1996) Laser Rangefinder Deflection Measurements of the Gbt Derrick, , Tech. Rep. GBT Memo 160, The National Radio Astronomy Observatory (NRAO) Nov.). Available from the NRAO Library; Bridges, R.E., Hoffer, J.M., (2008) Absolute Distance Meter That Measures A Moving Retroreflector, , US Patent; Bridges, R.E., Hoffer, J.M., (2010) Absolute Distance Meter That Measures A Moving Retroreflector, , US Patent; Parker, D.H., Payne, J.M., (2011) Method for Measuring the Structural Health of A Civil Structure, , US Patent; Parker, D.H., (1999) First Measurements of the GBT Feed Arm, , GBT Archive L0535, Available from NRAO Library; Marsh, B., VanScotter, K., (2010) Flight in Factory, , US Patent; Marsh, B.J., VanScotter, K., (2014) Flight in Factory, , US Patent; Marsh, B.J., Lazar, M.A., (2011) Calibrating Aircraft Surfaces, , US Patent; Marsh, B.J., Lazar, M.A., Vanscotter, K.D., Cooke, B.T., Bodziony, L.S., Coleman, R.M., Wel, M.M.V., Nobles, O.M., (2015) Aircraft Enhanced Reference System and Method, , US Patent Application Publication; Pettersson, B., (2015) Method and System for Virtual Assembly of A Structure, , US Patent Application Publication; Parker, D.H., Payne, J.M., (2012) Methods for Modeling the Structural Health of A Civil Structure Based on Electronic Distance Measurements, , US Patent; Parker, D.H., Payne, J.M., (2016) Methods for Measuring and Modeling the Process of Prestressing Concrete during Tensioning/detensioning Based on Electronic Distance Measurements, , US Patent Application Publication; (2006) The American Society of Mechanical Engineers, ASME B89. 4. 19-2006 Performance Evaluation of Laser-Based Spherical Coordinate Measurement Systems; Parker, D.H., (2010) Method for Calibrating A Laser-based Spherical Coordinate Measurement System by A Mechanical Harmonic Oscillator, , US Patent; Welty, V., (2015) Dynamic Evaluation of Laser Trackers, , mathesis, University of North Carolina at Charlotte","Parker, D.H.; Parker Intellectual Property Enterprises, 3919 Deepwoods Road, United States; email: david@parker-ip-ent.com","Yu T.-Y.Wu H.F.Shull P.J.Gyekenyesi A.L.","Fiberguide Industries;Frontiers Media;OZ Optics, Ltd.;Polytec, Inc.;The Society of Photo-Optical Instrumentation Engineers (SPIE)","SPIE","Conference on Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XI 2017","26 March 2017 through 29 March 2017",,128433,0277786X,9781510608238,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85021841536
"Szabó G., Völgyi I., Kenéz Á.","36543298600;36563002600;57223698865;","Vibration Assessment of a New Danube Bridge at Komárom",2022,"Periodica Polytechnica Civil Engineering","66","4",,"1014","1022",,1,"10.3311/PPci.19508","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139140674&doi=10.3311%2fPPci.19508&partnerID=40&md5=bb4d476157ac22ff3dd9323dab5dbcf5","Pont-TERV Ltd. Engineering Consultants, Mohai út 38, Budapest, H-1119, Hungary; Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary","Szabó, G., Pont-TERV Ltd. Engineering Consultants, Mohai út 38, Budapest, H-1119, Hungary; Völgyi, I., Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary; Kenéz, Á., Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary","In this paper the vortex induced vibration of a cable-stayed bridge with a main span of 252 m was studied at construction stages. Structural FEM and aerodynamic CFD models were made in order to calculate the vibration amplitude of this slender structure. The damping of the pure steel structure and the effect of the tuned mass dampers were measured through on-site vibration tests. Based on the validated structural dynamics model and the simulated aerodynamic parameters, the vortex induced vibration amplitudes were evaluated and compared with the monitoring data gained from accelerometers and wind sensors attached to the stiffening girder during the most critical construction period. © 2022, Budapest University of Technology and Economics. All rights reserved.","cable-stayed bridge; free cantilever construction; monitoring system; vortex induced vibration","Aerodynamics; Cables; Computational fluid dynamics; Fluid structure interaction; Structural dynamics; Structural health monitoring; Vibrations (mechanical); Vortex flow; Cable-stayed bridge; Cantilever construction; CFD-model; Construction stages; Free cantilever; Free cantilever construction; Monitoring system; Slender structures; Vibration amplitude; Vortex induced vibration; Cable stayed bridges; aerodynamics; bridge; computational fluid dynamics; construction method; damping; dynamic response; steel structure; stiffness; structural response; vibration; vortex flow; wind direction; wind field; wind velocity; Hungary; Komarom; Komarom-Esztergom",,,,,,,,,,,,,,,,"Zhao, L., Ge, Y., Emergency Measures for Vortex-induced Vibration of Humen Bridge (2020) Advances in Civil, Environmental, & Materials Research, , presented at: Seoul, South Korea, Aug. 25–28; Astiz, M. A., Wind-induced vibrations of the Alconétar Bridge, Spain (2010) Structural Engineering International, 20 (2), pp. 195-199. , https://doi.org/10.2749/101686610791283696; Fujino, Y., Yoshida, Y., Wind-Induced Vibration and Control of Trans-Tokyo Bay Crossing Bridge (2002) Journal of Structural Engineering, 128 (8), pp. 1012-1025. , https://doi.org/10.1061/(ASCE)0733-9445(2002)128:8(1012); (2005) Eurocode 1: Actions on structures-Part 1-4: General actions-Wind actions, , CEN ""EN 1991-1-4:2005, European Committee for Standardization, Brussels, Belgium; Hu, C., Zhao, L., Ge, Y., Mechanism of suppression of vortex-induced vibrations of a streamlined closed-box girder using additional small-scale components (2019) Journal of Wind Engineering and Industrial Aerodynamics, 189, pp. 314-331. , https://doi.org/10.1016/j.jweia.2019.04.015; Yang, Y., Kim, S., Hwang, Y., Kim, H.-K., Experimental study on suppression of vortex-induced vibration of bridge deck using vertical stabilizer plates (2021) Journal of Wind Engineering and Industrial Aerodynamics, 210, p. 104512. , https://doi.org/10.1016/j.jweia.2020.104512; Sun, Y., Li, M., Liao, H., Investigation on vortex-induced vibration of a suspension bridge using section and full aeroelastic wind tunnel tests (2013) Wind and Structures, 17 (6), pp. 565-587. , https://doi.org/10.12989/was.2013.17.6.565; Shimada, K., Ishihara, T., Predictability of unsteady two-dimensional k-ε model on the aerodynamic instabilities of some rectangular prisms (2012) Journal of Fluids and Structures, 28, pp. 20-39. , https://doi.org/10.1016/j.jfluidstructs.2011.08.013; Wu, T., Kareem, A., An overview of vortex-induced vibration (VIV) of bridge decks (2012) Frontiers of Structural and Civil Engineering, 6, pp. 335-347. , https://doi.org/10.1007/s11709-012-0179-1; Huang, Z., Li, Y., Hua, X., Chen, Z., Wen, Q., Automatic Identification of Bridge Vortex-Induced Vibration Using Random Decrement Method (2019) Applied Sciences, 9 (10), p. 2049. , https://doi.org/10.3390/app9102049; Li, Z., Zhou, Q., Liao, H., Ma, C., Numerical studies of the suppression of vortex-induced vibrations of twin box girders by central grids (2018) Wind and Structure, 26 (5), pp. 305-315. , https://doi.org/10.12989/was.2018.26.5.305; Mannini, C., Applicability of URANS and DES Simulations of Flow Past Rectangular Cylinders and Bridge Sections (2015) Computation, 3 (3), pp. 479-508. , https://doi.org/10.3390/computation3030479; Lee, L. W., Wang, Y. L., Aerodynamics of a circular cylinder of finite length on cross-flow (1987) ASME Applied Mechanics, Bioengineering and Fluids Engineering Conference, pp. 61-65. , Cincinnati, OH, USA","Szabó, G.; Pont-TERV Ltd. Engineering Consultants, Mohai út 38, Hungary; email: mr.gergely.szabo@gmail.com",,,"Budapest University of Technology and Economics",,,,,05536626,,,,"English","Period. Polytech. Civ. Eng.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85139140674
"Morgan C.J., Sparling B.F., Wegner L.D.","57215412595;15037238700;13805490300;","Use of structural health monitoring to extend the service life of the Diefenbaker Bridge",2022,"Journal of Civil Structural Health Monitoring","12","4",,"913","929",,1,"10.1007/s13349-022-00585-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131527281&doi=10.1007%2fs13349-022-00585-1&partnerID=40&md5=f1c267f9e80d99cbfc040b76777f528f","College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada; WSP Canada Inc., Saskatoon, SK, Canada","Morgan, C.J., College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada, WSP Canada Inc., Saskatoon, SK, Canada; Sparling, B.F., College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada; Wegner, L.D., College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada","A structural health monitoring system was installed on the 60-year-old Diefenbaker Bridge, located in Prince Albert, Saskatchewan, Canada, to investigate in-situ bridge behaviours, such as the degree of composite action, lateral load distribution, and dynamic load influence. The 304 m long, seven span bridge consists of two separate fracture critical superstructures, each comprising a cast-in-place concrete deck supported by two non-composite welded wide flange girders. Previous studies, based solely on a structural analysis, concluded that the connection of the lateral bracing to the girder web had less than 5 years of remaining fatigue life. Due to the uncertainty involved in this calculation, the data acquired from 6 months of field monitoring were used to define the structure’s response to live loading, and to calibrate a finite element model that was used to characterize the three-dimensional stress state at that connection. It was found that unexpected composite action, increased load sharing between the girders, and minimal dynamic load influence exist in the bridge. Results were compared with those obtained using the Canadian Highway Bridge Design Code (CAN/CSA S6–14). Based on the monitoring results, it was concluded that costly improvements to the connection detail were not required, since the remaining fatigue life was estimated to be at least 52 years. In addition, it was found that the exterior girders are more heavily loaded than the interior girders, and the northbound structure is more heavily loaded than the southbound, permitting the location of the most critical connection for fatigue life to be identified. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.","Bridge evaluation; Bridge inspection; Fatigue life evaluation; Finite element modelling; Model calibration; Structural health monitoring","Beams and girders; Dynamic loads; Fatigue of materials; Highway bridges; Highway planning; Stress analysis; Structural health monitoring; Bridge evaluation; Bridge inspection; Composite action; Fatigue life evaluation; Lateral load distributions; Load dynamics; Model calibration; Remaining fatigue life; Saskatchewan; Structural health monitoring systems; Finite element method",,,,,,,,,,,,,,,,"(2015) Diefenbaker Bridge Management Plan, City of Prince Albert, Prince Albert, Saskatchewan, Canada; (2016) Diefenbaker Assessment and Evaluation Report, Saskatchewan Ministry of Highways and Infrastructure, Saskatoon, Saskatchewan, Canada; Zhou, Y., Assessment of bridge remaining fatigue life through field strain measurement (2006) J Bridge Eng, 11 (6), pp. 737-744; Fasl, J., (2013) Estimating the Remaining Fatigue Life of Steel Bridges Using Field Measurements, , dissertation, the university of Texas at Austin; (2018) The manual for bridge evaluation, , 2, American association of state highway and transportation officials, Washington, DC, USA; Connor, R., Fisher, J., Identifying effective and ineffective retrofits for distortion fatigue cracking in steel bridges using field instrumentation (2006) J Bridge Eng, 11 (6), pp. 745-752; Fisher, J., Mertz, D., Zhong, A., (1983) Steel bridge members under variable amplitude long life fatigue loading, , Transportation research board national research council, Washington, D.C., USA; (2017) LRFD bridge design specification, , 8, American association of state highway and transportation officials, Washington, DC; Swenson, K., Frank, K., (1984) The application of cumulative damage fatigue theory to highway bridge fatigue design, , Center for transportation research, Austin, TX; Leander, J., Andersson, A., Karoumi, R., Monitoring and enhanced fatigue evaluation of a steel railway bridge (2010) Eng Struct, 32, pp. 854-863; (2013) Prince Albert Area Second Bridge River Crossing Study, City of Prince Albert, Saskatchewan, Canada; (2014) Canadian Highway Bridge Design Code, Canadian Standards Association, Rexdale, Ontario, Canada; Distortion induced fatigue cracking in steel bridges, NCHRP Report 335 (1990) Transportation Research Board, National Research Council, , Washington, DC, USA; Beer, F., Johnston, E., Dewolf, J., Mazurek, D., (2009) Mechanics of materials, , 5, McGraw-Hill, New York, NY, USA; (2011) Standard Practices for Cycle Counting in Fatigue Analysis, ASTM International, West Conshohocken, PA, USA; (2019) SAP 2000 integrated software for structural analysis and design, , Computers and structures Inc., Berkeley, California, USA; (2019) ANSYS Simulation Software, Version 19, , Delaware, USA; (2008) Ontario Structure Inspection Manual, , Toronto, ON, Canada; Feldman, L., Jackson, K., Sparling, B., Sparks, G., Comparison of load rating techniques for the red deer river bridge (2011) Can J Civ Eng, 38 (10), pp. 1072-1081; Miner, M., Cumulative damage in fatigue (1945) J Appl Mech, 12, pp. A159-A164; Moses, F., Schilling, C., Raju, K., (1987) Fatigue evaluation procedures for steel bridges, , Transportation research board, national research council, Washington, D.C. USA","Wegner, L.D.; College of Engineering, Canada; email: Leon.Wegner@usask.ca",,,"Springer Science and Business Media Deutschland GmbH",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85131527281
"Pereira M., Glisic B.","57202815487;57200346944;","A hybrid approach for prediction of long-term behavior of concrete structures",2022,"Journal of Civil Structural Health Monitoring","12","4",,"891","911",,1,"10.1007/s13349-022-00582-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131424067&doi=10.1007%2fs13349-022-00582-4&partnerID=40&md5=3e2a8e195664713b9165151bfcb339c0","Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States","Pereira, M., Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Glisic, B., Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States","Concrete displays long-term time-dependent behavior due to its rheological properties. The prediction of long-term behavior of concrete is difficult, even under laboratory conditions, due to the stochastic nature of its rheological phenomena. In concrete structures, long-term prediction is even more challenging due to the presence of uncontrolled conditions, such as variations in temperature, humidity, and loading. Current approaches for prediction of long-term time-dependent behavior at structural scale involve computationally intensive stochastic finite-element methods in which multiple creep and shrinkage models are implemented. However, these models are often calibrated using a database of experiments that are not the most informative for a specific structure. Structural health monitoring can improve prediction accuracy by providing structure-specific in-situ measurements of strain and temperature. Strain sensors, however, measure a multitude of effects simultaneously present in the structure, making it difficult to decouple effects of interest. In this work, a hybrid method employing probabilistic neural networks and engineering code models is proposed for the prediction of long-term behavior in concrete structures. A modular architecture is employed to decouple temperature-dependent environmental strain from long-term time-dependent strain. Generalized creep and shrinkage code models are fitted to the resulting time-dependent strain component data and used for prediction. The method is applied to a concrete pedestrian bridge instrumented with several embedded strain and temperature sensors. Excellent accuracy is achieved in the prediction of structural behavior multiple years beyond the training range. This, in turn, enables the detection of unusual structural behaviors with both gradual and sudden manifestation. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.","Anomaly detection; Creep and shrinkage; Long-term structural behavior; Machine learning; Predictive modeling; Structural health monitoring","Anomaly detection; Concrete construction; Concretes; Creep; Forecasting; Machine learning; Neural networks; Shrinkage; Stochastic models; Stochastic systems; Structural health monitoring; Anomaly detection; Creep and shrinkages; Hybrid approach; Long-term behavior; Long-term structural behavior; Predictive models; Rheological property; Structural behaviors; Time-dependent behaviour; Time-dependent strains; Concrete buildings",,,,,"Princeton University","The authors would like to thank the support of Princeton University for the financial support, and Hiba Abdel-Jaber and Vivek Kumar for the help with data preparation.",,,,,,,,,,"Bažant, Z.P., Jirásek, M., (2018) Creep and hygrothermal effects in concrete structures, , Springer, Dordrecht; Glasser, F.P., Marchand, J., Samson, E., Durability of concrete—degradation phenomena involving detrimental chemical reactions (2007) Cem Concr Res, 38, pp. 226-246; Abdellatef, M., Vorel, J., Wan-Wendner, R., Alnaggar, M., Predicting time-dependent behavior of post-tensioned concrete beams: discrete multiscale multiphysics formulation (2019) J Struct Eng, 145 (7), p. 04019060; Glisic, B., Inaudi, D., Lau, J.M., Fong, C.C., Ten-year monitoring of high-rise building columns using long-gauge fiber optic sensors (2013) Smart Mater Struct, 22; Abdel-Jaber, H., Glisic, B., Monitoring of long-term prestress losses in prestressed concrete structures using fiber optic sensors (2019) Struct Health Monit, 18, pp. 254-269; Mehta, P.K., Monteiro, P.J.M., (2013) Concrete microstructure, properties, and materials, , McGraw-Hill Education, New York; Di Luzio, G., Cusatis, G., Hygro-thermo-chemical modeling of high performance concrete. I: theory (2009) Cement Concr Compos, 31, pp. 301-309; Di Luzio, G., Cusatis, G., Hygro-thermo-chemical modeling of high performance concrete. II: numerical implementation, calibration, and validation (2009) Cement Concr Compos, 31, pp. 309-324; Farrar, C.R., Worden, K., (2013) Structural health monitoring: a machine learning perspective, , Wiley, West Sussex; Huston, D., (2011) Structural sensing, health monitoring, and performance evaluation, , CRC Press, Boca Raton; Alnaggar, M., Cusatis, G., Di Luzio, G., Lattice discrete particle modeling (LDPM) of alkali silica reaction (ASR) deterioration of concrete structures (2013) Cement Concr Compos, 41, pp. 45-59; Alnaggar, M., Di Luziom, G., Cusatis, G., Modeling time-dependent behavior of concrete affected by alkali silica reaction in variable environmental conditions (2017) Materials, 10 (5), p. 471; Bažant, Z., Hubler, M., Yu, Q., Pervasiveness of excessive segmental bridge deflections: wake-up call for creep (2011) ACI Struct J, 108 (6), pp. 766-774; Bažant, Z.P., Yu, Q., Li, G.-H., Excessive long-time deflections of prestressed box girders. I: record-span bridge in Palau and other paradigms (2012) J Struct Eng, 138 (6), pp. 676-686; Guide for modeling and calculating shrinkage and creep in hardened concrete (2008) Farmington Hills; CEB-FIP model code 1990 (1993) Committee Euro-International Du Béton; Bažant, Z.P., Baweja, S., Creep and shrinkage prediction model for analysis and design of concrete structures—model B3 (1995) Matériaux et Constructions, 28, pp. 357-365; Hubler, M.H., Wendner, R., Bažant, Z.P., Statistical justification of model B4 for drying and autogenous shrinkage of concrete and comparisons to other models (2015) Mater Struct, 48, pp. 797-814; Pham, A.-D., Ngo, N.-T., Nguyen, T.-K., Machine learning for predicting long-term deflections in reinforce concrete flexural structures (2020) J Comput Des Eng, 7 (1), pp. 95-106; Ghasemzadeh, F., Manafpour, A., Sajedi, S., Shekarchi, M., Hatami, M., Predicting long-term compressive creep of concrete using inverse analysis method (2016) Constr Build Mater, 124, pp. 496-507; Han, B., Xiang, T.-Y., Xie, H.-B., A Bayesian inference framework for predicting the long-term deflection of concrete structures caused by creep and shrinkage (2017) Eng Struct, 142, pp. 46-55; Sousa, H., Santos, L.O., Chryssanthopoulous, M., Quantifying monitoring requirements for predicting creep deformations through Bayesian updating methods (2019) Struct Saf, 76, pp. 40-50; Strauss, A., Wan-Wendner, R., Vidovic, A., Zambon, I., Yu, Q., Frangopol, D.M., Bergmeister, K., Gamma prediction models for long-term creep deformations of prestressed concrete bridges (2017) J Civ Eng Manag, 23 (6), pp. 681-698; Abdel-Jaber, H., Glisic, B., Systematic method for the validation of long-term temperature measurements (2016) Smart Mater Struct, 25; Che, Z., Purushotham, S., Cho, K., Recurrent neural networks for multivariate time series with missing values (2018) Sci Rep, 8, p. 6085; Hierarchical deep generative models for multi-rate multivariate time series (2018) Proceedings of the 35Th International Conference on Machine Learning, Ser. 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Master’s thesis (2019) Norwegian University of Science and Technology; Hu, W.-H., Cunha, Á., Caetano, E., Rohrmann, R.G., Said, S., Teng, J., Comparison of different statistical approaches for removing environmental/operational effects for massive data continuously collected from footbridges (2017) Struct Control Health Monit, 24; Cross, E.J., Worden, K., Chen, Q., Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data (2011) Proc R Soc A Math Phys Eng Sci, 467, pp. 2712-2732; Rubanova, Y., Chen, R.T.Q., Duvenaud, D.K., (2019) Latent Ordinary Differential Equations for Irregularly-Sampled Time Series, , http://papers.nips.cc/paper/8773-latent-ordinary-differential-equations-for-irregularly-sampled-time-series.pdf; Oh, B.K., Park, H.S., Glisic, B., Prediction of long-term strain in concrete structure using convolutional neural networks, air temperature and time stamp of measurements (2021) Autom Constr, 126; Sigurdardottir, D.H., Glisic, B., On-site validation of fiber-optic methods for structural health monitoring: Streicker bridge (2015) J Civ Struct Heal Monit, 5, pp. 529-549; Sigurdardottir, D.H., Glisic, B., Neutral axis as damage sensitive feature (2013) Smart Mater Struct, 22. , p 18; (2016) Guide to Estimating Prestress Losses; Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Asudevan, V., (2015) Software Available from Tensorflow.Org [Online], , https://www.tensorflow.org/; Kingma, D.P., Ba, J., Adam: A method for stochastic optimization. In: 3rd international conference for learning representations (2015) San Diego; Karniadakis, G.E., Levrelodos, I.G., Lu, L., Perdikaris, P., Wang, S., Yang, L., Physics-informed machine learning (2021) Nat Rev Phys, 3, pp. 422-440","Pereira, M.; Department of Civil and Environmental Engineering, United States; email: mp34@princeton.edu",,,"Springer Science and Business Media Deutschland GmbH",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85131424067
"Ghahremani B., Enshaeian A., Rizzo P.","57216525400;55175602800;57203255968;","Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis",2022,"Sensors","22","14","5172","","",,1,"10.3390/s22145172","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135132860&doi=10.3390%2fs22145172&partnerID=40&md5=af3b41ce8f2a1e4de3099da572a1310e","Laboratory for Nondestructive Evaluation and Structural Health Monitoring Studies, Department of Civil and Environmental Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, PA  15261, United States","Ghahremani, B., Laboratory for Nondestructive Evaluation and Structural Health Monitoring Studies, Department of Civil and Environmental Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, PA  15261, United States; Enshaeian, A., Laboratory for Nondestructive Evaluation and Structural Health Monitoring Studies, Department of Civil and Environmental Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, PA  15261, United States; Rizzo, P., Laboratory for Nondestructive Evaluation and Structural Health Monitoring Studies, Department of Civil and Environmental Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, PA  15261, United States","This article presented a physics-based structural health monitoring (SHM) approach applied to a pretensioned adjacent concrete box beams bridge in order to predict the deformations associated with the presence of transient loads. A detailed finite element model was generated using ANSYS software to create an accurate model of the bridge. The presence of concentrated loads on the deck at different locations was simulated, and a static analysis was performed to quantify the deformations induced by the loads. Such deformations were then compared to the strains recorded by an array of wireless strain gauges during a controlled truckload test performed by an independent third party. The test consisted of twenty low-speed crossings at controlled distances from the bridge parapets using a truck with a certified load. The array was part of a SHM system that consisted of 30 wireless strain gauges. The results of the comparative analysis showed that the proposed physics-based monitoring is capable of identifying sensor-related faults and of determining the load distributions across the box beams. In addition, the data relative to near two-years monitoring were presented and showed the reliability of the SHM system as well as the challenges associated with environmental effects on the strain reading. An ongoing study is determining the ability of the proposed physics-based monitoring at estimating the variation of strain under simulated damage scenarios. © 2022 by the authors.","bridge monitoring; finite element modeling; strain sensors; structural health monitoring","Deformation; Finite element method; Static analysis; Strain; Structural health monitoring; Trucks; Bridge health monitoring; Bridge monitoring; Data fidelity; Element models; Finite element modeling; Physics-based; Strain data; Strain sensors; Strain-gages; Structural health monitoring systems; Strain gages; finite element analysis; physiologic monitoring; reproducibility; software; Finite Element Analysis; Monitoring, Physiologic; Reproducibility of Results; Software",,,,,"Pennsylvania Department of Transportation, PennDOT","This research was supported by the Pennsylvania Department of Transportation (PennDOT) under the Work Order-003 titled “Data Management, Mining, and Inference for Bridge Monitoring”.",,,,,,,,,,"Rizzo, P., Enshaeian, A., Challenges in Bridge Health Monitoring: A Review (2021) Sensors, 21. , 34202875; https://www.fhwa.dot.gov/bridge/nbi/ascii.cfm, Available online; https://www.fhwa.dot.gov/bridge/fc.cfm, Available online; Zhang, Z., Sun, C., Structural Damage Identification via Physics-Guided Machine Learning: A Methodology Integrating Pattern Recognition with Finite Element Model Updating (2021) Struct. Health Monit, 20, pp. 1675-1688; Farrar, C.R., Worden, K., Structural Health Monitoring: A Machine Learning Perspective (2012) Structural Health Monitoring: A Machine Learning Perspective, , John Wiley & Sons, Ltd., Hoboken, NJ, USA; Rizzo, P., Lanza di Scalea, F., Wavelet-Based Unsupervised and Supervised Learning Algorithms for Ultrasonic Structural Monitoring of Waveguides (2007) Progress in Smart Materials and Structures Research, pp. 227-290. , Reece P.L., (ed), NOVA Science Publishers, New York, NY, USA; Liu, Y.Y., Ju, Y.F., Duan, C.D., Zhao, X.F., Structure Damage Diagnosis Using Neural Network and Feature Fusion (2011) Eng. Appl. Artif. Intell, 24, pp. 87-92; Gu, J., Gul, M., Wu, X., Damage Detection under Varying Temperature Using Artificial Neural Networks (2017) Struct. 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Struct, 26, p. 104007; Yang, D.-H., Yi, T.-H., Li, H.-N., Zhang, Y.-F., Correlation-Based Estimation Method for Cable-Stayed Bridge Girder Deflection Variability under Thermal Action (2018) J. Perform. Constr. Facil, 32, p. 04018070; Yang, D.H., Yi, T.H., Li, H.N., Liu, H., Liu, T., Train-Induced Dynamic Behavior Analysis of Longitudinal Girder in Cable-Stayed Bridge (2018) Smart Struct. Syst, 21, pp. 549-559; Yu, S., Ou, J., Structural Health Monitoring and Model Updating of Aizhai Suspension Bridge (2017) J. Aerosp. Eng, 30, p. B4016009; Schlune, H., PLoS, M., Gylltoft, K., Improved Bridge Evaluation through Finite Element Model Updating Using Static and Dynamic Measurements (2009) Eng. Struct, 31, pp. 1477-1485; Yang, H., Xu, X., Neumann, I., Laser Scanning-Based Updating of a Finite-Element Model for Structural Health Monitoring (2016) IEEE Sens. 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Health Monit, 10, pp. 497-511; Schommer, S., Nguyen, V.H., Maas, S., Zürbes, A., Model Updating for Structural Health Monitoring Using Static and Dynamic Measurements (2017) Procedia Eng, 199, pp. 2146-2153; Zanjani Zadeh, V., Patnaik, A., Finite Element Modeling of the Dynamic Response of a Composite Reinforced Concrete Bridge for Structural Health Monitoring (2014) Int. J. Adv. Struct. Eng, 6, p. 2; Gatti, M., Structural Health Monitoring of an Operational Bridge: A Case Study (2019) Eng. Struct, 195, pp. 200-209; Cheng, X.X., Dong, J., Han, X.L., Fei, Q.G., Structural Health Monitoring-Oriented Finite-Element Model for a Large Transmission Tower (2018) Int. J. Civ. Eng, 16, pp. 79-92; Weng, S., Zhu, H.P., Damage Identification of Civil Structures Based on Finite Element Model Updating (2021) Gongcheng Lixue/Eng. Mech, 38, pp. 1-16; Duan, Y.F., Xu, Y.L., Fei, Q.G., Wong, K.Y., Chan, K.W.Y., Ni, Y.Q., Ng, C.L., Advanced Finite Element Model of Tsing Ma Bridge for Structural Health Monitoring (2011) Int. J. Struct. Stab. Dyn, 11, pp. 313-344; Eiras, J.N., Payan, C., Rakotonarivo, S., Garnier, V., Experimental Modal Analysis and Finite Element Model Updating for Structural Health Monitoring of Reinforced Concrete Radioactive Waste Packages (2018) Constr. Build. Mater, 180, pp. 531-543; Haidarpour, A., Tee, K.F., Finite Element Model Updating for Structural Health Monitoring. SDHM Struct (2020) Durab. Health Monit, 14, pp. 1-17; Rizzo, P., Sorrivi, E., Lanza di Scalea, F., Viola, E., Wavelet-Based Outlier Analysis for Guided Wave Structural Monitoring: Application to Multi-Wire Strands (2007) J. Sound Vib, 307, pp. 52-68; Bagheri, A., Pistone, E., Rizzo, P., Outlier Analysis and Artificial Neural Network for the Noncontact Nondestructive Evaluation of Immersed Plates (2015) Res. Nondestruct. Eval, 26, pp. 154-173; Zheng, B., Rizzo, P., Nasrollahi, A., Outlier Analysis of Nonlinear Solitary Waves for Health Monitoring Applications (2020) Struct. Health Monit, 19, pp. 1160-1174","Rizzo, P.; Laboratory for Nondestructive Evaluation and Structural Health Monitoring Studies, 3700 O’Hara Street, United States; email: pir3@pitt.edu",,,"MDPI",,,,,14248220,,,"35890852","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85135132860
"Li Y., Ni P., Sun L., Zhu W.","57211568199;57337895400;7403956279;57215854862;","A convolutional neural network-based full-field response reconstruction framework with multitype inputs and outputs",2022,"Structural Control and Health Monitoring","29","7","e2961","","",,1,"10.1002/stc.2961","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127222363&doi=10.1002%2fstc.2961&partnerID=40&md5=387d8778b2cc752390c1f7fa2692e05b","Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, China; State Key Laboratory of Disaster Reduction in Civil Engineering, Dept. of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai Qizhi Institute, Shanghai, China; Sichuan Highway Planning, Survey, Design, and Research Institute Ltd, Chengdu, China","Li, Y., Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, China; Ni, P., Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, China; Sun, L., Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, China, State Key Laboratory of Disaster Reduction in Civil Engineering, Dept. of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai Qizhi Institute, Shanghai, China; Zhu, W., Sichuan Highway Planning, Survey, Design, and Research Institute Ltd, Chengdu, China","Structural health monitoring (SHM) systems evaluate the state of the infrastructures by analyzing the monitored responses. As measuring all target responses is difficult to accomplish due to technical or economic limitations, converting other easy-measuring responses to the target one is a popular way. Relative approaches are separated into data-driven and model-driven ones. This paper proposes a deep learning-based framework to reconstruct multitypes of full-field responses. The adopted architecture is a convolutional neural network (CNN) with an autoencoder structure and skip connections. Varied from other data-driven approaches, the training set in this paper is the responses computed by a finite element model (FEM), with which the CNN can learn the full-field mapping relationships among varied response types. Therefore, the proposed framework is data-model-co-driven. In the numerical simulation section, a simply-supported beam and a continuous beam bridge have been adopted to discuss the influence of hyperparameters (training epoch, kernel size, skip connection, and bottleneck size), sensor arrangement, modeling error, and measurement noise, which indicates that the framework applies to the in-field structures. Furtherly, a laboratory experiment has been conducted to validate the framework using a two-span continuous bridge with obvious FEM error. All results have shown that the deep-learning-based response reconstruction algorithms can obtain the training set from not only in-field measurements, but also numerical models to improve the diversity of training data. © 2022 John Wiley & Sons, Ltd.","autoencoder; convolutional neural network; data fusion and conversion; FEM-calculated training set; full-field response reconstruction; mapping relationship","Convolution; Convolutional neural networks; Data handling; Deep learning; Finite element method; Mapping; Numerical models; Structural health monitoring; Auto encoders; Convolutional neural network; Data fusion and conversion; Finite element model-calculated training set; Finite element modelling (FEM); Full field response; Full-field response reconstruction; Mapping relationships; Response reconstruction; Training sets; Data fusion",,,,,"XJ2021036; National Natural Science Foundation of China, NSFC: 51878482","This work was supported by the National Natural Science Foundation of China (grant number 51878482) and the Hong Kong Scholars Program (grant number XJ2021036).",,,,,,,,,,"Alkayem, N.F., Cao, M., Zhang, Y., Bayat, M., Su, Z., Structural damage detection using finite element model updating with evolutionary algorithms: a survey (2018) Neural Comput Applic, 30 (2), pp. 389-411; Sen, D., Nagarajaiah, S., Data-driven approach to structural health monitoring using statistical learning algorithms (2018) Mechatronics for Cultural Heritage and Civil Engineering, pp. 295-305. , Italy, Springer, p; Gherlone, M., Cerracchio, P., Mattone, M., Di Sciuva, M., Tessler, A., An inverse finite element method for beam shape sensing: theoretical framework and experimental validation (2014) Smart Materials and Structures, 23 (4); Baqersad, J., Niezrecki, C., Avitabile, P., Extracting full-field dynamic strain on a wind turbine rotor subjected to arbitrary excitations using 3D point tracking and a modal expansion technique (2015) J Sound Vib, 352, pp. 16-29; O'Callahan, J.C., System equivalent reduction expansion process, , Proc of the 7th Inter Modal Analysis Conf, 19891989; Zhao, Y., Bao, H., Duan, X., Fang, H., The application research of inverse finite element method for frame deformation Estimation (2017) International Journal of Aerospace Engineering, 2017; Tessler, A., Structural analysis methods for structural health management of future aerospace vehicles (2007) Key Engineering Materials, 347, pp. 57-66; Pak, C.-G., Wing shape sensing from measured strain, , AIAA Infotech@ Aerospace. 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Kim, S.G., Chae, Y.H., Seong, P.H., Development of a generative-adversarial-network-based signal reconstruction method for nuclear power plants (2021) Annals of Nuclear Energy, 142; Lei, X., Sun, L., Xia, Y., Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks (2021) Structural Health Monitoring., 20 (4), pp. 2069-2087; Wang, Z., Cha, Y.-J., Unsupervised deep learning approach using a deep auto-encoder with an one-class support vector machine to detect structural damage (2021) Structural Health Monitoring, 20 (1), pp. 406-425","Sun, L.; Department of Bridge Engineering, China; email: lmsun@tongji.edu.cn",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-85127222363
"Zacchei E., Lyra P.H.C., Lage G.E., Antonine E., Soares A.B., Jr., Caruso N.C., de Assis C.S.","57195836485;57219125236;57748727800;57747435500;57747750900;57746791400;57747435600;","Structural Health Monitoring of a Brazilian Concrete Bridge for Estimating Specific Dynamic Responses",2022,"Buildings","12","6","785","","",,1,"10.3390/buildings12060785","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132203637&doi=10.3390%2fbuildings12060785&partnerID=40&md5=64e1a30d5cd403363589c8316b747b46","Itecons, Coimbra, 3030-289, Portugal; CERIS (Civil Engineering Research and Innovation for Sustainability), University of Coimbra, Coimbra, 3004-531, Portugal; Mauá Institute of Technology (MIT), 1 Mauá Square,SP, São Caetano do Sul, 09580-900, Brazil; França e Associados Projetos Estruturais Company, 1768 Brigadeiro Faria Lima Avenue,SP, São Paulo, 01451-001, Brazil; HTB Engineer e Construction Company, 145 Alfredo Egídio de Souza Aranha Avenue,SP, São Paulo, 04726-170, Brazil","Zacchei, E., Itecons, Coimbra, 3030-289, Portugal, CERIS (Civil Engineering Research and Innovation for Sustainability), University of Coimbra, Coimbra, 3004-531, Portugal; Lyra, P.H.C., Mauá Institute of Technology (MIT), 1 Mauá Square,SP, São Caetano do Sul, 09580-900, Brazil; Lage, G.E., Mauá Institute of Technology (MIT), 1 Mauá Square,SP, São Caetano do Sul, 09580-900, Brazil, França e Associados Projetos Estruturais Company, 1768 Brigadeiro Faria Lima Avenue,SP, São Paulo, 01451-001, Brazil; Antonine, E., Mauá Institute of Technology (MIT), 1 Mauá Square,SP, São Caetano do Sul, 09580-900, Brazil; Soares, A.B., Jr., Mauá Institute of Technology (MIT), 1 Mauá Square,SP, São Caetano do Sul, 09580-900, Brazil; Caruso, N.C., HTB Engineer e Construction Company, 145 Alfredo Egídio de Souza Aranha Avenue,SP, São Paulo, 04726-170, Brazil; de Assis, C.S., Mauá Institute of Technology (MIT), 1 Mauá Square,SP, São Caetano do Sul, 09580-900, Brazil","A 3D coupled model to simulate vehicle–bridge interactions (VBI) to estimate its structural responses and impact factors (IMs) was developed in this study. By structural health monitoring (SHM) of a real concrete bridge, several data were collected to calibrate the bridge model by the finite element method (FEM). These models provide the bridge response in terms of vertical dis-placements and accelerations. VBI models provide reliable outputs without significantly altering the dynamic properties of the bridge. Modified recent analytical equations, which account for the effects of the asymmetric two-axle vehicles, were developed numerically. These equations, plus some proposed solutions, also quantified the vehicle response in terms of accelerations to estimate a more conservative driving comfort. The goal consisted in fitting the SHM with numerical and analytical models to find a more appropriate response for safety purposes and maintenance. From the codes and the literature, it was shown that a unique IM factor was not found. Moreover, most approaches underestimate the phenomena; in fact, results show that a monitored IM factor is 2.5 greater than IM from codes. Proposed equations for vehicle accelerations provided more conservative values up to about three times the standard comfort value. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","Brazilian bridge; concrete bridge; IM factors; SHM; VBI",,,,,,"POCI-01-0247-FEDER-039742; Programa Operacional Temático Factores de Competitividade, POFC","This work was framed within the “Probabilistic analysis applied to concrete road bridges”—Decision 11883/45/17; project funded by Mauá Institute of Technology (MIT), Brazil. This work was also carried out under POCI-01-0247-FEDER-039742 (SELF_Bridges—Long Span Modular Bridges: Smarter, Extensible, Lighter and Fast Assembly). Project funded by Portugal 2020 through COMPETE 2020.",,,,,,,,,,"Thierry, J.A., The Bailey bridge (1946) Mil. 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Built Environ, 5, pp. 1-11; Junior, A.B.S., Lage, G.E., Caruso, N.C., (2020) Desenvolvimento de um Sistema de Baixo Custo Para Monitoramento de Obras de Arte Especiais, p. 121. , Dissertation, Mauá Institute of Technology (MIT): São Caetano do Sul, Brazil; (2013) Sap2000, version 15.0.0, , Computers and Structures, Inc.: Walnut Creek, CA, USA; (2020) Autodesk InfraWorks, version 20 (student), , Autodesk, Inc.: San Rafael, CA, USA; (2010) AutoCAD, version 2010, , Autodesk, Inc.: San Rafael, CA, USA; González, A., Vehicle-bridge dynamic interaction using finite element modelling (2010) Finite Element Analysis, pp. 1-26. , Moratal, D., Ed.; IntechOpen: London, UK; Pedro, R.L., Demarche, J., Miguel., L.F.F., Lopez, R.H., An efficient approach for the optimization of simply supported steel-concrete composite I-girder bridges (2017) Adv. Eng. 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Struct, 30, pp. 2330-2337; Calçada, R., Montenegro, P., Castro, M., (2019) Numerical Evaluation of the Dynamic Load Allowance Factor in from ASTHOO in Steel Modular Bridges from the Peru Provias Project: Probabilistic Approach, , Technical Report for Faculdade de Engenharia da Universidade do Porto: Porto, Portugal; Clough, R.W., Penzien, J., (2003) Dynamics of Structures, p. 752. , 3rd ed.; McGraw-Hill: New York, NY, USA; (2016) Mechanical Vibration—Road Surface Profile—Reporting of Measured Data, p. 44. , International Organization for Standardization (ISO): Geneva, Switzerland; Neves, S.G.M., Azevedo, A.F.M., Calçada, R., A direct method for analyzing the vertical vehicle-structures interaction (2012) Eng. Struct, 34, pp. 414-420; (2019) Wolfram Mathematica 12, version number 12.0, , Wolfram Research, Inc.: Champaign, IL, USA; Yang, Y.B., Yau, J.D., Hsu, L.C., Vibration of simple beams due to trains moving as high speeds (1997) Eng. Struct, 19, pp. 936-944","Zacchei, E.; IteconsPortugal; email: enricozacchei@gmail.com",,,"MDPI",,,,,20755309,,,,"English","Buildings",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85132203637
"Svendsen B.T., Øiseth O., Frøseth G.T., Rønnquist A.","57208452088;36167100600;57188970692;25653400800;","A hybrid structural health monitoring approach for damage detection in steel bridges under simulated environmental conditions using numerical and experimental data",2022,"Structural Health Monitoring",,,,"","",,1,"10.1177/14759217221098998","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130588180&doi=10.1177%2f14759217221098998&partnerID=40&md5=728dca0df7c8e948ab2ba1c5704a791d","Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway","Svendsen, B.T., Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway; Øiseth, O., Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway; Frøseth, G.T., Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway; Rønnquist, A., Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway","This paper presents a novel hybrid structural health monitoring (SHM) framework for damage detection in bridges using numerical and experimental data. The framework is based on the hybrid SHM approach and combines the use of a calibrated numerical finite element (FE) model to generate data from different structural state conditions under varying environmental conditions with a machine learning algorithm in a supervised learning approach. An extensive experimental benchmark study is performed to obtain data from a local and global sensor setup on a real bridge under different structural state conditions, where structural damage is imposed based on a comprehensive investigation of common types of steel bridge damage reported in the literature. The experimental data are subsequently tested on the machine learning model. It is demonstrated that relevant structural damage can be established based on the hybrid SHM framework by separately evaluating different cases considering natural frequencies, mode shapes, and mode shape derivatives. Consequently, the work presented in this study represents a significant contribution toward establishing SHM systems that can be applied to existing steel bridges. The proposed framework is applicable to any bridge structure in which relevant structural damage can be simulated and experimental data obtained. © The Author(s) 2022.","bridge; damage detection; experimental study; fatigue; finite element model; hybrid approach; machine learning; modal parameters; statistical model development; stochastic subspace identification; Structural health monitoring; support vector machine; system identification","Damage detection; Fatigue of materials; Learning algorithms; Modal analysis; Steel bridges; Stochastic models; Stochastic systems; Structural health monitoring; Support vector machines; Experimental study; Finite element modelling (FEM); Hybrid approach; Modal parameters; Model development; Statistic modeling; Statistical model development; Stochastic subspace identification; Support vectors machine; System-identification; Finite element method",,,,,,"The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Hell Bridge Test Arena is financially supported by Bane NOR and the Norwegian Railway Directorate.",,,,,,,,,,"Haghani, R., Al-Emrani, M., Heshmati, M., Fatigue-prone details in steel bridges (2012) Buildings, 2, pp. 456-476; Sohn, H., Effects of environmental and operational variability on structural health monitoring (2007) Philos Trans R Soc A Math Phys Eng Sci, 365, pp. 539-560; Sun, L., Shang, Z., Xia, Y., Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection (2020) J Struct Eng, 146, p. 04020073; Vagnoli, M., Remenyte-Prescott, R., Andrews, J., Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges (2018) Struct Heal Monit, 17, pp. 971-1007; Moughty, J.J., Casas, J.R., A state of the art review of modal-based damage detection in bridges: Development, challenges, and solutions Appl Sci, 7. , Epub ahead of print 2017; Farrar, C.R., Worden, K., An introduction to structural health monitoring (2007) Philos Trans R Soc A Math Phys Eng Sci, 365, pp. 303-315; Farrar, C.R., Worden, K., (2012) Structural Health Monitoring: A Machine Learning Perspective, , Wiley; Barthorpe, R.J., (2010) On Model- and Data-Based Approaches to Structural Health Monitoring, , The University of Sheffield; Worden, K., Dulieu-Barton, J.M., An Overview of Intelligent Fault Detection in Systems and Structures (2004) Struct Heal Monit, 3, pp. 85-98; Rytter, A., (1993) Vibrational Based Inspection of Civil Engineering Structures, , Denmark, University of Aalborg; Reynders, E., Teughels, A., De Roeck, G., Finite element model updating and structural damage identification using OMAX data (2010) Mech Syst Signal Process, 24, pp. 1306-1323; Reynders, E., De Roeck, G., Bakir, P.G., Damage identification on the Tilff bridge by vibration monitoring using optical fiber strain sensors (2007) J Eng Mech, 133, pp. 185-193; Teughels, A., De Roeck, G., Structural damage identification of the highway bridge Z24 by FE model updating (2004) J Sound Vib, 278, pp. 589-610; Maeck, J., Peeters, B., De Roeck, G., Damage identification on the Z24 bridge using vibration monitoring (2001) Smart Mater Struct, 10, pp. 512-517; Huth, O., Feltrin, G., Maeck, J., Damage identification using modal data: experiences on a prestressed concrete bridge (2005) J Struct Eng, 131, pp. 1898-1910; Behmanesh, I., Moaveni, B., Probabilistic identification of simulated damage on the Dowling Hall footbridge through Bayesian finite element model updating (2015) Struct Control Heal Monit, 22, pp. 463-483; Farrar, C.R., Doebling, S.W., Nix, D.A., Vibration-based structural damage identification (2001) Philos Trans R Soc A Math Phys Eng Sci, 359, pp. 131-149; Sohn, H., Worden, K., Farrar, C.R., Statistical damage classification under changing environmental and operational conditions (2002) J Intell Mater Syst Struct, 13, pp. 561-574; Pan, H., Azimi, M., Yan, F., Time-frequency-based data-driven structural diagnosis and damage detection for cable-stayed bridges (2018) J Bridg Eng, 23, p. 04018033; Santos, A., Figueiredo, E., Silva, M.F.M., Machine learning algorithms for damage detection: Kernel-based approaches (2016) J Sound Vib, 363, pp. 584-599; Figueiredo, E., Park, G., Farrar, C.R., Machine learning algorithms for damage detection under operational and environmental variability (2011) Struct Heal Monit, 10, pp. 559-572; Magalhães, F., Cunha, A., Caetano, E., Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection (2012) Mech Syst Signal Process, 28, pp. 212-228; Santos, A., Figueiredo, E., Silva, M., Genetic-based EM algorithm to improve the robustness of Gaussian mixture models for damage detection in bridges (2017) Struct Control Heal Monit, 24, pp. 1-9; Reynders, E., Wursten, G., De Roeck, G., Output-only structural health monitoring in changing environmental conditions by means of nonlinear system identification (2014) Struct Heal Monit, 13, pp. 82-93; Figueiredo, E., Cross, E., Linear approaches to modeling nonlinearities in long-term monitoring of bridges (2013) J Civ Struct Heal Monit, 3, pp. 187-194; Malekzadeh, M., Atia, G., Catbas, F.N., Performance-based structural health monitoring through an innovative hybrid data interpretation framework (2015) J Civ Struct Heal Monit, 5, pp. 287-305; Figueiredo, E., Moldovan, I., Santos, A., Finite element–based machine-learning approach to detect damage in bridges under operational and environmental variations (2019) J Bridg Eng, 24, p. 04019061; Cross, E.J., Koo, K.Y., Brownjohn, J.M.W., Long-term monitoring and data analysis of the Tamar Bridge (2013) Mech Syst Signal Process, 35, pp. 16-34; Soyoz, S., Feng, M.Q., Long-term monitoring and identification of bridge structural parameters (2009) Comput Civ Infrastruct Eng, 24, pp. 82-92; Ko, J.M., Ni, Y.Q., Technology developments in structural health monitoring of large-scale bridges (2005) Eng Struct, 27, pp. 1715-1725; Farrar, C.R., Cornwell, P.J., Doebling, S.W., (2000) Structural health monitoring studies of the alamosa canyon and I-40 bridges, , Los Alamos National Laboratory, report LA-13635-MS; Gonzales, I., Ülker-Kaustell, M., Karoumi, R., Seasonal effects on the stiffness properties of a ballasted railway bridge (2013) Eng Struct, 57, pp. 63-72; Zabel, V., Brehm, M., Nikulla, S., (2010) The influence of temperature varying material parameters on the dynamic behavior of short span railway bridges, pp. 1519-1529. , Proceedings of ISMA 2010 - International Conference on Noise and Vibration Engineering, including USD 2010, In; Kim, J.T., Park, J.H., Lee, B.J., Vibration-based damage monitoring in model plate-girder bridges under uncertain temperature conditions (2007) Eng Struct, 29, pp. 1354-1365; Peeters, B., Maeck, J., Roeck, G.D., Vibration-based damage detection in civil engineering: excitation sources and temperature effects (2001) Smart Mater Struct, 10, pp. 518-527; Peeters, B., De Roeck, G., One-year monitoring of the Z24-Bridge: environmental effects versus damage events (2001) Earthq Eng Struct Dyn, 30, pp. 149-171; Svendsen, B.T., Frøseth, G.T., Rönnquist, A., Damage detection applied to a full-scale steel bridge using temporal moments (2020) Shock Vib, 2020, pp. 1-16; Svendsen, B.T., Frøseth, G.T., Øiseth, O., A data-based structural health monitoring approach for damage detection in steel bridges using experimental data J Civ Struct Heal Monit, , Epub ahead of print 2021; Brincker, R., Zhang, L., Andersen, P., (2000) Modal Identification from Ambient Responses using Frequency Domain Decomposition, pp. 625-630. , IMAC 18: Proceedings of the International Modal Analysis Conference (IMAC), In; Brincker, R., Zhang, L., Andersen, P., Modal identification of output-only systems using frequency domain decomposition (2001) Smart Mater Struct, 10, pp. 441-445; Van Overschee, P., De Moor, B., (1996) Subspace Identification for Linear Systems, , Boston, MA, Springer US, Epub ahead of print; Hermans, L., Auweraer, H.V.D., Modal testing and analysis of structures under operational conditions: industrial applications (1999) Mech Syst Signal Process, 13, pp. 193-216; Kvåle, K.A., Øiseth, O., Rønnquist, A., Operational modal analysis of an end-supported pontoon bridge (2017) Eng Struct, 148, pp. 410-423; Kvåle, K.A., KOMA toolbox, , Epub ahead of print 2021; Svendsen, B.T., Petersen, Ø.W., Frøseth, G.T., Improved finite element model updating of a full-scale steel bridge using sensitivity analysis (2021) Struct Infrastruct Eng, pp. 1-17; Svendsen, B.T., FE model updating in Python, , Epub ahead of print 2020; Pandey, A.K., Biswas, M., Samman, M.M., Damage detection from changes in curvature mode shapes (1991) J Sound Vib, 145, pp. 321-332; Pedregosa, F., Varoquaux, G., Gramfort, A., Scikit-learn: machine learning in python (2011) J Mach Learn Res, 12, pp. 2825-2830; Fawcett, T., (2004) ROC graphs: Notes and practical considerations for researchers, , Palo Alto, CA, HP Laboratories","Svendsen, B.T.; Department of Structural Engineering, Norway; email: bjorn.t.svendsen@ntnu.no",,,"SAGE Publications Ltd",,,,,14759217,,,,"English","Struct. Health Monit.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85130588180
"Barthorpe R.J., Hughes A.J., Gardner P.","26421949700;57211513237;57193994973;","A Forward Model Driven Structural Health Monitoring Paradigm: Damage Detection",2022,"Conference Proceedings of the Society for Experimental Mechanics Series",,,,"119","126",,1,"10.1007/978-3-030-77348-9_16","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122530285&doi=10.1007%2f978-3-030-77348-9_16&partnerID=40&md5=60935e4112db93f852ac5ec18c1d23fc","Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom","Barthorpe, R.J., Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom; Hughes, A.J., Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom; Gardner, P., Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom","Structural Health Monitoring (SHM) involves determining the health state of an engineered structure based upon measured, damage-sensitive features such as natural frequencies, modeshapes and time-domain model coefficients. One of the key challenges in SHM is the difficulty associated with gathering experimental data from a structure in its damaged state. This challenge is particularly acute for purely data-based supervised learning methods. Numerical modelling offers the potential to overcome the lack-of-data problem by making physically informed predictions of how the structure will behave once damaged. However, numerical modelling raises challenges of its own, with a major question being how one incorporates uncertainties and errors arising from the model prediction process within SHM decision-making. In addition, variability inevitably arises in the observed experimental responses and this, too, should be incorporated in the decision process. Finally, it is desirable that the cost of misclassification be incorporated within the decision process, with risk-based approaches being an attractive option for moving from classification to decision-making. This paper introduces a practical application of a Forward Model Driven (FMD) paradigm for SHM. A key tenet of the approach is that numerical model predictions may be used to inform a statistical classifier. The method is demonstrated for the case of damage detection on an experimental truss bridge structure for which an associated finite element (FE) model has been developed. A framework based upon a sequence of binary classifiers is introduced, with attention drawn to the importance both of the choice of individual classifier and the strategy for their combination. © 2022, The Society for Experimental Mechanics, Inc.","Damage identification; Finite element modelling; Structural health monitoring","Classification (of information); Damage detection; Decision making; Forecasting; Learning systems; Numerical models; Structural health monitoring; Time domain analysis; Trusses; Uncertainty analysis; Damage Identification; Damage-sensitive features; Decision process; Decisions makings; Engineered structures; Forward modeling; Health state; Model prediction; Model-driven; Structure-based; Finite element method",,,,,,,,,,,,,,,,"Farrar, C.R., Worden, K., (2012) Structural Health Monitoring: A Machine Learning Perspective, , Wiley, New York; Friswell, M.I., Damage identification using inverse methods (2007) Philos. Trans. R. Soc. a Math. Phys. Eng. Sci., 365 (1851), pp. 393-410; Friswell, M.I., Mottershead, J.E., Ahmadian, H., Combining subset selection and parameter constraints in model updating (1998) J. Vib. Acoust. Trans. ASME, 120, pp. 854-859; Barthorpe, R.J., (2011) On Model-And Data-Based Approaches to Structural Health Monitoring, , Ph.D. thesis; Gardner, P., (2019) On Novel Approaches to Model-Based Structural Health Monitoring, , Ph.D. thesis; Gardner, P., Barthorpe, R.J., On current trends in forward model-driven SHM (2019) 12Th International Workshop on Structural Health Monitoring; Flynn, E., Todd, M., A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing (2010) Mech. Syst. Signal Process., 24, pp. 891-903","Barthorpe, R.J.; Dynamics Research Group, United Kingdom; email: r.j.barthorpe@sheffield.ac.uk","Mao Z.",,"Springer","39th IMAC, A Conference and Exposition on Structural Dynamics, 2021","8 February 2021 through 11 February 2021",,264509,21915644,9783030773472,,,"English","Conf. Proc. Soc. Exp. Mech. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85122530285
"Ngoc-Nguyen L., Khatir S., Ngoc-Tran H., Nguyen-Tran H., Duc-Nguyen B., Bui-Tien T., Abdel Wahab M.","57388654100;6507792896;57388892300;57221116226;57388814200;57204859112;7102582536;","Finite Element Model Updating of Lifeline Truss Bridge Using Vibration-Based Measurement Data and Balancing Composite Motion Optimization",2022,"Lecture Notes in Civil Engineering","204",,,"3","12",,1,"10.1007/978-981-16-7216-3_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121926965&doi=10.1007%2f978-981-16-7216-3_1&partnerID=40&md5=42a40def04678a61f088be63ba98be6c","Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium; Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Viet Nam; Faculty of Information Technology, University of Transport and Communications, Hanoi, Viet Nam","Ngoc-Nguyen, L., Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Viet Nam; Khatir, S., Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium; Ngoc-Tran, H., Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Viet Nam; Nguyen-Tran, H., Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium, Faculty of Information Technology, University of Transport and Communications, Hanoi, Viet Nam; Duc-Nguyen, B., Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Viet Nam; Bui-Tien, T., Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Viet Nam; Abdel Wahab, M., Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium","Located in the heart of Hanoi (Vietnam), Chuong Duong bridge is a major truss bridge that connects one of the most heavily trafficked routes in the country-the 1A National road. Being built in the 80s of the twentieth century, after nearly 40 years of service, degradation and damages have threatened the integrity and safety of the structure. Physical and numerical evaluation of the bridge is required for the maintenance process. In this paper, we proposed a new approach to model updating of Chuong Duong bridge using vibration-based measurement data and Balancing Composite Motion Optimization (BCMO). BCMO is a newly developed meta-heuristic optimization algorithm based on individual’s balancing composite motion properties which has proved to provide highly-accurate result in determining the optimal solution in mathematical problem. BCMO is applied to update the different parameters of the baseline numerical model of Chuong Duong bridge, followed by comparing the obtained dynamic properties of the updated bridge with the measured one. The final result shows that BCMO has comprehensively updated the model with a high level of accuracy, thus could be potential used to solve practical problems of lifeline structures. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","Balancing composite motion optimization; Model updating; Structural health monitoring; Truss bridge","Finite element method; Structural health monitoring; Structural optimization; Balancing composite motion optimization; Finite-element model updating; Measurement data; Model updating; Motion optimization; National roads; Service degradation; Truss bridge; Twentieth century; Viet Nam; Trusses",,,,,"Vlaamse regering","Acknowledgements The authors acknowledge the financial support of VLIR-OUS TEAM Project, VN2018TEA479A103, ‘Damage assessment tools for Structural Health Monitoring of Vietnamese infrastructures’, funded by the Flemish Government.",,,,,,,,,,"Teughels, A., de Roeck, G., Structural damage identification of the highway bridge Z24 by FE model updating (2004) J Sound Vibrat, 278 (3), pp. 589-610; Jang, S., Li, J., Spencer, B., Corrosion estimation of a historic truss bridge using model updating (2013) J Bridg Eng, 18. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000403; Sierra, M., Coello, C., Improving PSO-based multi-objective optimization using crowding, mutation and ∈-dominance (2005) Lect Notes Comput Sci, 3410. , https://doi.org/10.1007/978-3-540-31880-4_35; Altiparmak, F., Gen, M., Lin, L., Paksoy, T., A genetic algorithm approach for multi-objective optimization of supply chain networks (2006) Comput Ind Eng, 51, pp. 196-215. , https://doi.org/10.1016/j.cie.2006.07.011; Mirjalili, S., Mirjalili, S.M., Lewis, A., Grey wolf optimizer (2014) Adv Eng Softw, 69, pp. 46-61. , https://doi.org/10.1016/j.advengsoft.2013.12.007, ISSN 0965-9978; Mirjalili, S., Lewis, A., The whale optimization algorithm (2016) Adv Eng Softw, 95, pp. 51-67. , https://doi.org/10.1016/j.advengsoft.2016.01.008, ISSN 0965-9978; Mirjalili, S., Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015) Knowl-Based Syst, 89, pp. 228-249. , https://doi.org/10.1016/j.knosys. 2015.07.006, ISSN 0950-7051; Faramarzi, A., Heidarinejad, M., Mirjalili, S., Gandomi, A., Marine predators algorithm: A nature-inspired metaheuristic (2020) Expert Syst Appl, 152. , https://doi.org/10.1016/j.eswa. 2020.113377; Tiachacht, S., Khatir, S., Le Thanh, C., Venkata Rao, R., Mirjalili, S., Abdel Wahab, M., Inverse problem for dynamic structural health monitoring based on slime mould algorithm (2021) Engineering with Computers, , https://doi.org/10.1007/s00366-021-01378-8; Tran-Ngoc, H., Khatir, S., de Roeck, G., Bui-Tien, T., Nguyen-Ngoc, L., Abdel WM Model updating for nam o bridge using particle swarm optimization algorithm and genetic algorithm (2018) Sensors, 18 (12), p. 4131; Ho, V.L., de Roeck, G., Bui Tien, T., Wahab, M., (2021) Determination of the Effective Stiffness of Half-Open Cross-Section Bars and Orthotropic Steel Deck of a Truss Bridge Using Model Updating, , https://doi.org/10.1007/978-981-15-9893-7_6; Nguyen-Ngoc, L., Tran-Ngoc, H., Nguyen-Tran, H., Nguyen-Duc, B., Nguyen-Le-minh, D., Bui-Tien, T., Wahab, M.A., Damaged detection in structures using artificial neural networks and genetic algorithms (2021) Proceedings of the 3Rd International Conference on Sustainability in Civil Engineering. Lecture Notes in Civil Engineering, 145. , https://doi.org/10.1007/978-981-16-0053-1_4, , vol , Springer, Singapore; Le-Duc, T., Nguyen, Q.-H., Nguyen-Xuan, H., Balancing composite motion optimization (2020) Inf Sci, 520. , https://doi.org/10.1016/j.ins.2020.02.013; Reynders, E., Schevenels, M., Roeck, G.D., (2011) MACEC 3.2: A Matlab Toolbox for Experimental and Operational Modal Analysis; user’s Manual, , Report BWM-2011-XX, Leuven, Belgium; Dooms, D., Jansen, M., de Roeck, G., Degrande, G., Lombaert, G., Schevenels, M., François, S., StaBIL: A finite element toolbox for Matlab, 2.0 ed. Structural Mechanics Section of the Department of Civil Engineering (2010) KU Leuven, , Leuven, Belgium","Ngoc-Nguyen, L.; Soete Laboratory, Belgium; email: ngoclan.nguyen@ugent.be","Abdel Wahab M.",,"Springer Science and Business Media Deutschland GmbH","2nd International Conference on Structural Damage Modelling and Assessment, SDMA 2021","4 August 2021 through 5 August 2021",,269639,23662557,9789811672156,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85121926965
"He L., Castoro C., Aloisio A., Zhang Z., Marano G.C., Gregori A., Deng C., Briseghella B.","24734031200;57208919038;57205733998;57216927763;57382102800;16241643500;7202302577;16314812100;","Dynamic assessment, FE modelling and parametric updating of a butterfly-arch stress-ribbon pedestrian bridge",2022,"Structure and Infrastructure Engineering","18","7",,"1064","1075",,1,"10.1080/15732479.2021.1995444","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119427979&doi=10.1080%2f15732479.2021.1995444&partnerID=40&md5=924e3942545eb7e4de48914e6754689e","Sustainable and Innovative Bridge Engineering Research Center, College of Civil Engineering, Fuzhou University, China; Department of Civil, Construction-Architectural and Environmental Engineering, University of L’Aquila, Italy; Department of Structural Engineering, Tongji University, China","He, L., Sustainable and Innovative Bridge Engineering Research Center, College of Civil Engineering, Fuzhou University, China; Castoro, C., Department of Civil, Construction-Architectural and Environmental Engineering, University of L’Aquila, Italy; Aloisio, A., Department of Civil, Construction-Architectural and Environmental Engineering, University of L’Aquila, Italy; Zhang, Z., Sustainable and Innovative Bridge Engineering Research Center, College of Civil Engineering, Fuzhou University, China; Marano, G.C., Sustainable and Innovative Bridge Engineering Research Center, College of Civil Engineering, Fuzhou University, China; Gregori, A., Department of Civil, Construction-Architectural and Environmental Engineering, University of L’Aquila, Italy; Deng, C., Department of Structural Engineering, Tongji University, China; Briseghella, B., Sustainable and Innovative Bridge Engineering Research Center, College of Civil Engineering, Fuzhou University, China","The article focuses on the dynamic identification and finite element (FE) modelling of a butterfly-arch stress-ribbon pedestrian bridge in Fuzhou, Fujian, China. The Stochastic Subspace Identification method yields an estimate of the operational modal parameters. A highly synchronous tri-axial wireless sensor network was deployed on the bridge deck to record the structure’s ambient vibration. Eight stable modes in the frequency range 3.59–14.92 Hz were found, associated with prevalent bending and torsional deformations. Four distinct FE models of the bridge with progressive complexity and accuracy were developed to investigate the sensitivity of the modal features to the modelling choices. The FE model characterised by the fittest agreement with the experimental modal parameters was used for the automatic parametric optimisation based on a sensitivity-based algorithm. The stiffness of the springs simulating the soil-structure interaction, the elastic modulus of the concrete deck and the elastic modulus of the tendons were chosen as updating parameters for a total of eight parameters. The effect of non-structural elements (handrails) and prestress on the modal features are also investigated. The final advanced FE model developed can serve as baseline for a long-term monitoring of the bridge during its life-cycle, and also provides some recommendations to practitioners and scholars all over the world for the modelling and analysis of this particular kind of footbridges. © 2021 Informa UK Limited, trading as Taylor & Francis Group.","Butterfly-arch stress-ribbon pedestrian bridge; finite element modelling; operational modal analysis; sensitivity-based model updating; structural health monitoring","Arch bridges; Arches; Elastic moduli; Footbridges; Life cycle; Modal analysis; Parameter estimation; Prestressed concrete; Sensitivity analysis; Stochastic systems; Structural health monitoring; Wireless sensor networks; Butterfly-arch stress-ribbon pedestrian bridge; Dynamic assessment; Dynamic identification; Finite element modelling (FEM); Modal parameters; Model updating; Operational modal; Operational modal analysis; Sensitivity-based model updating; Stochastic subspace identification methods; Finite element method",,,,,"National Natural Science Foundation of China, NSFC: 51778148; KU Leuven","The financial support from the National Natural Science Foundation of China (Grant No. 51778148) is gratefully acknowledged. Prof. Guido De Roeck is acknowledged for his guidance during the first author’s post-doctoral research period at KU Leuven and Dr. Edwin Reynders for his instruction of the identification software MACEC.",,,,,,,,,,"Aloisio, A., Alaggio, R., Fragiacomo, M., Dynamic identification and model updating of full-scale concrete box girders based on the experimental torsional response (2020) Construction and Building Materials, 264, p. 120146; Aloisio, A., Alaggio, R., Fragiacomo, M., Time-domain identification of the elastic modulus of simply supported box girders under moving loads: Method and full-scale validation (2020) Engineering Structures, 215, p. 110619; Bai-Jian, T., Fei, W., Song, C., Effect of prestress force on natural bending frequency of external prestressed steel beams (2018) The Open Civil Engineering Journal, 12 (1), pp. 62-70; Bleicher, A., Schlaich, M., Fujino, Y., Schauer, T., Model-based design and experimental validation of active vibration control for a stress ribbon bridge using pneumatic muscle actuators (2011) Engineering Structures, 33 (8), pp. 2237-2247; Briseghella, B., Chen, A., Li, X., Zordan, T., Lan, C., Mazzarolo, E., Analysis on applicability of health monitoring techniques on a curved cable stayed bridge (2012) Bridge Maintenance, Safety, Management, Resilience and Sustainability–Proceedings of the Sixth International Conference on Bridge Maintenance, Safety and Management (pp., pp. 2617-2624; Caetano, E., Cunha, A., Experimental and numerical assessment of the dynamic behaviour of a stress-ribbon footbridge (2004) Structural Concrete, 5 (1), pp. 29-38; Caetano, E., Cunha, Á., Magalhães, F., Moutinho, C., Studies for controlling human-induced vibration of the pedro e inês footbridge, Portugal. Part 1: Assessment of dynamic behaviour (2010) Engineering Structures, 32 (4), pp. 1069-1081; Cara, J., Magdaleno, A., Lorenzana, A., Input/output versus output only modal ana-lysis of a stress-ribbon footbridge (2017) IOMAC 2017–7th International Operational Modal Analysis Conference; Cunha, Á., Caetano, E., Moutinho, C., Magalhães, F., Damping identification in a stress-ribbon footbridge (2005) Structural Dynamics-EURODYN 2005, pp. 1-3; Deák, G., Discussion of “prestress force effect on vibration frequency of concrete bridges” by M. Saiidi, B. Douglas, and S. Feng (1996) Journal of Structural Engineering, 122 (4), pp. 458-459; Fa, G., He, L., Fenu, L., Mazzarolo, E., Briseghella, B., Zordan, T., (2016) Comparison of direct and iterative methods for model updating of a curved cable-stayed bridge using experimental modal data, pp. 8-11. , Guangzhou, China:, &, Proceedings of the IABSE Conference; Feldman, M., (2008) Hivoss—human-induced vibrations of steel structures, , Luxembourg: Office for Official Publications of the European Communities; Gregori, A., Castoro, C., Mercuri, M., Angiolilli, M., Numerical modelling of the mechanical behaviour of rubbercrete (2021) Computers & Structures, 242, p. 106393. , 106393; Hamed, E., Frostig, Y., Natural frequencies of bonded and unbonded prestressed beams–prestress force effects (2006) Journal of Sound and Vibration, 295 (1-2), pp. 28-39; He, L., Zhang, Z., Marano, G.C., Briseghella, B., Xue, J., Ni, Z., (2019), 11, p. 395). , Springer Nature, &, Dynamic characterization of a stress ribbon and butterfly arch pedestrian bridge using wireless measurements,. Proceedings of ARCH 2019: 9th International Conference on Arch Bridges; Hu, W.-H., Caetano, E., Cunha, Á., Structural health monitoring of a stress-ribbon footbridge (2013) Engineering Structures, 57, pp. 578-593; Law, S., Lu, Z., Time domain responses of a prestressed beam and prestress identification (2005) Journal of Sound and Vibration, 288 (4-5), pp. 1011-1025; Liu, T., Zhang, Q., Zordan, T., Briseghella, B., Finite element model updating of canonica bridge using experimental modal data and genetic algorithm (2016) Structural Engineering International, 26 (1), pp. 27-36; Mercuri, M., Pathirage, M., Gregori, A., Cusatis, G., Computational modeling of the out-of-plane behavior of unreinforced irregular masonry (2020) Engineering Structures, 223, p. 111181. , 111181; Miyamoto, A., Tei, K., Nakamura, H., Bull, J.W., Behavior of prestressed beam strengthened with external tendons (2000) Journal of Structural Engineering, 126 (9), pp. 1033-1044; Moré, J.J., The levenberg-marquardt algorithm: Implementation and theory (1978) Numerical analysis (, pp. 105-116). , Springer; Mottershead, J.E., Link, M., Friswell, M.I., The sensitivity method in finite element model updating: A tutorial (2011) Mechanical Systems and Signal Processing, 25 (7), pp. 2275-2296; Noble, D., Nogal, M., O׳Connor, A., Pakrashi, V., The effect of prestress force magnitude and eccentricity on the natural bending frequencies of uncracked prestressed concrete beams (2016) Journal of Sound and Vibration, 365, pp. 22-44; Peeters, B., De Roeck, G., Reference-based stochastic subspace identification for output-only modal analysis (1999) Mechanical Systems and Signal Processing, 13 (6), pp. 855-878; Pimentel, R., (1997) Vibrational performance of pedestrian bridges due to human-induced loads, , Univeristy of Sheffield, Sheffield, UK: (Ph.D. thesis; Reynders, E., Maes, C., Lombaert, G., Roeck, G.D., Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications (2016) Mechanical Systems and Signal Processing, 66-67, pp. 13-30; Reynders, E., Pintelon, R., Roeck, G.D., Uncertainty bounds on modal parameters obtained from stochastic subspace identification (2008) Mechanical Systems and Signal Processing, 22 (4), pp. 948-969; Reynders, E., Roeck, G.D., Gundes Bakir, P., Sauvage, C., Damage identification on the tilff bridge by vibration monitoring using optical fiber strain sensors (2007) Journal of Engineering Mechanics, 133 (2), pp. 185-193; Reynders, E., Schevenels, M., De Roeck, G., (2011) Macec 3.2: A MATLAB toolbox for experimental and operational modal analysis-user’s manual, , Leuven: Katholieke Universiteit; Saiidi, M., Douglas, B., Feng, S., Prestress force effect on vibration frequency of concrete bridges. discussion and closure (1996) Journal of Structural Engineering, 122 (4), p. 460; Sétra, F., Assessment of vibrational behaviour of footbridges under pedestrian loading (2006) Technical guide SETRA, , Paris, France; Soria, J.M., Díaz, I.M., García-Palacios, J.H., Ibán, N., Vibration monitoring of a steel-plated stress-ribbon footbridge: Uncertainties in the modal estimation (2016) Journal of Bridge Engineering, 21 (8), p. C5015002; Steel, B., (1978), Concrete and composite bridges. Specification for loads, bs 5400: Part 2., British Standard Institution; Strasky, J., (2005) Stress ribbon and cable-supported pedestrian bridges, , Thomas Telford; Strasky, J., Stress-ribbon pedestrian bridges supported by arches (2010) Concrete International, 32 (5), pp. 28-33; Živanović, S., Pavic, A., Reynolds, P., Vibration serviceability of footbridges under human-induced excitation: A literature review (2005) Journal of Sound and Vibration, 279 (1-2), pp. 1-74; Zordan, T., Briseghella, B., Liu, T., Finite element model updating of a tied-arch bridge using douglas-reid method and rosenbrock optimization algorithm (2014) Journal of Traffic and Transportation Engineering (English Edition), 1 (4), pp. 280-292","Castoro, C.; Department of Civil, Italy; email: chiara.castoro@graduate.univaq.it",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","",Scopus,2-s2.0-85119427979
"Li Y., Huang H., Zhang W., Sun L.","57211568199;55778544200;56646249600;7403956279;","Structural full-field responses reconstruction by the SVD and pseudo-inverse operator-estimated force with two-degree multi-scale models",2021,"Engineering Structures","249",,"112986","","",,1,"10.1016/j.engstruct.2021.112986","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118767848&doi=10.1016%2fj.engstruct.2021.112986&partnerID=40&md5=e585d53c10037f0d3f18a41bf7de26c4","Department of Bridge Engineering, Tongji Univ, Shanghai, 200092, China; Department of Bridge Engineering, State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ, Shanghai, 200092, China; Building Research & Fujian key laboratory of green building technology, No. 52 Jintang Road Fuzhou, Fujian, China; Department of Bridge Engineering, and State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ, Shanghai 200092, China, Shanghai Qi Zhi Institute, Shanghai, China","Li, Y., Department of Bridge Engineering, Tongji Univ, Shanghai, 200092, China; Huang, H., Department of Bridge Engineering, State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ, Shanghai, 200092, China; Zhang, W., Building Research & Fujian key laboratory of green building technology, No. 52 Jintang Road Fuzhou, Fujian, China; Sun, L., Department of Bridge Engineering, and State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ, Shanghai 200092, China, Shanghai Qi Zhi Institute, Shanghai, China","The measured structural response currently is in a deficient state for structural health monitoring (SHM). To obtain complete structural responses, a good solution is first estimating the external input, then applying the estimated input to the finite element model (FEM) of a structure, and calculating the complete structural responses by the FEM. The key content is estimating the unknown structural input from the limited measurements, which indicates that an under-determined differential equation should be solved. In the previous literature, the dynamic equilibrium equation is usually converted into the state space and solved as a discrete-time linear system. In this paper, the external input time history is estimated from the difference of the state space vectors at different moments combining with the singular value decomposition. The complete structural responses are then obtained by applying the estimated load vectors to the FEM. Adopting a single degree of freedom system, the robustness of the approach is theoretically deduced. The modeling error can merely change the estimated external force, while the reconstructed structural responses retain accuracy. To improve computational efficiency, the two-degree multi-scale FEM is proposed. The first model is a simplified model of the original structure, only adopting interior elements such as the beam elements. This model is to quickly estimate the external inputs and to reconstruct the displacement responses. The second model is a FEM adopting both interior and superior elements such as the beam and shell elements, and it can reconstruct both the displacement and detailed strain–stress responses. Finally, numerical simulations of a complex bridge FEM are carried out demonstrating that the proposed approach has high accuracy under different loading conditions, including the vehicle load, impact load, and random load. An in-field experiment has also validated the proposed framework applies to the quasi-static plus dynamic loading condition. © 2021","Full field response reconstruction; input estimation; multi-scale finite element model; singular value decomposition; state space","Computational efficiency; Degrees of freedom (mechanics); Differential equations; Dynamic loads; Dynamics; Inverse problems; Linear systems; Singular value decomposition; Structural health monitoring; Vector spaces; External input; Finite element modelling (FEM); Full field response; Full field response reconstruction; Input estimation; Multi-scale finite element model; Multi-scales; Response reconstruction; State-space; Structural response; Finite element method; computer simulation; decomposition analysis; displacement; dynamic analysis; dynamic response; finite element method; loading test; model test; monitoring; numerical model; reconstruction; stress-strain relationship; structural analysis; structural response",,,,,"SYXF0120020109; National Natural Science Foundation of China, NSFC: 51878482, XJ2021036","This work was supported by National Natural Science Foundation of China [grant number 51878482], the Hong Kong Scholars Program [grant number XJ2021036], and Science and Technology Cooperation Project of Shanghai Chizhi Research Institute [grant number SYXF0120020109].",,,,,,,,,,"Nakagiri, S., Suzuki, K., Finite element interval analysis of external loads identified by displacement input with uncertainty (1999) Comput Meth Appl Mech Eng., 168, pp. 63-72; Priestley, M., Displacement-based seismic assessment of existing reinforced concrete buildings (1996) Bulletin-New Zealand National Society for Earthquake Engineering., 29, pp. 256-272; Xu, B., Song, G., Masri, S.F., Damage detection for a frame structure model using vibration displacement measurement (2012) Structural Health Monitoring., 11, pp. 281-292; Rau, S., Morgenthal, G., An assessment framework for sensor-based detection of critical structural conditions with consideration of load uncertainty (2017), pp. 168-178. , Elsevier Structures; Pehlivan, H., Bayata, H.F., Usability of inclinometers as a complementary measurement tool in structural monitoring (2016) Struct Eng Mech., 58, pp. 1077-1085; Ye, X., Ni, Y., Wong, K., Ko, J., Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data (2012) Eng Struct, 45, pp. 166-176; Lee, H.S., Hong, Y.H., Park, H.W., Design of an FIR filter for the displacement reconstruction using measured acceleration in low-frequency dominant structures. International Journal for Numerical Methods in Engineering. 2009:n/a-n/a; Park, J.-W., Sim, S.-H., Jung, H.-J., Displacement Estimation Using Multimetric Data Fusion (2013) IEEE/ASME Trans Mechatron, 18, pp. 1675-1682; Cho, S., Yun, C.-B., Sim, S.-H., Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model (2015) Smart Structures and Systems., 15, pp. 645-663; Cho, S., Park, J.-W., Palanisamy, R.P., Sim, S.-H., Reference-Free Displacement Estimation of Bridges Using Kalman Filter-Based Multimetric Data Fusion (2016) Journal of Sensors., 2016, pp. 1-9; Smyth, A., Wu, M., Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring (2007) Mech Syst Sig Process, 21, pp. 706-723; Kim, J., Kim, K., Sohn, H., Autonomous dynamic displacement estimation from data fusion of acceleration and intermittent displacement measurements (2014) Mech Syst Sig Process, 42, pp. 194-205; Xu, Y., Brownjohn, J.M.W., Hester, D., Koo, K.Y., Long-span bridges: Enhanced data fusion of GPS displacement and deck accelerations (2017) Eng Struct, 147, pp. 639-651; Han, H., Wang, J., Meng, X., Liu, H., Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading (2016) Eng Struct, 122, pp. 238-250; Gillijns, S., De Moor, B., Unbiased minimum-variance input and state estimation for linear discrete-time systems (2007) Automatica., 43, pp. 111-116; Fang, H., de Callafon, R.A., On the asymptotic stability of minimum-variance unbiased input and state estimation (2012) Automatica., 48, pp. 3183-3186; Hsieh, C.-S., Extension of unbiased minimum-variance input and state estimation for systems with unknown inputs (2009) Automatica., 45, pp. 2149-2153; Nord, T.S., Lourens, E.-M., Øiseth, O., Metrikine, A., Model-based force and state estimation in experimental ice-induced vibrations by means of Kalman filtering (2015) Cold Reg Sci Technol, 111, pp. 13-26; Zhang, C.D., Xu, Y.L., Structural damage identification via response reconstruction under unknown excitation (2017) Structural Control and Health Monitoring., 24; Hsieh, C.-S., Chen, F.-C., Optimal solution of the two-stage Kalman estimator (1999) IEEE Trans Autom Control, 44, pp. 194-199; Hsieh, C.-S., Robust two-stage Kalman filters for systems with unknown inputs (2000) IEEE Trans Autom Control, 45, pp. 2374-2378; Pan, S., Xiao, D., Xing, S., Law, S.S., Du, P., Li, Y., A general extended Kalman filter for simultaneous estimation of system and unknown inputs (2016) Eng Struct, 109, pp. 85-98; Lei, Y., Lai, Z., Zhu, S., Zhang, X.-H., Experimental Study on Impact-Induced Damage Detection Using an Improved Extended Kalman Filter (2014) Int J Struct Stab Dyn, 14, p. 1440007; Niu, Y., Fritzen, C.-P., Jung, H., Buethe, I., Ni, Y.-Q., Wang, Y.-W., Online Simultaneous Reconstruction of Wind Load and Structural Responses-Theory and Application to Canton Tower (2015) Comput-Aided Civ Infrastruct Eng, 30, pp. 666-681; Zhi, L., Li, Q.S., Fang, M., Identification of Wind Loads and Estimation of Structural Responses of Super-Tall Buildings by an Inverse Method (2016) Comput-Aided Civ Infrastruct Eng, 31, pp. 966-982; Song, X., Zhang, Y., Liang, D., Dynamic input estimation and shape sensing for a nonlinear beam based on distributed fiber bragg grating sensor network (2018) Optik., 156, pp. 896-905; Song, X., Zhang, Y., Liang, D., Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter (2017) Materials., 10, p. 1162; Ding, Y., Law, S., Wu, B., Xu, G., Lin, Q., Jiang, H., Average acceleration discrete algorithm for force identification in state space (2013) Eng Struct, 56, pp. 1880-1892; Zhang, W., Sun, L., Sun, S., Bridge-Deflection Estimation through Inclinometer Data Considering Structural Damages (2016) J Bridge Eng, 22; Sun, L.M., Zhang, W., Nagarajaiah, S., Bridge Real-Time Damage Identification Method Using Inclination and Strain Measurements in the Presence of Temperature Variation (2019) J Bridge Eng, 24, p. 11; Reddy, A.N., Ananthasuresh, G.K., On computing the forces from the noisy displacement data of an elastic body (2008) Int J Numer Meth Eng, 76, pp. 1645-1677; Li, Y., Sun, L., (2020), pp. 1475921720952333. , Structural deformation reconstruction by the Penrose–Moore pseudo-inverse and singular value decomposition–estimated equivalent force. Structural Health Monitoring; Li, Z., Zhou, T., Chan, T.H., Yu, Y., Multi-scale numerical analysis on dynamic response and local damage in long-span bridges (2007) Eng Struct, 29, pp. 1507-1524; Li, Z., Chan, T.H., Yu, Y., Sun, Z., Concurrent multi-scale modeling of civil infrastructures for analyses on structural deterioration—Part I: Modeling methodology and strategy (2009) Finite Elem Anal Des, 45, pp. 782-794; Schilders, W.H., Van der Vorst, H.A., Rommes, J., Model order reduction: theory (2008) research aspects and applications, , Springer","Sun, L.; Department of Bridge Engineering, China; email: lmsun@tongji.edu.cn",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","",Scopus,2-s2.0-85118767848
"Locke W.R., Mokalled S.C., Abuodeh O.R., Redmond L.M., McMahan C.S.","57209638868;57221997965;57211625429;57212024018;42961867100;","An Intelligently Designed AI for Structural Health Monitoring of a Reinforced Concrete Bridge",2021,"American Concrete Institute, ACI Special Publication","SP-350",,,"103","112",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139871453&partnerID=40&md5=ba676ad3dacb20aa6e62943b18511d3e","The Buildings & Structures Practice, Exponent, Inc, Atlanta, GA, United States; Bank of America, Charlotte, NC, United States; The Glenn Department of Civil Engineering, Clemson University, United States; The Department of Civil and Mechanical Engineering, Clemson University, United States; The School of Mathematical and Statistical Sciences, Clemson University, United States","Locke, W.R., The Buildings & Structures Practice, Exponent, Inc, Atlanta, GA, United States; Mokalled, S.C., Bank of America, Charlotte, NC, United States; Abuodeh, O.R., The Glenn Department of Civil Engineering, Clemson University, United States; Redmond, L.M., The Department of Civil and Mechanical Engineering, Clemson University, United States; McMahan, C.S., The School of Mathematical and Statistical Sciences, Clemson University, United States","This research employs a novel Bayesian estimation technique to perform model updating on a coupled vehicle-bridge finite element model (FEM) for the purposes of classifying damage on a reinforced concrete bridge. Unlike existing Artificial intelligence (AI) techniques, the proposed methodology makes use of an embedded FEM, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM, and the results are compared against results obtained for “true” parameter values. Furthermore, a sensitivity study is conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when using experimental data to update vehicle-bridge FEMs with the Bayesian estimation technique. © 2021 American Concrete Institute. All rights reserved.","Artificial Intelligence; Bayesian Statistics; Highway Bridges; Model Uncertainty; Model Updating; Reinforced Concrete; Structural Health Monitoring; Vehicle-Bridge Interactions","Artificial intelligence; Bayesian networks; Concrete bridges; Highway bridges; Parameter estimation; Railroad bridges; Structural health monitoring; Uncertainty analysis; Vehicles; Artificial intelligence techniques; Bayesian estimation techniques; Bayesian statistics; Finite element modelling (FEM); Model updating; Modeling uncertainties; Parameters estimates; Reinforced concrete bridge; Vehicle bridges; Vehicle-bridge interaction; Reinforced concrete",,,,,,,,,,,,,,,,"Lu, P., Chen, S., Zheng, Y., Artificial intelligence in civil engineering (2012) Mathematical Problems in Engineering; Farrar, C.R., Worden, K., (2012) Structural health monitoring: a machine learning perspective, , John Wiley & Sons; Yuan, F.G., Zargar, S.A., Chen, Q., Wang, S., Machine learning for structural health monitoring: challenges and opportunities (2020) Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 2020, p. 1137903. , 11379; Asadollahi, P., (2018) Bayesian-based Finite Element Model Updating, Damage Detection, and Uncertainty Quantification for Cable-stayed Bridges, , (Doctoral dissertation, University of Kansas); Ziehl, P., Cousins, T., Ross, B., Huynh, N., (2020) Assessment of structural degradation for bridges and culverts, , FHWA-SC-20-03). South Carolina. Dept. of Transportation. Office of Materials and Research; Khouri Chalouhi, E., (2016) Structural Health Monitoring of Bridges using Machine Learning: The influence of Temperature on the health prediction; Yang, Y.B., Yau, J.D., Yao, Z., Wu, Y.S., Vehicle-bridge interaction dynamics: with applications to high-speed railways (2004) World Scientific; Chopra, A.K., Dynamics of structures. theory and applications to (2017) Earthquake Engineering; Agostinacchio, M., Ciampa, D., Olita, S., The vibrations induced by surface irregularities in road pavements-a Matlab® approach (2014) European Transport Research Review, 6 (3), pp. 267-275; ISO, I., (2016) Mechanical Vibration-Road Surface Profiles-Reporting of Measured Data, , 8608: 2016, BSI Standards Publication: London, UK; Locke, W., Redmond, L., Schmid, M., (2020) Experimental Evaluation of Drive-by Health Monitoring on a Short Span Bridge Using OMA Techniques, , Preprint submitted to Springer; Bakht, B., Jaeger, L.G., Bearing restraint in slab-on-girder bridges (1988) Journal of Structural Engineering, 114 (12), pp. 2724-2740; Locke, W., Redmond, L, A Comprehensive Approach to Modeling Freezing in Mechanical Bearings Considering Bridge Thermal Effect (2021) Preprint submitted to Engineering Structures; Kennedy, M.C., O'Hagan, A., Bayesian calibration of computer models (2001) Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63 (3), pp. 425-464; Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B., (2013) Bayesian data analysis, , CRC press; Hoff, P.D., (2009) A first course in Bayesian statistical methods, p. 580. , Springer; Jensen, H., Papadimitriou, C., (2019) Sub-structure Coupling for Dynamic Analysis: Bayesian Finite Element Model Updating, pp. 179-227. , Springer; Mokalled, S., Locke, W., Abuodeh, O., Redmond, L., McMahan, C., (2021) Structural Health Monitoring of Highway Bridges Using Bayesian Spike and Slab Models, , Preprint submitted to Structural Control and Health Monitoring; O'Brien, E.J., McGetrick, P., González, A., A drive-by inspection system via vehicle moving force identification (2014) Smart Structures and Systems, 13 (5), pp. 821-848; Kim, C.W., Chang, K.C., McGetrick, P.J., Inoue, S., Hasegawa, S., Utilizing moving vehicles as sensors for bridge condition screening-A laboratory verification (2017) Sensors and Materials, 29 (2), pp. 153-163; Lynch, J.P., Loh, K.J., A summary review of wireless sensors and sensor networks for structural health monitoring (2006) Shock and Vibration Digest, 38 (2), pp. 91-130; Capellari, G., Chatzi, E., Mariani, S., An optimal sensor placement method for SHM based on Bayesian experimental design and polynomial chaos expansion (2016) European Congress on Computational Methods in Applied Sciences and Engineering, pp. 6272-6282. , National Technical University of Athens (NTUA); Yi, T.H., Li, H.N., Gu, M., Optimal sensor placement for health monitoring of high-rise structure based on genetic algorithm (2011) Mathematical Problems in Engineering, , 2011; Capellari, G., Chatzi, E., Mariani, S., Cost-benefit optimization of sensor networks for SHM applications (2017) Multidisciplinary Digital Publishing Institute Proceedings, 2 (3), p. 132; Behmanesh, I., Moaveni, B., Accounting for environmental variability, modeling errors, and parameter estimation uncertainties in structural identification (2016) Journal of Sound and Vibration, 374, pp. 92-110; Hu, F., (2015) Road profile recovery using vertical acceleration data; Dimarogonas, A.D., Vibration of cracked structures: a state of the art review (1996) Engineering fracture mechanics, 55 (5), pp. 831-857; Friswell, M.I., Penny, J.E., Crack modeling for structural health monitoring (2002) Structural health monitoring, 1 (2), pp. 139-148; Huang, L.S., Chen, J., Analysis of variance, coefficient of determination and F-test for local polynomial regression (2008) Annals of Statistics, 36 (5), pp. 2085-2109; Zapico, J.L., Gonzalez, M.P., Friswell, M.I., Taylor, C.A., Crewe, A.J., Finite element model updating of a small scale bridge (2003) Journal of Sound and Vibration, 268 (5), pp. 993-1012",,"Naser M.Z.Mueller K.","ACI Committee 216;ACI Committee 444;ACI Committee 544","American Concrete Institute","The Concrete Industry in the Era of Artificial Intelligence 2020 - ACI Spring Concrete Convention 2020","29 March 2020 through 2 April 2020",,183072,01932527,9781641951623,,,"English","Am. Concr. Inst. ACI Spec. Publ.",Conference Paper,"Final","",Scopus,2-s2.0-85139871453
"Paral A., Samanta A.K., Shandilya A.N.","57209830554;25629860100;57289855300;","Corrosion-induced degradation assessment of steel beam using vibration-based scheme",2021,"International Journal of Structural Integrity","12","5",,"815","825",,1,"10.1108/IJSI-12-2020-0126","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116757121&doi=10.1108%2fIJSI-12-2020-0126&partnerID=40&md5=5cfc646c7e10437581aa4573d7cb5ec1","Department of Civil Engineering, National Institute of Technology Durgapur, Durgapur, India","Paral, A., Department of Civil Engineering, National Institute of Technology Durgapur, Durgapur, India; Samanta, A.K., Department of Civil Engineering, National Institute of Technology Durgapur, Durgapur, India; Shandilya, A.N., Department of Civil Engineering, National Institute of Technology Durgapur, Durgapur, India","Purpose: Catastrophe of steel-structured bridges due to progressive localized corrosion may lead to a major loss in terms of life and cost if not monitored continuously or periodically. The purpose of this paper is to present a vibration-based strategy to assess the severity and monitor the deterioration caused by corrosion-induced localized damage in a simply-supported steel beam. Design/methodology/approach: The threshold damage level is defined up to the yield limit of a simply supported steel beam of size ISMB 150 × 8 × 5 under three-point bending test and the progressive damage is induced through a continuous accelerated corrosion test. Change in the fundamental natural frequency due to localized damage in the experimental beam and the modulus of elasticity (E) in the corroded zone of an updated finite element (FE) model is evaluated. Findings: The updated FE model of the damaged beam shows a clear trend with the progressive damage of the beam and, hence, can be used to monitor the severity of damage and remaining capacity assessment of the monitored beam. Originality/value: Steel-structured bridges are prone to localized corrosion attack, and there are no standardized process or predictive model available by international steel design codes on how to consider corrosion damage in the condition assessment analysis. The vibration-based methods have gained popularity for condition assessment, and are mostly confined to damage assessment of corroded reinforced concrete (RC) beams. In this work, a vibration-based approach is presented for degradation assessment of steel beam due to progressive localized corrosion using modal hammer test. © 2021, Emerald Publishing Limited.","Condition assessment; Corrosion; Modal hammer test; Natural frequency; Steel beam; Structural health monitoring","Damage detection; Deterioration; Hammers; Pitting; Reinforced concrete; Steel beams and girders; Steel corrosion; Steel testing; Structural health monitoring; Condition assessments; Degradation assessment; Finite element modelling (FEM); Localized corrosion; Localized damage; Modal hammer; Modal hammer test; Progressive damage; Simply supported; Steel beams; Natural frequencies",,,,,"National Institute of Technology Durgapur, NITDGP","The authors would like to thank the Department of Civil Engineering, NIT Durgapur, India for providing necessary support and cooperation for this Investigation.",,,,,,,,,,"Ahn, J.H., Kim, I.T., Kainuma, S., Lee, M.J., Residual shear strength of steel plate girder due to web local corrosion (2013) Journal of Constructional Steel Research, 89, pp. 198-212; Alcántara, J., Fuente, D., Chico, B., Simancas, J., Díaz, I., Morcillo, M., Marine atmospheric corrosion of carbon steel: a review (2017) Materials, 10 (4), p. 406; (2013) Standard Practice for the Preparation of Substitute Ocean Water, , ASTM International; Atha, D.J., Jahanshahi, M.R., Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection (2018) Structural Health Monitoring, 17 (5), pp. 1110-1128; Ayas, H., Chabaat, M., Dynamic analysis of a cracked bar by the method of characteristics (2019) International Journal of Structural Integrity, 10 (4), pp. 438-453; Beaulieu, L.V., Legeron, F., Langlois, S., Compression strength of corroded steel angel members (2010) Journal of Constructional Steel Research, 66, pp. 1366-1373; Bhandari, J., Khan, F., Abbassi, R., Garaniya, V., Ojeda, R., Modelling of pitting corrosion in marine and offshore steel structures – a technical review (2015) Journal of Loss Prevention in the Process Industries, 37, pp. 39-62; Biezma, M.V., Schanack, F., Collapse of steel bridges (2007) Journal of Performance of Constructed Facilities, 21 (5), pp. 398-405; Ferreira, G.S., Guedes, T.O., Melo, L.F., Gonclaves, M.S., Pimental, R., Damage assessment of reinforced concrete flat slabs through modal tests using impact excitation (2019) International Journal of Structural Integrity, 10 (2), pp. 265-275; Gkatzogiannis, S., Weinert, J., Engelhardt, I., Knoedel, P., Ummenhofer, T., Correlation of laboratory and real marine corrosion for the investigation of corrosion fatigue of steel components (2019) International Journal of Fatigue, 126, pp. 90-102; (2017) Corrosion Tests in Artificial Atmospheres – Salt Spray Tests, , https://www.iso.org/standard/63543.html, 4th ed; Lehner, P., Křivý, V., Krejsa, M., Pařenica, P., Kozák, J., Stochastic service life prediction of existing steel structure loaded by overhead cranes (2018) Procedia Structural Integrity, 13, pp. 1539-1544; Li, L., Li, C.Q., Mahmoodian, M., Shi, W., Corrosion induced degradation of fatigue strength of steel in service for 128 years (2020) Structures, 23, pp. 415-424; Li, M., Cheng, X., Li, X., Hu, J., Pan, Y., Jin, Z., Indoor accelerated corrosion test and marine field test of corrosion-resistant low-alloy steel rebars (2016) Case Studies in Construction Materials, 5, pp. 87-99; Maalej, M., Chhoa, C.Y., Quek, S.T., Effect of cracking, corrosion and repair on the frequency response on RC beams (2010) Construction and Building Materials, 24, pp. 719-731; Ortega, N.F., Robles, S.I., Assessment of residual life of concrete structures affected by reinforcement corrosion (2016) HBRC Journal, 12 (2), pp. 114-122; Oszvald, K., Tomka, P., Dunai, L., The remaining load-bearing capacity of corroded steel angle compression members (2016) Journal of Constructional Steel Research, 120, pp. 188-198; Paral, A., Roy, D.K.S., Samanta, A.K., Application of a mode shape derivative-based damage index in artificial neural network for structural damage identification in shear frame building (2019) Journal of Civil Structural Health Monitoring, 9 (3), pp. 411-423; Paral, A., Roy, D.K.S., Samanta, A.K., A deep learning-based approach for condition assessment of semi-rigid joint of steel frame (2021) Journal of Building Engineering, 34; Razak, H.A., Choi, F.C., The effect of corrosion on the natural frequency and modal damping of reinforced concrete beams (2001) Engineering Structures, 23, pp. 1126-1133; Saad-Eldeen, S., Garbatov, Y., Guedes Soares, C., Corrosion-dependent ultimate strength assessment of aged box girders based on experimental results (2011) Journal of Ship Research, 55, pp. 289-300; Salehi, M., Azami, M., Structural damage localization through multi-channel empirical mode decomposition (2019) International Journal of Structural Integrity, 10 (1), pp. 102-117; Secer, M., Uzun, E.T., Corrosion damage analysis of steel frames considering lateral torsional buckling (2017) Procedia Engineering, 171, pp. 1234-1241; Sheng, J., Xia, J., Effect of simulated pitting corrosion on the tensile properties of steel (2017) Construction and Building Materials, 131, pp. 90-100; Silva, J.E., Garbatov, Y., Soares, C.G., Ultimate strength assessment of rectangular steel plates subjected to a random localized corrosion degradation (2013) Engineering Structures, 52, pp. 295-305; Verma, S.K., Bhadauria, S.S., Akhtar, S., Evaluating effect of chloride attack and concrete cover on the probability of corrosion (2013) Frontier of Structural and Civil Engineering, 7 (4), pp. 379-390; Wang, H.H., Du, M., Corrosion behavior of a low-carbon steel in simulated marine splash zone (2017) Acta Metallurgica Sinica (English. Letters), 30 (6), pp. 585-593; Wang, R., Shenoi, R.A., Sobey, A., Ultimate strength assessment of plated steel structures with random pitting corrosion damage (2018) Journal of Constructional Steel Research, 143, pp. 331-342; Wu, B., Cao, J.L., Kamg, L., Influence of local corrosion on behavior of steel I-beams subjected to endpatch loading: experiments (2017) Journal of Constructional Steel Research, 135, pp. 150-161; Xu, S.H., Qiu, B., Experimental study on fatigue behavior of corroded steel (2013) Material Science and Engineering: A, 584, pp. 163-169; Xu, S., Wang, Y., Estimating the effects of corrosion pits on the fatigue life of steel plate based on the 3D profile (2015) International Journal of Fatigue, 72, pp. 27-41","Paral, A.; Department of Civil Engineering, India; email: animesh.paral@yahoo.co.in",,,"Emerald Group Holdings Ltd.",,,,,17579864,,,,"English","Int. J. Struct. Integrity",Article,"Final","",Scopus,2-s2.0-85116757121
"Luo L., Shan D., He M.","57220060698;7007060453;57225098450;","Adaptive Sampling Method of Suspension-Bridge Finite Element Models Based on Coupled Modeling Approach",2021,"KSCE Journal of Civil Engineering","25","10",,"3802","3812",,1,"10.1007/s12205-021-1608-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109152792&doi=10.1007%2fs12205-021-1608-2&partnerID=40&md5=bcb86b389c2d0af3efbf87ce9069cf39","School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China","Luo, L., School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China; Shan, D., School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China; He, M., School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China","This paper presents an accurate sampling method for suspension-bridge finite element (FE) model updating. The proposed method utilizes a coupled suspension-bridge modeling approach, which integrates a numerical shape-finding method with conventional FE analysis to efficiently achieve parametric modeling during the sampling process. This study also employs the Nelder-Mead algorithm (NMA) to ensure the efficiency and robustness of the numerical shape-finding calculation. First, the accuracy of the NMA-based coupled modeling approach is examined by numerical investigation of the baseline model of an actual suspension bridge. Then, the computational results of two groups of samples with different sample sizes are compared to test the efficiency of the proposed method. Finally, by relying on the structural health monitoring (SHM) system of this bridge, the measured natural frequencies over half a year are identified, and almost all of them are within the calculated sample space. Therefore, the feasibility of the proposed suspension-bridge FE model sampling method is validated by the uncertainties of the measured responses over half a year. © 2021, Korean Society of Civil Engineers.","FE model; Nelder-Mead algorithm; Sampling method; Shape-finding analysis; Suspension bridge; Uncertainty","Efficiency; Numerical methods; Structural health monitoring; Struts; Suspension bridges; Adaptive sampling methods; Computational results; Coupled modeling; Finite-element model updating; Nelder-Mead algorithms; Numerical investigations; Parametric modeling; Structural health monitoring (SHM); Finite element method",,,,,"National Natural Science Foundation of China, NNSFC: 51978577, SCMQ-201728-ZB","This study was sponsored by National Natural Science Foundation of China (No. 51978577) and Science and Technology Project of Power China (No. SCMQ-201728-ZB).",,,,,,,,,,"Allemang, R., Brown, D., A correlation coefficient for modal vector analysis (1982) Proceedings of the 1St International Modal Analysis Conference, , November 8–10, Orlando, FL, USA; Cao, H., Qian, X., Chen, Z., Zhu, H., Layout and size optimization of suspension bridges based on coupled modeling approach and enhanced particle swarm optimization (2017) Engineering Structures, 146, pp. 170-183; Chen, Z., Cao, H., Ye, K., Zhu, H., Li, S., Improved particle swarm optimization-based form-finding method for suspension bridge installation analysis (2015) Journal of Computing in Civil Engineering, 29, p. 04014047; Fang, K., Winker, M.P., Centered L2-discrepancy of random sampling and Latin hypercube design, and construction of uniform designs (2002) Mathematics of Computation, 71 (237), pp. 275-296; Guo, T., Li, A., Wang, H., Research in form-finding of suspension structures based on Newton-Raphson iteration and zero order optimization arithmetic (2007) Engineering Mechanics, 24 (4), pp. 142-146. , (in Chinese; Hu, J., Wang, L., Song, X., Sun, Z., Cui, J., Huang, G., Field monitoring and response characteristics of longitudinal movements of expansion joints in long-span suspension bridges (2020) Measurements, 162, p. 107933; Jung, M.-R., Min, D.-J., Kim, M.-Y., Nonlinear analysis methods based on the unstrained element length for determining initial shaping of suspension bridges under dead loads (2013) Computers and Structures, 128, pp. 272-285; Karoumi, R., Some modeling aspects in the nonlinear finite element analysis of cable supported bridges (1999) Computers and Structures, 71, pp. 397-412; Lagarias, J.C., Reeds, J.A., Wright, M.H., Convergence properties of the Nelder—Mead simplex method in low dimensions (1998) SIAM Journal on Optimization, 9 (1), pp. 112-147; Liu, Y., Zhang, S., Probabilistic baseline of finite element model of bridges under environmental temperature changes (2017) Computer-Aided Civil and Infrastructure Engineering, 32 (7), pp. 581-598; Mao, J., Wang, H., Fu, Y., Spencer, B.F., Automated modal identification using principal component and cluster analysis: Application to a long-span cable-stayed bridge (2019) Structural Control and Health Monitoring, 26 (10); Marano, G.C., Quaranta, G., Monti, G., Modified genetic algorithm for the dynamic identification of structural systems using incomplete measurements (2011) Computer-Aided Civil and Infrastructure Engineering, 26 (2), pp. 92-110; Mottershead, J., Interval model updating with irreducible uncertainty using the Kriging predictor (2011) Mechanical Systems and Signal Processing, 25 (4), pp. 1204-1226; Ren, W., Chen, H., Finite element model updating in structural dynamics by using the response surface method (2010) Engineering Structures, 32 (8), pp. 2455-2465; Shabbir, F., Omenzetter, P., Particle swarm optimization with sequential niche technique for dynamic finite element model updating (2015) Computer-Aided Civil and Infrastructure Engineering, 30 (5), pp. 359-375; Shan, D., Li, Q., Khan, I., A novel finite element model updating method based on substructure and response surface model (2015) Engineering Structures, 103, pp. 147-156; Tang, M., Qiang, S., Shen, R., Segmental catenary method of calculating the cable curve of suspension bridge (2003) Journal of the China Railway Society, 25 (1), pp. 87-91. , (in Chinese; Wang, H., Li, A., Li, J., Progressive finite element model calibration of a long-span suspension bridge based on ambient vibration and static measurements (2010) Engineering Structures, 32 (9), pp. 2546-2556; Zhang, S., Chen, S., Wang, H., Wang, W., Chen, Z., Model updating with a neural network method based on uniform design (2013) Advances in Structural Engineering, 16 (7), pp. 1207-1222; Zhou, L., Yan, G., Ou, J., Response surface method based on radial basis functions for modeling large-scale structures in model updating (2013) Computer-Aided Civil and Infrastructure Engineering, 28 (3), pp. 210-226","Shan, D.; School of Civil Engineering, China; email: dsshan@163.com",,,"Springer Verlag",,,,,12267988,,,,"English","KSCE J. Civ. Eng.",Article,"Final","",Scopus,2-s2.0-85109152792
"Gunner S., Voyagaki E., Gavriel G., Carhart N., MacDonald J., Tryfonas T., Taylor C., Pregnolato M.","57216458982;15081790600;57303798600;36442104800;56583351100;23006808300;7404823270;57189259631;","Digital Twins for civil engineering: the Clifton Suspension Bridge (UK)",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1561","1566",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128350948&partnerID=40&md5=a1f6460c53c5c38a2a3fa7a023bfe218","Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom","Gunner, S., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Voyagaki, E., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Gavriel, G., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Carhart, N., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; MacDonald, J., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Tryfonas, T., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Taylor, C., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Pregnolato, M., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom","Society is dependent on aging infrastructure, which usually operates outside its expected life. Replacing this infrastructure is often an unviable option due to its cost and disruption. A structure's operational life might be extended if the features of its aging are better understood, enabling preventive maintenance to compensate. Digital Twins (the continuous comparison between sensor measurements and a mathematical model) are one way of enabling this sort of data-driven decision making. However, despite the possibilities for this technology, its take up amongst industry has been slow, in part because infrastructure managers are unsure of how the technology will support them. This work develops a methodological framework to enhance this uptake in the field of systems engineering and the system development life cycle, using the developed knowledge to inform how an operational Digital Twin should be created. The requirements capture is the most important part of any system design development process. We present a Digital Twin development method, grounded firmly in a thorough requirements capture, and illustrate how those requirements inform many of the later design decisions. We then present our method through a case study of the Clifton Suspension Bridge, UK. Our method provides a series of actionable steps, the completion of which will facilitate the creation of a Digital Twin able to support operational decisions. By fulfilling the requirements of infrastructure managers, we hope to encourage the uptake of the technology. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Bridge Management; Decision Support; Digital Twin; Finite Element Model; Resilience; Structural Health Monitoring","Decision making; Decision support systems; Life cycle; Managers; Preventive maintenance; Structural health monitoring; Ageing infrastructures; Bridge management; Decision supports; Expected life; Finite element modelling (FEM); Infrastructure managers; Operational life; Requirements Capture; Resilience; Sensor measurements; Finite element method",,,,,"EP/R00742X/2; EP/P016782/1, EP/R013411/1; Engineering and Physical Sciences Research Council, EPSRC","This work was supported by the Engineering and Physical Sciences Research Council (ESPRC) LWEC (Living With Environmental Change) Fellowship (EP/R00742X/2); UK Collaboratorium for Research in Infrastructure & Cities (UKCRIC): Urban Observatories (EP/P016782/1);UKCRIC City Observatory Research platfOrm for iNnovation and Analytics (CORONA) (EP/R013411/1). The authors gratefully acknowledge: CSBT, Trish, AMP, COWI","ACKNOWLEDGMENTS This work was supported by the Engineering and Physical Sciences Research Council (ESPRC) LWEC (Living With Environmental Change) Fellowship (EP/R00742X/2); UK Collaboratorium for Research in Infrastructure & Cities (UKCRIC): Urban Observatories (EP/P016782/1);UKCRIC City Observatory Research platfOrm for iNnovation and Analytics (CORONA) (EP/R013411/1). The authors gratefully acknowledge: CSBT, Trish, AMP, COWI",,,,,,,,,"(2018) Digital Twin - Towards a Meaningful Framework [online], , https://www.arup.com//media/arup/files/publications/d/digital-twin-report.pdf, (accessed 1/06/2020); Wright, L., Davidson, S., How to tell the difference between a model and a digital twin (2020) Adv. Model. and Simul. in Eng. Sci, 7 (13). , https://doi.org/10.1186/s40323-020-00147-4; (2020) The Approach to Delivering a National Digital Twin for the United Kingdom, , https://www.cdbb.cam.ac.uk/files/approach_summaryreport_final.pdf, [online]; Daskalova, M., (2018) The 'digital twin' - a bridge between the physical and the digital world, , https://cobuilder.com/en/the-digital-twin-a-bridge-between-thephysical-and-the-digital-world/, [online]. (accessed on 09.06.2020); Gunner, S., Vardanega, P. J., Tryfonas, T., Macdonald, J. H., Wilson, R. E., Rapid deployment of a WSN on the Clifton Suspension Bridge, UK (2017) Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 170 (3), pp. 59-71; Cook, R., (1995) Finite element modeling for stress analysis, , New York: Wiley; Balageas, D., Fritzen, Guemes, A., (2006) Structural health monitoring, , London Newport Beach, CA: ISTE; Sarget, G.R., Validation and Verification of Simulation Models (1992) Proceedings of the 1992 Winter Simulation Conference, pp. 104-114. , (Swain et al. eds): University Syracuse, New York; INCOSE, B., (2015) Systems engineering handbook: A guide for system life cycle processes and activities, , San Diego, US-CA: International Council on Systems Engineering; Maqsood, T., Finegan, A.D., Walker, D.H., (2001) Five Case Studies Applying Soft Systems Methodology to Knowledge Management, , Queensland University of Technology: Brisbane, Australia; Ewusi-Mensah, K., Przasnyski, Z. H., On information systems project abandonment: An exploratory study of organizational practices (1991) MIS Quarterly, 15 (1), pp. 67-84; Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Nee, A. Y. C., Enabling technologies and tools for digital twin (2019) Journal of Manufacturing Systems; Ko, J. M., Ni, Y. Q., Technology developments in structural health monitoring of large-scale bridges (2005) Engineering structures, 27 (12), pp. 1715-1725; Ballio, F., Ballio, G., Franzetti, S., Crotti, G., Solari, G., Actions monitoring as an alternative to structural rehabilitation: Case study of a river bridge (2018) Structural Control and Health Monitoring, 25 (11), p. e2250; Barlow, W. H., Description of the Clifton Suspension Bridge. Minutes of the Proceedings of the Institution of Civil Engineers, 1867,26, 243-257; Reprinted Proceedings of the Institution of Civil Engineers (1867), Bridge Engineering, 2003,156 1, 5-10; Macdonald, J.H.G., Pedestrian-induced vibrations of the Clifton Suspension Bridge, UK (2008) Proceedings of the Institution of Civil Engineers Bridge Engineering, 161 (BE2), pp. 69-77","Pregnolato, M.; Department of Civil Eng., United Kingdom",,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85128350948
"Erduran E., Demirlioglu K., Lau A., Aziz K., Willoughby I., Hyldmo E., Arsenovic T., Martinelli E.","16047219100;57204285680;57730532100;57262253800;57262109200;57261370000;57262253900;23967620900;","Stange Overpass: Finite Element Model Updating of an Unconventional Railway Bridge",2021,"Lecture Notes in Civil Engineering","156",,,"889","901",,1,"10.1007/978-3-030-74258-4_56","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115054648&doi=10.1007%2f978-3-030-74258-4_56&partnerID=40&md5=e7defa09c4c19ad9c9863706869adfc2","Department of Civil Eng. and Energy Tech., Oslo Metropolitan University, Oslo, Norway; Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway; Bane NOR, Oslo, Norway; Department of Civil Engineering, University of Salerno, Fisciano, Italy","Erduran, E., Department of Civil Eng. and Energy Tech., Oslo Metropolitan University, Oslo, Norway; Demirlioglu, K., Department of Civil Eng. and Energy Tech., Oslo Metropolitan University, Oslo, Norway; Lau, A., Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway; Aziz, K., Bane NOR, Oslo, Norway; Willoughby, I., Bane NOR, Oslo, Norway; Hyldmo, E., Bane NOR, Oslo, Norway; Arsenovic, T., Bane NOR, Oslo, Norway; Martinelli, E., Department of Civil Engineering, University of Salerno, Fisciano, Italy","Stange Overpass is a three-span railway reinforced concrete bridge on the Dovre line that connects the cities of Oslo and Trondheim in Norway. It was built in 2002 and has been extended by 1,5 m in each direction in 2004. During the extension operation, the part of the deck that has been extended from the abutments had not been placed on a foundation but, instead, had directly been constructed over the soil mass. Within the context of the Intercity project that has recently been undertaken by the Norwegian Railway Authority (BaneNOR), passenger trains that cross Stange Overpass will be upgraded to high-speed trains. For this, the existing bridge needs to be evaluated for the new train type. This requires a finite element model that correctly and reliably models the dynamic behavior of the bridge under various environmental and loading conditions. Preliminary analysis results show that the most significant parameter that governs the dynamic behavior of the bridge is the load-deformation behavior of the soil that directly supports the ends of the deck that extends outwards from the abutments. The load-deformation behavior of the soil can be expected to be highly sensitive to the environmental conditions and the changes in these conditions from summer to Nordic winter. To provide a reliable estimate of the load-deformation behavior of soil as well as a reliable finite element model, an instrumentation set-up will be installed in the bridge by Oslo Metropolitan University in collaboration with BaneNOR. A new finite element model updating algorithm will be developed that can not only estimate the traditional linear parameters but also the non-linear force-deformation relationship of the soil mass that supports the bridge deck. This article provides a summary of the project together with initial results that include sensitivity analysis that has been conducted to quantify the effects of the soil model on the dynamic behavior of the bridge. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Acceleration measurements; Finite element model updating; Railway bridge","Abutments (bridge); Bridge decks; Deformation; Overpasses; Passenger cars; Railroad bridges; Railroad transportation; Railroads; Reinforced concrete; Sensitivity analysis; Soils; Structural health monitoring; Dynamic behaviors; Environmental conditions; Finite-element model updating; High speed train (HST); Load deformation behavior; Loading condition; Preliminary analysis; Reliable estimates; Finite element method",,,,,,,,,,,,,,,,"Casas, J.R., Moughty, J.J., Bridge damage detection based on vibration data: Past and new developments (2017) Front Built Environ, 3, p. 4; EN: EN 1991-2 Eurocode 1 (2003) Actions on structures—part 2: Traffic loads on Bridges. CEN, Brussels; Chen, X., Omenzetter, P., Beskhyroun, S., Calibration of the finite element model of a twelve-span prestressed concrete bridge using ambient vibration data (2014) 7Th European Workshop on Structural Health Monitoring; Zacher, M., Baeßler, S., Dynamic behaviour of ballast on railway bridgese (2009) Dynamics of High-Speed Railway Bridges, pp. 99-112. , . pp , Taylor & Francis; Gsemyr, H., Aziz, K., (2019) Dynamic Modelling regarding Stresses on Existing Bridges in Heavy Haul Operation considering Track Irregularities on Ofotbanen, , International heavy haul conference; Friswell, M., Mottershead, J., Ahmedian, H., Finite element model updating using experimental test data: Parametization and regularization (2001) Philos Trans Roy Soc Lond Ser Math Phys Eng Sci, 359, pp. 169-186; Wang, H., Li, A., Li, J., Progressive finite element model calibration of a long-span suspension bridge based on ambient vibration and static measurements (2010) Eng Struct, 32, pp. 2456-2556; Moravej, H., Jamali, S., Chan, T., Nguyen, A., Finite element model updating of civil engineering infrastructures: A review literature (2017) 8Th International Conference on Structural Health Monitoring of Intelligent Infrastructure; Bowles, J., (1996) Foundation Analysis and Design, , McGraw-Hill Company, New York","Erduran, E.; Department of Civil Eng. and Energy Tech., Norway; email: emrah.erduran@oslomet.no","Rainieri C.Fabbrocino G.Caterino N.Ceroni F.Notarangelo M.A.",,"Springer Science and Business Media Deutschland GmbH","8th Civil Structural Health Monitoring Workshop, CSHM-8 2021","31 March 2021 through 2 April 2021",,264479,23662557,9783030742577,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85115054648
"Nagulapally P., Shamsuddoha M., Rajan G., Mohan M., Prusty B.G.","57221705927;24725829000;14037956800;57226448384;6603550457;","Distributed fiber optic sensor-based strain monitoring of a riveted bridge joint under fatigue loading",2021,"IEEE Transactions on Instrumentation and Measurement","70",,"9501955","","",,1,"10.1109/TIM.2021.3101324","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111605025&doi=10.1109%2fTIM.2021.3101324&partnerID=40&md5=908b3ccd868e161669062e801db5d857","ARC Training Center for Automated Manufacture of Advanced Composites, School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW  2052, Australia; Department of Applied Computing and Engineering, School of Technologies, Cardiff Metropolitan University at Llandaff, Cardiff, CF52YB, United Kingdom; Transport for NSW, Sydney, NSW  2008, Australia","Nagulapally, P., ARC Training Center for Automated Manufacture of Advanced Composites, School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW  2052, Australia; Shamsuddoha, M., ARC Training Center for Automated Manufacture of Advanced Composites, School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW  2052, Australia; Rajan, G., Department of Applied Computing and Engineering, School of Technologies, Cardiff Metropolitan University at Llandaff, Cardiff, CF52YB, United Kingdom; Mohan, M., Transport for NSW, Sydney, NSW  2008, Australia; Prusty, B.G., ARC Training Center for Automated Manufacture of Advanced Composites, School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW  2052, Australia","Riveted steel bridges, which were built in the early 20th century, require their regular structural integrity assessment to avoid any catastrophic failure. This article presents continuous strain monitoring of a single riveted lap joint, which is a representative critical element of riveted steel bridges through an optical frequency domain reflectometry (OFDR)-based distributed fiber optic sensor (DFOS). The aim of this study was to instrument a DFOS on a single riveted lap joint for monitoring the surface and critical strains experienced by the rivet joint under two fatigue loading conditions and also to compare the strain transfer between the two commonly used adhesives for bonding the DFOS. Initially, through finite element analysis (FEA), a location for installing the DFOS was identified, and also a strategy was developed for monitoring the critical location of the joint during fatigue loading. Subsequently, the DFOS was instrumented on the riveted joint at the identified location in two segments, where similar strain levels were expected with the aid of two types of adhesives: cyanoacrylate and epoxy. The strains on the rivet joint were monitored under high cycle fatigue (HCF) for up to 2 × 106 loading cycles with constant stress amplitude and followed by low cycle fatigue (LCF) loading with increasing stress amplitude until the failure of the specimen. The results showed that the DFOS could continuously sense the cyclic peak strain of - 223 μ ϵ under HCF conditions and a peak maximum strain of -1244 μ ϵ under LCF conditions. Furthermore, the internal critical strain on the rivet joint during loading could be monitored with the application of the developed damage monitoring strategy and DFOS strain data. Finally, the DFOS segment bonded using cyanoacrylate measured marginally high strains than epoxy adhesive during the HCF test. © 2021 IEEE.","Adhesive performance; Distributed fiber optic sensor (DFOS); High cycle fatigue (HCF); Low cycle fatigue (LCF); Riveted bridge joint; Structural health monitoring (SHM)","Adhesives; Fiber optic sensors; Frequency domain analysis; Joints (structural components); Location; Rivets; Steel bridges; Steel fibers; Strain; Stress analysis; Catastrophic failures; High cycle fatigue; Low cycle fatigues; Optical frequency domain reflectometry; Riveted lap joints; Riveted steel bridges; Stress amplitudes; Structural integrity assessment; Fatigue of materials",,,,,"Australian Government; Australian Research Council, ARC: IC160100040, LE140100082","Manuscript received April 16, 2021; revised July 1, 2021; accepted July 14, 2021. Date of publication July 30, 2021; date of current version August 11, 2021. This work was supported by Australian Government through Australian Research Council (ARC) Linkage Infrastructure, Equipment and Facilities (LIEF): An Australasian Facility for the Automated Fabrication of High-Performance Bespoke Components under Grant LE140100082 and ARC Industrial Transformation Training Centre (ITTC): ARC Training Centre for Automated Manufacture of Advanced Composites under Grant IC160100040. The Associate Editor coordinating the review process was Kok-Sing Lim. (Corresponding author: Prashanth Nagulapally.) Prashanth Nagulapally, Md Shamsuddoha, and B. Gangadhara Prusty are with the ARC Training Center for Automated Manufacture of Advanced Composites, School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia (e-mail: p.nagulapally@unsw.edu.au).",,,,,,,,,,"Regier, R., Hoult, N.A., Distributed strain behavior of a reinforced concrete bridge: Case study (2014) J. Bridge Eng., 19 (12); Barrias, A., Rodriguez, G., Casas, J.R., Villalba, S., Application of distributed optical fiber sensors for the health monitoring of two real structures in Barcelona (2018) Struct. Infrastruct. Eng., 14 (7), pp. 967-985. , Jul; Giraldo, C.D.M., (2018) Development of Optical Fiber Sensors for the Structural Health Monitoring in Aeronautical Composite Structures, , Ph.D. dissertation, Polytech. Univ. Madrid, Madrid, Spain; Rodriguez, G., Casas, J.R., Villalba, S., SHM by DOFS in civil engineering: A review (2015) Struct. Monitor. Maintenance, 2 (4), pp. 357-382. , Dec; Chen, Z., Zhou, X., Wang, X., Dong, L., Qian, Y., Deployment of a smart structural health monitoring system for long-span arch bridges: A review and a case study (2017) Sensors, 17 (9), p. 2151. , Sep; Nagulapally, P., Shamsuddoha, M., Rajan, G., Djukic, L., Prusty, G.B., Distributed fibre optic sensor-based continuous strain measurement along semicircular paths using strain transformation approach (2021) Sensors, 21 (3), p. 782. , Jan; Silva, A.L., Varanis, M., Mereles, A.G., Oliveira, C., Balthazar, J.M., A study of strain and deformation measurement using the Arduino microcontroller and strain gauges devices (2018) Revista Brasileira de Ensino de Física, 41 (3). , Dec; Maung, P., Prusty, G.B., Rajan, G., Li, E., Phillips, A.W., John, N.A., Distributed strain measurement using fibre optics in a high performance composite hydrofoil (2017) Proc. ICCM Int. Conf. Compos. Mater., pp. 20-25. , Aug; Matta, F., Bastianini, F., Galati, N., Casadei, P., Nanni, A., Distributed strain measurement in steel bridge with fiber optic sensors: Validation through diagnostic load test (2008) J. Perform. Constructed Facilities, 22 (4), pp. 264-273; Muanenda, Y., Oton, C.J., Di Pasquale, F., Application of Raman and Brillouin scattering phenomena in distributed optical fiber sensing (2019) Frontiers Phys., 7, pp. 1-14. , Oct; Glisic, B., Inaudi, D., Development of method for in-service crack detection based on distributed fiber optic sensors (2012) Struct. Health Monitor., 11 (2), pp. 161-171. , Mar; Rajan, G., Prusty, G., (2016) Structural Health Monitoring of Composite Structures Using Fibre Optic Methods, , G. Rajan and B. G. Prusty, Eds. Boca Raton, FL, USA: CRC Press; Li, E., Rayleigh scattering based distributed optical fiber sensing Proc. SPIE, p. 2017. , 10464 Oct; Kooi Der, K.Van, Hoult, N.A., Assessment of a steel model truss using distributed fibre optic strain sensing (2018) Eng. Struct., 171, pp. 557-568. , Sep; Minardo, A., Bernini, R., Amato, L., Zeni, L., Bridge monitoring using Brillouin fiber-optic sensors (2012) IEEE Sensors J., 12 (1), pp. 145-150. , Jan; Xu, J., Dong, Y., Zhang, Z., Li, S., He, S., Li, H., Full scale strain monitoring of a suspension bridge using high performance distributed fiber optic sensors (2016) Meas. Sci. Technol., 27 (12). , Dec; Gifford, D.K., Swept-wavelength interferometric interrogation of fiber Rayleigh scatter for distributed sensing applications (2007) Proc. SPIE, 6770. , Oct; (2013) Optical Backscatter Reflectometer 4600 User Guide, , Luna Technol., Roanoke, VA, USA; Liu, Z., Global-local fatigue assessment of an ancient riveted metallic bridge based on submodelling of the critical detail (2019) Fatigue Fract. Eng. Mater. Struct., 42 (2), pp. 546-560. , Feb; Zhou, Y.E., A97 steel (2003) Assessing Remaining Fatigue Life Existing Riveted Steel Bridges, pp. 193-204. , K.M. Mahmoud, Ed. Boca Raton, FL, USA: CRC Press; Guo, T., Chen, Y.-W., Field stress/displacement monitoring and fatigue reliability assessment of retrofitted steel bridge details (2011) Eng. Failure Anal., 18 (1), pp. 354-363. , Jan; Hebdon, M.H., Martin, F.J.B., Korkmaz, C., Connor, R.J., Load redistribution and remaining fatigue life of steel built-up members subjected to flexure following a component failure (2017) J. Bridge Eng., 22 (9); Collette, Q., (2014) Riveted Connections in Historical Metal Structures (1840-1940), , Ph.D. dissertation, Vrije Universiteit Brussel, Brussels, Belgium; (2018) Academic Research Mechanical Release ANSYS, , Canonsburg, PA, USA Jan; Moraes, J.F.C., Jordon, J.B., Ilieva, E.I., Influence of the friction coefficient in self-pierce riveting simulations: A statistical analysis (2018) SAE Int. J. Mater. Manuf., 11 (12), pp. 123-130; Leonetti, D., Maljaars, J., Snijder, H.H.B., Reliability-based fatigue life estimation of shear riveted connections considering dependency of rivet hole failures (2018) Proc. MATEC Web Conf., 165, pp. 1-9; Szolwinski, M.P., Farris, T.N., Linking riveting process parameters to the fatigue performance of riveted aircraft structures (2000) J. Aircr., 37 (1), pp. 130-137. , Jan; Amancio, S., (2007) Friction Riveting: Development and Analysis of A New Joining Technique for Polymer-metal Multi-materials Structures, , Ph.D. dissertation, Technische Universität Hamburg, Hamburg, Germany; Shan, Y., Xu, H., Zhou, Z., Yuan, Z., Xu, X., Wu, Z., State sensing of composite structures with complex curved surface based on distributed optical fiber sensor (2019) J. Intell. Mater. Syst. Struct., 30 (13), pp. 1951-1968. , Aug; Davis, C., Knowles, M., Rajic, N., Swanton, G., Evaluation of a distributed fibre optic strain sensing system for full-scale fatigue testing (2016) Proc. 21st Eur. Conf. Fracture, 2, pp. 3784-3791. , B. V. Catania, Ed. Amsterdam, The Netherlands: Elsevier; Moore, P., Booth, G., Structures under cyclic load (2015) The Welding Engineer's Guide to Fracture and Fatigue, pp. 65-73. , Oxford, U.K.: Woodhead Publishing, ch. 6; Davis, C., Knowles, M., Swanton, G., (2018) Evaluation of A Distributed Fibre Optic Strain Sensing System for Full-Scale Fatigue Testing, , Melbourne, VIC Australia: Fishermans Bend; Isah, B.W., Mohamad, H., Ahmad, N.R., Rock stiffness measurements fibre Bragg grating sensor (FBGs) and the effect of cyanoacrylate and epoxy resin as adhesive materials (2020) Ain Shams Eng. J., 12 (2), pp. 1677-1691","Nagulapally, P.; ARC Training Center for Automated Manufacture of Advanced Composites, Australia; email: p.nagulapally@unsw.edu.au",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,00189456,,IEIMA,,"English","IEEE Trans. Instrum. Meas.",Article,"Final","",Scopus,2-s2.0-85111605025
"Azim M.R.","57203927510;","A Data-Driven Damage Assessment Tool for Truss-Type Railroad Bridges Using Train Induced Strain Time-History Response",2021,"Australian Journal of Structural Engineering","22","2",,"147","162",,1,"10.1080/13287982.2021.1908710","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104238603&doi=10.1080%2f13287982.2021.1908710&partnerID=40&md5=6f90e73308a6d3c1e6228364535f052f","Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada","Azim, M.R., Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada","In this paper, a non-parametric damage detection method for truss-type railroad bridges is presented. The method uses operational strain time-history responses to detect damage in truss elements, and change in support behaviour. Dynamic strain time-history responses obtained under baseline and unknown-state bridge conditions are used to compute the magnitudes of differences in strain values between two successive time-steps. A new damage-sensitive feature (DSF) is proposed as the change in percentage of the square root of the sum of squared values of these magnitudes. After establishing a threshold DSF based on the baseline bridge, further structural change or damage in the bridge could be detected and located by observing the values of the DSFs. The validity of the method is investigated through finite element analysis of a steel-truss railway bridge. It is demonstrated that the proposed method yields promising results for identifying, locating, and relatively assessing the damage, and could be useful even when different operational conditions (i.e. different train speeds and loads) and measurement noise influence the strain data. Therefore, the proposed method has the potential to assist in developing effective maintenance strategies for railway bridges. © 2021 Engineers Australia.","damage detection; damage localisation; damage severity; dynamic strain response; railway truss bridges; Structural health monitoring","Railroad bridges; Railroads; Trusses; Damage assessments; Induced strain; Maintenance strategies; Measurement Noise; Operational conditions; Railway bridges; Time history response; Truss elements; Damage detection",,,,,,,,,,,,,,,,"Arangio, S., Beck, J.L., Bayesian Neural Networks for Bridge Integrity Assessment (2012) Structural Control & Health Monitoring, 19 (1), pp. 3-21; (2017) Infrastructure Report Card: Rail. American Society of Civil Engineers, , https://wwwinfrastructurereportcardorg/cat-item/rail/, Accessed from; Azim, M.R., Gül, M., Damage Detection of Steel Truss Railway Bridges Using Operational Vibration Data (2020) Journal of Structural Engineering- ASCE, 146 (3), p. 04020008; Azim, M.R., Zhang, H., Gül, M., Damage Detection of Railway Bridges Using Operational Vibration Data: Theory and Experimental Verifications (2020) Structural Monitoring and Maintenance, 7 (2), pp. 149-166; Bagchi, A., Humar, J., Xu, H., Noman, A.S., Model-Based Damage Identification in a Continuous Bridge Using Vibration Data (2010) Journal of Performance of Constructed Facilities, 24 (2), pp. 148-158; Balsamo, L., Betti, R., Beigi, H., A Structural Health Monitoring Strategy Using Cepstral Features (1994) Journal of Sound and Vibration, 169 (1), pp. 3-17; Banerji, P., Chikermane, S., Structural Health Monitoring of a Steel Railway Bridge for Increased Axle Loads (2011) Structural Engineering International, 21 (2), pp. 1-7; Barai, S.V., Pandey, P.C., Time-delay Neural Networks in Damage Detection of Railway Bridges (1997) Advances in Engineering Software, 28 (1), pp. 1-10; Beskhyroun, S., Oshima, T., Mikami, S., Wavelet-based Technique for Structural Damage Detection (2010) Structural Control & Health Monitoring, 17, pp. 473-494; Bowe, C., Quirke, P., Cantero, D., O’Brien, E.J., Drive-by Structural Health Monitoring of Railway Bridges Using Train Mounted Accelerometers (2015) 5th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, , Greece; Catbas, F.N., Ciloglu, S., Hasancebi, O., Grimmelsman, K., Aktan, A., Limitations in Structural Identification of Large Constructed Structures (2007) Journal of Structural Engineering, ASCE, 133 (8), pp. 1051-1066; Catbas, F.N., Gokce, H.B., Gül, M., Nonparametric Analysis of Structural Health Monitoring Data for Identification and Localization of Changes: Concept, Lab, and Real-Life Studies (2012) Structural Health Monitoring: An International Journal, 11 (5), pp. 1-14; (2014) Analysis Reference Manual for SAP2000, ETABS, SAFE, and CSiBridge, , Berkeley, California, USA: Computers and Structures; Dackermann, U., Li, J., Samali, B., Damage Identification in Timber Bridges Utilising the Damage Index Method and Neural Network Ensembles (2009) Australian Journal of Structural Engineering, 9 (3), pp. 181-194; Dos Santos, F.L.M., Peeters, B., Lau, J., Desmet, W., Goes, L.C.S., The Use of Strain Gauges in Vibration-Based Damage Detection. 11th International Conference on Damage Assessment of Structures (2015) Journal of Physics. 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J. Struct. Eng.",Article,"Final","",Scopus,2-s2.0-85104238603
"Hashlamon I., Nikbakht E., Topa A.","57205484515;56009891000;56051068600;","Indirect bridge health monitoring employing contact-point response of instrumented stationary vehicle",2021,"Lecture Notes in Civil Engineering","132",,,"883","890",,1,"10.1007/978-981-33-6311-3_100","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100745089&doi=10.1007%2f978-981-33-6311-3_100&partnerID=40&md5=49d54e6ad7baaff3a413caa7fb7455d0","Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia; Institute of Transportation Infrastructure, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia; Department of Maritime Technology, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Terengganu, 21300, Malaysia","Hashlamon, I., Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia; Nikbakht, E., Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia, Institute of Transportation Infrastructure, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia; Topa, A., Institute of Transportation Infrastructure, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia, Department of Maritime Technology, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Terengganu, 21300, Malaysia","Indirect bridge health monitoring requires an instrumented vehicle with an accelerometer to scan bridge vibration. The indirect method is more practical than the conventional direct method due to its cost efficiency and mobility. However, the vehicle’s own response may pollute the recorded vertical acceleration signal. This paper utilizes a newly developed contact-point calculation method to show its efficiency to reflect the true vibration of bridges. A finite element model is developed for a vehicle placed in stationary state at mid-span of a bridge that is excited by a moving vehicle. Two cases considering different property of the stationary vehicle and different speed of the moving vehicle are developed. The contact-point response is extracted from the stationary vehicle response using MATLAB. The results show the discrepancy between the stationary vehicle and bridge responses. In contrast, good agreement between the contact-point and bridge responses is presented in the time and frequency domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.","Bridge health monitoring; Contact-point response; Dynamic response; Fast fourier transform; Finite element method; Indirect method; Natural frequency; Stationary vehicle","Efficiency; Excited states; Offshore oil well production; Vehicles; Bridge health monitoring; Bridge vibration; Indirect methods; Instrumented vehicle; Its efficiencies; Time and frequency domains; Vehicle response; Vertical accelerations; Vibrations (mechanical)",,,,,,,,,,,,,,,,"Znidaric, A., Pakrashi, V., O’Brien, E.J., A review of road structure data in six European countries (2011) Proc. Insti. Civil Eng. J. Urban Des. Plan., 164 (4), pp. 225-232; (2013) Transport and Tourism Annual Report on Road Statistics: Current State of Bridges; Davis, S.L., (2013) The Fix we’re in For: The State of Our nation’s Bridges; Zhu, X., (2019) Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads. Features Eval. 19(3, p. 463; Carden, E.P., Fanning, P., Vibration based condition monitoring: A review (2004) Struct. Health Monitor., 3 (4), pp. 355-377; Zhu, X., Jaise, S.S., Law, structural health monitoring based on vehicle-bridge interaction: Accomplishments and challenges. Adv. Struct (2015) Eng, 18 (12), pp. 1999-2015; Yang, Y.B., Lin, C.W., Yau, J.D., Extracting bridge frequencies from the dynamic response of a passing vehicle (2004) J. Sound Vibr., 272 (3), pp. 471-493; Yang, Y.B., Yang, J.P., State-of-the-art review on modal identification and damage detection of bridges by moving test vehicles (2018) Int. J. Struct. Stab. Dyn., 18 (2); Yang, Y.B., Contact-point response for modal identification of bridges by a moving test vehicle (2018) Int. J. Struct. Stab. Dyn., 18 (5); Li, J., Indirect bridge modal parameters identification with one stationary and one moving sensors and stochastic subspace identification (2019) J. Sound Vibr., 446, pp. 1-21; Yang, Y.B., Lin, C.W., Vehicle–bridge interaction dynamics and potential applications (2005) J. Sound Vibr., 284 (1-2), pp. 205-226","Hashlamon, I.; Department of Civil and Environmental Engineering, Malaysia; email: ibrahim_19001027@utp.edu.my","Mohammed B.S.Kutty S.R.M.Balogun A.-L.Shafiq N.Mohamad H.",,"Springer Science and Business Media Deutschland GmbH","6th International Conference on Civil, Offshore and Environmental Engineering, ICCOEE 2020","13 July 2021 through 15 July 2021",,253689,23662557,9789813363106,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85100745089
"Venglár M., Sokol M.","57191739008;53985383700;","Case study: The Harbor Bridge in Bratislava",2020,"Structural Concrete","21","6",,"2736","2748",,1,"10.1002/suco.201900190","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092370356&doi=10.1002%2fsuco.201900190&partnerID=40&md5=49dc4bede8d108cbe59d8cc927105847","Slovak University of Technology, Faculty of Civil Engineering, Bratislava, Slovakia","Venglár, M., Slovak University of Technology, Faculty of Civil Engineering, Bratislava, Slovakia; Sokol, M., Slovak University of Technology, Faculty of Civil Engineering, Bratislava, Slovakia","Bratislava Harbor Bridge is an important part of Slovak highway and railway network. The authorities of the bridge (Railways of the Slovak Republic and National Motorway Company) cannot afford to close it. In the case of closure, traffic in Bratislava would be paralyzed, as up to 100,000 vehicles pass the bridge daily. Because of that, only dynamic tests during operation are possible, but the mentioned companies do not possess a sophisticated measurement system for structural health monitoring purposes. Therefore, a preliminary proposal of a measuring system has been prepared, in accordance with the fact that the bridge is one of the most complex structures in Slovakia. An initial and subsequent validated and verified FEM model have been used for numerical calculations. Time history analysis has also been used for verification and validation of the FEM model. This article also presents a comparison of current results of operational modal analysis and results of numerical analysis. It includes the recommendations for the fib Model Code 2020 (MC2020) as well. © 2020 fib. International Federation for Structural Concrete","FEM model; in situ measurements; model code 2020; monitoring system; verification and validation of FEM model","Modal analysis; Railroads; Structural health monitoring; Complex structure; Measurement system; Measuring systems; Numerical calculation; Operational modal analysis; Structural health; Time history analysis; Verification-and-validation; Highway bridges",,,,,"Ministerstvo školstva, vedy, výskumu a športu Slovenskej republiky: 1/0749/19; Slovenská technická univerzita v Bratislave, STU","This paper was supported by the Grant Agency of the Ministry of Education, Science, Research and Sports of the Slovak Republic VEGA No. 1/0749/19. The paper was also supported by a grant from the research program of Slovak University of Technology – Excellent teams of young researchers 2018.",,,,,,,,,,"Comisu, C.-C., Taranu, N., Boaca, G., Scutaru, M.-C., Structural health monitoring system of bridges (2017) Procedia Eng, 2054-2059, p. 199. , https://doi.org/10.1016/j.proeng.2017.09.472; Seo, J., Hu, J.W., Lee, J., Summary review of structural health monitoring applications for highway bridges (2016) J Perform Construct Facil, 30 (4). , https://doi.org/10.1061/(ASCE)CF.1943-5509.0000824; Sousa, H., Félix, C., Bento, J., Figueiras, J., Design and implementation of a monitoring system applied to a long-span prestressed concrete bridge (2011) Struct Concrete, 12 (2), pp. 82-93. , https://doi.org/10.1002/suco.201000014; Strauss, A., Karimi, S., Kopf, F., Capraru, C., Bergmeister, K., Monitoring-based performance assessment of rail-bridge interaction based on structural reliability (2015) Struct Concrete, 16 (3), pp. 342-355. , https://doi.org/10.1002/suco.201500019; Yarnold, M.T., Moon, F.L., Temperature-based structural health monitoring baseline for long-span bridges (2015) Eng Struct, 86, pp. 157-167. , https://doi.org/10.1016/j.engstruct.2014.12.042; Guan, H., (2006) Vibration-based structural health monitoring of highway bridges. PhD. Thesis, University of California, San Diego; Haardt, P., Holst, R., (2017) Monitoring during life cycle of bridges to establish performance indicators. Paper presented at: Proceedings of the Joint COST TU1402 - COST TU1406 - IABSE WC1 Workshop: The Value of Structural Health Monitoring for the reliable Bridge Management. University of Zagreb Faculty of Civil Engineering, 2017, pp. 1-9; (2009) Railways of Slovak Republic: Regulation S5 - Performance management of railway bridges, , (in Slovak); Strauss, A., Mandić Ivanković, A., Matos, J.C., Casas, J.R., (2017) Performance indicators for road bridges - overview of findings and future progress. Paper presented at: Proceedings of the Joint COST TU1402 - COST TU1406 - IABSE WC1 Workshop: The Value of Structural Health Monitoring for the reliable Bridge Management. University of Zagreb Faculty of Civil Engineering, pp. 1-6; (2015) The results of national traffic survey in Sleovak Republic in 2015, , Bratislava, (in slovak); Ároch, R., Sokol, M., Venglár, M., (2016) Structural health monitoring of major danube bridges in Bratislava. Paper presented at: Procedia Engineering: 9th International Conference ""Bridges in Danube Basin 2016"", Žilina, SR; September 30-October 1, pp. 24-31. , 156; Sung, Y.-C., Lin, T.-K., Chiu, Y.-T., Chang, K.-C., Chen, K.-L., Chang, C.-C., A bridge safety monitoring system for prestressed composite box-girder bridges with corrugated steel webs based on in-situ loading experiments and a long-term monitoring database (2016) Eng Struct, 126, pp. 571-585. , https://doi.org/10.1016/j.engstruct.2016.08.006; (2008) Operating instructions and specifications NI 9234. Austin, Texas; Zhou, G.-D., Yi, T.-H., A summary review of correlations between temperatures and vibration properties of long-span bridges (2014) Math Probl Eng, 2014. , https://doi.org/10.1155/2013/217983; Wong, K.-Y., Instrumentation and health monitoring of cable-supported bridges (2004) Struct Control Health Monit, 11 (2), pp. 91-124. , https://doi.org/10.1002/stc.33; Oppenheim, A.V., Schafer, R.W., Buck, J.R., (1999) Discrete-time signal processing, , 2nd ed., Upper Saddle River, NJ, Prentice Hall; Reynders, E., Degrauwe, D., De Roeck, G., Magalhães, F., Caetano, E., Combined experimental-operational modal testing of footbridges (2010) J Eng Mech, 136 (6), pp. 687-696. , https://doi.org/10.1061/(ASCE)EM.1943-7889.0000119; Peeters, B., (2008) System identification and damage detection in civil engineering. PhD thesis, Katholieke Universiteit Leuven, Leuven; Peeters, B., Lau, J., Lanslots, J., van der Auweraer, H., Automatic modal analysis—Myth or reality? (2008) Sound Vib, 3, pp. 17-21; Wang, L., Lie, S.T., Zhang, Y., Damage detection using frequency shift path (2016) Mech Syst Signal Process, 66-67, pp. 298-313. , https://doi.org/10.1016/j.ymssp.2015.06.028; Wang, L., Chan, T.H.T., (2009) Review of vibration-based damage detection and condition assessment of bridge structures using structural health monitoring. Paper presented at: The Second Infrastructure Theme Postgraduate Conference: Rethinking Sustainable Development: Planning, Engineering, Design and Managing Urban Infrastructure., Queensland University; March 26; (2010) European Committee for Standardization. Eurocode 1: Actions on structures - Part 2. CEN, Brussels, , EN 1991–2; Shokrani, Y., Dertimanis, V.K., Chatzi, E.N., Savoia, M.N., On the use of mode shape curvatures for damage localization under varying environmental conditions (2018) Structural Control and Health Monitoring, 25 (4). , http://dx.doi.org/10.1002/stc.2132; Peeters, B., De Roeck, G., One-year monitoring of the Z24-bridge: Environmental effects versus damage events (2001) Earthquake Eng Struct Dyn, 30, pp. 149-171; Limongelli, M.P., Orcesi, A., (2017) A proposal for classification of key performance indicators for roadbridges. Paper presented at: Engineering the Future: proceedings. 39th IABSE Symposium, September 21-23, 2017, Vancouver, Canada. 1. ed., Zurich: International Association for Bridge and Structural Engineering, pp. 191-197","Venglár, M.; Slovak University of Technology, Slovakia; email: michal.venglar@stuba.sk",,,"Wiley-Blackwell",,,,,14644177,,,,"English","Struct. Concr.",Article,"Final","",Scopus,2-s2.0-85092370356
"Kaium S.A., Hossain S.A., Ali J.S.","57211715006;57209473356;56644087200;","Modal parameter extraction from measured signal by frequency domain decomposition (FDD) technique",2020,"International Journal of Structural Integrity","11","2",,"324","337",,1,"10.1108/IJSI-06-2019-0062","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074876658&doi=10.1108%2fIJSI-06-2019-0062&partnerID=40&md5=3375f44b8c00c2f92e84eedc46104a73","Department of Civil Engineering, Brainware Group of Institutions, Kolkata, India; Department of Civil Engineering, Camellia Institute of Engineering and Technology, Bardhaman, India; Department of Civil Engineering, Aliah University, Kolkata, India","Kaium, S.A., Department of Civil Engineering, Brainware Group of Institutions, Kolkata, India; Hossain, S.A., Department of Civil Engineering, Camellia Institute of Engineering and Technology, Bardhaman, India; Ali, J.S., Department of Civil Engineering, Aliah University, Kolkata, India","Purpose: The purpose of this paper is to highlight that the need for improved system identification methods within the domain of modal analysis increases under the impulse of the broadening field of applications, e.g., damage detection and vibro-acoustics, and the increased complexity of today’s structures. Although significant research efforts during the last two decades have resulted in an extensive number of parametric identification algorithms, most of them are certainly not directly applicable for modal parameter extraction. So, based on this, the aim of the present work is to develop a technique for modal parameter extraction from the measured signal. Design/methodology/approach: A survey and classification of the different modal analysis methods are made; however, the focus of this thesis is placed on modal parameter extraction from measured time signal. Some of the methods are examined in detail, including both single-degree-of-freedom and multi-degree-of-freedom approaches using single and global frequency-response analysis concepts. The theory behind each of these various analysis methods is presented in depth, together with the development of computer programs, theoretical and experimental examples and discussion, in order to evaluate the capabilities of those methods. The problem of identifying properties of structures that possess close modes is treated in particular detail, as this is a difficult situation to handle and yet a very common one in many structures. It is essential to obtain a good model for the behavior of the structure in order to pursue various applications of experimental modal analysis (EMA), namely: updating of finite element models, structural modification, subsystem-coupling and calculation of real modes from complex modes, to name a few. This last topic is particularly important for the validation of finite element models, and for this reason, a number of different methods to calculate real modes from complex modes are presented and discussed in this paper. Findings: In this paper, Modal parameters like mode shapes and natural frequencies are extracted using an FFT analyzer and with the help of ARTeMiS, and subsequently, an algorithm has been developed based on frequency domain decomposition (FDD) technique to check the accuracy of the results as obtained from ARTeMiS. It is observed that the frequency domain-based algorithm shows good agreement with the extracted results. Hence the following conclusion may be drawn: among several frequency domain-based algorithms for modal parameter extraction, the FDD technique is more reliable and it shows a very good agreement with the experimental results. Research limitations/implications: In the case of extraction techniques using measured data in the frequency domain, it is reported that the model using derivatives of modal parameters performed better in many situations. Lack of accurate and repeatable dynamic response measurements on complex structures in a real-life situation is a challenging problem to analyze exact modal parameters. Practical implications: During the last two decades, there has been a growing interest in the domain of modal analysis. Evolved from a simple technique for troubleshooting, modal analysis has become an established technique to analyze the dynamical behavior of complex mechanical structures. Important examples are found in the automotive (cars, trucks, motorcycles), railway, maritime, aerospace (aircrafts, satellites, space shuttle), civil (bridges, buildings, offshore platforms) and heavy equipment industry. Social implications: Presently structural health monitoring has become a significantly important issue in the area of structural engineering particularly in the context of safety and future usefulness of a structure. A lot of research is being carried out in this area incorporating the modern sophisticated instrumentations and efficient numerical techniques. The dynamic approach is mostly employed to detect structural damage, due to its inherent advantage of having global and location-independent responses. EMA has been attempted by many researchers in a controlled laboratory environment. However, measuring input excitation force(s) seems to be very expensive and difficult for the health assessment of an existing real-life structure. So Ambient Vibration Analysis is a good alternative to overcome those difficulties associated with the measurement of input excitation force. Originality/value: Three single bay two storey frame structure has been chosen for the experiment. The frame has been divided into six small elements. An algorithm has been developed to determine the natural frequency of those frame structures of which one is undamaged and the rest two damages in single element and double element, respectively. The experimental results from ARTeMIS and from developed algorithm have been compared to verify the effectiveness of the developed algorithm. Modal parameters like mode shapes and natural frequencies are extracted using an FFT analyzer and with the help of ARTeMiS, and subsequently, an algorithm has been programmed in MATLAB based on the FDD technique to check the accuracy of the results as obtained from ARTeMiS. Using singular value decomposition, the power Spectral density function matrix is decomposed using the MATLAB program. It is observed that the frequency domain-based algorithm shows good consistency with the extracted results. © 2019, Emerald Publishing Limited.","Modal analysis; Modal parameter; Mode shape; Natural frequency; Parameter extraction","Automotive industry; Composite beams and girders; Damage detection; Degrees of freedom (mechanics); Domain decomposition methods; Extraction; Fast Fourier transforms; Finite element method; Frequency division multiplexing; Frequency response; MATLAB; Natural frequencies; Offshore oil well production; Offshore structures; Parameter estimation; Parameter extraction; Singular value decomposition; Soil structure interactions; Space platforms; Space shuttles; Spectral density; Structural analysis; Structural frames; Structural health monitoring; Ultrasonic devices; Vibration analysis; Design/methodology/approach; Experimental modal analysis; Frequency domain decomposition; Frequency response analysis; Modal parameters; Mode shapes; Parametric identification algorithms; System identification methods; Modal analysis",,,,,,,,,,,,,,,,"Asmussen, J.C., Brincker, R., (1996) Estimation of Frequency Response Function by Random Decrement, , Department of Building Technology and Structural Engineering Aalborg University; Bendat, J.S., Piersol, A.G., (1986) Random Data, Analysis and Measurement Procedures, , John Wiley & Sons; Brincker, R., Andersen, P., Cantieni, R., (2001) Identification and level 1 damage detection of the Z24 highway bridge by frequency domain decomposition; Brincker, R., Zhang, L., Andersen, P., Output-only modal analysis by frequency domain decomposition (2000) Proceedings of ISMA 25, Vol. 2; Cremona, C.F., Brandon, J.A., Modal identification algorithm with unmeasured input (1992) Journal of Aerospace Engineering, 5 (4), pp. 442-449; Giraldo, D.F., Song, W., Dyke, S.J., Caicedo, J.M., Modal identification through ambient vibration: comparative study (2009) Journal of Engineering Mechanics, 135 (8), pp. 759-770; James Hu, S.-L., Li, P., Vincent, H.T., Li, H., (2011) Journal of waterway, port, coastal, and ocean engineering, 137 (5), pp. 234-245; Juang, J.N., Pappa, R.S., An eigensystem realization algorithm for modal parameter identification and modal reduction (1985) Journal of Guidance, Control and Dynamics, 8 (5), pp. 620-627; Randall, R.B., Zurita, G., Wardrop, T., Extraction of modal parameters from response measurements (2004) Investigation & Desarrollo, (4), pp. 5-12; Zhang, D.-W., Wei, F.-S., Extracting modes of constrained structure with elastic supports from free test data (2007) Journal of Aerospace Engineering, 20 (1), pp. 1-9; Zhang, L., Brincker, R., Andersen, P., (2002) An unified approach for two-stage time domain modal identification; Bendat, J.S., Piersol, A.G., (1993) Engineering Applications of Correlation and Spectral Analysis, , John Wiley & Sons; Brincker, R., Zhang, L., Andersen, P., Modal identification from ambient responses using frequency domain decomposition (2000) Proceedings of the 18th International Modal Analysis Conference, pp. 625-630. , San Antonio, TX; Felber, A.J., (1993) Development of a hybrid bridge evaluation sytem, , PhD thesis, Department of Civil Engineering, University of British Columbia, Vancouver; Ibrahim, S.R., Milkulcik, E.C., The experimental determination of vibration test parameters from time responses (1976) The Shock and Vibration Bulletin, 46 (5), pp. 187-196; Rodrigues, J., Brincker, R., Andersen, P., Improvement of frequency domain output-only modal identification from the application of the random decrement technique; Van Overshcee, P., De Moor, B., (1996) Subspace Identification for Linear Systems, , Kluwer Academic Publishers; Vold, H., Kundrat, J., Rocklin, G.T., Russel, R., (1982) A multi-input modal estimation algorithm for mini-computer, , SAE Technical Paper Series, 820194","Kaium, S.A.; Department of Civil Engineering, India; email: sakaium@hotmail.com",,,"Emerald Group Holdings Ltd.",,,,,17579864,,,,"English","Int. J. Struct. Integrity",Article,"Final","",Scopus,2-s2.0-85074876658
"Seventekidis P., Giagopoulos D., Arailopoulos A., Markogiannaki O.","57079023500;37064557500;56884701500;48662606100;","Damage Identification of Structures Through Machine Learning Techniques with Updated Finite Element Models and Experimental Validations",2020,"Conference Proceedings of the Society for Experimental Mechanics Series",,,,"143","154",,1,"10.1007/978-3-030-47638-0_16","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120399556&doi=10.1007%2f978-3-030-47638-0_16&partnerID=40&md5=ab3ec0d65b5f40697a3caa2a9db983d7","Department of Mechanical Engineering, University of Western Macedonia, Kozani, Greece","Seventekidis, P., Department of Mechanical Engineering, University of Western Macedonia, Kozani, Greece; Giagopoulos, D., Department of Mechanical Engineering, University of Western Macedonia, Kozani, Greece; Arailopoulos, A., Department of Mechanical Engineering, University of Western Macedonia, Kozani, Greece; Markogiannaki, O., Department of Mechanical Engineering, University of Western Macedonia, Kozani, Greece","Structural Health Monitoring (SHM) Techniques have recently started to draw significant attention in engineering applications due to the need of maintenance cost reductions and catastrophic failures prevention. Most of the current research on SHM focuses on developing either purely experimental models or stays on purely numerical data without real application validation. The potential of SHM methods however could be unlocked, having accurate enough numerical models and classifiers that not only recognize but also locate or quantify the structural damage. The present study focuses on the implementation of a methodology to bridge the gap between SHM models with numerically generated data and correspondence with measurements from the real structure to provide reliable damage predictions. The methodology is applied in a composite carbon fiber tube truss structure which is constructed, using aluminum elements and steel bolts for the connections. The composite cylindrical parts are produced on a spinning axis by winded carbon fibers, cascaded on specified number of plies, in various angles and directions. 3D FE models of the examined cylindrical parts are developed in robust finite element analysis software simulating each carbon fiber ply and resin matrix and analyzed against static and dynamic loading to investigate their linear and nonlinear response. In addition, experimental tests on composite cylindrical parts are conducted based on the corresponding analysis tests. The potential damage to the structure is set as loose bolts defining a multiclass damage identification problem. The SHM procedure starts with optimal modeling of the structure using an updated Finite Element (FE) model scheme, for the extraction of the most accurate geometrical and physical numerical model. To develop a high-fidelity FE model for reliable damage prediction, modal residuals and mode shapes are combined with response residuals and time-histories of strains and accelerations by using the appropriate updating algorithm. Next, the potential multiclass damage is simulated with the optimal model through a series of stochastic FE load cases for different excitation characteristics. The acceleration time series obtained through a network of optimally placed sensors are defined as the feature vectors of each load case, which are to be fed in a supervised Neural Network (NN) classifier. The necessary data processing, feature learning and limitations of the NN are discussed. Finally, the learned NN is tested against the real structure for different damage cases identification. © 2020, The Society for Experimental Mechanics, Inc.","Damage identification; Modal identification; Neural networks; Optimal modeling; Structural health monitoring","Carbon fibers; Cost engineering; Cost reduction; Damage detection; Data handling; Dynamic loads; Machine learning; Neural networks; Numerical methods; Numerical models; Spinning (fibers); Steel fibers; Stochastic models; Stochastic systems; Structural dynamics; Structural health monitoring; Structural optimization; Damage Identification; Damage prediction; Finite element modelling (FEM); Identification of structures; Machine learning techniques; Modal identification; Model validation; Neural-networks; Optimal model; Real structure; Finite element method",,,,,"T6YBII-00478","Acknowledgments This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call INDUSTRIAL MATERIALS (project code: T6YBII-00478).",,,,,,,,,,"Soutis, C., Carbon fiber reinforced plastics in aircraft construction (2005) Mater. Sci. Eng. A., 412 (1), pp. 171-176; Hongfei, Z., Xuesen, Z., Jianbao, Z., Hongjie, S., The Application of Carbon Fiber Composites in Cryotank (2018) Ares, A.E. (Ed.) Solidification. Intechopen; Park, S.Y., Choi, W.J., Choi, C.H., Choi, H.S., An experimental study into aging unidirectional carbon fiber epoxy composite under thermal cycling and moisture absorption (2019) Compos. Struct., 207, pp. 81-92; Obradovic, J., Boria, S., Belingardi, G., Lightweight design and crash analysis of composite frontal impact energy absorbing structures (2012) Compos. Struct., 94 (2), pp. 423-430; Zhu, C., Zhu, P., Liu, Z., Uncertainty analysis of mechanical properties of plain woven carbon fiber reinforced composite via stochastic constitutive modeling (2019) Compos. Struct., 207, pp. 684-700; Ryou, H., Chung, K., Yu, W.-R., Constitutive modeling of woven composites considering asymmetric/anisotropic, rate dependent, and nonlinear behavior (2007) Compos. A: Appl. Sci. Manuf., 38 (12), pp. 2500-2510; Karkkainen, R.L., Sankar, B.V., A direct micromechanics method for analysis of failure initiation of plain weave textile composites (2006) Compos. Sci. Technol., 66 (1), pp. 137-150; Giagopoulos, D., Natsiavas, S., Hybrid (Numerical-experimental) modeling of complex structures with linear and nonlinear components (2007) Nonlinear Dynamics, 47 (1), pp. 193-217; Giagopoulos, D., Natsiavas, S., Dynamic response and identification of critical points in the superstructure of a vehicle using a combination of numerical and experimental methods (2015) Exp. Mech., 55 (3), pp. 529-542; Spottswood, S.M., Allemang, R.J., On the investigation of some parameter identification and experimental modal filtering issues for nonlinear reduced order models (2007) Exp. Mech., 47 (4), pp. 511-521; Hadjidoukas, P.E., Angelikopoulos, P., Papadimitriou, C., Koumoutsakos, P., 4U: A high performance computing framework for Bayesian uncertainty quantification of complex models (2015) J. Comput. Phys., 284, pp. 1-21; Hansen, N., Müller, S.D., Koumoutsakos, P., Reducing the time complexity of the derandomized evolution strategy with Covariance Matrix Adaptation (CMA-ES) (2003) Evol. Comput., 11 (1), pp. 1-18; Giagopoulos, D., Arailopoulos, A., Dertimanis, V., Papadimitriou, C., Chatzi, E., Grompanopoulos, K., Computational framework for online estimation of fatigue damage using vibration measurements from a limited number of sensors (2017) Proced. Eng., 199, pp. 1906-1911; Giagopoulos, D., Arailopoulos, A., Dertimanis, V., Papadimitriou, C., Chatzi, E., Grompanopoulos, K., Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating (2018) Struct. Health Monit., 18 (4). , https://doi.org/10.1177/1475921718790188; Papadimitriou, C., Ntotsios, E., Giagopoulos, D., Natsiavas, S., Variability of updated finite element models and their predictions consistent with vibration measurements (2012) Struct. Control. Health Monit., 19 (5), pp. 630-654; Giagopoulos, D., Papadioti, D.-C., Papadimitriou, C., Natsiavas, S., Bayesian Uncertainty Quantification and Propagation in Nonlinear Structural Dynamics (2013) Proceedings of the 31St IMAC, a Conference on Structural Dynamics, 5, pp. 33-41. , In: Simmermacher, T., Cogan, S., Moaveni, B., Papadimitriou, C. (eds.) Topics in model validation and uncertainty quantification, volume, pp., Springer New York, New York; Bornn, L., Farrar, C.R., Park, G., Farinholt, K., Structural health monitoring with autoregressive support vector machines (2009) J. Vib. Acoust., 131 (2); Fassois, S.D., Kopsaftopoulos, F.P., Statistical Time Series Methods for Vibration Based Structural Health Monitoring (2013) New Trends in Structural Health Monitoring, pp. 209-264. , W. Ostachowicz and J.A. Güemes, Editors, Springer: Vienna. p; Hasni, H., Jiao, P., Alavi, A.H., Lajnef, N., Masri, S.F., Structural health monitoring of steel frames using a network of self-powered strain and acceleration sensors: A numerical study (2018) Autom. Constr., 85, pp. 344-357; Arailopoulos, A., Giagopoulos, D., Zacharakis, I., Pipili, E., Integrated Reverse Engineering Strategy for Large-Scale Mechanical Systems: Application to a Steam Turbine Rotor (2018) Front. Built Environ., 4 (55); Giagopoulos, D., Arailopoulos, A., Computational framework for model updating of large scale linear and nonlinear finite element models using state of the art evolution strategy (2017) Comput. Struct., 192, pp. 210-232; Grafe, H., (1999) Model Updating of Large Structural Dynamics Models Using Measured Response Function, , London; McCulloch, W.S., Pitts, W., A logical calculus of the ideas immanent in nervous activity (1943) Bull. Math. Biophys., 5 (4), pp. 115-133; Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning representations by back-propagating errors (1986) Nature, 323 (6088), pp. 533-536; Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J., 1D Convolutional Neural Networks and Applications (2019) A Survey. Arxiv Preprint Arxiv, 1905, p. 03554; Beta Cae Systems, S.A., ANSA & META-Post. BETA CAE Systems, S.A (2018) Thessaloniki; Giagopoulos, D., Chatziparasidis, I., Optimum design, finite element model updating and dynamic analysis of a full laminated glass panoramic car elevator (2016) 7Th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS Congress 2016. National Technical University of Athens; Ewins, D.J., (1984) Modal Testing: Theory and Practice, , Research Studies Press, Somerset; Mohanty, P., Rixen, D.J., Identifying mode shapes and modal frequencies by operational modal analysis in the presence of harmonic excitation (2005) Exp. Mech., 45 (3), pp. 213-220; Hagan, M.T., Demuth, H.B., Beale, M.H., De Jesus, O.: Neural network design, PWS Publishing Company, Boston (2014). ISBN 9780971732117; Chollet, F.: keras, GitHub: https://github.com/fchollet/keras (2015)","Giagopoulos, D.; Department of Mechanical Engineering, Greece; email: dgiagopoulos@uowm.gr","Mao Z.",,"Springer","38th IMAC, A Conference and Exposition on Structural Dynamics, 2020","10 February 2020 through 13 February 2020",,245349,21915644,9783030487782,,,"English","Conf. Proc. Soc. Exp. Mech. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85120399556
"Mostafa N., Loendersloot R., Di Maio D., Tinga T.","57194079699;8504539700;14619027400;16308137200;","Application of wavelet synchro-squeezed transform (WSST) method to railway bridge health monitoring",2020,"Proceedings of the International Conference on Structural Dynamic , EURODYN","1",,,"1388","1396",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099728717&partnerID=40&md5=e22509a9854357d25fdfe88675eb40ba","University Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands","Mostafa, N., University Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands; Loendersloot, R., University Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands; Di Maio, D., University Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands; Tinga, T., University Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands","Typically, the identification of resonant frequencies in railway bridges is carried out from free-decay stationary signals as a train leaves the bridge. The same identification proves very challenging when nonstationary vibrations are measured as a train traverses the bridge. Despite the numerous attempts, nonstationary signals with low modulating frequencies are still difficult to be processed. This paper attempts to evaluate the bridge-vehicle first bending resonance by a method known as Wavelet Synchro-Squeezed Transform (WSST). The significant advantage of this signal processing method is to deal with low-frequency modulations, which are typical of long bridges. This research focusses on a Finite Element Model (FEM) of a bridge simulating the nonstationary vibration responses exerted by a spring-mass model traversing the bridge. The paper sets two objectives, and the first one is to investigate how the WSST analyses nonstationary signals generated by the FE model. The instantaneous frequency trace of the bridge-vehicle system will be compared to a similar frequency trace, that is created by performing several modal analyses at different locations of the bridge. The second objective of the paper is to investigate if the instantaneous frequency obtained from WSST is suitable for damage detection, as the FE model is fitted with damages. Both objectives are met, and the results will be presented. The trace of the first natural frequency matches well the one calculated by the WSST, and the instantaneous frequency shows to be capable of detecting damages included in the model. © 2020 European Association for Structural Dynamics. All rights reserved.","Health monitoring; Vehicle-bridge interaction; WSST","Damage detection; Modal analysis; Natural frequencies; Railroad bridges; Railroads; Structural dynamics; Synchros; Wavelet transforms; Bending resonance; Bridge-vehicle systems; Instantaneous frequency; Low frequency modulation; Modulating frequencies; Non-stationary vibration; Nonstationary signals; Spring mass models; Frequency modulation",,,,,,,,,,,,,,,,"He, W.-Y., Zhu, S., Moving load-induced response of damaged beam and its application in damage localization (2016) Journal of Vibration and Control, 22 (16), pp. 3601-3617; Khorram, A., Rezaeian, M., Bakhtiari-Nejad, F., Multiple cracks detection in a beam subjected to a moving load using wavelet analysis combined with factorial design (2013) European Journal of Mechanics - A/Solids, 40, pp. 97-113; Zhang, W., Damage detection in bridge structures under moving loads with phase trajectory change of multi-type vibration measurements (2017) Mechanical Systems and Signal Processing, 87, pp. 410-425; Roveri, N., Carcaterra, A., Damage detection in structures under traveling loads by Hilbert-Huang transform (2012) Mechanical Systems and Signal Processing, 28, pp. 128-144; Meredith, J., Gonzalez, A., Hester, D., Empirical Mode Decomposition of the acceleration response of a prismatic beam subject to a moving load to identify multiple damage locations (2012) Shock and Vibration, 19 (5), pp. 845-856; Aied, H, Gonzalez, A., Cantero, D., Identification of sudden stiffness changes in the acceleration response of a bridge to moving loads using ensemble empirical mode decomposition (2016) Mechanical Systems and Signal Processing, 66-67, pp. 314-338; Doebling, S.W., Farrar, C. R., Prime, M.B., A summary review of vibration-based damage identification methods (1998) The Shock and Vibration Digest, 30, pp. 91-105; Moughty, J. J., Casas, J. R., A state of the art review of modal-based damage detection in bridges: Development, challenges, and solutions (2017) Applied Sciences-Base, 7 (5), p. 510. , https://doi.org/10.3390/app7050510; Friswell, M. I., Damage identification using inverse methods (2007) Philos Trans A Math Phys Eng Sci, 365, pp. 393-410; Farahani, R. V., Penumadu, D., Damage identification of a full-scale five-girder bridge using time-series analysis of vibration data (2016) Engineering Structures, 115, pp. 129-139; Li, J., Law, S. S., Hao, H., Improved damage identification in bridge structures subject to moving loads: Numerical and experimental studies (2013) International Journal of Mechanical Sciences, 74, pp. 99-111; Silva, M., Santos, A., Figueiredo, E., Santos, R., Sales, C., Costa, J. C. W. A., A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges (2016) Engineering Applications of Artificial Intelligence, 52, pp. 168-180; Comanducci, G., Magalhaes, F., Ubertini, F., Cunha, A., On vibration-based damage detection by multivariate statistical techniques: Application to a long-span arch bridge (2016) Structural Health Monitoring, 15 (5), pp. 505-524; Santos, A., Figueiredo, E., Silva, M., Santos, R., Sales, C., Costa, J. C.W. A., Genetic-based EM algorithm to improve the robustness of gaussian mixture models for damage detection in bridges (2017) Structural Control & Health Monitoring, 24 (3); Gonzalez, A., Hester, D., An investigation into the acceleration response of a damaged beam-type structure to a moving force (2013) Journal of Sound and Vibration, 332, pp. 3201-3217; Yang, J. N., Lei, Y., Lin, S., Huang, N., Hilbert-Huang based approach for structural damage detection (2004) Journal of Engineering Mechanics, 130 (1), pp. 85-95; Roveri, N., Carcaterra, A., Damage detection in structures under traveling loads by hilbert-huang transform (2012) Mechanical Systems and Signal Processing, 28, pp. 128-144; Yan, B.F., Miyamoto, A., A comparative study of modal parameter identification based on wavelet and Hilbert-Huang transforms (2006) Computer-Aided Civil and Infrastructure Engi-neering, 21, pp. 119-123; Nguyen, K.V., Comparison studies of open and breathing crack detections of a beam-like bridge subjected to a moving vehicle (2013) Engineering Structures, 51, pp. 306-314; Hester, D., Gonzalez, A., A wavelet-based damage detection algorithm based on bridge acceleration response to a vehicle (2012) Mechanical Systems and Signal Processing, 28, pp. 145-166; Feng, Z., Liang, M., Chu, F., Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples (2013) Mechanical Systems and Signal Processing, 38 (1), pp. 165-205; Aied, H., Gonzalez, A., Cantero, D., Identification of sudden stiffness changes in the acceleration response of a bridge to moving loads using ensemble empirical mode decomposition (2016) Mechanical Systems and Signal Processing, 66-67, pp. 314-338; Zhu, X. Q., Law, S.S., Wavelet-based crack identification of bridge beam from operational deflection time history (2006) International Journal of Solids and Structures, 43 (7-8), pp. 2299-2317; Mahmoud, M. A., Effect of cracks on the dynamic response of a simple beam subject to a moving load (2001) Proceedings of the Institution of Mechanical Engineers Part F-Journal of Rail and Rapid Transit, 215 (3), pp. 207-215; Saleeb, A., Kumar, A., Automated finite element analysis of complex dynamics of primary system traversed by oscillatory subsystem (2011) Computational Methods in Engineering Science and Mechanics, 12 (4), pp. 184-202; Poulimenos, A., Fassois, S., Parametric time-domain methods for nonstationary random vibration modelling and analysis - a critical survey and comparison (2005) Elsevier, 20 (4), pp. 763-816; Zhang, W., Li, J., Hao, H., Ma, H., Damage detection in bridge structures under moving loads with phase trajectory change of multi-type vibration measurements (2017) Mechanical Systems and Signal Processing, 87, pp. 410-425","Mostafa, N.; University Twente, Drienerlolaan 5, Netherlands; email: n.mostafa@utwente.nl","Papadrakakis M.Fragiadakis M.Papadimitriou C.",,"European Association for Structural Dynamics","11th International Conference on Structural Dynamics, EURODYN 2020","23 November 2020 through 26 November 2020",,165382,23119020,9786188507203,,,"English","Proc. Int. Conf. Struct. Dyn., EURODYN",Conference Paper,"Final","",Scopus,2-s2.0-85099728717
"Del Rio I., Cabaleiro M., Conde B., Riveiro B., Caamaño J.C.","57220199817;56294619500;56875345700;35096575300;23977387000;","Hbim application to historical steel structures: The case study of lapela bridge",2020,"World Congress on Civil, Structural, and Environmental Engineering",,,,"160-1","160-7",,1,"10.11159/icsect20.160","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097250535&doi=10.11159%2ficsect20.160&partnerID=40&md5=b2b744d78e6d3cb0f5e4c46274b893ef","Department of Materials Engineering Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, Vigo, CP 36208, Spain","Del Rio, I., Department of Materials Engineering Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, Vigo, CP 36208, Spain; Cabaleiro, M., Department of Materials Engineering Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, Vigo, CP 36208, Spain; Conde, B., Department of Materials Engineering Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, Vigo, CP 36208, Spain; Riveiro, B., Department of Materials Engineering Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, Vigo, CP 36208, Spain; Caamaño, J.C., Department of Materials Engineering Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, Vigo, CP 36208, Spain","Historical steel structures are present all around the world. Besides being a common part of the cultural heritage, many of them are still in service. As an example, we can cite the case of riveted bridges, railway stations, exhibition pavilions, or industrial buildings. The maintenance of these ancient constructions is crucial since they are prone to have suffered significant damage over time due to effects such as corrosion, human actions, or the exposure to heavy loads for which they were not originally conceived. Further, in many cases, these steel structures have to be strengthened in order to adapt them to new uses. HBIM (Historical Buildings Information Modeling) is a new methodology in structural design and construction that could be used as the ideal tool for the maintenance management of these historical structures. HBIM technology is adopted in this paper for structural engineering purposes. Departing from the geometric survey carried out by terrestrial laser scanning, a 3D model is obtained which apart of gathering the main dimensions and details regarding the composition of the structure, it allows collecting all the information concerning the deterioration grade or the different inspections and retrofitting actions performed over time. Thus, by introducing different time stages in the 3D model, the evolution of the structural health over time can be analyzed, which allows the decision-making regarding maintenance and, if required, the undertaking of repairing works. The proposed methodology will be applied to the case study of the Lapela Bridge, in Portugal. © 2020, Avestia Publishing. All rights reserved.","3D Modelling; FEM simulation; Laser scanner; Maintenance; Structural health monitoring",,,,,,"Interreg; Ministerio de Ciencia, Innovación y Universidades, MCIU: RTI2018-095893-B-C21; European Regional Development Fund, FEDER: EAPA_826/2018","This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities through the project Ref. RTI2018-095893-B-C21, and the SIRMA project, which is co-financed by the INTERREG Atlantic Area Programme through the European Regional Development Fund (ERDF) with application code: EAPA_826/2018.",,,,,,,,,,"López, F.J., Lerones, P.M., Llamas, J., Gómez-García-Bermejo, J., Zalama, E., A review of heritage building information modelling (H-BIM) (2018) Multimodal Technologies and Interaction, 2 (2), p. 21; Cabaleiro, M., Riveiro, B., Arias, P., Caamano, J.C., Algorithm for beam deformation modeling from LiDAR data (2015) Measurement, 76, pp. 20-31; Herraez, J., Navarro, P., Denia, J.L., Martin, M.T., Rodriguez, J., Modeling the thickness of vaults in the church of Santa Maria de Magdalena (Valencia, Spain) with laser scanning techniques (2014) J. Cult. Herit, 15 (6), pp. 679-686; Cabaleiro, M., Riveiro, B., Arias, P., Caamano, J.C., Vilan, J.A., Automatic 3D modelling of metal frame connections from LIDAR data for structural engineering purposes (2014) ISPRS J. Photogramm. Remote Sens, 96, pp. 47-56; Yin, X., Liu, H., Chen, Y., Al-Hussein, M., Building information modelling for off-site construction: Review and future directions (2019) Autom. Constr, 101, pp. 72-91; Santos, R., Costa, A.A., Silvestre, J.D., Pyl, L., Informetric analysis and review of literature on the role of BIM in sustainable construction (2019) Autom. Constr, 103, pp. 221-234; Garzia, F., Costantino, D., Baiocchi, V., Security and safety management and role of laser scanning in unique and peculiar cultural heritage sites such as the papal basilica and the sacred convent of Saint Francis in Assisi in Italy International Journal of Heritage Architecture, 2 (2), pp. 271-282; Cuartero, J., Cabaleiro, M., Sousa, H. S., Branco, J. M., Tridimensional parametric model for prediction of structural safety of existing timber roofs using laser scanner and drilling resistance tests (2019) Engineering Structures, 185, pp. 58-67; Sanchez-Aparicio, L.J., Riveiro, B., Gonzalez-Aguilera, D., Ramos, L.F., The combination of geomatic approaches and operational modal analysis to improve calibration of finite element models: A case of study in Saint Torcato church (Guimaraes, Portugal) (2014) Constr. Build. Mater, 70; Yang, L., Cheng, J. C., Wang, Q., Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data (2020) Automation in Construction, 112, p. 103037; Basta, A., Serror, M. H., Marzouk, M., A BIM-based framework for quantitative assessment of steel structure deconstructability (2020) Automation in Construction, 111, p. 103064; Laefer, D. F., Truong-Hong, L., Toward automatic generation of 3D steel structures for building information modelling (2017) Automation in Construction, 74, pp. 66-77; Donato, V., Biagini, C. G., Bertini, Marsugli, F., Challenges and opportunities for the implementation of h-bim with regards to historical infrastructures: A case study of the ponte giorgini in castiglione della pescaia (grossetoitaly) (2017) International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, p. 42; Morganti, R., Tosone, A., Di Donato, D., Abita, M., Hbim and the 20th century steel building heritage-a procedure suitable for the construction history in italy (2019) International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences; Mol, A., Cabaleiro, M., Sousa, H. S., Branco, J. M., HBIM for storing life-cycle data regarding decay and damage in existing timber structures (2020) Automation in Construction, 117, p. 103262",,"El Naggar H.Barros J.",,"Avestia Publishing","5th World Congress on Civil, Structural, and Environmental Engineering, CSEE 2020","18 October 2020 through 20 October 2020",,251919,23715294,,,,"English","World Cong. Civ., Struct., Environ. Eng.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85097250535
"Moravej H., Chan T.H.T., Nguyen K.D., Jesus A.","57188768669;7402687570;39262319400;56150440500;","Application of Gaussian process metamodel in structural finite element model updating applying dynamic measured data",2019,"9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings","1",,,"9","17",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091431419&partnerID=40&md5=1deb226d0a55ebd3166541f1370a437e","Queensland University of Technology, Australia; University of West London, United Kingdom","Moravej, H., Queensland University of Technology, Australia; Chan, T.H.T., Queensland University of Technology, Australia; Nguyen, K.D., Queensland University of Technology, Australia; Jesus, A., University of West London, United Kingdom","Civil infrastructure is vital linking component whose behavior is necessary to be monitored continuously since any fault in performance will cause significant risks. Recently, structural health monitoring (SHM) has obtained a significant contribution in preparing information related to structural behavior during functional life. Though, determining real infrastructure's behavior is intricate, since it relies on structural parameters that cannot be obtained directly from observed data and such identification is prone to uncertainties. Finite element model updating (FEMU) is an approach to address this issue. The current study employs a Modular Bayesian approach (MBA) to update a finite element model (FEM) of a lab-scaled box girder bridge applying natural frequencies. This approach is performed in two stages as undamaged and damaged. These stages can be denoted as the change in structural parameters due to incidences such as impact or fatigue effect. The performed MBA deals with uncertainties thoroughly in all steps. In this study, a discrepancy function is applied to detect the discrepancy in natural frequencies between the FEM and the experimental counterpart. A Gaussian process (GP) is used as a metamodel for the simulated model and the model discrepancy function. In this research, updating the initial FEM of the lab-scale Box Girder Bridge (BGB) by calibrating multi parameters is highlighted. Results specify a considerable drop in stiffness of concrete in damaged phase which is well matched with the cracks observed on the structure's body. Also, discrepancy records reach satisfying range in both stages which implies the structure's properties are predicted accurately. © 2019 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings. All rights reserved.",,"Bayesian networks; Box girder bridges; Gaussian distribution; Gaussian noise (electronic); Natural frequencies; Steel bridges; Structural health monitoring; Civil infrastructures; Discrepancy functions; Finite-element model updating; Model discrepancies; Structural behaviors; Structural finite elements; Structural health monitoring (SHM); Structural parameter; Finite element method",,,,,"Australian Research Council, ARC; Queensland University of Technology, QUT","The first author would like to express his sincere appreciation to Queensland University of Technology (QUT) for the financial support for his research. The support provided by Australian Research Council (ARC) is also gratefully acknowledged. Furthermore, the support provided by technical support from FEMtools is acknowledged.",,,,,,,,,,"Arendt, P.D., Apley, D.W., Chen, W., Quantification of model uncertainty: Calibration, model discrepancy, and identifiability (2012) Journal of Mechanical Design, 134 (10), p. 100908; Arendt, P.D., Apley, D.W., Chen, W., Lamb, D., Gorsich, D., Improving identifiability in model calibration using multiple responses (2012) Journal of Mechanical Design, 134 (10), p. 100909; Abaqus, F.E.A., (2017) Abaqus Inc, , Providence, Rhode Island, United States; Beck, J.L., Katafygiotis, L.S., Updating models and their uncertainties. I: Bayesian statistical framework (1998) Journal of Engineering Mechanics, 124 (4), pp. 455-461; Beck, J.L., Au, S.K., Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation (2002) Journal of engineering mechanics, 128 (4), pp. 380-391; Darmawan, M.S., Stewart, M.G., Spatial time-dependent reliability analysis of corroding pretensioned prestressed concrete bridge girders (2007) Structural Safety, 29 (1), pp. 16-31; Erdogan, Y.S., Gul, M., Catbas, F.N., Bakir, P.G., Investigation of uncertainty changes in model outputs for finite-element model updating using structural health monitoring data (2014) Journal of Structural Engineering, 140 (11), p. 04014078; FEMtools, UM, (2012) FEMtools Dynamic Design Solutions N.V. (DDS); Frangopol, D.M., Life-cycle performance, management, and optimisation of structural systems under uncertainty: accomplishments and challenges 1 (2011) Structure and Infrastructure Engineering, 7 (6), pp. 389-413; General principles on reliability for structures (AS 5104); Higdon, D., Gattiker, J., Williams, B., Rightley, M., Computer model calibration using high-dimensional output (2008) Journal of the American Statistical Association, 103 (482), pp. 570-583; Jesus, A.H., Dimitrovová, Z., Silva, M.A., A statistical analysis of the dynamic response of a railway viaduct (2014) Engineering Structures, 71, pp. 244-259; Jesus, A., Brommer, P., Zhu, Y., Laory, I., Comprehensive Bayesian structural identification using temperature variation (2017) Engineering Structures, 141, pp. 75-82; Jesus, A., Brommer, P., Westgate, R., Koo, K., Brownjohn, J., Laory, I., Bayesian structural identification of a long suspension bridge considering temperature and traffic load effects (2018) Structural Health Monitoring, p. 1475921718794299; Jin, R., Chen, W., Simpson, T.W., Comparative studies of metamodeling techniques under multiple modelling criteria (2001) Structural and multidisciplinary optimization, 23 (1), pp. 1-13; Kennedy, M.C., O'Hagan, A., Bayesian calibration of computer models (2001) Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63 (3), pp. 425-464; Kennedy, M.C., O'Hagan, A., (2001) Supplementary details on Bayesian calibration of computer, , Rap. tech., University of Nottingham. Statistics Section; Lam, H.F., Yang, J., Au, S.K., Bayesian model updating of a coupled-slab system using field test data utilizing an enhanced Markov chain Monte Carlo simulation algorithm (2015) Engineering Structures, 102, pp. 144-155; Li, H.N., Li, D.S., Ren, L., Yi, T.H., Jia, Z.G., Li, K.P., Structural health monitoring of innovative civil engineering structures in Mainland China (2016) Structural Monitoring and Maintenance, 3 (1), pp. 1-32; Lophaven, S.N., Nielsen, H.B., Søndergaard, J., DACE: a Matlab kriging toolbox (2002), 2. , IMM, Informatics and Mathematical Modelling, the Technical University of Denmark; Mirza, S.A., MacGregor, J.G., Hatzinikolas, M., Statistical descriptions of strength of concrete (1979) Journal of the Structural Division, 105 (6), pp. 1021-1037; Mirza, S.A., Kikuchi, D.K., MacGregor, J.G., Flexural strength reduction factor for bonded prestressed concrete beams (1980) Journal Proceedings, 77 (4), pp. 237-246; Moravej, H, Jamali, S, Chan, THT, Nguyen, A., Finite Element Model Updating of civil engineering infrastructures: a review (2017) International Conference on Structural Health Monitoring of Intelligent Infrastructure, , Brisbane, Australia 2017; Nishio, M., Marin, J., Fujino, Y., Uncertainty quantification of the finite element model of existing bridges for dynamic analysis (2012) Journal of Civil Structural Health Monitoring, 2 (3-4), pp. 163-173; Pathirage, TS., (2017) Identification of prestress force in prestressed concrete box girder bridges using vibration-based techniques, , Queensland University of Technology; Rasmussen, C., Williams, C., Gaussian Processes for Machine Learning (2006) Adaptive Computation and Machine Learning; Sacks, J., Welch, W.J., Mitchell, T.J., Wynn, H.P., Design and analysis of computer experiments (1989) Statistical science, pp. 409-423; Shahidi, S.G., Pakzad, S.N., Generalized response surface model updating using time domain data (2013) Journal of Structural Engineering, 140 (8), p. A4014001; Spiridonakos, M.D., Chatzi, E.N., Metamodeling of dynamic nonlinear structural systems through polynomial chaos NARX models (2015) Computers & Structures, 157, pp. 99-113; (2011), Structural Vibration Solutions A/S SVS-ARTeMIS Extractor-Release 5.3, User's manual. Aalborg-Denmark; Wan, H.P., Ren, W.X., Parameter selection in finite-element-model updating by global sensitivity analysis using Gaussian process metamodel (2014) Journal of Structural Engineering, 141 (6), p. 04014164; Weng, S., Xia, Y., Zhou, X.Q., Xu, Y.L., Zhu, H.P., Inverse substructure method for model updating of structures (2012) Journal of Sound and Vibration, 331 (25), pp. 5449-5468; Yuen, K.V., (2010) Bayesian methods for structural dynamics and civil engineering, , John Wiley & Sons","Moravej, H.; Queensland University of TechnologyAustralia; email: h.moravej@qut.edu.au","Chen G.Alampalli S.",,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019","4 August 2019 through 7 August 2019",,161240,,9780000000002,,,"English","Int. Conf. Struct. Health Monit. Intell. Infrastruct.: Transf. Res. Pract., SHMII - Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85091431419
"Abdullahi M., Oyadiji S.O.","57210173210;6701499380;","Simulation and experimental measurement of acoustic wave reflectometry for leak detection in pipes",2019,"Proceedings of SPIE - The International Society for Optical Engineering","10972",,"109721S","","",,1,"10.1117/12.2528686","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069785499&doi=10.1117%2f12.2528686&partnerID=40&md5=c6185b4c2672538c586546fb430e27ad","School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, M13 9PL, United Kingdom","Abdullahi, M., School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, M13 9PL, United Kingdom; Oyadiji, S.O., School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, M13 9PL, United Kingdom","Leakage of oil and gas pipe systems, water pipes and other pipe networks can cause serious environmental, health and economic problems. In order to minimise the damages brought to the environment, human health and the economic issues, rapid non-destructive detection of pipeline leakage is imperative. In recent works, number of non-destructive testing (NDT) methods was used in detecting this defect in pipeline systems such ultrasonic, magnetic particle inspection, pressure transient and acoustic wave methods. In this study, the acoustic wave method and a modal frequency technique are used to detect leakage in pipeline system. Finite element analysis (FEA) was employed to simulate acoustic wave propagation in fluid-filled pipes with leakage. Furthermore, experimental testing was conducted to validate some of the numerical results. The experiment performed consisted of the measurement of acoustic wave propagation in a straight fluid-filled pipe. The FEA analysis of fluidfilled pipe can be used to simulate the acoustic wave propagation and acoustic wave reflectometry of a fluid-filled pipe with leakage of different using the ACAX element in order for accurate predictions. Also, the measured signal of acoustic wave propagation in pipeline from the experiment can be decomposed and de-noised to identify and locate leakages of different sizes. © 2019 SPIE.","Acoustic Reflectometry; Acoustic Wave Propagation; FEA; Leakage Detection; NDT; Non-Destructive Testing; Time of Flight","Acoustic wave propagation; Acoustic waves; Acoustics; Biological systems; Bridge decks; Damage detection; Finite element method; Health; Leak detection; Nondestructive examination; Particle size analysis; Pipelines; Piping systems; Reflection; Reflectometers; Structural health monitoring; Water pipelines; Acoustic reflectometry; Acoustic wave method; Experimental testing; Leakage detection; Magnetic particle inspection; Non destructive testing; Nondestructive detection; Time of flight; Ultrasonic testing",,,,,"Petroleum Technology Development Fund, PTDF","This research was funded by the Petroleum Technology Development Fund (PTDF), Nigeria.",,,,,,,,,,"(2012) Abaqus 6. 10 User's Documentation, , Getting Started with Abaqus Interactive Edition; Abdullahi, M., Oyadiji, S., Acoustic wave propagation in air-filled pipes using finite element analysis (2018) Applied Sciences, 8 (8), p. 1318; Raichel, D.R., The science and applications of acoustics (2006) Springer Science and Business Media, , Second Edi. Edited by Y. Ray. New York; Jihoon, C., Joonho, S., Choonggeun, S., Suyong, H., Doo, I.I.P., Leak detection and location of water pipes using vibration sensors and modified ml prefilter (2017) Sensors, pp. 1-17; Kri, A., Barauskas, R., Ma, L., Minimization of numerical dispersion errors in 2d finite element models of short acoustic wave propagation (2016) International Conference on Information and Software Technologies, pp. 745-752; Liu, C., Li, Y., Meng, L., Wang, W., Zhao, F., Fu, J., Computational fluid dynamic simulation of pressure perturbations generation for gas pipelines leakage (2015) Computers and Fluids. Elsevier Ltd, 119, pp. 213-223; Liu, C., Li, Y., Yan, Y., Fu, J., Zhang, Y., A new leak location method based on leakage acoustic waves for oil and gas pipelines (2015) Journal of Loss Prevention in the Process Industries. Elsevier Ltd, 35, pp. 236-246; Nomura, T., Sato, S., Takagi, K., Finite element simulation of sound propagation concerning meteorological conditions (2010) International Journal for Numerical Methods in Fluids, 64 (10), pp. 1296-1318; Papadopoulou, K.A., Shamout, M.N., Lennox, B., Mackay, D., Taylor, A.R., Turner, J.T., Wang, X., An evaluation of acoustic reflectometry for leakage and blockage detection (2008) Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 222, pp. 959-966; Sharp, D.B., Campbell, D.M., Leak detection in pipes using acoustic pulse reflectometry (1997) Acta Acustica, 83, pp. 560-566; Smith, J., Peters, R., Owen, S., (1982) Acoustics and Noise Control, , https://Trove.Nla.Gov.Au/Work/151589084, New York: Longman Inc",,"Fromme P.Su Z.","OZ Optics, Ltd.;Polytec, Inc.;The Society of Photo-Optical Instrumentation Engineers (SPIE)","SPIE","Health Monitoring of Structural and Biological Systems XIII 2019","4 March 2019 through 7 March 2019",,149643,0277786X,9781510625990,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85069785499
"Xia Y., Zhang C.","57204663183;57206683994;","Bridge management integrating big data of structural health monitoring",2019,"Lecture Notes in Mechanical Engineering",,,,"745","751",,1,"10.1007/978-3-319-95711-1_73","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056647772&doi=10.1007%2f978-3-319-95711-1_73&partnerID=40&md5=1b94de15a7c297efb6a51fc8b8f020a1","Qingdao University of Technology, Shandong, China","Xia, Y., Qingdao University of Technology, Shandong, China; Zhang, C., Qingdao University of Technology, Shandong, China","In bridge condition assessment, structural health monitoring (SHM) data can reduce the level of uncertainty in the operational loadings and structural responses, which increases the reliability of the evaluation, and the effectiveness of the management activities. Therefore, it has a potential to decrease the life-cycle cost of bridges. To realize the economic benefit of the SHM systems, a bridge management system (BMS) integrating big data of SHM collected continuously from the bridges is proposed in this paper. The BMS includes four modules: Structural inventory, inspection/SHM/finite-element model (FEM), bridge condition assessment, and maintenance decision. The SHM data is utilized in module of bridge condition assessment, which derives a three-dimensional bridge condition rating system. The proposed methodology is expected to assist priority ranking of bridge management activities throughout the bridge’s life time. © Springer Nature Switzerland AG 2019.",,"Big data; Bridges; Condition based maintenance; Costs; Data integration; Information management; Life cycle; Bridge management; Bridge management system; Condition assessments; Economic benefits; Finite element modelling (FEM); Loading response; Management activities; Structural health monitoring systems; Structural response; Uncertainty; Structural health monitoring",,,,,,,,,,,,,,,,"Abe, M., Fujino, Y., (2009) Bridge Monitoring in Japan, , Encyclopedia of structural health monitoring. Wiley Online Library; Brownjohn, J.M., Structural health monitoring of civil infrastructure (2007) Philos Trans Royal Soc Lond a Math Phys Eng Sci, 365 (1851), pp. 589-622; Coles, S., (2001) An Introduction to Statistical Modeling of Extreme Values, , Springer; Ou, J.P., Li, H., Structural health monitoring research in China: Trends and applications (2009) Structural Health Monitoring of Civil Infrastructure Systems, , Woodhead Publishing, Cambridge; Pines, D., Aktan, A.E., Status of structural health monitoring of long-span bridges in the United States (2002) Prog Struct Mat Eng, 4 (4), pp. 372-380; Rakoczy, A.M., Nowak, A.S., Reliability-based strength limit state for steel railway bridges (2014) Struct Infrastruct Eng, 10 (9), pp. 1248-1261; Wong, K.Y., Instrumentation and health monitoring of cable-supported bridges (2004) Struct Control Health Monit, 11 (2), pp. 91-124; Xia, Y.X., Ni, Y.Q., Extrapolation of extreme traffic load effects on bridges based on long-term SHM data (2016) Smart Struct Syst, 17 (6), pp. 995-1015; Xia, Y.X., Ni, Y.Q., Wong, K.Y., Development of a 3D bridge rating system incorporating structural health monitoring data (2013) Proceedings of the 6Th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII) (CD-ROM), , Hong Kong, 9– 11 December; Xia, Y.X., (2017) Integration of Long-Term SHM Data into Bridge Condition Assessment, , Ph.D. thesis, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong","Xia, Y.; Qingdao University of TechnologyChina; email: xiayunxia@qut.edu.cn",,,"Pleiades journals",,,,,21954356,,,,"English","Lect. Notes Mech. Eng.",Book Chapter,"Final","",Scopus,2-s2.0-85056647772
"Chen Q., Ju B., Xi R., Meng X., Jiang W., Fan W.","57193114062;57200142497;56150036700;8702243900;26643102900;57200142020;","GNSS for real-time monitoring of bridge dynamic responses",2017,"2017 Forum on Cooperative Positioning and Service, CPGPS 2017",,,"8075121","184","189",,1,"10.1109/CPGPS.2017.8075121","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039911550&doi=10.1109%2fCPGPS.2017.8075121&partnerID=40&md5=24d4ddf96ab9d8405d1e200c273abc10","School of Geodesy and Geomatics, Wuhan University, Wuhan, China; Nottingham Geospatial Institute, University of Nottingham, Nottingham, United Kingdom; GNSS Research Center, Wuhan University, Wuhan, China","Chen, Q., School of Geodesy and Geomatics, Wuhan University, Wuhan, China, Nottingham Geospatial Institute, University of Nottingham, Nottingham, United Kingdom; Ju, B., School of Geodesy and Geomatics, Wuhan University, Wuhan, China; Xi, R., School of Geodesy and Geomatics, Wuhan University, Wuhan, China; Meng, X., Nottingham Geospatial Institute, University of Nottingham, Nottingham, United Kingdom; Jiang, W., GNSS Research Center, Wuhan University, Wuhan, China; Fan, W., GNSS Research Center, Wuhan University, Wuhan, China","Global Navigation Satellite Systems (GNSS) Realtime Kinematic (RTK) positioning technique has been widely used for the structural health monitoring (SHM) of different structures in the past two decades. Through post processing and analysis, it has been demonstrated that the displacements and natural frequencies identified with GNSS data are highly consistent with those obtained by using a finite element (FE) model. However, structural health monitoring needs to measure all spectrum of the dynamic responses of bridges such as deformations, natural frequencies, damping, etc. in real-time in order to support the timely decision making for the bridge operation and maintenance, particularly under extreme loading conditions caused by busy traffic, severe wind or even earthquake etc. This paper proposes a new quasi real time time-frequency analysis strategy based on the Fast Fourier Transform (FFT). With the support of the European Space Agency (ESA) and the University of Nottingham in the UK, one week of real-life GNSS data gathered from the Forth Road Bridge in Scotland has been used since the traffic loading has an approximate repetition period of one week from the weekdays to weekend. Firstly, the approximate frequency distribution is achieved by using the whole date set. Then a sliding window method is proposed to simulate a quasi real time mode for the time-frequency analysis, and a set of experiments are carried out to decide the optimal window length and the overlapped sliding step, through which the natural frequencies and relevant deformation amplitudes can be calculated at the same time. Finally, the results show that the natural frequencies calculated by FFT are quite stable which indicates the frequency responses are not sensitive enough to the changing loadings. However, the relevant amplitude time series of each frequency can clearly display the influence caused by different kinds of loading respectively, such as vehicles and wind etc., which would be a reliable indicator of bridge dynamic responses to assess the structural health conditions in the future. © 2017 IEEE.","Fast Fourier Transform; GNSS; Structural Dynamocs; Structural Health Monitoring","Bridges; Decision making; Deformation; Dynamic response; Fast Fourier transforms; Finite element method; Frequency response; Global positioning system; Natural frequencies; Space flight; Structural analysis; Structural health monitoring; Traffic surveys; Frequency distributions; Global Navigation Satellite Systems; GNSS; Operation and maintenance; Real-time kinematic positioning; Structural Dynamocs; Structural health monitoring (SHM); University of Nottingham; Loading",,,,,,,,,,,,,,,,"Ashkenazi, V., Dodson, A.H., Real time OTF GPS monitoring of the Humber Bridge (1996) Surveying World, 4 (4), pp. 26-28; Brown, C.J., Karuna, R., Monitoring of structures using the Global Positioning System (1999) Proceedings of the Institution of Civil Engineers. Structures and Buildings, 134 (1), pp. 97-105; Celebi, M., Sanli, A., GPS in pioneering dynamic monitoring of long-period structures (2002) Earthquake Spectra, 18 (1), pp. 47-61; Chan, W., Xu, Y., Assessment of dynamic measurement accuracy of GPS in three directions (2006) Journal of Surveying Engineering, 132 (3), pp. 108-117; Curry, S., Griffioen, P., Real-time kinematic GPS for surveying: Centimeters in seconds (1993) ACSM ASPRS ANNUAL CONVENTION, American SOC PHOTOGRAMMETRY & REMOTE SENSING+ AMER CONG on; Guo, J., Xu, L., Application of the real-time kinematic global positioning system in bridge safety monitoring (2005) Journal of Bridge Engineering, 10 (2), pp. 163-168; Hofmann-Wellenhof, B., Lichtenegger, H., (2012) Global Positioning System: Theory and Practice, , Springer Science & Business Media; Ko, J.M., Ni, Y.Q., Technology developments in structural health monitoring of large-scale bridges (2005) Engineering Structures, 27 (12), pp. 1715-1725; Lovse, J.W., Teskey, W.F., Dynamic deformation monitoring of tall structure using GPS technology (1995) Journal of Surveying Engineering; Meng, X., (2002) Real-time Deformation Monitoring of Bridges Using GPS/accelerometers, , University of Nottingham; Meng, X., Dodson, A., Hybrid sensor system for bridge deformation monitoring: Interfacing with structural engineers (2005) A Window on the Future of Geodesy, pp. 89-94. , Springer; Meng, X., Dodson, A.H., Detecting bridge dynamics with GPS and triaxial accelerometers (2007) Engineering Structures, 29 (11), pp. 3178-3184; Meng, X., Roberts, G.W., Impact of GPS satellite and pseudolite geometry on structural deformation monitoring: Analytical and empirical studies (2004) Journal of Geodesy, 77 (12), pp. 809-822; Roberts, G.W., Meng, X., Integrating a global positioning system and accelerometers to monitor the deflection of bridges (2004) Journal of Surveying Engineering, 130 (2), pp. 65-72; Sumitro, S., Wang, M.L., Sustainable structural health monitoring system (2005) Structural Control and Health Monitoring, 12 (34), pp. 445-467; Xu, L., Guo, J.J., Timefrequency analysis of a suspension bridge based on GPS (2002) Journal of Sound and Vibration, 254 (1), pp. 105-116; Yang, J., Li, J.B., A simple approach to integration of acceleration data for dynamic soil-structure interaction analysis (2006) Soil Dynamics and Earthquake Engineering, 26 (8), pp. 725-734; Yi, T.H., Li, H.N., Recent research and applications of GPS-based monitoring technology for high rise structures (2013) Structural Control and Health Monitoring, 20 (5), pp. 649-670",,,"","Institute of Electrical and Electronics Engineers Inc.","2017 Forum on Cooperative Positioning and Service, CPGPS 2017","19 May 2017 through 21 May 2017",,131222,,9781509050222,,,"English","Forum Cooperative Position. Serv., CPGPS",Conference Paper,"Final","",Scopus,2-s2.0-85039911550
"Kwad J., Kripakaran P.","57194651377;22980560500;","Fatigue assessment in steel bridges: Integrating field measurements and numerical modelling to compute hot spot stresses",2017,"SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings",,,,"894","903",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050138178&partnerID=40&md5=e672b6a854ee0fb715b461d8e0f93084","University of Exeter, College of Engineering, Mathematics and Physical Sciences, Vibration Engineering Section, North Park Road, EX4 4QF,+44 (0)1392, Exeter, 72 6421, United Kingdom; Civil Engineering Department, University of Anbar, Ramadi, Anbar, Iraq","Kwad, J., University of Exeter, College of Engineering, Mathematics and Physical Sciences, Vibration Engineering Section, North Park Road, EX4 4QF,+44 (0)1392, Exeter, 72 6421, United Kingdom, Civil Engineering Department, University of Anbar, Ramadi, Anbar, Iraq; Kripakaran, P., University of Exeter, College of Engineering, Mathematics and Physical Sciences, Vibration Engineering Section, North Park Road, EX4 4QF,+44 (0)1392, Exeter, 72 6421, United Kingdom","The hot spot stress approach is often employed to evaluate the fatigue strength of welded structures in cases where the nominal stress is difficult to estimate reliably because of geometric and/or loading complexities. Its predictions are mostly conservative since the nominal stresses are estimated for code-specified loadings that are much larger than the loads experienced by a bridge. This paper presents a novel methodology to evaluate the hot spot stress at a weld detail in a fatigue critical connection using strain data measured further away from areas of significant stress concentration with a finite element model (FEM) of the connection. Strains measured at various points around the physical connection are used to compute the forces and moments applied at the connection. These forces are then applied to a numerical model of the connection to predict the stresses at the detail. The methodology is investigated with a full-scale case study: The Bascule Bridge in Exeter (UK). The stress time-history produced using the proposed approach with in-situ strain measurements is compared to that generated using the modified hot spot stress approach. Results show that the predicted stress time-history is more accurate and reliable, and hence promises to be more appropriate for fatigue evaluation. © 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved.",,"Joints (structural components); Numerical models; Strain; Stress analysis; Structural health monitoring; Welding; Critical connections; Fatigue assessments; Fatigue evaluation; Field measurement; Hot spot stress approach; Novel methodology; Physical connections; Welded structures; Fatigue of materials",,,,,"Higher Committee for Education Development in Iraq, HCED: D11000218","The first author would like to acknowledge the financial support of The Higher Committee for Education Development in Iraq (HCED IRAQ) scholarship reference D11000218. The authors would also like to acknowledge the Vibration Engineering Section (VES) at the University of Exeter for providing the NI equipment used in this investigation and the on-site support given by VES research team. The authors would like to thank the Bridges and Structures team at Devon County Council for sharing data and providing access to the Bascule Bridge.",,,,,,,,,,"(2005) Standard Practices for Cycle Counting in Fatigue Analysis 1, 85, pp. 1-10. , ASTM E1049 85, ASTM International, West Conshohocken, PA, Reapproved 2011); Aygul, M., (2012) Fatigue Analysis of Welded Structures Using The Finite Element Method, pp. 1-56; Aygül, M., Al-Emrani, M., Urushadze, S., Modelling and fatigue life assessment of orthotropic bridge deck details using FEM (2012) International Journal of Fatigue, 40, pp. 129-142; UK national annex to eurocode 1. Actions on structures. Traffic loads on bridges Fatigue Reliability Analysis for The Crack Propagation Compared with LRFD Specification, 10 (1), pp. 35-49. , BS NA EN 1991-2 2003 English Cho, T. et al., 2010; (2005) Eurocode 3: Design of Steel Structures - Part 1-9: Fatigue Fatigue Strength of Steel Structures, , EN 1993-1-9, European Committee for Standardization, Brussels; Fricke, W., Fatigue analysis of welded joints: State of development (2003) Marine Structures, 16, pp. 185-200; Garg, V.K., Chu, K.H., Wiriyachai, A., Fatigue life of critical members in a railway truss bridge (1982) Earthquake Engineering and Structural Dynamics, 10, pp. 779-795; Hobbacher, A.F., The new IIW recommendations for fatigue assessment of welded joints and components - A comprehensive code recently updated (2009) International Journal of Fatigue, 31 (1), pp. 50-58; Kwad, J., Alencar, G., Correia, J., Jesus, A., Calçada, R., Kripakaran, P., Fatigue assessment of an existing steel bridge by finite element modelling and field measurements (2017) Unpublished Conference Paper at: 6th International Conference on Fracture Fatigue and Wear, , 26-27 July 2017, Porto, Portugal; Kwon, K., Probabilistic fatigue life estimation of steel bridges by using a bilinear s - n approach (2012) Journal of Bridge Engineering (ASCE), 17 (1), pp. 58-70; Liu, R., Hot spot stress analysis on rib-deck welded joint in orthotropic steel decks (2014) Journal of Constructional Steel Research, 97, pp. 1-9. , http://dx.doi.org/10.1016/j.jcsr.2014.01.012; Miner, M., Miner cumulative damage in fatigue.pdf (1945) Journal of Applied Mechanics, 12 (3), pp. A159-A164; Ni, Y.Q., Ye, X.W., Ko, J.M., Monitoring-based fatigue reliability assessment of steel bridges: Analytical model and application (2010) Journal of Structural Engineering, 136 (12), pp. 1563-1573; Niemi, E., Fricke, W., Maddox, S.J., (2006) Fatigue Analysis of Welded Components: Designer'S Guide to The Structural Hot-Spot Stress Approach [M], , Woodhead Pub; Park, J.Y., Kim, H., (2014) Fatigue Life Assessment for A Composite Box Girder Bridge, 14 (4), pp. 843-853; Pasquier, R., D'Angelo, L., Goulet, J.-A., Acevedo, C., Nussbaumer, A., Smith, I.F.C., Measurement, data interpretation and uncertainty propagation for fatigue assessments of structures (2016) Journal of Structural Engineering; Saini, D.S., Karmakar, D., Ray-Chaudhuri, S., A review of stress concentration factors in tubular and non-tubular joints for design of offshore installations (2016) Journal of Ocean Engineering and Science, 1 (3), pp. 186-202; Schumacher, A., (2003) Fatigue Behaviour of Welded Circular Hollow Section Joints in Bridges, , PhD thesis EPFL n°2727, Swiss Federal Institute of Technology (EPFL), Lausanne; Schumacher, A., Nussbaumer, A., Experimental study on the fatigue behaviour of welded tubular K-joints for bridges (2006) Engineering Structures, 28 (5), pp. 745-755; Tveiten, B.W.T., Fatigue assessment of aluminum ship details by hot-spot stress approach (2007) ABS Technical Papers, 135, pp. 13-17. , M, X.W. & S.B, November); Yang, M., Fatigue behavior and strength evaluation of vertical stiffener welded joint in orthotropic steel decks (2016) Engineering Failure Analysis, 70, pp. 222-236. , http://dx.doi.org/10.1016/j.engfailanal.2016.05.001; Ye, X.W., Ni, Y.Q., Ko, J.M., Experimental evaluation of stress concentration factor of welded steel bridge T-joints (2012) Journal of Constructional Steel Research, 70, pp. 78-85; Zamiri Akhlaghi, F., (2009) Fatigue Life Assessment of Welded Bridge Details Using Structural Hot Spot Stress Method, A Numerical and Experimental Case Study, , Master's thesis. Chalmers University of technology; Zhou, T.Q., Chan, T.H.T., Hot spot stress analysis of fatigue for Tsing Ma Bridge critical members under traffic using finite element method (2007) Progresses in Fracture and Strength of Materials and Structures, 1-4 (3), pp. 925-928. , 353-358 <Go to; Zhao, X.L., Packer, J.A., Iiw (2000) Fatigue Design Procedure for Welded Hollow Section Joints, XIII-, pp. 99-1035. , ternational Institute of Welding. Doc. 1804-99",,"Mahini S.Mahini S.Chan T.","Geomotion;Mainmark;Queensland Government;Worldsensing","International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2017","5 December 2017 through 8 December 2017",,137293,,9781925553055,,,"English","SHMII - Int. Conf. Struct. Health Monit. Intell. Infrastruct., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85050138178
"Nguyen V.V., Li J., Yu Y., Dackermann U., Alamdari M.M.","56785028500;7410077355;56430081600;29667491700;55814161700;","Simulation of various damage scenarios using finite element modelling for structural health monitoring systems",2017,"Mechanics of Structures and Materials: Advancements and Challenges - Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM24 2016",,,,"1541","1546",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036459502&partnerID=40&md5=1729f8c87c227af846cae537b40b9d24","Centre for Built Infrastructure Research, University of Technology Sydney, Sydney, Australia; Smart Infrastructure, Data61, CSIRO, Sydney, Australia","Nguyen, V.V., Centre for Built Infrastructure Research, University of Technology Sydney, Sydney, Australia; Li, J., Centre for Built Infrastructure Research, University of Technology Sydney, Sydney, Australia; Yu, Y., Centre for Built Infrastructure Research, University of Technology Sydney, Sydney, Australia; Dackermann, U., Centre for Built Infrastructure Research, University of Technology Sydney, Sydney, Australia; Alamdari, M.M., Smart Infrastructure, Data61, CSIRO, Sydney, Australia","Structural Health Monitoring (SHM) is a developing technology for asset management of structures including bridge assets. A crucial benefit of SHM is its ability to monitor the health status of structures using continuous measurements. As a key in SHM, the application of damage detection algorithms to assess the condition of a structure using vibration measurements can be enhanced by providing structural information under various damaged scenarios, which can be obtained from updated numerical models that realistically represent the in-situ structure. However, the dynamic characteristics of a structure are sensitive to uncertainties of various parameters, including material properties and boundary conditions, which require updating in the Finite Element (FE) model to ensure that the model replicates the actual structure. This study focuses on the development of an FE model for the accurate simulation of a jack arch replica structure of the Sydney Harbour Bridge. An experimental jack arch replica is produced to simulate various damage scenarios for laboratory testing. A matching FE model of the jack arch replica is generated and updated using Genetic Algorithm (GA) based on experimental measurements. Damage is simulated in the updated model and the results are validated using the experimental test results. The successful simulation of damage using updated FE models enables the generation of a large number of damage cases that can be trained into an SHM system to improve its damage detection capabilities. © 2017 Taylor & Francis Group, London.",,"Arch bridges; Arches; Damage detection; Finite element method; Genetic algorithms; Mechanics; Continuous measurements; Detection capability; Dynamic characteristics; Finite element modelling; Management of structures; Structural health monitoring (SHM); Structural health monitoring systems; Structural information; Structural health monitoring",,,,,,,,,,,,,,,,"Brehm, M., Zabel, V., Bucher, C., An automatic mode pairing strategy using an enhanced modal assurance criterion based on modal strain energies (2010) Journal of Sound and Vibration, 329, pp. 5375-5392; Friswell, M.I., Mottershead, J.E., (1995) Finite Element Model Updating in Structural Dynamics, , Swansea: Springer; Hsieh, K.H., Halling, M.W., Barr, P.J., Overview of vibrational structural health monitoring with representative case studies (2006) Journal of Bridge Engineering, 11, pp. 707-715; Ko, J.M., Ni, Y.Q., Technology developments in structural health monitoring of large-scale bridges (2005) Engineering Structures, 27 (12), pp. 1715-1725; Kotze, R., Ngo, H., Seskis, J., (2015) Improved Bridge Deterioration Models, Predictive Tools and Costs, , Sydney: Austroads; Marwala, T., (2010) Finite-Element-Model Updating Using Computational Intelligence Techniques, , London: Springer; Montgomery, P.L., A Block Lanczos algorithm for finding dependencies over GF(2) (1995) Lecture Notes in Computer Science, 921, pp. 106-120; Mustapha, S., Pattern recognition based on time series analysis using vibration data for structural health monitoring in civil structures (2015) Electronic Journal of Structural Engineering, 14 (1), pp. 106-115; Nguyen, K., Freytag, B., Ralbovsky, M., Rio, O., Assessment of serviceability limit stage of vibrations in the UHPFRC-Wild bridge through an updated FEM using vehicle-bridge interation (2015) Computers & Structures, pp. 29-41; Salawu, O.S., Detection of structural damage through changes in frequency: A review (1997) Engineering Structures, 19 (9), pp. 718-723",,"Hao H.Zhang C.","","CRC Press/Balkem","24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM 2016","6 December 2016 through 9 December 2016",,191339,,9781138029934,,,"English","Mech. Struct. Mater.: Adv Chall. - Proc. Aust. Conf. Mech. Struct. Mater.",Conference Paper,"Final","",Scopus,2-s2.0-85036459502
"Xia Q., Wu W.-L., Li F.-N., Zhou X.-Q., Xu Y.-L., Xia Y.","56565997900;57222992316;57920600200;57203870911;55695003100;8673901000;","Temperature behaviors of an arch bridge through integration of field monitoring and unified numerical simulation",2022,"Advances in Structural Engineering","25","16",,"3492","3509",,,"10.1177/13694332221130797","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139484029&doi=10.1177%2f13694332221130797&partnerID=40&md5=0d3454593a1a609875a509f019714b56","School of Civil Engineering, Southeast University, Nanjing, China; Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong; School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, China; College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China","Xia, Q., School of Civil Engineering, Southeast University, Nanjing, China; Wu, W.-L., Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong; Li, F.-N., School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, China; Zhou, X.-Q., College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China; Xu, Y.-L., Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong; Xia, Y., Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong","Varying temperature may cause a non-uniform temperature distribution of a bridge and lead to excessive movement and stresses of the structure. Traditional thermal analyses of bridges adopt a divide-and-conquer approach, which conducts a simplified 2D or local 3D heat-transfer analysis and then a global 3D structural analysis by inputting the calculated temperature into another bridge model. This process requires considerable manual intervention and is inefficient and may lead to inaccurate results. This study develops a unified approach of heat-transfer and structural analyses for the first time to calculate the temperature distribution and the associated responses of an entire structure by integrating the field monitoring data. The arch footbridge at the Hong Kong Polytechnic University is used as a testbed, and a detailed finite element model (FEM) of the bridge is established. The measured air temperature and solar radiation are used as the thermal boundary conditions. The hemisphere technique is adopted to calculate the view factor between different surfaces of the bridge, which are then used to obtain the solar radiation on all external surfaces in different instants on different dates. The 3D global hear-transfer analysis is conducted to obtain the temperature distribution of the entire bridge. The calculated temperature data of the bridge are then automatically input into the same FEM of the bridge to calculate the temperature-induced bridge responses via the structural analysis. The heat-transfer analysis and structural analysis share the same FEM while using different element types. Therefore, the manual intervention is avoided. The calculated and monitored temperature data and responses show a good agreement. The developed new unified approach enables an automatic and efficient analysis of thermal behaviors of bridges. This approach can be extended to other types of bridges. © The Author(s) 2022.","arch bridge; heat-transfer analysis; structural health monitoring; temperature behaviors; temperature distribution","Arch bridges; Arches; Heat transfer; Solar radiation; Structural health monitoring; Thermoanalysis; Field monitoring; Finite element modelling (FEM); Heat transfer analysis; Manual intervention; Nonuniform temperature; Temperature behavior; Temperature data; Thermal; Unified approach; Varying temperature; Temperature distribution",,,,,"JSSCBS20210129; 1-ZVFH, 1-ZVJN, PolyU 152125/17E; Natural Science Foundation of Jiangsu Province: BK20220852; Fundamental Research Funds for the Central Universities: 3205002208A1","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by RGC-GRF (Project No. PolyU 152125/17E), PolyU Strategic Development Special Projects (Project Nos. 1-ZVFH and 1-ZVJN), the Jiangsu Provincial Double-Innovation Doctoral Program (Grant No. JSSCBS20210129), the Natural Science Foundation of Jiangsu Province (Grant No. BK20220852), and the Fundamental Research Funds for the Central Universities (Grant No.3205002208A1).",,,,,,,,,,"[Computer Software], , Southpointe, PA, ANSYS; Al-Mostafa, Z.A., Maghrabi, A.H., Al-Shehri, S.M., Sunshine-based global radiation models: a review and case study (2014) Energy Conversion and Management, 84, pp. 209-216; Branco, F.A., Mendes, P.A., Thermal actions for concrete bridge design (1993) Journal of Structural Engineering, 119 (8), pp. 2313-2331; Defraeye, T., Blocken, B., Carmeliet, J., Convective heat transfer coefficients for exterior building surfaces: existing correlations and CFD modelling (2011) Energy Conversion and Management, 52 (1), pp. 512-522; Deraemaeker, A., Reynders, E., De Roeck, G., Vibration-based structural health monitoring using output-only measurements under changing environment (2008) Mechanical Systems and Signal Processing, 22 (1), pp. 34-56; Elbadry, M.M., Ghali, A., Temperature variations in concrete bridges (1983) Journal of Structural Engineering, 109 (10), pp. 2355-2374; Emanuel, J.H., Hulsey, J.L., Temperature distributions in composite bridges (1978) Journal of the Structural Division, 104 (1), pp. 65-78; Fu, H., Ng, S., Cheung, M.S., Thermal behavior of composite bridges (1990) Journal of Structural Engineering, 116 (12), pp. 3302-3323; Gupta, M.K., Bumtariya, K.J., Shukla, H.A., Methods for evaluation of radiation view factor: a review (2017) Materials Today Proceedings, 4 (2), pp. 1236-1243; Han, N., Zhao, W., Zhang, B., Performance assessment of railway multispan steel truss bridge bearing by thermal excitation (2022) Journal of Civil Structural Health Monitoring, 12, pp. 163-178; Hios, J.D., Fassois, S.D., A global statistical model based approach for vibration response-only damage detection under various temperatures: a proof-of-concept study (2014) Mechanical Systems and Signal Processing, 49 (1-2), pp. 77-94; Hou, R.R., Wang, X.Y., Xia, Q., Sparse Bayesian learning for structural damage detection under varying temperature conditions (2020) Mechanical Systems and Signal Processing, 145, p. 106965; John, H., Lienhard, I., Lienhard, V., (2006) A Heat Transfer Textbook, , Cambridge, MA; Mirambell, E., Aguado, A., Temperature and stress distributions in concrete box girder bridges (1990) Journal of Structural Engineering, 116 (9), pp. 2388-2409; Moorty, S., Roeder, C.W., Temperature-dependent bridge movements (1992) Journal of Structural Engineering, 118 (4), pp. 1090-1105; Nayeri, R.D., Masri, S.F., Ghanem, R.G., A novel approach for the structural identification and monitoring of a full-scale 17-story building based on ambient vibration measurements (2008) Smart Materials and Structures, 17 (2), pp. 1-19; Peeters, B., Roeck, G.D., One year monitoring of the Z24-bridge: environmental effects versus damage events (2001) Earthquake Engineering & Structural Dynamics, 30 (2), pp. 149-171; Sohn, H., Dzwonczyk, M., Straser, E.G., An experimental study of temperature effect on modal parameters of the Alamosa Canyon bridge (1999) Earthquake Engineering & Structural Dynamics, 28 (8), pp. 879-897; Tan, X., Chen, W., Wang, L., The impact of uneven temperature distribution on stability of concrete structures using data analysis and numerical approach (2021) Advances in Structural Engineering, 24 (2), pp. 279-290; Wang, X.Y., Li, L.F., Beck, J.L., Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditions (2021) Mechanical Systems and Signal Processing, 154, p. 107563; Wang, G.X., Shao, J.S., Xu, W.Z., Real-time quantitative evaluation on the cable damage of cable-stayed bridges using the correlation between girder deflection and temperature (2022) Structural Health Monitoring, 21 (4), pp. 1483-1500; Westgate, R., Koo, K.Y., Brownjohn, J.M.W., Effect of solar radiation on suspension bridge performance (2015) Journal of Bridge Engineering, 20 (5), p. 04014077; Whitaker, S., (2013) Fundamental Principles of Heat Transfer, , Elsevier; Xia, Q., Zhang, J., Tian, Y.D., Experimental study of thermal effects on a long-span suspension bridge (2017) Journal of Bridge Engineering, 22 (7), p. 04017034; Xia, Q., Xia, Y., Wan, H.P., Condition analysis of expansion joints of a long-span suspension bridge through metamodel based model updating considering thermal effect (2020) Structural Control and Health Monitoring, 27 (5), pp. 1-16; Xia, Q., Zhou, L.M., Zhang, J., Thermal performance analysis of a long-span suspension bridge with long-term monitoring data (2019) Journal of Civil Structural Health Monitoring, 8, pp. 543-553; Xia, Q., Wu, W.L., Li, F.N., System design and demonstration of performance monitoring of a butterfly-shaped arch footbridge (2021) Structural Control and Health Monitoring, 28 (7), pp. 1-21; Xia, Y., Chen, B., Weng, S., Temperature effect on vibration properties of civil structures: a review and case studies (2012) Journal of Civil Structural Health Monitoring, 2 (1), pp. 29-46; Xia, Y., Chen, B., Zhou, X.Q., Field monitoring and numerical analysis of tsing ma suspension bridge temperature behavior (2013) Structural Control and Health Monitoring, 20 (4), pp. 560-575; Xu, Y.L., Chen, B., Ng, C.L., Monitoring temperature effect on a long suspension bridge (2010) Structural Control and Health Monitoring, 17 (6), pp. 632-653; Yan, A.M., Kerschen, G., De Boe, P., Structural damage diagnosis under changing environmental conditions - part 1: linear analysis (2005) Mechanical Systems and Signal Processing, 19 (4), pp. 847-864; Yang, D.H., Yi, T.H., Li, H.N., Correlation-based estimation method for cable-stayed bridge girder deflection variability under thermal action (2018) Journal of Performance of Constructed Facilities, 32 (5), p. 04018070; Zhang, F., Shen, J., Liu, J., Effect of encased concrete on section temperature gradient of corrugated steel web box girder (2021) Advances in Structural Engineering, 24 (11), pp. 2321-2335. , (, a; Zhang, H., Liu, Y., Deng, Y., Temperature gradient modeling of a steel box-girder suspension bridge using Copulas probabilistic method and field monitoring (2021) Advances in Structural Engineering, 24 (5), pp. 947-961. , (, b; Zhao, H.W., Ding, Y.L., Nagarajaiah, S., Behavior analysis and early warning of girder deflections of a steel-truss arch railway bridge under the effects of temperature and trains: case study (2019) Journal of Bridge Engineering, 24 (1), p. 05018013; Zhou, G.D., Yi, T.H., Thermal load large-scale bridges: a state-of-the-art review (2013) International Journal of Distributed Sensor Networks, 9 (12), pp. 1-17; Zhou, L.R., Xia, Y., Brownjohn, J.M.W., Temperature analysis of a long-Span suspension bridge based on field monitoring and numerical simulation (2016) Journal of Bridge Engineering, 21 (1), p. 04015027; Zhou, Y., Xia, Y., Sun, Z., Analytical formulation of the temperature-induced deformation of multi-span suspension bridges (2022) Structural Control and Health Monitoring, 29 (6), p. e2937; Zhu, J.S., Meng, Q.L., Effective and fine analysis for temperature effect of bridges in natural environments (2017) Journal of Bridge Engineering, 22 (6), pp. 1-19; Zhu, Q.X., Wang, H., Xu, Z.D., Mapping temperature contours for a long-span steel truss arch bridge based on field monitoring data (2021) Journal of Civil Structural Health Monitoring, 11, pp. 725-743","Xia, Y.; Department of Civil and Environmental Engineering, Hong Kong; email: ceyxia@polyu.edu.hk",,,"SAGE Publications Inc.",,,,,13694332,,ASEDD,,"English","Adv. Struct. Eng.",Article,"Final","",Scopus,2-s2.0-85139484029
"Han X., Frangopol D.M.","57209237266;7103084807;","Fatigue reliability analysis considering corrosion effects and integrating SHM information",2022,"Engineering Structures","272",,"114967","","",,,"10.1016/j.engstruct.2022.114967","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139033315&doi=10.1016%2fj.engstruct.2022.114967&partnerID=40&md5=5a0b30b82a04de6157c6de22605f1d03","Dept. of Civil and Environmental Engineering, ATLSS Engineering Research Center, Lehigh Univ, 117 ATLSS Dr, Bethlehem, PA  18015, United States; Professor and the Fazlur R. Khan Endowed Chair of Structural Engineering and Architecture, Dept. of Civil and Environmental Engineering, ATLSS Engineering Research Center, Lehigh Univ, 117 ATLSS Dr, Bethlehem, PA  18015, United States","Han, X., Dept. of Civil and Environmental Engineering, ATLSS Engineering Research Center, Lehigh Univ, 117 ATLSS Dr, Bethlehem, PA  18015, United States; Frangopol, D.M., Professor and the Fazlur R. Khan Endowed Chair of Structural Engineering and Architecture, Dept. of Civil and Environmental Engineering, ATLSS Engineering Research Center, Lehigh Univ, 117 ATLSS Dr, Bethlehem, PA  18015, United States","Fatigue cracking is a common problem facing steel bridges. S-N curves have been adopted in design specifications to offer guidance on fatigue design of these bridges. Structural health monitoring (SHM) techniques can provide reliable information on stress states at fatigue-prone details using strain sensors, thereby circumventing complex theoretical analyses. When corrosion is present, simply relying on SHM information obtained through the monitoring period may not be sufficient to reliably estimate the damage incurred by corrosion-enhanced fatigue as corrosion is a long-term process and the time-variant effect of corrosion on fatigue needs to be determined. In this paper, time-variant analysis is used to estimate the reliability of fatigue-prone details at the coped region of a transverse diaphragm in a tied-arch bridge. Stress histograms obtained based on SHM information associated with this case study are utilized to establish the distribution of the original effective stress. Finite element analysis is conducted for (a) determining the spatial adjustment factor (SAF) to adjust the stress range obtained at the location of strain sensors, and (b) assessing the effect of corrosion on the effective stress range. The reliability analysis results show that for the fatigue details investigated in this case study, corrosion has a significant effect on the fatigue reliability and, therefore, should be considered in the life-cycle management of fatigue-prone details. © 2022 Elsevier Ltd","Corrosion; Fatigue; Finite element analysis; Reliability; SHM","Arch bridges; Corrosion fatigue; Corrosive effects; Life cycle; Reliability analysis; Structural health monitoring; Case-studies; Corrosion effects; Effective stress; Fatigue cracking; Fatigue reliability; Finite element analyse; Monitoring information; Strain sensors; Stress range; Time variant; Finite element method; bridge; corrosion; cracking (fracture); effective stress; fatigue; finite element method; health monitoring; reliability analysis; strain analysis",,,,,,,,,,,,,,,,"Committee, A.S.C.E., Fatigue reliability: Introduction (1982) J Struct Div, 108, pp. 3-23; Miner, M.A., Cumulative fatigue damage (1945) J Appl Mech, 12, pp. A159-A164; (2017), AASHTO. 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Proc 10th Int Conf Appl Stat Probab Civ Eng, New York: Taylor & Francis;; Righiniotis, T.D., Imam, B.M., Chryssanthopoulos, M.K., Fatigue analysis of riveted railway bridge connections using the theory of critical distances (2008) Eng Struct, 30 (10), pp. 2707-2715; Imam, B.M., Righiniotis, T.D., Fatigue evaluation of riveted railway bridges through global and local analysis (2010) J Constr Steel Res, 66 (11), pp. 1411-1421; Cousins, T.E., Stallings, J.M., Lower, D.A., Stafford, T.E., Field evaluation of fatigue cracking in diaphragm-girder connections (1998) J Perform Constr Facil, 12 (1), pp. 25-32; Connor, R.J., Fisher, J.W., Hodgson, I.C., Bowman, C.A., (2004), ATLSS Report No. 04-04: Results of Field Monitoring Prototype Floorbeam Connection Retrofit Details on the Birmingham Bridge. 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Washington, D.C.: Federal Highway Administration;; Decò, A., Frangopol, D.M., Life-cycle risk assessment of spatially distributed aging bridges under seismic and traffic hazards (2013) Earthq Spectra, 29 (1), pp. 127-153; Akiyama, M., Frangopol, D.M., Ishibashi, H., Toward life-cycle reliability-, risk-, and resilience-based design and assessment of bridges and bridge networks under independent and interacting hazards: Emphasis on earthquake, tsunami and corrosion (2020) Struct Infrastruct Eng, 16 (1), pp. 26-50","Frangopol, D.M.; Professor and the Fazlur R. Khan Endowed Chair of Structural Engineering and Architecture, 117 ATLSS Dr, United States; email: dan.frangopol@lehigh.edu",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","",Scopus,2-s2.0-85139033315
"Nguyen D.C., Salamak M., Katunin A., Gerges M.","57982334400;25028351300;38661200300;57193212502;","Finite Element Model Updating of RC Bridge Structure with Static Load Testing: A Case Study of Vietnamese ThiThac Bridge in Coastal and Marine Environment",2022,"Sensors","22","22","8884","","",,,"10.3390/s22228884","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142752735&doi=10.3390%2fs22228884&partnerID=40&md5=a3c450b36576354d017b4e07410e15a7","Department of Mechanics and Bridges, Faculty of Civil Engineering, Silesian University of Technology, Akademicka 5, Gliwice, 44-100, Poland; Department of Fundamentals of Machinery Design, Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, Gliwice, 44-100, Poland; Faculty of Science and Engineering, University of Wolverhampton, Alan Turing Building, Wulfruna Street, Wolverhampton, WV1 1LY, United Kingdom","Nguyen, D.C., Department of Mechanics and Bridges, Faculty of Civil Engineering, Silesian University of Technology, Akademicka 5, Gliwice, 44-100, Poland; Salamak, M., Department of Mechanics and Bridges, Faculty of Civil Engineering, Silesian University of Technology, Akademicka 5, Gliwice, 44-100, Poland; Katunin, A., Department of Fundamentals of Machinery Design, Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, Gliwice, 44-100, Poland; Gerges, M., Faculty of Science and Engineering, University of Wolverhampton, Alan Turing Building, Wulfruna Street, Wolverhampton, WV1 1LY, United Kingdom","Diagnostic load testing refers to the use of the measured historical responses of the structure in the field data to better understand its dynamic and static structural behaviours. It is important and necessary to predict the health state, load capacity, and aging of the structure by updating the finite element (FE) model, which can give useful information to aid the design of retrofits and the maintenance of the existing bridge in the future. The paper presents an update of the full-scale FE model for the reinforced concrete (RC) bridge structure over the seawater river based on the experimental strains under the static load testing in which the representative FE model of the actual structure is determined from the optimisation procedures. The optimisation variables are applied, including the cross-sectional properties and concrete material calibrated through the genetic algorithm (GA) optimisation in the MATLAB software, which interfaces with the FE modelling in the scripting of the SOFISTIK TEDDY software automatically. The bending moments at the mid-span of the RC girders are determined in the FE modelling to compute stresses, which are compared with the measured stresses through optimisation scenarios with a percentage error of the objective function less than 10%. The measured data of concrete strains are recorded from reusable strain transducers installed on the mid-span girders for every bridge span, which are used to calibrate the bridge model in static load testing. The novelty of the solution is to implement innovative techniques using field data as an improved approach for calibrating automatically the analytical FE model parameters of all RC spans of the bridge until its static behaviours are very similar to those of the actual bridge. The final updated FE modelling is used to apply truck load configurations according to bridge design standards such as the AASHTO specifications, which can predict the load limits of the existing bridge structure more accurately and reliably. These proposed approaches can be applied to large bridges as well as complex structures with supporting FE analysis software and data processing software. © 2022 by the authors.","FE model update; GA optimisation; RC bridge; SOFISTIK TEDDY; structural health monitoring","Bridges; Concrete testing; Data handling; Finite element method; Genetic algorithms; Load testing; MATLAB; Reinforced concrete; Shape optimization; Strain; Concrete bridge structures; Element models; Field data; Finite element model update; Finite element modelling (FEM); Genetic-algorithm optimizations; Model updates; Reinforced concrete bridge; SOFISTIK TEDDY; Static load testing; Structural health monitoring; aging; Asian; finite element analysis; human; information processing; river; Aging; Asians; Finite Element Analysis; Humans; Records; Rivers",,,,,,,,,,,,,,,,"Commander, B., Evolution of Bridge Diagnostic Load Testing in the USA (2019) Front. 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Eng, 21, p. 04016005; Harris, D.K., Civitillo, J.M., Gheitasi, A., Performance and Behavior of Hybrid Composite Beam Bridge in Virginia: Live Load Testing (2016) J. Bridg. Eng, 21, p. 04016022; Colombani, I.A., Andrawes, B., Comparison of load rating of reinforced concrete slab bridge using analytical and field testing approaches (2021) Innov. Infrastruct. Solutions, 7, pp. 1-12; Deng, Y., Phares, B.M., Automated bridge load rating determination utilizing strain response due to ambient traffic trucks (2016) Eng. Struct, 117, pp. 101-117; Ding, L., Hao, H., Xia, Y., Deeks, A., Evaluation of Bridge Load Carrying Capacity Using Updated Finite Element Model and Nonlinear Analysis (2012) Adv. Struct. Eng, 15, pp. 1739-1750; Karimpour, A., Rahmatalla, S., Ghadikolaee, H.B., Finite Element Model-Based Weight-Over Process Philosophy for Bridge Loading Capacity Evaluation and Rating Factor Estimation (2021) Adv. Civ. 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Struct, 241, p. 112465; (2014) AASHTO: LRFD Bridge Design Specifications, 7. , American Association of State Highway and Transportation Officials, Washington, DC, USA; Sanayei, M., Phelps, J.E., Sipple, J.D., Bell, E.S., Brenner, B.R., Instrumentation, Nondestructive Testing, and Finite-Element Model Updating for Bridge Evaluation Using Strain Measurements (2012) J. Bridg. Eng, 17, pp. 130-138; (2018) The Manual for Bridge Evaluation, , American Association of State Highway and Transportation Officials, Washington, DC, USA; Asadollahi, P., Huang, Y., Li, J., Bayesian Finite Element Model Updating and Assessment of Cable-Stayed Bridges Using Wireless Sensor Data (2018) Sensors, 18","Nguyen, D.C.; Department of Mechanics and Bridges, Akademicka 5, Poland; email: cong.nguyen@polsl.pl",,,"MDPI",,,,,14248220,,,"36433489","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85142752735
"González A., Feng K., Casero M.","12782485200;57208625402;55848371300;","The Use of the Forced Frequency of a Bridge Due to a Truck Fleet for Estimating Stiffness Losses at Low Speed",2022,"Applied Sciences (Switzerland)","12","22","11380","","",,,"10.3390/app122211380","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142533454&doi=10.3390%2fapp122211380&partnerID=40&md5=7ab9d40eceb0e8d51656ce5950ec2ba9","School of Civil Engineering, University College Dublin, Dublin, D04 V1W8, Ireland","González, A., School of Civil Engineering, University College Dublin, Dublin, D04 V1W8, Ireland; Feng, K., School of Civil Engineering, University College Dublin, Dublin, D04 V1W8, Ireland; Casero, M., School of Civil Engineering, University College Dublin, Dublin, D04 V1W8, Ireland","The influence of traffic loads on the dynamic features of a bridge is an external factor that can hinder the true condition of the structure. This paper aims to effectuate a shift in the way this factor is viewed. If the interaction between vehicle and bridge is modeled using the finite element method, the response is based on mass, stiffness, and damping matrices of a coupled vehicle-bridge system that vary with the location of the load at each point in time. The time-varying forced frequencies of a beam bridge model due to a fleet of 3-axle trucks based on eigenvalue analysis (i.e., derived from the matrices of the coupled system) are compared to those obtained using dynamic transient analysis (i.e., derived from the frequency content of the acceleration response of the beam due to a truck crossing). Truck properties are randomly varied within a realistic range to obtain a pattern for the forced vibration due to a truck fleet traveling at an ideal speed of 1 m/s on a 15 m bridge with a smooth surface, and at 10 m/s on a 30 m bridge. These patterns reveal a trend that allows for locating and quantifying the stiffness loss associated with a crack using only the forced frequency. The implementation of this methodology requires the installation of accelerometers on the bridge, and a nearby weigh-in-motion system to identify the traffic fleet of interest. High requirements for frequency resolution limit the application to bridges located on low speed routes. © 2022 by the authors.","bridge dynamics; damage detection; forced vibration; frequency evolution; mahalanobis distance; structural health monitoring; truck fleet; vehicle bridge interaction",,,,,,"Science Foundation Ireland, SFI: 16/US/I3277","This research has received funding from Science Foundation Ireland (SFI)’s US-Ireland R&D partnership programme under the proposal id. 16/US/I3277 titled MARS-Fly.",,,,,,,,,,"Doebling, S.W., Farrar, C.R., Prime, M.B., A summary review of vibration-based damage identification methods (1998) Shock. Vib. 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Dyn, 30, pp. 149-171; Gunawan, T.S., On the optimal window shape for genomic signal processing Proceedings of the 2008 International Conference on Computer and Communication Engineering, pp. 252-255. , Kuala Lumpur, Malaysia, 13–15 May 2008; Amezquita-Sanchez, J.P., Adeli, H., Signal processing techniques for vibration-based health monitoring of smart structures (2016) Arch. Comput. Method Eng, 23, pp. 1-15; González, A., OBrien, E.J., McGetrick, P.J., Identification of damping in a bridge using a moving instrumented vehicle (2012) J. Sound Vib, 331, pp. 4115-4131; Frýba, L., (1999) Vibration of Solids and Structures under Moving Loads, , 3rd ed., Thomas Telford, London, UK; Yang, Y.B., Wang, B., Wang, Z., Shi, K., Xu, H., Scanning of bridge surface roughness from two-axle vehicle response by EKF-UI and contact residual: Theoretical study (2022) Sensors, 22. , 35591100; (2016) Mechanical Vibration—Road Surface Profiles—Reporting of Measured Data, p. 36. , ISO, Geneva, Switzerland","González, A.; School of Civil Engineering, Ireland; email: arturo.gonzalez@ucd.ie",,,"MDPI",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85142533454
"Hashlamon I., Nikbakht E.","57205484515;56009891000;","The Use of a Movable Vehicle in a Stationary Condition for Indirect Bridge Damage Detection Using Baseline-Free Methodology",2022,"Applied Sciences (Switzerland)","12","22","11625","","",,,"10.3390/app122211625","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142429581&doi=10.3390%2fapp122211625&partnerID=40&md5=96d65438bb5ec7bb66b6e5f49a421a30","Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia","Hashlamon, I., Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia; Nikbakht, E., Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Perak, 32610, Malaysia","The use of an instrumented scanning vehicle has become the center of focus for bridge health monitoring (BHM) due to its cost efficiency, mobility, and practicality. However, indirect BHM still faces challenges such as the effects of road roughness on vehicle response, which can be avoided when the vehicle is in a stationary condition. This paper proposes a baseline-free method to detect bridge damage using a stationary vehicle. The proposed method is implemented in three steps. First, the contact-point response (CPR) of the stationary vehicle is computed. Secondly, the CPR is decomposed into intrinsic mode functions (IMFs) using the variational mode decomposition (VMD) method. Finally, instantaneous amplitude (IA) of a high frequency IMF is computed. The peak represents the existence and location of the damage. A finite element model of a bridge with damage is created. The results show that the method can identify the damage location under different circumstances, such as a vehicle with and without damping, different speeds of the moving vehicle, different sizes of damage, and multiple damage. A higher speed was found to provide better visibility of damages. In addition, smaller damage was less visible than wider damage. © 2022 by the authors.","contact-point response; finite element method; instantaneous amplitude; intrinsic mode functions; signal processing; stationary vehicle; variational mode decomposition",,,,,,"YUTP 015LC0-177","This research was funded by Universiti Teknologi PETRONAS Malaysia under grant number YUTP 015LC0-177.","The authors wish to acknowledge the financial support received from the Universiti Teknologi PETRONAS Malaysia.",,,,,,,,,"Kim, C.W., Chang, K.C., McGetrick, P.J., Inoue, S., Hasegawa, S., Utilizing moving vehicles as sensors for bridge condition screening—A laboratory verification (2017) Sens. 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Struct, 203, p. 109859; Hashlamon, I., Nikbakht, E., Topa, A., Elhattab, A., Numerical Parametric Study on the Effectiveness of the Contact-Point Response of a Stationary Vehicle for Bridge Health Monitoring (2021) Appl. Sci, 11; Hashlamon, I., Nikbakht, E., Topa, A., Indirect Bridge Health Monitoring Employing Contact-Point Response of Instrumented Stationary Vehicle (2021) Proceedings of the International Conference on Civil, Offshore and Environmental Engineering, , Dubai, United Arab Emirates, 20–21 December 2021, Springer, Berlin/Heidelberg, Germany; Carden, E.P., Fanning, P., Vibration Based Condition Monitoring: A Review (2004) Struct. Health Monit, 3, pp. 355-377; Corbally, R., Malekjafarian, A., Examining changes in bridge frequency due to damage using the contact-point response of a passing vehicle (2021) J. Struct. Integr. Maint, 6, pp. 148-158; Singh, P., Sadhu, A., A hybrid time-frequency method for robust drive-by modal identification of bridges (2022) Eng. Struct, 266, p. 114624; ElHattab, A., Uddin, N., Obrien, E., Drive-by bridge damage detection using non-specialized instrumented vehicle (2017) Bridg. Struct, 12, pp. 73-84; Janeliukstis, R., Ručevskis, S., Wesolowski, M., Chate, A., Multiple Damage Identification in Beam Structure Based on Wavelet Transform (2017) Procedia Eng, 172, pp. 426-432; Bandara, S., Rajeev, P., Gad, E., Sriskantharajah, B., Flatley, I., Damage detection of in service timber poles using Hilbert-Huang transform (2019) NDT E Int, 107, p. 102141; Kim, C., Isemoto, R., McGetrick, P., Kawatani, M., Obrien, E., Drive-by bridge inspection from three different approaches (2014) Smart Struct. Syst, 13, pp. 775-796; Zhu, X., Cao, M., Ostachowicz, W., Xu, W., Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation (2019) Sensors, 19; Mousavi, M., Holloway, D., Olivier, J., Gandomi, A.H., Beam damage detection using synchronisation of peaks in instantaneous frequency and amplitude of vibration data (2021) Measurement, 168, p. 108297; McGetrick, P.J., Kim, C.W., A Parametric Study of a Drive by Bridge Inspection System Based on the Morlet Wavelet (2013) Key Eng. Mater, 569–570, pp. 262-269; Hester, D., Gonzalez, A., A discussion on the merits and limitations of using drive-by monitoring to detect localised damage in a bridge (2017) Mech. Syst. Signal Process, 90, pp. 234-253; Fitzgerald, P., Malekjafarian, A., Cantero, D., Obrien, E.J., Prendergast, L.J., Drive-by scour monitoring of railway bridges using a wavelet-based approach (2019) Eng. Struct, 191, pp. 1-11; Obrien, E.J., Malekjafarian, A., González, A., Application of empirical mode decomposition to drive-by bridge damage detection (2017) Eur. J. Mech.—A/Solids, 61, pp. 151-163; Kildashti, K., Alamdari, M.M., Kim, C.W., Gao, W., Samali, B., Drive-by-bridge inspection for damage identification in a cable-stayed bridge: Numerical investigations (2020) Eng. Struct, 223, p. 110891; Zhang, B., Qian, Y., Wu, Y., Yang, Y., An effective means for damage detection of bridges using the contact-point response of a moving test vehicle (2018) J. Sound Vib, 419, pp. 158-172; Yang, Y.-B., Zhang, B., Qian, Y., Wu, Y., Further Revelation on Damage Detection by IAS Computed from the Contact-Point Response of a Moving Vehicle (2018) Int. J. Struct. Stab. Dyn, 18, p. 1850137; Zhan, Y., Au, F.T.K., Zhang, J., Bridge identification and damage detection using contact point response difference of moving vehicle (2021) Struct. Control Health Monit, 28, p. e2837; Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Liu, H.H., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis (1998) Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci, 454, pp. 903-995; Roveri, N., Carcaterra, A., Damage detection in structures under traveling loads by Hilbert–Huang transform (2012) Mech. Syst. Signal Process, 28, pp. 128-144; Cheraghi, N., Taheri, F., A damage index for structural health monitoring based on the empirical mode decomposition (2007) J. Mech. Mater. Struct, 2, pp. 43-61; Meredith, J., González, A., Hester, D., Empirical mode decomposition of the acceleration response of a prismatic beam subject to a moving load to identify multiple damage locations (2012) Shock Vib, 19, pp. 845-856; Dragomiretskiy, K., Zosso, D., Variational mode decomposition (2013) IEEE Trans. Signal Process, 62, pp. 531-544; (2016), ISO, London, UK; He, W.-Y., Ren, W.-X., Structural damage detection using a parked vehicle induced frequency variation (2018) Eng. Struct, 170, pp. 34-41","Hashlamon, I.; Department of Civil and Environmental Engineering, Malaysia; email: ibrahim_19001027@utp.edu.my
Nikbakht, E.; Department of Civil and Environmental Engineering, Malaysia; email: ehsan.nikbakht@utp.edu.my",,,"MDPI",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85142429581
"Luo L., Xia Y., Wang A., Lei X., Jian X., Sun L.","57337744100;55553987200;57222349047;57190377935;57209266680;7403956279;","Finite element model updating method for continuous girder bridges using monitoring responses and traffic videos",2022,"Structural Control and Health Monitoring","29","11","e3062","","",,,"10.1002/stc.3062","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135784390&doi=10.1002%2fstc.3062&partnerID=40&md5=33160247d0cb079b3a1745f39fd50c37","Department of Bridge Engineering, Tongji University, Shanghai, China; Shanghai Qizhi Institute, Shanghai, China; State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China","Luo, L., Department of Bridge Engineering, Tongji University, Shanghai, China; Xia, Y., Department of Bridge Engineering, Tongji University, Shanghai, China, Shanghai Qizhi Institute, Shanghai, China; Wang, A., Department of Bridge Engineering, Tongji University, Shanghai, China; Lei, X., Department of Bridge Engineering, Tongji University, Shanghai, China; Jian, X., Department of Bridge Engineering, Tongji University, Shanghai, China; Sun, L., Department of Bridge Engineering, Tongji University, Shanghai, China, Shanghai Qizhi Institute, Shanghai, China, State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China","Accurate finite element (FE) models play an essential role in the health monitoring of operational bridges. Static structural deflections caused by vehicles, which are used in traditional finite element model updating (FEMU) methods, are often procured from field tests, interrupting the traffic and limiting the test loading scenarios. This study proposes a FEMU method that directly applies the massive, multi-source structural and traffic data in the operation phase to update the FE model, effectively solving the defects above. We use the computer vision-based vehicle load identification technique to accurately locate and weigh vehicle loads and carry out static simulations in the FE model based on the identified vehicle loads. The proposed FEMU objective function is established using indices including dynamic structural characteristics and vehicle-induced static structural responses. The static error index in the objective function integrates the curve shape and extrema difference of theoretic and measured static responses. Finally, we deploy a parallel particle swarm optimization (PSO) algorithm to find the global optimal updated FE model. A continuous scale bridge model is employed in the experimental studies with four typical scenarios. Compared to the results from the initial FE model, the updated FE model provides significantly better results both in dynamic and static aspects in all scenarios, and the static error indexes reduce by 75% on average. The proposed method offers a practical approach to deploying the monitoring data for real-time FEMU, which considers dynamic and static features and provides a basis for damage detection, performance assessment, and management. © 2022 John Wiley & Sons Ltd.","dynamic structural characteristics; finite element model updating (FEMU); parallel particle swarm optimization; static response; traffic data; vehicle load identification","Damage detection; Finite element method; Information management; Particle swarm optimization (PSO); Structural health monitoring; Dynamic structural characteristic; Finite element model updating; Finite-element model updating; Load identification; Parallel particle swarm optimization; Static response; Structural characteristics; Traffic data; Vehicle load; Vehicle load identification; Vehicles",,,,,"SYXF0120020109; 2021B51; National Natural Science Foundation of China, NSFC: 51878482, 51978508","This paper is supported by National Natural Science Foundation of China (51878482 and 51978508), the Technology Cooperation Project of Shanghai Qizhi Institute (SYXF0120020109), and the Transportation Science and Technology Program of Shandong Province (2021B51).",,,,,,,,,,"Song, M., Yousefianmoghadam, S., Mohammadi, M.-E., Moaveni, B., Stavridis, A., Wood, R.L., An application of finite element model updating for damage assessment of a two-story reinforced concrete building and comparison with lidar (2018) Struct Health Monit, 17 (5), pp. 1129-1150; Friswell, M.I., (2013) Finite element model updating in structural dynamics; Soize, C., A comprehensive overview of a non-parametric probabilistic approach of model uncertainties for predictive models in structural dynamics (2005) J Sound Vib, 288 (3), pp. 623-652; Arora, V., Comparative study of finite element model updating methods (2011) J Vib Control, 17 (13), pp. 2023-2039; Ren, W.X., Fang, S.E., Deng, M.Y., Response surface-based finite-element-model updating using structural static responses (2011) J Eng Mech-Asce, 137 (4), pp. 248-257; Ren, W.X., Chen, H.B., Finite element model updating in structural dynamics by using the response surface method (2010) Eng Struct, 32 (8), pp. 2455-2465; Bhowmick, S., Nagarajaiah, S., Identification of full-field dynamic modes using continuous displacement response estimated from vibrating edge video (2020) J Sound Vib, 489; Jana, D., Nagarajaiah, S., Computer vision-based real-time cable tension estimation in Dubrovnik cable-stayed bridge using moving handheld video camera (2021) Struct Control Health Monit, 28 (5); Xiao, X., Xu, Y.L., Zhu, Q., Multiscale modeling and model updating of a cable-stayed bridge. II: Model updating using modal frequencies and influence lines (2015) J Bridge Eng, 20 (10), p. 04014113; Wang, Y., Li, Z., Wang, C., Wang, H., Concurrent multi-scale modelling and updating of long-span bridges using a multi-objective optimisation technique (2013) Struct Infrastruct E, 9 (12), pp. 1251-1266; Kim, S., Kim, N., Park, Y.S., Jin, S.S., A sequential framework for improving identifiability of FE model updating using static and dynamic data (2019) Sensors-Basel, 19 (23); Moses, F., Weigh-in-motion system using instrumented bridges (1979) Transport Eng-J Asce, 105 (3), pp. 233-249; Yu, Y., Cai, C.S., Deng, L., State-of-the-art review on bridge weigh-in-motion technology (2016) Adv Struct Eng, 19 (9), pp. 1514-1530; Dan, D.H., Ge, L.F., Yan, X.F., Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision (2019) Measurement, 144, pp. 155-166; Jian, X.D., Xia, Y., Lozano-Galant, J.A., Sun, L.M., Traffic sensing methodology combining influence line theory and computer vision techniques for girder Bridges (2019) J Sensors, 2019; Ge, L.F., Dan, D.H., Li, H., An accurate and robust monitoring method of full-bridge traffic load distribution based on YOLO-v3 machine vision (2020) Struct Control Hlth, 27 (12); Xia, Y., Jian, X.D., Yan, B., Su, D., Infrastructure safety oriented traffic load monitoring using multi-sensor and single camera for short and medium span bridges (2019) Remote Sens-Basel, 11 (22); Sanayei, M., Khaloo, A., Gul, M., Catbas, F.N., Automated finite element model updating of a scale bridge model using measured static and modal test data (2015) Eng Struct, 102, pp. 66-79; Liu, Y., Ma, J., Nie, J.G., Zhang, S.Y., Virtual distortion method-based finite element model updating of bridges by using static deformation (2017) J Eng Mech, 143 (3); Ebrahimian, H., Astroza, R., Conte, J.P., de Callafon, R.A., Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation (2017) Mech Syst Signal Pr., 84, pp. 194-222; Lin, K., Xu, Y.-L., Lu, X., Guan, Z., Li, J., Time history analysis-based nonlinear finite element model updating for a long-span cable-stayed bridge (2020) Struct Health Monit; Lam, H.F., Adeagbo, M.O., Yang, Y.B., Time-domain Markov chain Monte Carlo-based Bayesian damage detection of ballasted tracks using nonlinear ballast stiffness model (2020) Struct Health Monit; Kim, G.H., Park, Y.S., An improved updating parameter selection method and finite element model update using multiobjective optimisation technique (2004) Mech Syst Signal Pr, 18 (1), pp. 59-78; Perera, R., Ruiz, A., A multistage FE updating procedure for damage identification in large-scale structures based on multiobjective evolutionary optimization (2008) Mech Syst Signal Pr., 22 (4), pp. 970-991; Lei, X.M., Sun, L.M., Xia, Y., Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks (2021) Struct Health Monit, 20 (4), pp. 2069-2087. , Jul; Xia, Y., Lei, X.M., Wang, P., Liu, G.M., Sun, L.M., Long-term performance monitoring and assessment of concrete beam bridges using neutral axis indicator (2020) Struct Control Health, 27 (12); Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M., (2020) YOLOv4: optimal speed and accuracy of object detection, , https://ui.adsabs.harvard.edu/abs/2020arXiv200410934B, ., Accessed April 01, 2020; Xu, G., Zhang, Z., (1996) Epipolar geometry in stereo, motion and object recognition: A unified approach, 6. , Springer Science & Business Media; Peeters, B., de Roeck, G., Reference-based stochastic subspace identification for output-only modal analysis (1999) Mech Syst Signal Pr., 13 (6), pp. 855-878; Zhang, L., Wang, T., Tamura, Y., A frequency–spatial domaindecomposition (FSDD) method for operational modal analysis (2010) Mech Syst Signal Pr, 24 (5), pp. 1227-1239; Cleveland, W.S., Devlin, S.J., Locally weighted regression - an approach to regression-analysis by local fitting (1988) J am Stat Assoc, 83 (403), pp. 596-610; Jang, J., Smyth, A.W., Model updating of a full-scale FE model with nonlinear constraint equations and sensitivity-based cluster analysis for updating parameters (2017) Mech Syst Signal Pr, 83, pp. 337-355; Tran-Ngoc, H., Khatir, S., de Roeck, G., Bui-Tien, T., Nguyen-Ngoc, L., Wahab, M.A., Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm (2018) Sensors-Basel, 18 (12); Lin, K.Q., Xu, Y.L., Lu, X.Z., Guan, Z.G., Li, J.Z., Cluster computing-aided model updating for a high-fidelity finite element model of a long-span cable-stayed bridge (2020) Earthq Eng Struct D, 49 (9), pp. 904-923; Papadimitriou, C., Optimal sensor placement methodology for parametric identification of structural systems (2004) J Sound Vib, 278 (4-5), pp. 923-947; Jana, D., Mukhopadhyay, S., Ray-Chaudhuri, S., Fisher information-based optimal input locations for modal identification (2019) J Sound Vib, 459; Zhu, Y.-C., Au, S.-K., Bayesian operational modal analysis with asynchronous data, part I: most probable value (2018) Mech Syst Signal Pr., 98, pp. 652-666; Zhu, Y.-C., Au, S.-K., Bayesian operational modal analysis with asynchronous data, Part II: Posterior uncertainty (2018) Mech Syst Signal Pr., 98, pp. 920-935; Araujo, I.G., Maldonado, E., Cho, G.C., Ambient vibration testing and updating of the finite element model of a simply supported beam bridge (2011) Front Struct Civ Eng, 5 (3), pp. 344-354","Lei, X.; Department of Bridge Engineering, China; email: leixiaoming@outlook.com
Sun, L.; Department of Bridge Engineering, China; email: lmsun@tongji.edu.cn",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-85135784390
"Venglár M., Lamperová K., Sokol M.","57191739008;57202967108;53985383700;","PERFORMANCE ASSESSMENT OF STEEL TRUSS RAILWAY BRIDGE WITH CURVED TRACK",2022,"Acta Polytechnica","62","5",,"558","566",,,"10.14311/AP.2022.62.0558","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142102492&doi=10.14311%2fAP.2022.62.0558&partnerID=40&md5=75b63b49294cf023a3b659c90c2ae74a","Slovak University of Technology, Faculty of Civil Engineering, Department of Structural Mechanics, Radlinského 11, Bratislava, 810 05, Slovakia","Venglár, M., Slovak University of Technology, Faculty of Civil Engineering, Department of Structural Mechanics, Radlinského 11, Bratislava, 810 05, Slovakia; Lamperová, K., Slovak University of Technology, Faculty of Civil Engineering, Department of Structural Mechanics, Radlinského 11, Bratislava, 810 05, Slovakia; Sokol, M., Slovak University of Technology, Faculty of Civil Engineering, Department of Structural Mechanics, Radlinského 11, Bratislava, 810 05, Slovakia","Non-destructive Structural Health Monitoring techniques can be incorporated into bridge integrity management by assessing structural conditions. This paper describes a performance assessment of a steel truss railway bridge in Bratislava using vibration-based techniques as a further part of maintenance in addition to standard visual inspections. To obtain the necessary data, a multipurpose measuring system was used. Various types of data were measured, e.g. accelerations, strains, and displacements. The advantage of the multipurpose measuring system was that the traffic over the bridge was not restricted, even though the bridge carries only a single curved track. Two test campaigns were conducted to assess the performance of the bridge. One campaign was devoted to measuring ambient vibrations in order to perform the operational modal analysis, and the second was carried out to measure strains and displacements during a train passage. The results show a successful system identification of the structure using ambient vibrations; and a finite element model was verified and validated by a comparison of strains and displacements, as well as by modal parameters. According to the results obtained, the structural health of the investigated bridge was satisfactory. © 2022 The Author(s).","ambient vibration; curved track; FEM model; performance assessment; steel truss bridge; System identification; train passage",,,,,,"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV, VEGA: 1/0230/22","The authors gratefully acknowledge the contribution of the Scientific Grant Agency of the Slovak Republic under the grant VEGA no. 1/0230/22.",,,,,,,,,,"Siriwardane, S. C., Vibration measurement-based simple technique for damage detection of truss bridges: A case study (2015) Case Studies in Engineering Failure Analysis, 4, pp. 50-58. , https://doi.org/10.1016/j.csefa.2015.08.001; Ryjáček, P., Rotter, T., Stančík, V., (2022) Metody pro zajištění udržitelnosti ocelových mostních konstrukcí industriálního kulturního dědictví [Methods for Achieving Sustainability of Industrial Heritage Steel Bridges], , First printing. 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Bratislava-Raca, , https://www.google.sk/maps, [2022-08-02]; Dertimanis, V., Chatzi, E., Sensor networks in structural health monitoring: From theory to practice (2020) Journal of Sensor and Actuator Networks, 9 (4), p. 47. , https://doi.org/10.3390/jsan9040047; Venglár, M., Lamperová, K., Beutelhauser, D., (2022) Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and Sustainability, chap. Application of multipurpose measuring system on various bridges – pros and cons, pp. 324-331. , https://doi.org/10.1201/9781003322641-36, CRC Press; Gocal, J., Ortyl, L., Owerko, T., (2013) Determination of displacements and vibrations of engineering structures using ground-based radar interferometry, , First printing. Wydawnictwa AGH, Krakow; Venglár, M., Sokol, M., Case study: The Harbor Bridge in Bratislava (2020) Structural Concrete, 21 (6), pp. 2736-2748. , https://doi.org/10.1002/suco.201900190; Krejčí, J., Beneš, P., A comparison of two modal analysis methods: An impact hammer versus laser vibrometry (2020) Acta Polytechnica, 60 (5), pp. 420-427. , https://doi.org/10.14311/AP.2020.60.0420; Wang, L., Lie, S., Zhang, Y., Damage detection using frequency shift path (2016) Mechanical Systems and Signal Processing, 66-67, pp. 298-313. , https://doi.org/10.1016/j.ymssp.2015.06.028; Sokol, M., Venglár, M., System identification of a composite beam (2017) Pol lack Periodica, 12 (3), pp. 43-54. , https://doi.org/10.1556/606.2017.12.3.5; (2006) STN EN 1991-2 Eurocode 1: Actions on structures. Part 2: Traffic loads on bridges, , SÚTN. nationaannex EN 1991-2/NA","Venglár, M.; Slovak University of Technology, Radlinského 11, Slovakia; email: michal.venglar@stuba.sk",,,"Czech Technical University in Prague",,,,,12102709,,,,"English","Acta Polytech.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85142102492
"Febrianto E., Butler L., Girolami M., Cirak F.","56493217600;55795448200;7005215170;6602445202;","Digital twinning of self-sensing structures using the statistical finite element method",2022,"Data-Centric Engineering","3","3","e31","","",,,"10.1017/dce.2022.28","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141456151&doi=10.1017%2fdce.2022.28&partnerID=40&md5=d194732f86500890c15708ba91c4ef33","Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom; The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, United Kingdom; Lassonde School of Engineering, York University, Toronto, ON  M3J 1P3, Canada","Febrianto, E., Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom, The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, United Kingdom; Butler, L., The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, United Kingdom, Lassonde School of Engineering, York University, Toronto, ON  M3J 1P3, Canada; Girolami, M., Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom, The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, United Kingdom; Cirak, F., Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom, The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, United Kingdom","The monitoring of infrastructure assets using sensor networks is becoming increasingly prevalent. A digital twin in the form of a finite element (FE) model, as commonly used in design and construction, can help make sense of the copious amount of collected sensor data. This paper demonstrates the application of the statistical finite element method (statFEM), which provides a principled means of synthesizing data and physics-based models, in developing a digital twin of a self-sensing structure. As a case study, an instrumented steel railway bridge of length located along the West Coast Mainline near Staffordshire in the UK is considered. Using strain data captured from fiber Bragg grating sensors at 108 locations along the bridge superstructure, statFEM can predict the true system response while taking into account the uncertainties in sensor readings, applied loading, and FE model misspecification errors. Longitudinal strain distributions along the two main I-beams are both measured and modeled during the passage of a passenger train. The statFEM digital twin is able to generate reasonable strain distribution predictions at locations where no measurement data are available, including at several points along the main I-beams and on structural elements on which sensors are not even installed. The implications for long-term structural health monitoring and assessment include optimization of sensor placement and performing more reliable what-if analyses at locations and under loading scenarios for which no measurement data are available. © 2022 The Author(s).","Bayesian learning; digital twin; FBG sensing; physics-informed machine learning; statistical finite element method; structural health monitoring","Fiber Bragg gratings; Fiber optic sensors; Finite element method; Location; Machine learning; Sensor networks; Statistical Physics; Strain; Bayesian learning; FBG sensing; Finite element modelling (FEM); I beams; Machine-learning; Measurement data; Physic-informed machine learning; Self-sensing; Statistical finite element methods; Strain distributions; Structural health monitoring",,,,,"Alan Turing Institute, ATI; UK Research and Innovation, UKRI; Engineering and Physical Sciences Research Council, EPSRC: EP/T001569/1","This work was supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the “Digital twins for complex engineering systems” theme within that grant, and The Alan Turing Institute.",,,,,,,,,,"Abdulkarem, M., Samsudin, K., Rokhani, F.Z., Rasid, M.F.A., Wireless sensor network for structural health monitoring: A contemporary review of technologies, challenges, and future direction (2020) Structural Health Monitoring, 19, pp. 693-735; Arendt, P.D., Apley, D.W., Chen, W., Quantification of model uncertainty: Calibration, model discrepancy, and identifiability (2012) Journal of Mechanical Design, 134, pp. 1009081-10090812; Beck, J.L., Bayesian system identification based on probability logic (2010) Structural Control and Health Monitoring, 17, pp. 825-847; Bolton, A., Enzer, M., Schooling, J., (2018) The Gemini Principles: Guiding Values for the National Digital Twin and Information Management Framework, , Technical report, Centre for Digital Built Britain and Digital Framework Task Group; Brownjohn, J.M.W., Structural health monitoring of civil infrastructure (2007) Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365, pp. 589-622; Butler, L.J., Lin, W., Xu, J., Gibbons, N., Elshafie, M.Z.E.B., Middleton, C.R., Monitoring, modeling, and assessment of a selfsensing railway bridge during construction (2018) Journal of Bridge Engineering, 23, pp. 1-16; Cirak, F., Long, Q., Subdivision shells with exact boundary control and non-manifold geometry (2011) International Journal for Numerical Methods in Engineering, 88, pp. 897-923; Cirak, F., Ortiz, M., Schröder, P., Subdivision surfaces: A new paradigm for thin-shell finite-element analysis (2000) International Journal for Numerical Methods in Engineering, 47, pp. 2039-2072; Cirak, F., Scott, M.J., Antonsson, E.K., Ortiz, M., Schröder, P., Integrated modeling, finite-element analysis, and engineering design for thin-shell structures using subdivision (2002) Computer-Aided Design, 34, pp. 137-148; De Battista, N., Cheal, N., Harvey, R., Kechavarzi, C., Monitoring the axial displacement of a high-rise building under construction using embedded distributed fibre optic sensors (2017) SHMII 2017-8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings, pp. 1058-1067; Di, J., Ruan, X., Zhou, X., Wang, J., Peng, X., Fatigue assessment of orthotropic steel bridge decks based on strain monitoring data (2021) Engineering Structures, 228, p. 111437; Di Murro, V., Pelecanos, L., Soga, K., Kechavarzim, C., Morton, R., Distributed fibre optic long-term monitoring of concrete-lined tunnel section tt10 at cern (2016) International Conference on Smart Infrastructure and Construction; Frangopol, D.M., Soliman, M., Life-cycle of structural systems: Recent achievements and future directions (2016) Structure and Infrastructure Engineering, 12, pp. 1-20; Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B., (2013) Bayesian Data Analysis, , 3rd Edn. Boca Raton: CRC Press; Ghanem, R.G., Spanos, P.D., (1991) Stochastic Finite Elements: A Spectral Approach, , New York: Springer; Girolami, M., Febrianto, E., Yin, G., Cirak, F., The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions (2021) Computer Methods in Applied Mechanics and Engineering, 375, pp. 1135331-11353332; Huang, Y., Shao, C., Wu, B., Beck, J.L., Li, H., State-of-the-art review on Bayesian inference in structural system identification and damage assessment (2019) Advances in Structural Engineering, 22, pp. 1329-1351; Hughes, T.J.R., Cottrell, J.A., Bazilevs, Y., Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement (2005) Computer Methods in Applied Mechanics and Engineering, 194, pp. 4135-4195; Kaipio, J., Somersalo, E., (2006) Statistical and Computational Inverse Problems, , New York: Springer; Kennedy, M.C., O'Hagan, A., Bayesian calibration of computer models (2001) Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63, pp. 425-464; Lau, F.D.-H., Adams, N.M., Girolami, M.A., Butler, L.J., Elshafie, M.Z.E.B., The role of statistics in data-centric engineering (2018) Statistics & Probability Letters, 136, pp. 58-62; Lin, W., Butler, L.J., Elshafie, M.Z.E.B., Middleton, C.R., Performance assessment of a newly constructed skewed halfthrough railway bridge using integrated sensing (2019) Journal of Bridge Engineering, 24, pp. 1-14; Lynch, J.P., An overview of wireless structural health monitoring for civil structures (2007) Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365, pp. 345-372; MacKay, D.J.C., Bayesian interpolation (1992) Neural Computation, 4, pp. 415-447; MacKay, D.J.C., Comparison of approximate methods for handling hyperparameters (1999) Neural Computation, 11, pp. 1035-1068; Malekzadeh, M., Atia, G., Catbas, F.N., Performance-based structural health monitoring through an innovative hybrid data interpretation framework (2015) Journal of Civil Structural Health Monitoring, 5, pp. 287-305; Mikkola, P., Martin, O.A., Chandramouli, S., Hartmann, M., Pla, O.A., Thomas, O., Pesonen, H., Klami, A., Prior knowledge elicitation: The past, present, and future (2021) Preprint; Murphy, K.P., (2012) Machine Learning: A Probabilistic Perspective, , Cambridge: MIT Press; Nagel, J.B., Sudret, B., A unified framework for multilevel uncertainty quantification in Bayesian inverse problems (2016) Probabilistic Engineering Mechanics, 43, pp. 68-84; Oden, J.T., Moser, R., Ghattas, O., Computer predictions with quantified uncertainty, part I (2010) SIAM News, 43, pp. 1-3; Pasquier, R., Smith, I., Iterative structural identification framework for evaluation of existing structures (2016) Engineering Structures, 106, pp. 179-194; Rasheed, A., San, O., Kvamsdal, T., Digital twin: values, challenges and enablers from a modeling perspective (2020) IEEE Access, 8, pp. 21980-22012; Scarth, C., Adhikari, S., Cabral, P.H., Silva, G.H.C., Do Prado, A.P., Random field simulation over curved surfaces: Applications to computational structural mechanics (2019) Computer Methods in Applied Mechanics and Engineering, 345, pp. 283-301; Stuart, A.M., Inverse problems: A Bayesian perspective (2010) Acta Numerica, 19, pp. 451-559; Sudret, B., Der Kiureghian, A., (2000) Stochastic Finite Element Methods and Reliability: A State-of-the-Art Report, , Technical Report UCB/SEMM-2000/08, Department of Civil & Environmental Engineering, University of California, Berkeley; Tsialiamanis, G., Wagg, D.J., Dervilis, N., Worden, K., On generative models as the basis for digital twins (2021) Data-Centric Engineering 2; Worden, K., Cross, E., Barthorpe, R., Wagg, D., Gardner, P., On digital twins, mirrors, and virtualizations: Frameworks for model verification and validation (2020) ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6 (3), p. 30902; Wu, R.-T., Jahanshahi, M.R., Data fusion approaches for structural health monitoring and system identification: Past, present, and future (2020) Structural Health Monitoring, 19, pp. 552-586; Zhang, Q., Sabin, M., Cirak, F., Subdivision surfaces with isogeometric analysis adapted refinement weights (2018) Computer-Aided Design, 102, pp. 104-114","Cirak, F.; Department of Engineering, Trumpington Street, United Kingdom; email: f.cirak@eng.cam.ac.uk",,,"Cambridge University Press",,,,,26326736,,,,"English","Data-Centric Eng.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85141456151
"de Souza E.F., Bittencourt T.N., Ribeiro D., Carvalho H.","57942017700;6603036318;24476782300;56656114300;","Feasibility of Applying Mel-Frequency Cepstral Coefficients in a Drive-by Damage Detection Methodology for High-Speed Railway Bridges",2022,"Sustainability (Switzerland)","14","20","13290","","",,,"10.3390/su142013290","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140595547&doi=10.3390%2fsu142013290&partnerID=40&md5=570f453da1c2fac9a2843be442542f42","Department of Civil Engineering (COECI), Federal University of Technology—Parana (UTFPR), Guarapuava, 85053-525, Brazil; Department of Structural Engineering and Geotechnical (PEF), University of São Paulo (USP), São Paulo, 05508-900, Brazil; Institute of R&D in Structures and Construction (CONSTRUCT), School of Engineering, Polytechnic of Porto, Porto, 4200-072, Portugal; Department of Structural Engineering, Federal University of Minas Gerais (UFMG), Belo Horizonte, 31270-901, Brazil","de Souza, E.F., Department of Civil Engineering (COECI), Federal University of Technology—Parana (UTFPR), Guarapuava, 85053-525, Brazil, Department of Structural Engineering and Geotechnical (PEF), University of São Paulo (USP), São Paulo, 05508-900, Brazil; Bittencourt, T.N., Department of Structural Engineering and Geotechnical (PEF), University of São Paulo (USP), São Paulo, 05508-900, Brazil; Ribeiro, D., Institute of R&D in Structures and Construction (CONSTRUCT), School of Engineering, Polytechnic of Porto, Porto, 4200-072, Portugal; Carvalho, H., Department of Structural Engineering, Federal University of Minas Gerais (UFMG), Belo Horizonte, 31270-901, Brazil","In this paper, a drive-by damage detection methodology for high-speed railway (HSR) bridges is addressed, to appraise the application of Mel-frequency cepstral coefficients (MFCC) to extract the Damage Index (DI). A finite element (FEM) 2D VTBI model that incorporates the train, ballasted track and bridge behavior is presented. The formulation includes track irregularities and a damaged condition induced in a specified structure region. The feasibility of applying cepstrum analysis components to the indirect damage detection in HSR by on-board sensors is evaluated by numerical simulations, in which dynamic analyses are performed through a code implemented in MATLAB. Different damage scenarios are simulated, as well as external excitations such as measurement noises and different levels of track irregularities. The results show that MFCC-based DI are highly sensitive regarding damage detection, and robust to the noise. Bridge stiffness can be recognized satisfactorily at high speeds and under different levels of track irregularities. Moreover, the magnitude of DI extracted from MFCC is related to the relative severity of the damage. The results presented in this study should be seen as a first attempt to link cepstrum-based features in an HSR drive-by damage detection approach. © 2022 by the authors.","drive-by damage detection; high-speed railway bridges; mel-frequency cepstral coefficients; structural health monitoring; vehicle-bridge interaction","bridge; damage mechanics; railway construction; signal processing; structural stability",,,,,,,,,,,,,,,,"Li, Z., Li, S., Lv, J., Li, H., Condition Assessment for High-Speed Railway Bridges Based on Train-Induced Strain Response (2015) Struct. Eng. Mech, 54, pp. 199-219; Montenegro, P.A., Calçada, R., Carvalho, H., Bolkovoy, A., Chebykin, I., Stability of a Train Running over the Volga River High-Speed Railway Bridge during Crosswinds (2020) Struct. Infrastruct. 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Sound Vib, 333, pp. 4526-4542; Tronci, E.M., Beigi, H., Feng, M.Q., Betti, R., Damage Assessment in Structural Systems through Transfer Learning from Audio Domains (2021) Proceedings of the International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII, 2021, pp. 805-811. , International Society for Structural Health Monitoring of Intelligent Infrastructure ISHMII, Brussels, Belgium; Tronci, E.M., Beigi, H., Feng, M.Q., Betti, R., Transfer Learning from Audio Domains a Valuable Tool for Structural Health Monitoring (2022) Proceedings of the Conference Proceedings of the Society for Experimental Mechanics Series, pp. 99-107. , Grimmelsman K., (ed), Springer, Berlin/Heidelberg, Germany; Tronci, E.M., Beigi, H., Feng, M.Q., Betti, R., A Transfer Learning SHM Strategy for Bridges Enriched by the Use of Speaker Recognition X-Vectors (2022) J. Civ. Struct. 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Eng, 20, pp. 647-655; Lee, J., Choi, H., Park, D., Chung, Y., Kim, H.-Y., Yoon, S., Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis (2016) Sens, 16. , 27092509; Calçada, R., Delgado, R., Campos e Matos, A., (2008) Bridges for High-Speed Railways: Revised Papers from the Workshop, Porto, Portugal, 3–4 June 2004, , 1st ed., CRC Press, Boca Raton, FL, USA","de Souza, E.F.; Department of Civil Engineering (COECI), Brazil; email: eflorentino@usp.br",,,"MDPI",,,,,20711050,,,,"English","Sustainability",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85140595547
"Loh T.B., Wu Y., Goh S.H., Kong K.H., Goh K.L., Chong J.J.","57486482800;57903484900;55457689300;57903485000;57902865200;57902234400;","An Integrated Approach for the Determination of Young’s Modulus of a Cantilever Beam Using Finite Element Analysis and the Digital Image Correlation (DIC) Technique",2022,"Electronics (Switzerland)","11","18","2826","","",,,"10.3390/electronics11182826","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138652554&doi=10.3390%2felectronics11182826&partnerID=40&md5=61b1cb6be488b7596cf14ddd941cb502","Faculty of Science, Agriculture Engineering, Newcastle University in Singapore, SIT Building @ Nanyang Polytechnic, 172A Ang Mo Kio Avenue 8 #05-01, Singapore, 567739, Singapore; Department of Civil and Environmental Engineering, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore","Loh, T.B., Faculty of Science, Agriculture Engineering, Newcastle University in Singapore, SIT Building @ Nanyang Polytechnic, 172A Ang Mo Kio Avenue 8 #05-01, Singapore, 567739, Singapore; Wu, Y., Department of Civil and Environmental Engineering, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore; Goh, S.H., Department of Civil and Environmental Engineering, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore; Kong, K.H., Department of Civil and Environmental Engineering, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore; Goh, K.L., Faculty of Science, Agriculture Engineering, Newcastle University in Singapore, SIT Building @ Nanyang Polytechnic, 172A Ang Mo Kio Avenue 8 #05-01, Singapore, 567739, Singapore; Chong, J.J., Faculty of Science, Agriculture Engineering, Newcastle University in Singapore, SIT Building @ Nanyang Polytechnic, 172A Ang Mo Kio Avenue 8 #05-01, Singapore, 567739, Singapore","This paper is an extended paper from the 24th International Conference on Mechatronics Technology, ICMT 2021. The basic mechanical characteristic that gauges the stiffness of a solid material is known as the Young’s modulus. To evaluate the Young’s modulus, destructive material testing is frequently used. This paper describes how to determine a material’s dynamic Young’s modulus using Digital Image Correlation (DIC) in conjunction with numerical back-analysis. Three different materials (brass, aluminum, and steel) were examined for their static and dynamic reactions. A static transverse displacement was first applied at the free end of the beam before it was released and the beam was allowed to vibrate freely. The resulting vibrations at the free end were monitored using the DIC method, following which the natural frequencies of the beam were derived by applying the Fast Fourier Transform (FFT) to the DIC measured time history. The Young’s modulus corresponding to the fundamental natural frequency of the beam was then obtained via modal back-analysis using the finite element program Ansys 2022 R1. In this way, the Young’s modulus of the material may be calculated using a combination of numerical and DIC techniques, thus allowing for the non-contact evaluation of the structural integrity without subjecting the material to destructive testing. Potential applications of this method include bridge and building assessments, and structural health monitoring (SHM). © 2022 by the authors.","cantilever beam; digital image correlation; natural frequency; numerical; static and dynamic Young’s modulus",,,,,,,,,,,,,,,,,"Mott, R., Untener, J., (2021) Applied Strength of Materials, , CRC Press, Boca Raton, FL, USA; Lord, J.D., Morrell, R., (2007) Elastic Modulus Measurement, , National Physical Laboratory, London, UK; Lord, J.D., Morrell, R.M., Comparison of static and dynamic methods for measuring stiffness of high modulus steels and metal composites (2014) Can. Metall. 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Eng, 165, pp. L9-L10; Kotsanis, D., Nomikos, P., Rozos, D., Comparison of Static and Dynamic Young’s Modulus of Prasinites (2021) Mater. Proc, 5, p. 54; Asef, M., Farrokhrouz, M., A semi-empirical relation between static and dynamic elastic modulus (2017) J. Pet. Sci. Eng, 157, pp. 359-363; Hammond, J.P., Ratcliff, L.T., Brinkman, C.R., Moyer, M.W., Nestor, C.W., Jr., (1979) Dynamic and Static Measurements of Elastic Constants with Data on 2 1/4 Cr-1 Mo Steel, Types 304 and 316 Stainless Steels, and Alloy 800H, , Oak Ridge National Lab, Oak Ridge, TN, USA; Digilov, R.M., Abramovich, H., Flexural vibration test of a beam elastically restrained at one end: A new approach for Young’s modulus determination (2013) Adv. Mater. Sci. Eng, 2013; Pan, B., Qian, K., Xie, H., Asundi, A., Two-dimensional digital image correlation for in-plane displacement and strain measurement: A review (2009) Meas. Sci. Technol, 20, p. 062001; McCormick, N., Lord, J., Digital Image Correlation (2010) Mater. 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Technol, 29, p. 082001; Shirole, D., Walton, G., Hedayat, A., Experimental investigation of multi-scale strain-field heterogeneity in rocks (2020) Int. J. Rock Mech. Min. Sci, 127, p. 104212; Kant, S., Jawalkar, C.S., Comparative modal analysis of cantilever beam made of biocomposites using finite element analysis (2022) Mater. Today: Proc, 49, pp. 2330-2334; Barhli, S., Mostafavi, M., Cinar, A.F., Hollis, D., Marrow, T.J., J-integral calculation by finite element processing of measured full-field surface displacements (2017) Exp. Mech, 57, pp. 997-1009; Hunt, J.F., Zhang, H., Guo, Z., Fu, F., Cantilever beam static and dynamic response comparison with mid-point bending for thin MDF composite panels (2013) BioResources, 8, pp. 115-129; Budynas, R., Sadegh, A., (2020) Roark’s Formulas for Stress and Strain, , McGraw-Hill Education, New York, NY, USA; Tong, J., Full-field characterisation of crack tip deformation and fatigue crack growth using digital image correlation—A review. Fatigue Fract (2018) Eng. Mater. Struct, 41, pp. 1855-1869; Video Gauge—How it works | Imetrum. 10 August 2021, , https://www.imetrum.com/video-gauge/how-it-works/, Available online; Scenini, F., Azough, F., Read-Jennings, M.C.L., Waterfall, P., Shoesmith, J.P., Use of Digital Image Correlation (DIC) for detection of defects and monitoring of structural integrity in the nuclear industry Proceedings of the 9th International Conference on NDE in Relation to Structural Integrity for Nuclear and Pressurized Components, , Seattle, WA, USA, 22–24 May 2012; Liu, C., Yuan, Y., Zhang, M., Uncertainty analysis of displacement measurement with Imetrum Video Gauge (2016) ISA Trans, 65, pp. 547-555; (2017) Video Gauge User Manual: Version 5.4.8, , I. Imetrum Limited, New Albany, OH, USA; https://www.ansys.com/products/release-highlights, Available online","Loh, T.B.; Faculty of Science, 172A Ang Mo Kio Avenue 8 #05-01, Singapore; email: t.b.loh2@newcastle.ac.uk",,,"MDPI",,,,,20799292,,,,"English","Electronics (Switzerland)",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85138652554
"Qu H., Chang A., Li T., Guan Z.","57191477542;57889540500;57843515500;8987056300;","Synchro-Squeezed Adaptive Wavelet Transform-Based Optimized Multiple Analytical Mode Decomposition: Parameter Identification of Cable-Stayed Bridge under Earthquake Input",2022,"Buildings","12","8","1285","","",,,"10.3390/buildings12081285","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138008684&doi=10.3390%2fbuildings12081285&partnerID=40&md5=55d7819a4729488c858ae9dc029930a4","Department of Bridge Engineering, Tongji University, 1239 Siping Road, Shanghai, 200092, China; Shanghai Typhoon Institute of China Meteorological Administration, 166 Puxi Road, Shanghai, 200030, China","Qu, H., Department of Bridge Engineering, Tongji University, 1239 Siping Road, Shanghai, 200092, China; Chang, A., Department of Bridge Engineering, Tongji University, 1239 Siping Road, Shanghai, 200092, China; Li, T., Shanghai Typhoon Institute of China Meteorological Administration, 166 Puxi Road, Shanghai, 200030, China; Guan, Z., Department of Bridge Engineering, Tongji University, 1239 Siping Road, Shanghai, 200092, China","Deriving critical parametric information from recorded signals for system identification is critical in structural health monitoring and damage detection, while the time-varying nature of most signals often requires significant processing efforts due to structural nonlinearity. In this study, synchro-squeezed adaptive wavelet transform-based optimized multiple analytical mode decomposition (SSAWT-oMAMD) is proposed. The SSAWT algorithm acts as the preprocessing algorithm for clear signal component separation, high temporal and frequency resolution, and accurate time–frequency representation. Optimized MAMD is then utilized for signal denoising, decomposition, and identification, with the help of AWT for bisecting frequency determination. The SSAWT-oMAMD is first verified by the analytical model of two Duffing systems, where clear separation of the two signals is presented and identification of complex time-varying stiffness is achieved with errors less than 2.9%. The algorithm is then applied to system identification of a cable-stayed bridge model subjected to earthquake loading. Based on both numerical and experimental results, the proposed method is effective in identifying the structural state and viscous damping coefficient. © 2022 by the authors.","cable-stayed bridge; finite element analysis; Hilbert transform; shake table test; signal processing; wavelet transform",,,,,,"National Natural Science Foundation of China, NSFC: 52008316; Science and Technology Commission of Shanghai Municipality, STCSM: 20PJ1413900, 20ZR1461400; National Key Research and Development Program of China, NKRDPC: 2021YFF0502200","This research was funded by National Key Research and Development Program of China, grant number 2021YFF0502200; National Natural Science Foundation of China, grant number 52008316; Science and Technology Commission of Shanghai Municipality, grant numbers 20ZR1461400, 20PJ1413900.",,,,,,,,,,"Wigner, E.P., On the Quantum Correction for Thermodynamic Equilibrium (1997) Part I: Physical Chemistry. 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Sound Vib, 517, p. 116477; Zhao, J., Bao, Y., Guan, Z., Zuo, W., Li, J., Li, H., Video-based multiscale identification approach for tower vibration of a cable-stayed bridge model under earthquake ground motions (2019) Struct. Control Health Monit, 26, p. e2314; Wang, Z.-C., Xin, Y., Xing, J.-F., Ren, W.-X., Hilbert low-pass filter of non-stationary time sequence using analytical mode decomposition (2017) J. Vib. Control, 23, pp. 2444-2469; Qu, H., Li, T., Wang, R., Li, J., Guan, Z., Chen, G., Application of adaptive wavelet transform based multiple analytical mode decomposition for damage progression identification of Cable-Stayed bridge via shake table test (2021) Mech. Syst. Signal Process, 149, p. 107055","Guan, Z.; Department of Bridge Engineering, 1239 Siping Road, China; email: guanzhongguo@tongji.edu.cn",,,"MDPI",,,,,20755309,,,,"English","Buildings",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85138008684
"Yu S., Li D., Ou J.","57752060000;57219460395;57211125450;","Digital twin-based structure health hybrid monitoring and fatigue evaluation of orthotropic steel deck in cable-stayed bridge",2022,"Structural Control and Health Monitoring","29","8","e2976","","",,,"10.1002/stc.2976","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128285952&doi=10.1002%2fstc.2976&partnerID=40&md5=aa6732c09732e03ebecef25e44fe3194","School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China; School of Civil Engineering and State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China","Yu, S., School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China; Li, D., School of Civil Engineering and State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China; Ou, J., School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China, School of Civil Engineering and State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China","Digital twin bridges are virtual replicas of real physical entity bridges in computers. A digital twin bridge in the form of a finite element model can help in making sense of the structural responses monitored by the structure health monitoring system. This study proposes the structure health hybrid monitoring method, which provides a mean for synthesizing monitoring data and finite element model to reconstruct the un-monitoring structure responses, in developing a digital twin of a cable-stayed bridge. The considered structure is the orthotropic steel deck, in which the welding residual stress is an important cause of fatigue cracking. The submodel technology is employed to study the distribution characteristics of welding residual stress and the coupling effect with vehicle-induced stress and temperature-induced stress near the weld in the U-ribs to top deck joint in orthotropic steel deck. Aiming at the defect that cannot consider welding residual stress for the S-N curves based on fatigue evaluation and life prediction method, a nonlinear fatigue damage model based on the continuum damage mechanics, which has been verified by orthotropic steel deck fatigue test, is employed to evaluate the fatigue performance of U-ribs to top deck joint in orthotropic steel deck for an in-service cable-stayed bridge. © 2022 John Wiley & Sons Ltd.","digital twin bridge; fatigue performance; orthotropic steel deck; structural health hybrid monitoring; welding residual stress","Cable stayed bridges; Cables; Continuum damage mechanics; Fatigue testing; Finite element method; Structural health monitoring; Welding; Deck joints; Digital twin bridge; Fatigue evaluation; Fatigue performance; Finite element modelling (FEM); Induced stress; Orthotropic steel decks; Structural health; Structural health hybrid monitoring; Welding residual stress; Residual stresses",,,,,"National Natural Science Foundation of China, NSFC: 51778104, 51921006, U1709207","The National Natural Science Foundation of China (NSFC) with grant Nos.U1709207, 51921006, and 51778104.","The authors are grateful for the financial support from the National Natural Science Foundation of China with Grant Nos. 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"Pan Y., Ventura C.E., Li T.","56937401900;7101926223;56424221000;","Sensor placement and seismic response reconstruction for structural health monitoring using a deep neural network",2022,"Bulletin of Earthquake Engineering","20","9",,"4513","4532",,,"10.1007/s10518-021-01266-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125539034&doi=10.1007%2fs10518-021-01266-y&partnerID=40&md5=9ca331bbf535395a3d2458a01f70d34a","Earthquake Engineering Research Facility, Department of Civil Engineering, The University of British Columbia, Vancouver, BC  V6T 1Z4, Canada; School of Control Science and Engineering, Shandong University, Shandong, Jinan, 250061, China","Pan, Y., Earthquake Engineering Research Facility, Department of Civil Engineering, The University of British Columbia, Vancouver, BC  V6T 1Z4, Canada; Ventura, C.E., Earthquake Engineering Research Facility, Department of Civil Engineering, The University of British Columbia, Vancouver, BC  V6T 1Z4, Canada; Li, T., School of Control Science and Engineering, Shandong University, Shandong, Jinan, 250061, China","In seismic structural health monitoring (SHM), a structure is normally instrumented with limited sensors at certain locations to monitor its structural behavior during an earthquake event. To reconstruct the responses at non-instrumented locations, an effective regression method has to be used given the measured data from the sensed locations. In addition, determination of where to place the sensors directly affects the ability of the system to infer the behaviour of the entire structure. In this study, a practical framework is proposed for sensor placement and seismic response reconstruction at non-instrumented locations, which adopts a novel attention-based deep neural network (DNN). The developed DNN model is trained by using structural displacements at measured locations as input and the structural displacements at unmeasured locations of interest as output. The proposed framework is demonstrated by a case study of an instrumented long-span girder bridge in California. Different sensor placement schemes are investigated using the proposed DNN model. Real-time seismic assessment of the bridge is achieved by issuing each reconstructed output in 1.5 ms. The case study validates the effectiveness and accuracy of the proposed method to be used as part of a seismic SHM system. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.","Deep neural network; Finite element model; Inclinometer; Seismic response reconstruction; Sensor placement; Time history analysis","Earthquakes; Finite element method; Location; Regression analysis; Seismic response; Structural health monitoring; Case-studies; Finite element modelling (FEM); Inclinometer; Neural network model; Response reconstruction; Seismic response reconstruction; Sensor placement; Structural behaviors; Structural displacement; Time history analysis; Deep neural networks; artificial neural network; earthquake; health monitoring; reconstruction; seismic response; Calabarzon; California; Philippines; Quezon; Real; United States",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; fr:CRSNG: RGPIN-2017–04988; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada, CANRIMT, NSERC; fr:CRSNG","This work is supported in part by a Discovery Grant by the Natural Sciences and Engineering research Council of Canada awarded to the second author (Grant No. RGPIN-2017–04988).",,,,,,,,,,"Alcazar-Pastrana, O., (2016) Operational Modal Analysis, Model Updating and Response Prediction Bridge under the 2014 Napa Earthquake, Master of Applied Science, , Thesis, The University of British Columbia, Vancouver, Canada; Chang, M., Pakzad, S.N., Optimal sensor placement for modal identification of bridge systems considering number of sensing nodes (2014) J Bridg Eng, 19 (6), p. 04014019; Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y., Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014) Conference on Empirical Methods in Natural Language Processing (EMNLP), , Doha, Qatar; Chow, H.M., Lam, H.F., Yin, T., Au, S.K., Optimal sensor configuration of a typical transmission tower for the purpose of structural model updating (2011) Struct Control Health Monit, 18 (3), pp. 305-320; Civera, M., Pecorelli, M.L., Ceravolo, R., Surace, C., Zanotti Fragonara, L., A multi-objective genetic algorithm strategy for robust optimal sensor placement (2021) Comput-Aid Civ Infrastruct Eng; (2016) CSiBridge version 18, , Computers and Structures Inc., Berkeley, California, USA; Dolce, M., Nicoletti, M., De Sortis, A., Marchesini, S., Spina, D., Talanas, F., Osservatorio sismico delle strutture: the Italian structural seismic monitoring network (2017) Bull Earthq Eng, 15 (2), pp. 621-641; Fan, G., Li, J., Hao, H., Xin, Y., Data driven structural dynamic response reconstruction using segment based generative adversarial networks (2021) Eng Struct, 234, p. 111970; (2009) Quantification of Building Seismic Performance Factors, FEMA, p. 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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I., Attention is all you need (2017) 31St Conference on Neural Information Processing Systems, , (NIPS)Long Beach, CA, USA; Wu, R.T., Jahanshahi, M.R., Deep convolutional neural network for structural dynamic response estimation and system identification (2019) J Eng Mech, 145 (1), p. 04018125; Xu, Y.L., Zhang, X.H., Zhu, S., Zhan, S., Multi-type sensor placement and response reconstruction for structural health monitoring of long-span suspension bridges (2016) Sci Bull, 61 (4), pp. 313-329; Yi, T.H., Li, H.N., Gu, M., Sensor placement for structural health monitoring of Canton Tower (2012) Smart Struct Syst, 10 (4), pp. 313-329; Yu, S., Zhang, J., Fast bridge deflection monitoring through an improved feature tracing algorithm (2020) Comput-Aid Civ Infrastruct Eng, 35 (3), pp. 292-302; Yuen, K.V., Kuok, S.C., Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi-type sensory systems (2015) Earthq Eng Struct Dyn, 44 (5), pp. 757-774; Zhang, F.L., Ni, Y.C., Lam, H.F., Bayesian structural model updating using ambient vibration data collected by multiple setups (2017) Struct Control Health Monit, 24 (12); Zhang, F.L., Yang, Y.P., Xiong, H.B., Yang, J.H., Yu, Z., Structural health monitoring of a 250-m super-tall building and operational modal analysis using the fast Bayesian FFT method (2019) Struct Control Health Monit, 26 (8); Zhang, R., Chen, Z., Chen, S., Zheng, J., Büyüköztürk, O., Sun, H., Deep long short-term memory networks for nonlinear structural seismic response prediction (2019) Comput Struct, 220, pp. 55-68; Zhang, W., Sun, L., Sun, S., Bridge-deflection estimation through inclinometer data considering structural damages (2017) J Bridg Eng, 22 (2), p. 04016117; Zhang, Y., Ayyub, B., Huang, H., Enhancing civil infrastructure resilience with structural health monitoring (2018) Resil Eng Urban Tunn","Li, T.; School of Control Science and Engineering, Shandong, China; email: li.teng@sdu.edu.cn",,,"Springer Science and Business Media B.V.",,,,,1570761X,,,,"English","Bull. Earthquake Engin.",Article,"Final","",Scopus,2-s2.0-85125539034
"Finotti R.P., Gentile C., Barbosa F., Cury A.","57195837755;7005059400;57219564129;35221434500;","Structural novelty detection based on sparse autoencoders and control charts",2022,"Structural Engineering and Mechanics","81","5",,"647","664",,,"10.12989/sem.2022.81.5.647","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129191288&doi=10.12989%2fsem.2022.81.5.647&partnerID=40&md5=3db08bab192da1a4f3e377b5da2019c9","Graduate Program in Computational Modeling, Federal University of Juiz de Fora, Brazil; Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Italy; Graduate Program in Civil Engineering, Federal University of Juiz de Fora, Brazil","Finotti, R.P., Graduate Program in Computational Modeling, Federal University of Juiz de Fora, Brazil; Gentile, C., Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Italy; Barbosa, F., Graduate Program in Computational Modeling, Federal University of Juiz de Fora, Brazil, Graduate Program in Civil Engineering, Federal University of Juiz de Fora, Brazil; Cury, A., Graduate Program in Civil Engineering, Federal University of Juiz de Fora, Brazil","The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations. Copyright © 2022 Techno-Press, Ltd.","Damage detection; Deep learning; Machine learning; Sparse autoencoder; Structural health monitoring","Anomaly detection; Control charts; Deep learning; Dynamic response; Flowcharting; Learning algorithms; Structural health monitoring; Academic work; Auto encoders; Data mappings; Deep learning; Finite element modelling (FEM); Learning methods; Mapping capabilities; Novelty detection; Sparse autoencoder; Structural alterations; Damage detection",,,,,"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES: 88881.068530/2014-0; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq: 304329/2019-3-PQ, 311576/2018-4-PQ; Fundação de Amparo à Pesquisa do Estado de Minas Gerais, FAPEMIG: PPM-00001-18, PPM-00106-17; Politecnico di Milano; Universidade Federal de Juiz de Fora, UFJF","The authors would like to thank UFJF (Universidade Federal de Juiz de Fora - Programa de Pós-Graduação em Modelagem Computacional), CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, PROCAD 88881.068530/2014-0), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, grants 311576/2018-4-PQ and 304329/2019-3-PQ), FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais, grants PPM-00106-17 and PPM-00001-18) and Politecnico di Milano.",,,,,,,,,,"Agis, D., Pozo, F., A frequency-based approach for the detection and classification of structural changes using t-SNE (2019) Sensor, 19, p. 5097. , https://doi.org/10.3390/s19235097; Alves, V., Meixedo, A., Ribeiro, D., Calçada, R., Cury, A., Evaluation of the performance of different damage indicators in railway bridges (2015) Procedia Eng, 114, pp. 746-753. , https://doi.org/10.1016/j.proeng.2015.08.020; Amezquita-Sanchez, J.P., Adeli, H., Signal processing techniques for vibration-based health monitoring of smart structures (2016) Arch. Comput. Meth. Eng, 23 (1), pp. 1-15. , https://doi.org/10.1007/s11831-014-9135-7; Anowar, F., Sadaoui, S., Selim, B., Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE) (2021) Comput. Sci. Rev, 40, p. 100378. , https://doi.org/10.1016/j.cosrev.2021.100378; Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., Inman, D.J., A review of vibration-based damage detection in civil structures: From traditional methods to machine learning and deep learning applications (2021) Mech. Syst. Signal Pr, 147, p. 107077. , https://doi.org/10.1016/j.ymssp.2020.107077; Azim, M.R., Gül, M., Data-driven damage identification technique for steel truss railroad bridges utilizing principal component analysis of strain response (2021) Struct. Infrastr. Eng, 17 (8), pp. 1019-1035. , https://doi.org/10.1080/15732479.2020.1785512; Azim, M.R., Zhang, H., Gül, M., Damage detection of railway bridges using operational vibration data: theory and experimental verifications (2020) Struct. Monit. Mainten, 7 (2), pp. 149-166. , https://doi.org/10.12989/smm.2020.7.2.149; Baldi, P., Hornik, K., Neural networks and principal component analysis: learning from examples without local minima (1989) Neur. Network, 2 (1), pp. 53-58. , https://doi.org/10.1016/0893-6080(89)90014-2; Bao, Y., Tang, Z., Li, H., Zhang, Y., Computer vision and deep learning-based data anomaly detection method for structural health monitoring (2019) Struct. Hlth. Monit, 18, p. 401421. , https://doi.org/10.1177/1475921718757405; Cabboi, A., Gentile, C., Saisi, A., From continuous vibration monitoring to FEM-based damage assessment: Application on a stone-masonry tower (2017) Constr. Build. Mater, 156, pp. 252-265. , https://doi.org/10.1016/j.conbuildmat.2017.08.160; Carden, E.P., Fanning, P., Vibration based condition monitoring: A review (2004) Struct. Hlth. Monit, 3 (4), pp. 355-377. , https://doi.org/10.1177/1475921704047500; Cardoso, R.A., Cury, A., Barbosa, F., A clustering-based strategy for automated structural modal identification (2018) Struct. Hlth. Monit, 17 (2), pp. 201-217. , https://doi.org/10.1177/1475921716689239; Cardoso, R.A., Cury, A., Barbosa, F., Automated real-time damage detection strategy using raw dynamic measurements (2019) Eng. Struct, 196, p. 109364. , https://doi.org/10.1016/j.engstruct.2019.109364; Cardoso, R.A., Cury, A., Barbosa, F., Gentile, C., Unsupervised real-time SHM technique based on novelty indexes (2019) Struct. Control Hlth. Monit, 26, p. e2364. , https://doi.org/10.1002/stc.2364; Chang, M., Kim, J.K., Lee, J., Hierarchical neural network for damage detection using modal parameters (2019) Struct. Eng. Mech, 70 (4), pp. 457-466. , https://doi.org/10.12989/sem.2019.70.4.457; Cremona, C., Santos, J., Structural health monitoring as a big-data problem (2018) Struct. Eng. Int, 28, p. 243254. , https://doi.org/10.1080/10168664.2018.1461536; Dan, J., Feng, W., Huang, X., Wang, Y., Global bridge damage detection using multi-sensor data based on optimized functional echo state networks (2021) Struct. Hlth. Monit, 20 (4), pp. 1924-1937. , https://doi.org/10.1177/1475921720948206; Das, S., Saha, P., Patro, S.K., Vibration-based damage detection techniques used for health monitoring of structures: a review (2016) J. Civil Struct. Hlth. Monit, 6 (3), pp. 477-507. , https://doi.org/10.1007/s13349-016-0168-5; De Roeck, G., Peeters, B., Maeck, J., Dynamic monitoring of civil engineering structures (2000) Proceedings of IASS-IACM 2000, 4th International Colloquium on Computational Methods for Shell and Spatial Structures, , Athens; Doebling, S.W., Farrar, C.R., Prime, M.B., A summary review of vibration-based damage identification methods (1998) Shock Vib. Dig, 30 (2), pp. 91-105; Eftekhar Azam, S., Rageh, A., Linzell, D., Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition (2019) Struct. Control Hlth. Monit, 26 (2), p. e2288. , https://doi.org/10.1002/stc.2288; Esfandiari, A., Nabiyan, M.S., Rofooei, F.R., Structural damage detection using principal component analysis of frequency response function data (2020) Struct. Control Hlth. Monit, 27 (7), p. e2550. , https://doi.org/10.1002/stc.2550; Fan, W., Qiao, P., Vibration-based damage identification methods: a review and comparative study (2011) Struct. Hlth. Monit, 10 (1), pp. 83-129. , https://doi.org/10.1177/1475921710365419; Finotti, R.P., Barbosa, F.D.S., Cury, A.A., Pimentel, R.L., Numerical and experimental evaluation of structural changes using sparse auto-encoders and SVM applied to dynamic responses (2021) Appl. Sci, 11 (24), p. 11965. , https://doi.org/10.3390/app112411965; Finotti, R.P., Cury, A.A., Barbosa, F.S., An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements (2019) Lat. Am. J. Solid. Struct, 16 (2), p. e165. , https://doi.org/10.1590/1679-78254942; Garcia-Macias, E., Ubertini, F., MOVA/MOSS: Two integrated software solutions for comprehensive Structural Health Monitoring of structures (2020) Mech. Syst. Signal Pr, 143, p. 106830. , https://doi.org/10.1016/j.ymssp.2020.106830; Gentile, C., Saisi, A., Cabboi, A., Structural identification of a masonry tower based on operational modal analysis (2015) Int. J. Arch. Heritage, 9 (2), pp. 98-110. , https://doi.org/10.1080/15583058.2014.951792; Gillich, G.R., Furdui, H., Wahab, M.A., Korka, Z.I., A robust damage detection method based on multi-modal analysis in variable temperature conditions (2019) Mech. Syst. Signal Pr, 115, pp. 361-379. , https://doi.org/10.1016/j.ymssp.2018.05.037; Goodfellow, I., Bengio, Y., Courville, A., (2016) Deep Learning, , MIT press; Gu, J., Gul, M., Wu, X., Damage detection under varying temperature using artificial neural networks (2017) Struct. Control Hlth. Monit, 24 (11), p. e1998. , https://doi.org/10.1002/stc.1998; Guo, G., Zhang, N., A survey on deep learning based face recognition (2019) Comput. Vis. Image Understand, 189, p. 102805. , https://doi.org/10.1016/j.cviu.2019.102805; Hou, R., Xia, Y., Review on the new development of vibration-based damage identification for civil engineering structures: 2010-2019 (2020) J. Sound Vib, 491, p. 115741. , https://doi.org/10.1016/j.jsv.2020.115741; Kullback, S., Leibler, R.A., On information and sufficiency (1951) Ann. Math. Statist, 22 (1), pp. 79-86; Liu, G., Zhai, Y., Leng, D., Tian, X., Mu, W., Research on structural damage detection of offshore platforms based on grouping modal strain energy (2017) Ocean Eng, 140, pp. 43-49. , https://doi.org/10.1016/j.oceaneng.2017.05.021; Zhou, G.D., Yi, T.H., A summary review of correlations between temperatures and vibration properties of long-span bridges (2014) Math. Prob. Eng, 2014, p. 638209. , https://doi.org/10.1155/2014/638209, Article ID","Cury, A.; Graduate Program in Civil Engineering, Brazil; email: alexandre.cury@ufjf.edu.br",,,"Techno-Press",,,,,12254568,,SEGME,,"English","Struct Eng Mech",Article,"Final","",Scopus,2-s2.0-85129191288
"Lin S.T.K., Lu Y., Alamdari M.M., Khoa N.L.D.","57201885621;7405478104;57211214264;16642825600;","Neural network based numerical model updating and verification for a short span concrete culvert bridge by incorporating Monte Carlo simulations",2022,"Structural Engineering and Mechanics","81","3",,"293","303",,,"10.12989/sem.2022.81.3.293","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129200133&doi=10.12989%2fsem.2022.81.3.293&partnerID=40&md5=120d70026caffb31ea9a36fc5cd913db","Department of Civil Engineering, Monash University, Melbourne, VIC, Australia; School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia; Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, NSW, Australia","Lin, S.T.K., Department of Civil Engineering, Monash University, Melbourne, VIC, Australia; Lu, Y., Department of Civil Engineering, Monash University, Melbourne, VIC, Australia; Alamdari, M.M., School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia; Khoa, N.L.D., Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, NSW, Australia","As infrastructure ages and traffic load increases, serious public concerns have arisen for the well-being of bridges. The current health monitoring practice focuses on large-scale bridges rather than short span bridges. However, it is critical that more attention should be given to these behind-the-scene bridges. The relevant information about the construction methods and as-built properties are most likely missing. Additionally, since the condition of a bridge has unavoidably changed during service, due to weathering and deterioration, the material properties and boundary conditions would also have changed since its construction. Therefore, it is not appropriate to continue using the design values of the bridge parameters when undertaking any analysis to evaluate bridge performance. It is imperative to update the model, using finite element (FE) analysis to reflect the current structural condition. In this study, a FE model is established to simulate a concrete culvert bridge in New South Wales, Australia. That model, however, contains a number of parameter uncertainties that would compromise the accuracy of analytical results. The model is therefore updated with a neural network (NN) optimisation algorithm incorporating Monte Carlo (MC) simulation to minimise the uncertainties in parameters. The modal frequency and strain responses produced by the updated FE model are compared with the frequency and strain values on-site measured by sensors. The outcome indicates that the NN model updating incorporating MC simulation is a feasible and robust optimisation method for updating numerical models so as to minimise the difference between numerical models and their real-world counterparts. Copyright © 2022 Techno-Press, Ltd.","Finite element model updating; Neural network optimization; Short span bridge; Unidentified construction method","Bridges; Concretes; Culverts; Deterioration; Finite element method; Intelligent systems; Numerical methods; Numerical models; Optimization; Structural health monitoring; 'current; Construction method; Finite element modelling (FEM); Finite-element model updating; Model updating; Monte Carlo's simulation; Neural network optimization; Neural-networks; Short-span bridges; Unidentified construction method; Monte Carlo methods",,,,,,,,,,,,,,,,"Abbasnia, R., Shayanfar, M., Khodam, A., Reliability-based design optimization of structural systems using a hybrid genetic algorithm (2014) Struct. Eng. 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Mech, 59 (4), pp. 703-718. , https://doi.org/10.12989/sem.2016.59.4.703; Aygül, M., Al-Emrani, M., Urushadze, S., Modelling and fatigue life assessment of orthotropic bridge deck details using FEM (2012) Int. J. Fatig, 40, pp. 129-142. , https://doi.org/10.1016/j.ijfatigue.2011.12.015; Basterrech, S., Mohammed, S., Rubino, G., Soliman, M., Levenberg-Marquardt training algorithms for random neural networks (2011) Comput. J, 54 (1), pp. 125-135. , https://doi.org/10.1093/comjnl/bxp101; Brandimarte, P., (2014) Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics, , John Wiley & Sons; Cha, Y.J., Choi, W., Büyüköztürk, O., Deep learning-based crack damage detection using convolutional neural networks (2017) Comput. Aid. Civil Infrastr. Eng, 32 (5), pp. 361-378. , https://doi.org/10.1111/mice.12263; Chan, T.H., Zhou, T., Li, Z., Guo, L., Hot spot stress approach for Tsing Ma Bridge fatigue evaluation under traffic using finite element method (2005) Struct. Eng. Mech, 19 (3), pp. 261-280. , https://doi.org/10.12989/sem.2005.19.3.261; Chan, W.K.V., (2013) Theory and Applications of Monte Carlo Simulations, , https://doi.org/10.1007/978-3-642-25349-2_111, BoD-Books on Demand; Chen, Y., Yan, J., Feng, J., Sareh, P., Particle swarm optimization-based metaheuristic design generation of nontrivial flat-foldable origami tessellations with degree-4 vertices (2021) J. Mech. Des, 143 (1), p. 011703. , https://doi.org/10.1115/1.4047437; Deng, L., Cai, C., Bridge model updating using response surface method and genetic algorithm (2010) J. 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Monit, 27 (10), p. e2614. , https://doi.org/10.1002/stc.2614; Lu, Y., Tu, Z., A two-level neural network approach for dynamic FE model updating including damping (2004) J. Sound Vib, 275 (3-5), pp. 931-952. , https://doi.org/10.1016/S0022-460X(03)00796-X; Maind, S.B., Wankar, P., Research paper on basic of artificial neural network (2014) Int. J. Recent Innov. Trend. Comput. Commun, 2 (1), pp. 96-100. , https://doi.org/10.17762/ijritcc.v2i1.2920; Miao, S., Koenders, E., Knobbe, A., Automatic baseline correction of strain gauge signals (2015) Struct. Control Hlth. Monit, 22 (1), pp. 36-49. , https://doi.org/10.1002/stc.1658; Momeni, E., Nazir, R., Armaghani, D.J., Maizir, H., Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN (2014) Measure, 57, pp. 122-131. , https://doi.org/10.1016/j.measurement.2014.08.007; Mordechai, S., (2011) Applications of Monte Carlo Method in Science and Engineering, , Intech; Mortazavi, A., Togan, V., Nuhoglu, A., An integrated particle swarm optimizer for optimization of truss structures with discrete variables (2017) Struct. Eng. Mech, 61 (3), pp. 359-370. , https://doi.org/10.12989/sem.2017.61.3.359; Nikoo, M., Torabian Moghadam, F., Sadowski, Ł., Prediction of concrete compressive strength by evolutionary artificial neural networks (2015) Adv. Mater. Sci. Eng, 2015, p. 849126. , https://doi.org/10.1155/2015/849126, Article ID; Okasha, N.M., Frangopol, D.M., Advanced modeling for efficient computation of life-cycle performance prediction and service-life estimation of bridges (2010) J. Comput. Civil Eng, 24 (6), pp. 548-556. , https://doi.org/10.1061/(ASCE)CP.19435487.0000060; Park, Y.S., Kim, S., Kim, N., Lee, J.J., Finite element model updating considering boundary conditions using neural networks (2017) Eng. Struct, 150, pp. 511-519. , https://doi.org/10.1016/j.engstruct.2017.07.032; Satoh, K., Yoshikawa, N., Nakano, Y., Yang, W.J., Whole learning algorithm of the neural network for modeling nonlinear and dynamic behavior of RC members (2001) Struct. Eng. Mech, 12 (5), pp. 527-540. , https://doi.org/10.12989/sem.2001.12.5.527; Shabbir, F., Omenzetter, P., Particle swarm optimization with sequential niche technique for dynamic finite element model updating (2015) Comput. Aid. Civil Infrastr. Eng, 30 (5), pp. 359-375. , https://doi.org/10.1111/mice.12100; Shafahi, Y., Bagherian, M., A customized particle swarm method to solve highway alignment optimization problem (2013) Comput. Aid. Civil Infrastr. Eng, 28 (1), pp. 52-67. , https://doi.org/10.1111/j.1467-8667.2012.00769.x; Shi, L., Lin, S., Lu, Y., Ye, L., Zhang, Y., Artificial neural network based mechanical and electrical property prediction of engineered cementitious composites (2018) Constr. Build. Mater, 174, pp. 667-674. , https://doi.org/10.1016/j.conbuildmat.2018.04.127; Su, Z., Ye, L., Lamb wave propagation-based damage identification for quasi-isotropic CF/EP composite laminates using artificial neural algorithm: Part I-methodology and database development (2005) J. Intel. Mater. Syst. Struct, 16 (2), pp. 97-111. , https://doi.org/10.1177%2F1045389X05047599; Wang, S.C., (2012) Interdisciplinary Computing in Java Programming, 743. , Springer Science & Business Media; Yan, G.R., Duan, Z.D., Ou, J.P., Application of genetic algorithm on structural finite element model updating (2007) J. Harbin Inst. Technol, 2, pp. 181-186; Živanović, S., Pavic, A., Reynolds, P., Finite element modelling and updating of a lively footbridge: The complete process (2007) J. Sound Vib, 301 (1-2), pp. 126-145. , https://doi.org/10.12989/sem.2014.52.6.1099","Lu, Y.; Department of Civil Engineering, Australia; email: ye.lu@monash.edu",,,"Techno-Press",,,,,12254568,,SEGME,,"English","Struct Eng Mech",Article,"Final","",Scopus,2-s2.0-85129200133
"Pourtarki A., Badri Ghavifekr H., Afshin H.","57986109900;35226199400;55874683100;","Study on the dynamic behaviour of Bafgh-Bandar Abbas lane railway bridge for structural health monitoring purpose",2022,"Australian Journal of Structural Engineering",,,,"","",,,"10.1080/13287982.2022.2149975","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142898613&doi=10.1080%2f13287982.2022.2149975&partnerID=40&md5=5601da2946005488f683a9dbcf076e45","Structural Engineering, Shahr Ab Sahand Co, Iran; Microelectronics, Sahand University of Technology, Tabriz, Iran; Structural Engineering, Sahand University of Technology, Tabriz, Iran","Pourtarki, A., Structural Engineering, Shahr Ab Sahand Co, Iran; Badri Ghavifekr, H., Microelectronics, Sahand University of Technology, Tabriz, Iran; Afshin, H., Structural Engineering, Sahand University of Technology, Tabriz, Iran","This paper uses the vibration-based damage detection method for structural health monitoring of a Railway Bridge. Hence, the bridge had been exactly modelled in finite element analysis software. Due to the elastomeric bridge bearing, direct calculating of the natural frequencies and extracting the mode shapes of the bridge is not suitable and effective. Therefore, a time-dependent transient analysis of the train movement on the bridge is done and then the vibrations of all truss cells during the train crossing period were extracted. Afterwards, the relevant data is transformed, which represents the natural frequencies of the bridge vibration and their amplitude at each point. In the following, the mode shapes of vibration and the distribution of vibration energy are calculated. Applying any artificial damages in the model, occurring changes in the natural frequencies, mode shapes, and the vibration energies of the modes, are examined. Consequently, detecting and locating damages in the structure has been done with acceptable accuracy. Finally, for validation of the results, an accelerometer is installed on the bridge truss in the middle part of the bridge to extract the acceleration of bridge vibration at the train crossing time interval. In the end, the results are compared and presented. © 2022 Engineers Australia.","dynamic behaviour; mode shape; natural frequency; railway bridge; Structural health monitoring",,,,,,,,,,,,,,,,,"Alamdari, M.M., Vibration-Based Structural Health Monitoring (2015) Ph.D. thesis, , University of Technology Sydney, Australia; Alwash, M.B., Excitation Sources for Structural Health Monitoring of Bridges (2010) Ph.D. thesis, , University of Saskatchewan, Canada; Balageas, D., Fritzen, C.-P., Güemes, A., (2006) Structural Health Monitoring, , U.S.: John Wiley & Sons; Comanducci, G., Magalhães, F., Ubertini, F., Cunha, Á., On vibration-based Damage Detection by Multivariate Statistical Techniques: Application to a long-span Arch Bridge (2016) Structural Health Monitoring, 15 (5), pp. 505-524; Cruz, P.J.S., Salgado, R., Performance of vibration-based Damage Detection Methods in Bridges (2008) Computer-Aided Civil and Infrastructure Engineering, 24 (1), pp. 62-79; Galchev, T.V., McCullagh, J., Peterson, R.L., Najafi, K., Harvesting traffic-induced Vibrations for Structural Health Monitoring of Bridges (2011) Journal of Micromechanics andMicroengineering, 21 (10), p. 104005; Guan, H., Karbhari, V.M., Vibration-Based Structural Health Monitoring of Highway Bridges (2008) Technical report, , California Department of Transportation Engineering Services Center, Report CA06-0081; Haghighi, A.A.M.K., (2010) Vibration-based Damage Detection and Health Monitoring of Bridges, , Ph.D thesis, North Carolina State University, United States; Magalhães, F., Cunha, A.A.M.F., De Sa Caetano, E., Vibration Based Structural Health Monitoring of an Arch Bridge: From Automated OMA to Damage Detection (2012) Mechanical Systems and Signal Processing, 28, pp. 212-228; Min, X., Oliveira Santos, L., Dynamic Assessment of the São João Bridge Structural Integrity (2017) 2nd International Conference on Structural Integrity, 5, pp. 325-331. , Funchal, Madeira, Portugal, Procedia Structural Integrity:, and; Moughty, J.J., Ramon CASAS, J., Vibration Based Damage Detection Techniques for Small to Medium Span Bridges: A Review and Case Study (2016) 8th European Workshop On Structural Health Monitoring, , Spain, Bilbao; Wenzel, H., (2009) Health Monitoring of Bridges, , U.S.: John Wiley & Sons","Pourtarki, A.; Structural Engineering, Iran; email: amir.pourtarki@Gmail.com",,,"Taylor and Francis Ltd.",,,,,13287982,,,,"English","Aust. J. Struct. Eng.",Article,"Article in Press","",Scopus,2-s2.0-85142898613
"Si Y., Sun L., Li Y.","57985758800;7403956279;57211568199;","Sensitivity-based Structural Damage Identification via Response Reconstruction",2022,"IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation, Report",,,,"1248","1255",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142887639&partnerID=40&md5=76719d09cf854268035fe8171eb04cf5","Department of Bridge Engineering, Tongji University, Shanghai, 200092, China; State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai, 200092, China; Department of Civil and Environment, Hong Kong Polytechnic University, Hong Kong; Dept. of Bridge Engineering, College of Civil Engineering, Tongji University, Hong Kong","Si, Y., Department of Bridge Engineering, Tongji University, Shanghai, 200092, China; Sun, L., Department of Bridge Engineering, Tongji University, Shanghai, 200092, China, State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai, 200092, China; Li, Y., Department of Civil and Environment, Hong Kong Polytechnic University, Hong Kong, Dept. of Bridge Engineering, College of Civil Engineering, Tongji University, Hong Kong","The limitation of sensor number and results that the monitored data is difficult to reflect the true state of the structures, which limits the reliability of bridge damage identification and deterioration assessment. There are many full-field response reconstruction methods developed to obtain the complete responses. However, the quality of the reconstructed data is comprehensively affected by finite element model errors, noise interference and strong coupling environmental effects, resulting in the low accuracy of dynamic-based damage indicators. Thus, this paper explores a damage identification method based on static response reconstruction and solve the damage factor using the relationship between the sensitivity matrix and the structural deformation. Taking simply-supported and cantilevered beam as examples, numerical calculations confirm that the method can effectively locate the damage of the structural system. © IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation, Report. All rights reserved.","damage identification; response reconstruction; sensitivity analysis; structural health monitoring","Damage detection; Deterioration; Numerical methods; Structural health monitoring; Bridge damage; Complete response; Damage deteriorations; Damage Identification; Finite element modelling (FEM); Full field response; Model errors; Reconstruction method; Response reconstruction; Structural damage identification; Sensitivity analysis",,,,,,,,,,,,,,,,"DOEBLING, S W, FARRAR, C R, PRIME, M B, Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review (1996) Los Alamos National Laboratory Report LA-13070-MS, 30 (11), pp. 2043-2049. , [J]; YANG, C, WANG, X J, QIU, Z P., A fusion sensitivity structural damage identification method based on dynamic and static testing [J] (2013) Chinese Journal of Applied Mechanics, 30 (5); ZHU, H P, MAO, L, WENG, S., A sensitivity-based structural damage identification method with unknown input excitation using transmissibility concept (2014) Journal of Sound & Vibration, 333 (26), pp. 7135-7150. , [J]; LUO, S, YANG, H, SHEN, J B, Structural damage identification based on stiffness sensitivity matrix (2020) Journal of Guangxi University (Natural Science Edvition), 45 (6), pp. 1293-1300. , [J]; MASOUMI, M, JAMSHIDI, E, BAMDAD, M., Application of generalized flexibility matrix in damage identification using Imperialist Competitive Algorithm (2015) Ksce Journal of Civil Engineering, 19 (4), pp. 1-8. , [J]; ZHANG, C D, XU, Y L., Structural damage identification via response reconstruction under unknown excitation (2016) Structural Control & Health Monitoring, 24 (8), p. e19531. , [J]. e.11; LI, J, LAW, S S, DING, Y., Substructure damage identification based on response reconstruction in frequency domain and model updating (2012) Engineering Structures, 41 (3), pp. 270-284. , [J]; ZHANG, Q, JANKOWSKI, L, DUAN, Z., Simultaneous identification of excitation time histories and parametrized structural damages (2012) Mechanical Systems & Signal Processing, 33, pp. 56-68. , [J]. (NOV); LI, X Y, LAW, S S., Adaptive Tikhonov regularization for damage detection based on nonlinear model updating (2010) Mechanical Systems & Signal Processing, 24 (6), pp. 1646-1664. , [J]; LI, Y, SUN, L., Structural deformation reconstruction by the Penrose-Moore pseudo-inverse and singular value decomposition-estimated equivalent force (2020) Structural Health Monitoring, p. 1475921720952333. , [J]; SUN, L M, SI, Y N, LI, Y X., Analyze a mechanical system by PCA: a full-field load estimation and response reconstruction framework [J], , unpublished manuscript; ZHANG, C D, XU, Y L., Comparative studies on damage identification with Tikhonov regularization and sparse regularization (2016) Structural Control & Health Monitoring, 23 (3). , [J]","Sun, L.; Department of Bridge Engineering, China; email: lmsun@tongji.edu.cn",,,"International Association for Bridge and Structural Engineering (IABSE)","IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation","21 September 2022 through 23 September 2022",,184084,,9783857481840,,,"English","IABSE Congr. Nanjing - Bridg. Struct.: Connect., Integr. Harmon., Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85142887639
"Luo L., Xia Y., Wang A., Sun L.","57337744100;55553987200;57222349047;7403956279;","Computer Vision-based Finite Element Model Updating Method Using Measured Static Data: An Experimental Study",2022,"IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation, Report",,,,"1473","1479",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142874111&partnerID=40&md5=5e0499da70a565d3d410f78b4d3d93e8","Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, China; State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai, 200092, China","Luo, L., Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, China; Xia, Y., Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, China; Wang, A., Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, China; Sun, L., State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai, 200092, China","Accurate FE models play an important role in structure health monitoring (SHM). In the traditional static finite element model updating (FEMU) process, loading tests interrupting the traffic are required for obtaining static data, which is inconvenient. This paper proposes a novel static FEMU method based on computer vision technology and WIM system, avoiding the mentioned defects. Firstly, the static response simulation under traffic load is carried out with the computer vision determining the load location and the BIW system deciding the load value. Secondly, signal processing technology extracts the measured static data from the monitoring data. Thirdly, the PSO method is utilized to perform the FEMU. An experiment is designed on a bridge model with an SHM system, and results verify the convenience and accuracy of the proposed method. © IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation, Report. All rights reserved.","computer vision; FEMU; parallel calculation; PSODE","Bridges; Finite element method; Structural health monitoring; Computer vision technology; FE model; Finite-element model updating; Loading tests; Parallel calculation; PSODE; Static datum; Structure health monitoring; Updating methods; Vision based; Computer vision",,,,,,,,,,,,,,,,"Giagopoulos, D, Arailopoulos, A, Dertimanis, V, Papadimitriou, C, Chatzi, E, Grompanopoulos, K., Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating (2018) Structural Health Monitoring, 18 (4), pp. 1189-1206; Friswell, M. I., (2013) Finite Element Model Updating in Structural Dynamics: Finite element model updating in structural dynamics; Alkayem, NF, Cao, MS, Zhang, YF, Bayat, M, Su, ZQ., Structural damage detection using finite element model updating with evolutionary algorithms: a survey (2018) Neural Comput Appl, 30 (2), pp. 389-411; Xiao, X, Xu, YL, Zhu, Q., Multiscale Modeling and Model Updating of a Cable-Stayed Bridge. II: Model Updating Using Modal Frequencies and Influence Lines (2015) J Bridge Eng, 20 (10); Wang, Y, Li, Z, Wang, C, Wang, H., Concurrent multi-scale modelling and updating of long-span bridges using a multi-objective optimisation technique (2013) Struct Infrastruct E, 9 (12), pp. 1251-1266; Dan, DH, Ge, LF, Yan, XF., Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision (2019) Measurement, 144, pp. 155-166; Jian, XD, Xia, Y, Lozano-Galant, JA, Sun, LM., Traffic Sensing Methodology Combining Influence Line Theory and Computer Vision Techniques for Girder Bridges (2019) J Sensors; Xia, Y, Jian, XD, Yan, B, Su, D., Infrastructure Safety Oriented Traffic Load Monitoring Using Multi-Sensor and Single Camera for Short and Medium Span Bridges (2019) Remote Sens-Basel, 11 (22); Cleveland, WS., Lowess - a Program for Smoothing Scatterplots by Robust Locally Weighted Regression (1981) Am Stat, 35 (1), p. 54","Sun, L.; State Key Laboratory for Disaster Reduction in Civil Engineering, China; email: lmsun@tongji.edu.cn",,,"International Association for Bridge and Structural Engineering (IABSE)","IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation","21 September 2022 through 23 September 2022",,184084,,9783857481840,,,"English","IABSE Congr. Nanjing - Bridg. Struct.: Connect., Integr. Harmon., Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85142874111
"Zacchei E., Lyra P.H.C., Lage G.E., Antonine E., Soares A.B., Jr., Caruso N.C., de Assis C.S.","57195836485;57219125236;57748727800;57747435500;57747750900;57746791400;57747435600;","Structural Health Monitoring and Mathematical Modelling of a Site-Specific Concrete Bridge Under Moving Two-Axle Vehicles",2022,"International Journal of Civil Engineering",,,,"","",,,"10.1007/s40999-022-00770-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140976003&doi=10.1007%2fs40999-022-00770-9&partnerID=40&md5=76f5b5770ca785dd23c3cfa9a134ab0d","Itecons, Coimbra, Portugal; University of Coimbra, CERIS, Coimbra, Portugal; Mauá Institute of Technology (MIT), 1 Mauá Square, São Caetano do Sul-SP, Brazil; França e Associados Projetos Estruturais, 1768 Brigadeiro Faria Lima Avenue, São Paulo-SP, Brazil; HTB Engineer e Construction, 145 Alfredo Egídio de Souza Aranha Avenue, São Paulo-SP, Brazil","Zacchei, E., Itecons, Coimbra, Portugal, University of Coimbra, CERIS, Coimbra, Portugal; Lyra, P.H.C., Mauá Institute of Technology (MIT), 1 Mauá Square, São Caetano do Sul-SP, Brazil; Lage, G.E., Mauá Institute of Technology (MIT), 1 Mauá Square, São Caetano do Sul-SP, Brazil, França e Associados Projetos Estruturais, 1768 Brigadeiro Faria Lima Avenue, São Paulo-SP, Brazil; Antonine, E., Mauá Institute of Technology (MIT), 1 Mauá Square, São Caetano do Sul-SP, Brazil; Soares, A.B., Jr., Mauá Institute of Technology (MIT), 1 Mauá Square, São Caetano do Sul-SP, Brazil; Caruso, N.C., HTB Engineer e Construction, 145 Alfredo Egídio de Souza Aranha Avenue, São Paulo-SP, Brazil; de Assis, C.S., Mauá Institute of Technology (MIT), 1 Mauá Square, São Caetano do Sul-SP, Brazil","3D coupled models to simulate vehicle–bridge interaction (VBI) for estimating its structural responses and impact factors (IMs) have been developed. By structural health monitoring (SHM), with building information modelling (BIM) of a real bridge, several data have been collected to calibrate the bridge model by finite element method (FEM). Modified recent analytical equations, which account for the effects of the asymmetric two-axle vehicles, have been also developed. These models provide the displacements and accelerations of the bridge and vehicle. From codes and literature, it is shown that a unique IM factor is not found. Also, most approaches underestimate the dynamic responses for bridges. Therefore, the goal consisted in fitting the SHM with FEM and analytical models to find more appropriate response for safety purposes. The monitoring provided a maximum acceleration and displacement of the bridge of about 8.0 m/s2 and 6.0 mm, respectively. By analytical models, more conservative vertical accelerations with values up to 3.0 times the standard riding comfort was estimated. Also, results show that the monitored IM factor is 1.50 greater than IM from codes. © 2022, Iran University of Science and Technology.","BIM; Brazilian bridge; Concrete bridge; IM factors; SHM; VBI","Architectural design; Axles; Concrete bridges; Concretes; Estimation; Structural health monitoring; Vehicles; Brazilian bridge; Bridge model; Building Information Modelling; Coupled models; Impact factor; Impact factor factor; Site-specific; Structural impact; Structural response factor; Vehicle-bridge interaction; Analytical models",,,,,"Education Department of Hunan Province: 20B005; National Natural Science Foundation of China, NSFC: 51809011, 52270195, U21A2010; Natural Science Foundation of Hunan Province: 2021JJ40601","This research was financially supported by the National Natural Science Foundation of China (52270195, 51809011, and U21A2010), the Natural Science Foundation of Hunan Province (2021JJ40601), and the Scientific Research Project of the Education Department of Hunan province (20B005).",,,,,,,,,,"Cai, C., He, Q., Zhu, S., Zhai, W., Wang, M., Dynamic interaction of suspension-type monorail vehicle and bridge: numerical simulation and experiment (2019) Mech Syst Signal Process, 118, pp. 388-407; Alexander, N.A., Kashani, M.M., Exploring bridge dynamics for ultra-high-speed (2018) Hyperloop, trains, Structures, 14, pp. 69-74; Xia, H., Zhang, N., Dynamic analysis of railway bridge under high-speed trains (2005) Comput Struct, 83, pp. 1891-1901; Galvín, P., Romero, A., Moliner, E., Martínez-Rodrigo, M.D., Two FE models to analyse the dynamic response of short span simply-supported oblique high-speed railway bridges: comparison and experimental validation (2018) Eng Struct, 167, pp. 48-64; Xia, H., Zhang, N., De Roeck, G., Dynamic analysis of high speed railway bridge under articulated trains (2003) Comput Struct, 81, pp. 2467-2478; Huseynov, F., Kim, C., Obrien, E.J., Brownjohn, J.M.W., Hester, D., Chang, K.C., Bridge damage detection using rotation measurements – Experimental validation (2020) Mech Syst Sign Process, 135, pp. 1-22; André, A., Fernandes, J., Ferraz, I., Pacheco, P., New modular bridges solutions (2018) Mater Sci Eng, 419, pp. 1-9; Lantsoght, E.O.L., Veen, C.V.D., Boer, D.A., Hordijk, D.A., State-of-the-art in load testing of concrete bridges (2017) Eng Struct, 150, pp. 231-241; Leitão, F.N., Da Silva, J.G.S., Vellasco, P.C.G.S., De Andrade, S.A.L., De Lima, L.R.O., Composite (stell-concrete) highway bridge fatigue assessment (2011) J Constr Steel Res, 67, pp. 14-24; Li, H., Wu, G., Fatigue evaluation of steel bridge details integrating multi-scale dynamic analysis of coupled train-track-bridge system and fracture mechanics (2020) Appl Sci, 10, pp. 1-21; Siwowski, T., Fatigue assessment of existing riveted truss bridges: case study (2015) Bull Acad Pol Sci, 63, pp. 125-133; Svendsen, B.T., Gunnstein, T.F., Ronnquist, A., Damage detection applied to a full-scale steel bridge using temporal moments (2020) Shock Vib, 1-16, p. 2020; Siriwardane, S., Ohga, M., Dissanayake, R., Taniwaki, K., Application of new damage indicator-based sequential law for remaining fatigue life estimation of railway bridges (2008) J Constr Steel Res, 64, pp. 228-237; Mousavi, A.A., Zhang, C., Masri, S.F., Gholipour, G., Structural damage localization and quantification based on a CEEMDAN Hilbert transform neural network approach: a model steel truss bridge case study (2020) Sensors, 20, pp. 1-23; Carneiro, A.L., Portela, E.L., Bittencourt, T.N., Beck, A.T., Fatigue safety level provided by Brazilian design standards for a prestressed girder highway bridge (2021) Ibracon Struct Mater J, 14, pp. 1-24; Wang, T.L., Shahawy, M., Huang, D.Z., Impact in highway prestressed concrete bridges (1992) Comput Struct, 44, pp. 525-534; Liu, C., Huang, D., Wang, T.L., Analytical dynamic impact study based on correlated road roughness (2002) Comput Struct, 80, pp. 1639-1650; Cai, C.S., Shi, X.M., Araujo, M., Chen, S.R., Effect of approach span condition on vehicle-induced dynamic response of slab-on-girder road bridges (2007) Eng Struct, 29, pp. 3210-3226; Brady, S.P., O’Brien, E.J., Znidaric, A., Effect of vehicle velocity on the dynamic amplification of a vehicle crossing a simply supported bridge (2006) J Bridg Eng, 11, pp. 241-249; Yang, Y.B., Yau, G.B., Wu, Y.S., (2004) Vehicle-Bridge Interaction Dynamics, with applications to high-speed railways, p. 565. , World Scientific Publishing Co. Pte, Ltd., Singapore; Yang, Y.B., Wu, Y.S., A versatile element for analyzing vehicle-bridge interaction response (2001) Eng Struct, 23, pp. 452-469; Yang, Y.B., Zhang, B., Wang, T., Xu, H., Wu, Y., Two-axle test vehicle for bridges: theory and applications (2019) Int J Mech Sci, 152, pp. 51-62; Yang, Y.B., Wang, Z.L., Shi, K., Xu, H., Mo, X.Q., Wu, Y.T., Two-axle test vehicle for damage detection for railway tracks modelled as simply supported beams with elastic foundation (2020) Eng Struct, 219, pp. 1-13; Camara, A., Kavrakov, I., Nguyen, K., Morgenthal, G., Complete framework of wind-vehicle-bridge interaction with random real surfaces (2019) J Sound Vib, 458, pp. 197-217; Agostinacchio, M., Ciampa, D., Olita, S., The vibrations induced by surface irregularities in road pavements – a Matlab approach (2014) Eur Transp Res Rev, 6, pp. 267-275; Deng, L., Cai, C.S., Development of dynamic impact factor for performance evaluation of existing multi-girder concrete bridges (2010) Eng Struct, 32, pp. 21-31; 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Montenegro, P.A., Barbosa, D., Carvalho, H., Calçada, R., Dynamic effects on a train-bridge system caused by stochastically generated turbulent wind fields (2020) Eng Struct, 211, pp. 1-16; Zacchei, E., Lyra, P., Stucchi, F., Pushover analysis for flexible and semi-flexible pile-supported wharf structures accounting the dynamic magnification factors due to torsional effects (2020) Struct Concr, 1-20, p. 2020; Marrana, J.R.M.S.S., (2016) Analise Comparativa E Regulamentação Internacional Em ações De Trafego rodoviário, p. 114. , Porto, Portugal, Master Dissertation; Nouri, M., Mohammadzadeh, S., Probabilistic estimation of dynamic impact factor for masonry arch bridges using health monitoring data and new finite element method (2020) Struct Control Health Monit, 27, pp. 1-19; (2012) American Association of State Highway and Transportation Officials (ASHTOO), p. 1661. , Washington, DC, USA; Liu, K., Zhou, H., Wang, Y.Q., Shi, Y.J., De Roeck, G., Fatigue assessment of a composite railway bridge for high speed trains part II: conditions for which a dynamic analysis is needed (2013) J Constr Steel Res, 82, pp. 246-254; (2002) ) Standard Specifications for Highway Bridges, p. 740. , Washington, DC, USA; (1978) Steel, Concrete and Composite Bridges – Part 2: Specification for loads, pp. p5400-p5402. , London, BS, England; (2013) Road and Pedestrian Live Load on Bridges Viaducts, , Footbridges and other Structures ABNT NBR 7188, Brasilia, Brazil; (2008) Norme Tecniche per le Costruzioni (NTC), NTC 2008, , Italy, Rome; (1983) Ministério da Habilitação, Obras Públicas e Transportes, p. 34. , Lisbon, Portugal; Mohseni, I., Khalim, A.R., Nikbakht, E., Effectiveness of skewness in dynamic impact factor of concrete multicell box-girder bridges subjected to truck loads (2014) Arab J Sci Eng, 39, pp. 6083-6097; Jung, H., Kim, G., Cheolwoo, P., Impact factors of bridges based on natural frequency for various superstructure types (2013) KSCE J Civ Eng, 17, pp. 458-464; (2003) Eurocode 1: Actions on structures – Part 2: Traffic loads on bridges, pp. 1991-1993. , EN, Brussels, Belgium; Instrucción De Acciones a Considerer En Puentes De Ferrocarril (IAPF) (2010) Ministerio De Fomento, p. 134. , Madrid, Spain; Rodrigues, J.F.S., Casas, J.R., Almeida, P.A.O., Fatigue-safety assessment of reinforced concrete (RC) bridges: application to the Brazilian highway network (2013) Struct Infrastruct Eng, 9, pp. 601-616; Chang, D., Lee, H., Impact factors for simple-span highway girder bridges (1992) J Struct Eng, 120, pp. 1-12; Commander, B., Evolution of bridge diagnostic load testing in the USA (2019) Front Built Environ, 5, pp. 1-11; Junior, A.B.S., Lage, G.E., Caruso, N.C., (2020) Desenvolvimento de um sistema de baixo custo para monitoramento de Obras de Arte Especiais Dissertation, p. 121. , São Paulo Brazil, Mauá Institute of Technology (MIT) São Caetano do Sul; Revit, A., (2020) Software, , Autodesk, Inc; Autodesk Fusion 360 (2020) Software, , Autodesk, Inc; (2010) Software, , Autodesk, Inc; Software (2013) Version 16.0.0 Plus, , Computers and Structures, Inc; Zacchei, E., Lyra, P.H.C., Lage, G.E., Antonine, E., Soares, A.B., Jr., Caruso, N.C., de Assis, C.S., Structural health monitoring of a Brazilian concrete bridge for estimating specific dynamic responses (2022) Buildings, 12, pp. 1-22; PRISM Software, Earthquake Engineering Research Group, Department of Architectural Engineering, , http://sem.inha.ac.kr/prism/; Ghindea, C.L., Cruciat, R.I., Racanel, I.R., Dynamic test of a bridge over the Danube – Black Sea Channel at Agigea (2019) Mater Tools: Proc, 12, pp. 491-498; González, A., Finite element analysis, Cap. 26: vehicle-bridge dynamic interaction using finite element modelling (2013) IntechOpen; Calçada, R., Montenegro, P., Castro, M., (2019) Numerical Evaluation of the Dynamic Load Allowance Factor in from ASTHOO in Steel Modular Bridges from the Peru Provias Project: Probabilistic Approach, p. 59. , Porto, Portugal, Technical Report; Clough, R.W., Penzien, J., (2003) Dynamics of Structures, p. 752. , 3, McGraw-Hill, New York NY USA; Mechanical vibration – road surface profiles – Reporting of measured data, ISO 8608 (2016) Switzerland, Geneva, p. 44; Wolfram Research (2019) Inc.; Yang, Y.B., Yau, J.D., Hsu, L.C., Vibration of simple beams due to trains moving as high speeds (1997) Eng Struct, 19, pp. 936-944; Instrucción Sobre Las Acciones a Considerar En El Proyecto De Puentes De Carretera (IAP-11) (2011) Ministerio De Fomento, p. 88. , Madrid, Spain; Zacchei, E., Lyra, P.H.C., Recalibration of low seismic excitations in Brazil through probabilistic and deterministic analyses: Application for shear buildings structures (2022) Struct Concr, 1-19, p. 2022; Zhong, H., Yang, M., Gao, Z.J., Dynamic responses of prestressed bridge and vehicle through bridge–vehicle interaction analysis (2015) Eng Struct, 87, pp. 116-125; Yang, M., Liu, C., Possibility of bridge inspection through drive-by vehicles (2021) Appl Sci, 11, pp. 1-27; Matsouka, K., Kaito, K., Sogabe, M., Bayesian time–frequency analysis of the vehicle–bridge dynamic interaction effect on simple-supported resonant railway bridges (2020) Mech Syst Signal Process, 135, pp. 1-30; Jin, Z., Huang, B., Pei, S., Zhang, Y., Energy-based additional damping on bridges to account for vehicle-bridge interaction (2021) Eng Struct, 229, pp. 1-17; Mousavi, M., Holloway, D., Olivier, J.C., Gandomi, A.H., A baseline-free damage detection method using VBI incomplete measurement data (2021) Measurement, 174, pp. 1-16","Zacchei, E.; IteconsPortugal; email: enricozacchei@gmail.com",,,"Springer Science and Business Media Deutschland GmbH",,,,,17350522,,,,"English","Int. J. Civ. Eng.",Article,"Article in Press","",Scopus,2-s2.0-85140976003
"Fawad M., Koris K., Salamak M., Gerges M., Bednarski L., Sienko R.","57949408000;6506285955;25028351300;57193212502;24365952700;56866373700;","Nonlinear modelling of a bridge: A case study-based damage evaluation and proposal of Structural Health Monitoring (SHM) system",2022,"Archives of Civil Engineering","68","3",,"569","584",,,"10.24425/ace.2022.141903","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140970221&doi=10.24425%2face.2022.141903&partnerID=40&md5=4525f2f44a3d5872ac9f7c313d059f90","Silesian University of Technology, Faculty of Civil Engineering, ul. Akademicka 2A, Gliwice, 44-100, Poland; Budapest University of Technology and Economics, Faculty of Civil Engineering, Műegyetem rkp. 3, Budapest, 1111, Hungary; University of Wolverhampton, Wulfruna St, Wolverhampton, WV1 1LY, United Kingdom; AGH University of Science, Mechanical Engineering and Robotics, ul. Mickiewicza 30, Kraków, 30-059, Poland; Cracow University of Technology, Faculty of Civil Engineering, ul. Warszawska 24, Kraków, 31-155, Poland","Fawad, M., Silesian University of Technology, Faculty of Civil Engineering, ul. Akademicka 2A, Gliwice, 44-100, Poland; Koris, K., Budapest University of Technology and Economics, Faculty of Civil Engineering, Műegyetem rkp. 3, Budapest, 1111, Hungary; Salamak, M., Silesian University of Technology, Faculty of Civil Engineering, ul. Akademicka 2A, Gliwice, 44-100, Poland; Gerges, M., University of Wolverhampton, Wulfruna St, Wolverhampton, WV1 1LY, United Kingdom; Bednarski, L., AGH University of Science, Mechanical Engineering and Robotics, ul. Mickiewicza 30, Kraków, 30-059, Poland; Sienko, R., Cracow University of Technology, Faculty of Civil Engineering, ul. Warszawska 24, Kraków, 31-155, Poland","Monitoring and structural health assessment are the primary requirements for performance evaluation of damaged bridges. This paper highlights the case-study of a damaged Reinforced Concrete (RC) bridge structure by considering the outcomes of destructive testing, Non-Destructive Testing (NDT) evaluations, static and 3D non-linear analysis methods. Finite element (FE) modelling of this structure is being done using the material properties extracted by the in-situ testing. Analysis is carried out to evaluate the bridge damage based on the data recorded after the static linear (AXIS VM software) and 3D non-linear analysis (ATENA 3D software). Extensive concrete cracking and high value of crack width are found to be the major problems, leading to lowering the performance of the bridge. As a solution, this paper proposes a proper Structural Health Monitoring (SHM) system, that will extend the life cycle of the bridge with minimal repair costs and reduced risk of failure. This system is based on the installation of three different types of sensors: Liquid Levelling sensors (LLS) for measurement of vertical displacement, Distributed Fiber Optic Sensors (DFOS) for crack monitoring, and Weigh in Motion (WIM) devices for monitoring of moving loads on bridge. © 2022. Muhammad Fawad, Kalman Koris, Marek Salamak, Michael Gerges, Lukasz Bednarski, Rafal Sienko.","bridges; finite element method; non-destructive techniques; reinforced concrete; sensors; structural health monitoring","Fiber optic sensors; Finite element method; Life cycle; Nondestructive examination; Reinforced concrete; Case-studies; Damage evaluation; Health assessments; Non-linear analysis; Non-linear modelling; Nondestructive technique; Performances evaluation; Reinforced concrete bridge; Structural health; Structural health monitoring systems; Structural health monitoring",,,,,,,,,,,,,,,,"Bittencourt, T.N., (2016) Maintenance, Monitoring, Safety, Risk and Resilience of bridges and bridge networks, pp. 145-153. , D.M. Frangopol, A. Beck, Eds. Taylor & Francis Group; Xie, L., Qu, Z., On Civil Engineering Disasters and their Mitigation (2018) Earthquake Engineering and Engineering Vibrations, 17 (1), pp. 1-10; Papakonstantinou, K.G., Shinozuka, M., Stochastic Control Approaches for Structural Maintenance (2014) Maintenance and Safety of Aging Infrastructure, pp. 535-572. , D. Frangopol, Y. Tsompanakis, Eds. CRC Press; de Jong, F.B.P., Boersma, P.B., Lifetime Calculations for Orthotropic Steel Bridge Decks (2003) Structural Faults + Repair 2003 Conference, pp. 1-17. , https://trid.trb.org/view/746169, CD-rom. Engineering Technics Press, [Online]. Available; Shokrieh, M.M., Mohammadi, A.R.G., Nondestructive testing (NDT) techniques in the measurement of residual stresses in composite materials: An overview (2021) Residual Stresses in Composite Materials, 2, pp. 71-109. , M. Shokrieh, Ed. 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Available; Biondni, F., Frangopol, D.M., Assessment and retrofitting of existing bridges (2012) Bridge Maintenance, Safety, Manageent, Resilance and Sustainability – Proceedings of Sixth International IABMAS Conference, , CRC Press; Biliszczuk, J., Hawryszków, P., Teichgraeber, M., SHM system and a FEM model-based force analysis assessment in stay cables (2021) Sensors, 21 (6), pp. 1-27; Fujino, Y., Vibration, control and monitoring of long-span bridges – Recent research, developments and practice in Japan (2002) Journal of Construction Steel Research, 58 (1), pp. 71-97; Chen, Z., Zhou, X., Wang, X., Deployment of a smart structural health monitoring system for long-span arch bridges: A review and a case study (2017) Sensors (Switzerland), 17 (9); Haidarpour, A., Tee, K.F., Finite element model updating for structural health monitoring (2020) SDHM Structural Durability and Health Monitoring, 14 (1), pp. 1-17; Bayane, I., Brühwiler, E., Structural condition assessment of reinforced-concrete bridges based on acoustic emission and strain measurements (2020) Journal of Civil Structural Health Monitoring, 10 (5), pp. 1037-1055; Arena, M., Viscardi, M., Strain state detection in composite structures: Review and new challenges (2020) Journal of Composite Science, 4 (2); Kocaman, M., Akay, E.S., Yılmaz, E., Monitoring the Damage State of Fiber Reinforced Composites Using an FBG Network for Failure Prediction (2017) Materials MDPI, 10 (1); Kim, N.S., Cho, N.S., Estimating deflection of a simple beam model using fiber optic Bragg-grating sensors (2004) Experimental Mechanics, 44 (4), pp. 433-439; Burdet, O., Automatic deflection and temperature monitoring of a balanced cantilever concrete bridge (1998) 5th International Conference of Short and Medium Span Bridges, , presented at Calgary, Canada; Suangga, M., Haripriambodo, T., Maximum deflection of three span continuous bridge using rotation data based on 3 dimensional model (2021) IOP Conference Series Earth and Environmental Science, 794, p. 012016. , art. ID; Ye, X., Sun, Z., Cai, X., Mei, L., An improved step-type liquid level sensing system for bridge structural dynamic deflection monitoring (2019) Sensors (Switzerland), 19 (9); Feng, D., Feng, M. 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ID; Kim, J.T., Park, J.H., Hong, D.S., Park, W.S., Hybrid health monitoring of prestressed concrete girder bridges by sequential vibration-impedance approaches (2010) Engineering Structures, 32 (1), pp. 115-128; Kral, Z., Horn, W., Steck, J., Crack propagation analysis using acoustic emission sensors for structural health monitoring systems (2013) The Scientific World Journal, 2013, p. 823603. , art. ID; Wang, S., Zhang, B., Yang, G., Ji, C., Zhou, Z., Electrical Analysis of Smart Film-Based Crack-Width Monitoring in Bridge Infrastructure System (2016) Journal of Infrastructure Systems, 22 (1); Bassil, A., Casas, R., Leduc, D., (2019) Distributed Fiber Optics Sensing for Crack Monitoring of Concrete, , PhD thesis, Nantes University, France; Ferdinand, P., The evolution of optical fiber sensors technologiesduring the 35 last years and their applications instructure health monitoring (2014) 7th European workshop on structural health monitoring, 16, pp. 914-929. , https://hal.inria.fr/hal-01021251/document, Jul Nantes, France, 2014, [Online]. Available; Sieńko, R., Bednarski, Ł., Howiacki, T., Distributed Optical Fibre Sensors for Strain and Temperature Monitoring of Early-Age Concrete: Laboratory and In-situ Examples (2021) RILEM Bookseries, 31, pp. 77-87. , Springer; Ye, X.W., Su, Y.H., Han, J.P., Structural health monitoring of civil infrastructure using optical fiber sensing technology: A comprehensive review (2014) The Scientific World Journal, 2014; Wu, T., Liu, G., Fu, S., Xing, F., Recent progress of fiber-optic sensors for the structural health monitoring of civil infrastructure (2020) Sensors (Switzerland), 20 (16), pp. 1-25; Feng, X., Pavement Crack Detection and Segmentation Method Based on Improved Deep Learning Fusion Model (2020) Mathematical Problems in Engineering, 2020; Lang, H., Wen, T., Lu, J., 3D pavement crack detection method based on deep learning (2021) Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal Southeast Univ, 51 (1), pp. 53-60; Sieńko, R., Bednarski, Ł., Howiacki, T., Smart Composite Rebars Based on DFOS Technology as Nervous System of Hybrid Footbridge Deck: A Case Study (2021) Lecture Notes Civil Engineering, 128, pp. 331-341. , Springer; Güemes, A., Fernández-López, A., Soller, B., Optical fiber distributed sensing – physical principles and applications (2010) Structural Health Monitoring, 9 (3), pp. 233-245; Choo, J.F., Ha, D.H., Chang, S.G., New Bridge Weigh-in-Motion System Using Piezo-Bearing (2018) Shock and Vibration, 2018; Asnachinda, P., Pinkaew, T., Laman, J.A., Multiple vehicle axle load identification from continuous bridge bending moment response (2008) Engineering Structures, 30 (10), pp. 2800-2817; Žnidarič, A., Kalin, J., Using bridge weigh-in-motion systems to monitor single-span bridge influence lines (2020) Journal of Civil Structural Health Monitoring, 10 (5), pp. 743-756; EN 1992-2:2005 Eurocode 2 Design of concrete structures – Concrete bridges – Design and detailing rules (2005), CEN, 2011; (1967) MSZ–07–3701–67 Hungarian Standard: Statical calculation of road bridges, 1; Fawad, M., Kalman, K., Khushnood, R.A., Usman, M., Retrofitting of damaged reinforced concrete bridge structure (2019) Procedia Structural Integrity, 18, pp. 189-197",,,,"Polska Akademia Nauk",,,,,12302945,,ACIEE,,"English","Arch Civ Eng",Article,"Final","",Scopus,2-s2.0-85140970221
"Wu W.-H., Chen C.-C., Kusbiantoro A., Lai G.","7407079422;56328506200;57466735900;56783716600;","Finite element model of a cable-stayed bridge updated with vibration measurements and its application to investigate the variation of modal frequencies in monitoring",2022,"Structure and Infrastructure Engineering",,,,"","",,,"10.1080/15732479.2022.2132517","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139781282&doi=10.1080%2f15732479.2022.2132517&partnerID=40&md5=d5a4cbccf7be68ba01c3212d68ecf26a","Department of Civil and Construction Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan; Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, Taiwan","Wu, W.-H., Department of Civil and Construction Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan; Chen, C.-C., Department of Civil and Construction Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan; Kusbiantoro, A., Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, Taiwan; Lai, G., Department of Civil and Construction Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan","Ambient vibration measurements of a cable-stayed bridge in Taiwan were first conducted to identify its dominant modal parameters with a reliable stochastic subspace identification algorithm recently developed. A finite element model is then constructed based on the identified frequencies and shape vectors of four vertically bending, one torsional, and one horizontally bending modes. This model is also updated by adjusting the girder-pier connection with the addition of a rotational spring to reflect the actual conditions and its accuracy is demonstrated with less than 1% of error for the first two vertically bending modes. As a solid application for probing the environmental effects in structural health monitoring, the vibration measurements of 24 hours were further conducted and combined with the well calibrated model to investigate the controlling factor for its variation in modal frequencies. It is found that the frequencies of the first two vertically bending modes are closely correlated to the root-mean-square velocity representing the traffic excitation intensity. Moreover, it is shown with the updated model that the daily variations in the frequencies of the first two vertically bending modes can be reasonably simulated by modifying the coefficient of the rotational spring merely in a limited range. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","Ambient vibration measurement; cable-stayed bridge; environmental effects; finite element model; modal parameters; stochastic subspace identification; structural health monitoring; traffic excitation","Cable stayed bridges; Cables; Composite beams and girders; Finite element method; Modal analysis; Parameter estimation; Stochastic models; Stochastic systems; Vectors; Vibration analysis; Vibration measurement; Ambient vibration measurement; Ambient vibrations; Bending modes; Cable-stayed bridge; Finite element modelling (FEM); Measurements of; Modal frequency; Modal parameters; Stochastic subspace identification; Traffic excitation; Structural health monitoring",,,,,,,,,,,,,,,,"Alamdari, M.M., Khoa, N.L.D., Wang, Y., Samali, B., Zhu, X., A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge (2019) Structural Health Monitoring, 18 (1), pp. 35-48; Au, F.T.K., Tham, L.G., Lee, P.K.K., Su, C., Han, D.J., Yan, Q.S., Wong, K.Y., Ambient vibration measurements and finite element modelling for the Hong Kong Ting Kau Bridge (2003) Structural Engineering and Mechanics, 15 (1), pp. 115-134; Bakir, P.G., Automation of the stabilization diagram for subspace based system identification (2011) Expert Systems with Applications, 38 (12), pp. 14390-14397; Benedettini, F., Gentile, C., Operational modal testing and FE model tuning of a cable-stayed bridge (2011) Engineering Structures, 33 (6), pp. 2063-2073; Cao, Y., Yim, J., Zhao, Y., Wang, M.L., Temperature effects on cable stayed bridge using health monitoring system: A case study (2011) Structural Health Monitoring, 10 (5), pp. 523-537; Carden, E.P., Brownjohn, J.M.W., Fuzzy clustering of stability diagrams for vibration-based structural health monitoring (2008) Computer-Aided Civil and Infrastructure Engineering, 23 (5), p. 360; Clemente, P., Bongiovanni, G., Buffarini, G., Saitta, F., Structural health status assessment of a cable-stayed bridge by means of experimental vibration analysis (2019) Journal of Civil Structural Health Monitoring, 9 (5), pp. 655-669; Chang, K.C., Mo, Y.L., Chen, C.C., Lai, L.C., Chou, C.C., Lessons learned from the damaged Chi-Lu cable-stayed bridge (2004) Journal of Bridge Engineering, 9 (4), pp. 343-352; Chen, C.C., Wu, W.H., Shih, F., Wang, S.W., Scour evaluation for foundation of a cable-stayed bridge based on ambient vibration measurements of superstructure (2014) NDT & E International, 66, pp. 16-27; Daneshvar, M.H., Gharighoran, A., Zareei, S.A., Karamodin, A., Early damage detection under massive data via innovative hybrid methods: Application to a large-scale cable-stayed bridge (2021) Structure and Infrastructure Engineering, 17 (7), pp. 902-920; Ding, Y., Li, A., Du, D., Liu, T., Multi-scale damage analysis for a steel box girder of a long-span cable-stayed bridge (2010) Structure and Infrastructure Engineering, 6 (6), pp. 725-739; Elsaid, A., Seracino, R., Rapid assessment of foundation scour using the dynamic features of bridge superstructure (2014) Construction and Building Materials, 50 (1), pp. 42-49; Felber, A., Cantieni, R., Advances in ambient vibration testing: Ganter Bridge, Switzerland (1996) Structural Engineering International, 6 (3), pp. 187-190; Foti, D., Gattulli, V., Potenza, F., Output-only identification and model updating by dynamic testing in unfavorable conditions of a seismically damaged building (2014) Computer-Aided Civil and Infrastructure Engineering, 29 (9), pp. 659-675; Hoa, T.N., Khatir, S., De Roeck, G., Long, N.N., Thanh, B.T., Abdel Wahab, M., An efficient approach for model updating of a large-scale cable-stayed bridge using ambient vibration measurements combined with a hybrid metaheuristic search algorithm (2020) Smart Structures and Systems, 25 (4). , 487 − 499; Hu, W.-H., Cunha, Á., Caetano, E., Magalhães, F., Moutinho, C., LabVIEW toolkits for output-only modal identification and long-term dynamic structural monitoring (2010) Structure and Infrastructure Engineering, 6 (5), p. 557; Jang, S., Jo, H., Cho, S., Mechitov, K., Rice, J.A., Sim, S.-H., Agha, G., Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation (2010) Smart Structures and Systems, 6 (5-6), pp. 439-459. , …; Kang, J., Zhang, X., Cao, H., Qin, S., Research on multi-alternatives problem of finite element model updating based on IAFSA and Kriging model (2020) Sensors, 20 (15), p. 4274; Kibboua, A., Farsi, M.N., Chatelain, J.L., Guillier, B., Bechtoula, H., Mehani, Y., Modal analysis and ambient vibration measurements on Mila-Algeria cable stayed bridge (2008) Structural Engineering and Mechanics, 29 (2), pp. 171-186; Lin, S.W., Du, Y.L., Yi, T.H., Yang, D.H., Model updating using bridge influence lines based on an adaptive metamodel global optimization method (2022) Journal of Bridge Engineering, 27 (3), p. 4022003; Mao, J.X., Wang, H., Feng, D.M., Tao, T.Y., Zheng, W.Z., Investigation of dynamic properties of long-span cable-stayed bridges based on one-year monitoring data under normal operating condition (2018) Structural Control and Health Monitoring, 25 (5), p. e2146; Macdonald, J.H., Daniell, W.E., Variation of modal parameters of a cable-stayed bridge identified from ambient vibration measurements and FE modelling (2005) Engineering Structures, 27 (13), pp. 1916-1930; Magalhaes, F., Cunha, A., Caetano, E., Online automatic identification of the modal parameters of a long span arch bridge (2009) Mechanical Systems and Signal Processing, 23 (2), pp. 316-329; Magalhaes, F., Cunha, A., Caetano, E., Brincker, R., Damping estimation using free decays and ambient vibration tests (2010) Mechanical Systems and Signal Processing, 24 (5), pp. 1274-1290; Ni, Y.C., Alamdari, M.M., Ye, X.W., Zhang, F.L., Fast operational modal analysis of a single-tower cable-stayed bridge by a Bayesian method (2021) Measurement, 174, p. 109048; Rahbari, R., Niu, J., Brownjohn, J.M.W., Koo, K.Y., Structural identification of Humber Bridge for performance prognosis (2015) Smart Structures and Systems, 15 (3), pp. 665-682; Ren, W.X., Peng, X.L., Lin, Y.Q., Experimental and analytical studies on dynamic characteristics of a large span cable-stayed bridge (2005) Engineering Structures, 27 (4), pp. 535-548; Reynders, E., De Roeck, G., Reference-based combined deterministic-stochastic subspace identification for experimental and operational modal analysis (2008) Mechanical Systems and Signal Processing, 22 (3), pp. 617-637; Reynders, E., Houbrechts, J., De, Roeck, G., Fully automated (operational) modal analysis (2012) Mechanical Systems and Signal Processing, 29, pp. 228-250; Scionti, M., Lanslots, J.P., Stabilisation diagrams: Pole identification using fuzzy clustering techniques (2005) Advances in Engineering Software, 36 (11-12), pp. 768-779; Shabbir, F., Omenzetter, P., Particle swarm optimization with sequential niche technique for dynamic finite element model updating (2015) Computer-Aided Civil and Infrastructure Engineering, 30 (5), pp. 359-375; Sun, L., Zhou, Y., Min, Z., Experimental study on the effect of temperature on modal frequencies of bridges (2018) International Journal of Structural Stability and Dynamics, 18 (12), p. 1850155; Ubertini, F., Gentile, C., Materazzi, A.L., Automated modal identification in operational conditions and its application to bridges (2013) Engineering Structures, 46, p. 264; Whelam, M.J., Janoyan, K.D., In-service diagnostics of a highway bridge from a progressive damage case study (2010) Journal of Bridge Engineering, ASCE, 15 (5). , 597 − 607; Wu, W.H., Chen, C.C., Chen, Y.J., Lai, G., Effects of vehicle mass and excitation on the modal frequencies of girder bridges in structural health monitoring (2019) Proceedings of 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, , Chen G., Alampalli S., (eds), 665 − 670, INSPIRE Conference Contributions, &,. (Eds; Wu, W.H., Chen, C.C., Wang, S.W., Huang, C.M., Assessment of environmental effects in scour monitoring of a cable-stayed bridge simply based on pier vibration measurements (2017) Smart and Structures and Systems, 20 (2). , 231 − 246; Wu, W.H., Jhou, J.W., Chen, C.C., Lai, G., A novel recursive stochastic subspace identification algorithm with its application in long-term structural health monitoring of office buildings (2019) Smart and Structures and Systems, 24 (4). , 459 − 474; Wu, W.H., Wang, S.W., Chen, C.C., Lai, G., Application of stochastic subspace identification for stay cables with an alternative stabilization diagram and hierarchical sifting process (2016) Structural Control and Health Monitoring, 23 (9), pp. 1194-1213; Wu, W.H., Wang, S.W., Chen, C.C., Lai, G., Mode identifiability of a cable-stayed bridge under different excitation conditions assessed with an improved algorithm based on stochastic subspace identification (2016) Smart Structures and Systems, 17 (3), pp. 363-389; Wu, W.H., Wang, S.W., Chen, C.C., Lai, G., Assessment of environmental and non-destructive earthquake effects on modal parameters of an office building based on long-term vibration measurements (2017) Smart Materials and Structures, 26 (5), p. 55034; Wu, W.H., Wang, S.W., Chen, C.C., Lai, G., Stable modal identification for civil structures based on a stochastic subspace algorithm with appropriate selection of time lag parameter (2017) Structural Monitoring and Maintenance, 4 (4). , 331 − 350; Wu, W.H., Wang, S.W., Chen, C.C., Lai, G., Modal parameter identification for closely spaced modes of civil structures based on an upgraded stochastic subspace methodology (2019) Structure and Infrastructure Engineering, 15 (3), pp. 296-313; Zhou, K., Tang, J., Structural model updating using adaptive multi-response Gaussian process meta-modeling (2021) Mechanical Systems and Signal Processing, 147, p. 107121","Wu, W.-H.; Department of Civil and Construction Engineering, Yunlin, Taiwan; email: wuwh@yuntech.edu.tw",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Article in Press","",Scopus,2-s2.0-85139781282
"Veit-Egerer R., Bursa J., Synek J.","25634796100;57208152064;57783508500;","Periodic assessment of an old concrete road bridge based on operational dynamic bridge behaviour with regard to structural integrity and the remaining load bearing capacity",2022,"IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report",,,,"322","329",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133516973&partnerID=40&md5=4afb921bf7c98ccec228ea94581aaea8","VCE Vienna Consulting Engineers, Vienna, Austria; MDS Projekt, Vysoké Mýto, Czech Republic; Road Construction and Maintenance Department of Pardubice Region, Pardubice, Czech Republic","Veit-Egerer, R., VCE Vienna Consulting Engineers, Vienna, Austria; Bursa, J., MDS Projekt, Vysoké Mýto, Czech Republic; Synek, J., Road Construction and Maintenance Department of Pardubice Region, Pardubice, Czech Republic","The aim of the investigation was the analysis of a concrete road bridge structure crossing the Elbe river and having been operated since 1924. In accordance to previous investigations from 2015 & 2017 in depth dynamic measurements were conducted in 2020 with regard to the remaining load bearing capacity based on a dense grid of accelerometer data and additional displacement measurements. © 2022 IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report. All rights reserved.","Dynamic Measurement; FE Model Update; Performance Assessment; Structural Health Monitoring","Bearing capacity; Concretes; Finite element method; Highway bridges; Loads (forces); Roads and streets; Bridge structures; Concrete roads; Dynamic measurement; FE model; FE model update; Load-bearing capacity; Model updates; Performance assessment; Periodic assessment; Road bridge; Structural health monitoring",,,,,,,,,,,,,,,,"Veit-Egerer, R., Hubka, M., Silniční most přes trať ČD za obcí Komořany - Dynamické chování vzhledem k provoznímu stavu a zatížitelnosti metodou BRIMOS® (2008) 13th International Symposium BRIDGES, , Sekurkon. Brno. Czech Republic. ISBN 978-80-86604-35-0; Wenzel, H., (2009) Health Monitoring of Bridges, , J. Wiley and Sons Ltd, Chichester England. ISBN 978-0-470-03173-5; (2005) Zatěžovací zkoušky mostů (Loading tests of bridges), , Praha, Český normalizační institut. Czech Republic; (2013) Zatížitelnost mostů pozemních komunikací (Load bearing capacity of road bridges), Praha, Úřad pro technickou normalizaci, metrologii a státní zkušebnictví, , Czech Republic; (1998) Prohlídky mostu ev. č. 3227 - 3, , (-2020). Pardubice, Czech Republic; Wenzel, H., Pichler, D., (2005) Ambient Vibration Monitoring, , J. Wiley and Sons Ltd, Chichester England. ISBN 0470024305; (2020) Most ev. č. 3227 - 3. Následná nedestruktivní diagnostika mostu 2020 založená na průzkumném měření dynamického chování metodou BRIMOS®, Analýza provozního stavu, stanovení realné zatížitelnosti a porovnání s výsledky diagnostiky 2015 a 2017, , Project Report. Vienna, Austria; Veit-Egerer, R., Bursa, J., Synek, J., Follow-up assessment on an old concrete road bridge based on operational dynamic bridge behaviour - analysis of structural integrity and determination of loading capacity (2018), IALCCE 2018, Ghent, Belgium, ISBN 978-1-138-62633-1","Veit-Egerer, R.; VCE Vienna Consulting EngineersAustria; email: veit-egerer@vce.at",,"ALLPlan;BBR VT International Ltd;Ceska asociace ocelovych konstrukci (Czech Constructional Steelwork Association);et al.;IDEA Statica;REDAELLI TECNA S.p.A.","International Association for Bridge and Structural Engineering (IABSE)","IABSE Symposium Prague 2022: Challenges for Existing and Oncoming Structures","25 May 2022 through 27 May 2022",,180214,,9783857481833,,,"English","IABSE Symp. Prague,: Challenges Exist. Oncoming Struct. - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85133516973
"Islam N., Miyashita T., Shill S.K., Takeda K., Fukada S., Takasu A., Al-Deen S., Subhani M.","57219147393;7201920776;57192814704;57208693038;19336785300;7005461524;36774302300;55293906800;","Assessment of structural health of an existing prestressed concrete bridge by finite element analysis",2022,"Australian Journal of Civil Engineering",,,,"","",,,"10.1080/14488353.2022.2092253","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132889910&doi=10.1080%2f14488353.2022.2092253&partnerID=40&md5=2ae01ae9c50babcf6c8e587932ace792","Civil and Environmental Engineering, Nagaoka University of Technology, Nagaoka, Japan; School of Engineering, Deakin University, Geelong, VIC, Australia; School of engineering and information technology (SEIT), UNSW at ADFA, Canberra, ACT, Australia; Architecture, Civil Engineering and Industrial Management Engineering Nagoya, Nagoya Institute of Technology, Nagoya, Japan; Faculty of Geosciences and Civil Engineering, Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan; Digital Content and Media Sciences Research Division, National Institute of Informatics, Tokyo, Japan","Islam, N., Civil and Environmental Engineering, Nagaoka University of Technology, Nagaoka, Japan; Miyashita, T., Civil and Environmental Engineering, Nagaoka University of Technology, Nagaoka, Japan; Shill, S.K., School of Engineering, Deakin University, Geelong, VIC, Australia, School of engineering and information technology (SEIT), UNSW at ADFA, Canberra, ACT, Australia; Takeda, K., Architecture, Civil Engineering and Industrial Management Engineering Nagoya, Nagoya Institute of Technology, Nagoya, Japan; Fukada, S., Faculty of Geosciences and Civil Engineering, Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan; Takasu, A., Digital Content and Media Sciences Research Division, National Institute of Informatics, Tokyo, Japan; Al-Deen, S., School of engineering and information technology (SEIT), UNSW at ADFA, Canberra, ACT, Australia; Subhani, M., School of Engineering, Deakin University, Geelong, VIC, Australia","This study investigates the feasibility of an alternative structural health monitoring (SHM) technique for an existing, post-tensioned, prestressed concrete bridge in Niigata, Japan. Currently, a static SHM system is in place to detect the progress of damages within the bridge. However, the existing system cannot properly monitor the structural health of the bridge including the periodic vibration, which is one of the damage-sensitive features of interest. Therefore, to effectively detect the real-time performances of the bridge, a three-dimensional (3D) Finite Element (FE) model was developed as a reference and verified using on-site load-deflection test results. After validating the reference FE model, different damage scenarios, such as degradation of concrete, corrosion & rupture of steel tendons and missing tendons, were incorporated in the FE models. Based on non-linear structural and Eigenvalue analyses, natural frequencies and mode shapes of the bridge remain constant even after careful consideration of all types of damages in the FE model. However, vertical displacements are observed to increase for the damage scenarios. Although the effect of tendon rupture and corrosion showed negligible influences on the vertical displacement, the deterioration of the concrete largely influenced the vertical displacement. Additionally, crack widths were found to vary with damage types. Brifely, this study recommends some effective indicators to monitor the structural conditions of the bridge using FE analysis (FEA). ©, Engineers Australia.","bridge structure; crack width; mode shape; natural frequency; Structural health; vertical displacement","Concrete beams and girders; Concrete bridges; Cracks; Eigenvalues and eigenfunctions; Natural frequencies; Prestressed concrete; Steel bridges; Steel corrosion; Structural health monitoring; Wire; Bridge structures; Crack-width; Damage scenarios; Finite element analyse; Finite element modelling (FEM); Health monitoring technique; Mode shapes; Prestressed concrete bridges; Structural health; Vertical displacements; Finite element method",,,,,"New Energy and Industrial Technology Development Organization, NEDO; Japan Science and Technology Agency, JST; Cabinet Office, Government of Japan; Ministry of Land, Infrastructure, Transport and Tourism, MLIT","This research was supported by the Cabinet Office, Government of Japan, Cross-ministerial Strategic Innovation Promotion Program (SIP), “Infrastructure Maintenance, Renovation and Management” (funding agency: Japan Science and Technology Agency and New Energy and Industrial Technology Development Organization). Additionally, the authors gratefully acknowledge the sincere support from the Ministry of Land, Infrastructure, Transport and Tourism, Hokuriku Regional Development Bureau, Takada Office of Rivers and National Highways.",,,,,,,,,,"Agdas, D., Rice, J.A., Martinez, J.R., Lasa, I.R.J.J.O.P.O.C.F., Comparison of Visual Inspection and structural-health Monitoring as Bridge Condition Assessment Methods (2016) Journal of Performance of Constructed Facilities, 30 (3), p. 04015049; Aktan, A., Catbas, F., Grimmelsman, K., Tsikos, C.J.J.O.E.M., Issues in Infrastructure Health Monitoring for Management (2000) Journal of Engineering Mechanics, 126 (7), pp. 711-724; Alampalli, S., Fu, G., Dillon, E.W.J.J.O.S.E., Signal versus Noise in Damage Detection by Experimental Modal Analysis (1997) Journal of Structural Engineering, 123 (2), pp. 237-245; Bader, J., (2008) Nondestructive Testing and Evaluation of Steel Bridges, , College Park, MD: University of Maryland; Banks, H., Inman, D., Leo, D., Wang, Y.J.J.O.S., vibration, An Experimentally Validated Damage Detection Theory in Smart Structures (1996) Journal of Sound and vibration, 191 (5), pp. 859-880; Carden, E.P., Fanning, P.J.S.H.M., Vibration Based Condition Monitoring: A Review (2004) Structural Health Monitoring, 3 (4), pp. 355-377; Cawley, P., Adams, The Location of Defects in Structures from Measurements of Natural Frequencies (1979) Journal of Strain Analysis for Engineering Design, 14 (2), pp. 49-57; Chan, T.H., Thambiratnam, D.P., (2011) Structural Health Monitoring in Australia, , https://novapublishers.com/shop/structural-health-monitoring-in-australia/, Australia: Nova Science Publishers; Chen, H., Spyrakos, C., Venkatesh, G.J.J.O.S.E., Evaluating Structural Deterioration by Dynamic Response (1995) Journal of Structural Engineering-American Society of Civil Engineers, 121 (8), pp. 1197-1204; Das, S., Saha, P., Patro, S.J.J.O.C.S.H.M., Vibration-based Damage Detection Techniques Used for Health Monitoring of Structures: A Review (2016) Journal of Civil Structural Health Monitoring, 6 (3), pp. 477-507; De Roeck, G., Peeters, B., Maeck, J.J.C.M.F.S., Structures, S., (2000) Dynamic Monitoring of Civil Engineering Structures; Dessi, D., Camerlengo, G.J.M.S., Processing, S., Damage Identification Techniques via Modal Curvature Analysis: Overview and Comparison (2015) Mechanical Systems and Signal Processing, 52, pp. 181-205; Enckell-El Jemli, M., Karoumi, R., Lanaro, F., Monitoring of the New Årsta Railway Bridge Using Traditional and Fiber Optic Sensors, Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures (2003) International Society for Optics and Photonics, pp. 279-288; Ercolani, G., Felix, D., Ortega, N.J.J.O.C.S.H.M., Crack Detection in Prestressed Concrete Structures by Measuring Their Natural Frequencies (2018) Journal of Civil Structural Health Monitoring, 8 (4), pp. 661-671; Farrar, C., Doebling, S., Damage Detection and Evaluation II (1999) Modal Analysis and Testing, pp. 345-378. , Dordrecht: Springer, and; Fatemi, S., Ali, M.M., Sheikh, A., Wei, Z., Finite Element Analysis of Prestressed Bridge Decks Using Ultra High Performance Concrete (2017) Australian Journal of Civil Engineering, 15 (2), pp. 93-102; Feng, D., Feng, M.Q.J.E.S., Computer Vision for SHM of Civil Infrastructure: From Dynamic Response Measurement to Damage detection–A Review (2018) Engineering Structures, 156, pp. 105-117; Flanagan, D., Belytschko, T.J.I.J.F.N.M.I.E., A Uniform Strain Hexahedron and Quadrilateral with Orthogonal Hourglass Control (1981) Annali dell’Istituto superiore di sanita, 17 (5), pp. 679-706; Gentile, C., Bernardini, G.J.S., Engineering, I., An Interferometric Radar for non-contact Measurement of Deflections on Civil Engineering Structures: Laboratory and full-scale Tests (2010) Structure and Infrastructure Engineering, 6 (5), pp. 521-534; (2012) Specifications for Highway Bridges, Part III, Concrete Bridges, , Tokyo: Maruzen; Kessler, S.S., Spearing, S.M., Atalla, M.J., Cesnik, C.E., Soutis, C.J.C.P.B.E., Damage Detection in Composite Materials Using Frequency Response Methods (2002) Composites Part B: Engineering, 33 (1), pp. 87-95; Li, S., Wu, Z.J.S.H.M., Development of Distributed long-gage Fiber Optic Sensing System for Structural Health Monitoring (2007) Structural Health Monitoring, 6 (2), pp. 133-143; Lynch, J.P., Loh, K.J.J.S., Digest, V., A Summary Review of Wireless Sensors and Sensor Networks for Structural Health Monitoring (2006) Shock and Vibration Digest, 38 (2), pp. 91-130; Manie, J., Schreppers, G.J.D.F.B., Delftechpark 19a, DIANA–Finite Element Analysis (2017) User’s Manual: Release, 10 (2), p. 2628; Phares, B.M., Rolander, D.D., Graybeal, B.A., Washer, G.A.J.P.R., Reliability of Visual Bridge Inspection (2001) Public Roads, 64 (5), pp. 22-29; Rashidi, M., Samali, B., Sharafi, P., A New Model for Bridge Management: Part A: Condition Assessment and Priority Ranking of Bridges (2016) Australian Journal of Civil Engineering, 14 (1), pp. 35-45; Salawu, O.S., Williams, C.J.J., Bridge Assessment Using forced-vibration Testing (1995) Journal of Structural Engineering, 121 (2), pp. 161-173; Salawu, O.J.E.S., Detection of Structural Damage through Changes in Frequency: A Review (1997) Engineering Structures, 19 (9), pp. 718-723; Shi, Z., Law, S., Zhang, L.J.J.O.E.M., Damage Localization by Directly Using Incomplete Mode Shapes (2000) Journal of Engineering Mechanics, 126 (6), pp. 656-660; Shill, S.K., Al-Deen, S., Ashraf, M., Concrete Durability Issues Due to Temperature Effects and Aviation Oil Spillage at Military airbase–A Comprehensive Review (2018) Construction and Building Materials, 160, pp. 240-251; Wang, K., Nelsen, D.E., Nixon, W.A.J.C., Damaging Effects of Deicing Chemicals on Concrete Materials (2006) Cement and Concrete composites, 28 (2), pp. 173-188; Wang, J., Qiao, P.J.S.H.M., Improved Damage Detection for beam-type Structures Using a Uniform Load Surface (2007) Structural Health Monitoring, 6 (2), pp. 99-110; Webb, G., Vardanega, P.J., Middleton, C.R.J.J.O.B.E., (2015) Categories of SHM Deployments: Technologies and Capabilities, 20 (11), p. 04014118; Xu, Y.-L., Zhang, C.-D., Zhan, S., Spencer, B.F.J.E.S., Multi-level Damage Identification of a Bridge Structure: A Combined Numerical and Experimental Investigation (2018) Engineering Structures, 156, pp. 53-67; Yeum, C.M., Dyke, S.J.J.C.A.C., Engineering, I., Vision‐based Automated Crack Detection for Bridge Inspection (2015) Computer‐Aided Civil and Infrastructure Engineering, 30 (10), pp. 759-770","Shill, S.K.; School of Engineering and Information Technology (SEIT), Australia; email: sukanta.shill@deakin.edu.au",,,"Taylor and Francis Ltd.",,,,,14488353,,,,"English","Aust. J. Civ. Eng.",Article,"Article in Press","",Scopus,2-s2.0-85132889910
"Markogiannaki O., Arailopoulos A., Giagopoulos D., Papadimitriou C.","48662606100;56884701500;37064557500;7103065916;","Vibration-based Damage Localization and Quantification Framework of Large-Scale Truss Structures",2022,"Structural Health Monitoring",,,,"","",,,"10.1177/14759217221100443","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132691703&doi=10.1177%2f14759217221100443&partnerID=40&md5=a7b02db2e195d84f889acfe702457582","Department of Mechanical Engineering, University of Western Macedonia, Kozani, Greece; Department of Mechanical Engineering, University of Thessaly, Volos, Greece","Markogiannaki, O., Department of Mechanical Engineering, University of Western Macedonia, Kozani, Greece; Arailopoulos, A., Department of Mechanical Engineering, University of Western Macedonia, Kozani, Greece; Giagopoulos, D., Department of Mechanical Engineering, University of Western Macedonia, Kozani, Greece; Papadimitriou, C., Department of Mechanical Engineering, University of Thessaly, Volos, Greece","The use of structural health monitoring (SHM) systems on a regular basis is critical to achieve early damage detection, avoid unpredicted failures, and perform cost-effective maintenance planning. The main objective of this work is to present a model-based Damage Detection Framework for truss structural systems that uses output-only vibration measurements. Model-based methods provide much more comprehensive information about the condition of the monitored system than the data-driven and also allow the prediction of the location and level of damage. The measured vibration response of a healthy structural system under operational vibrations is employed to tune a parameterized FE model using state-of-the-art FE model updating techniques to obtain an optimal numerical model of the structural system. Based on the optimal FE model, a set of damaged FE models is generated for selected damage scenarios. A damage approximation approach that represents local damage with uniform stiffness reduction is also presented. In the Damage Detection Framework, the vibration data records for both the “healthy” and the “damaged” structure and the results from multiple numerical analysis on the “healthy” and the “damaged” FE models are used. The transmittance functions for the “healthy” and “damaged” states of the structure and the FE models are derived to calculate the damage indicators. Using these indicators, potentially damaged structural members are identified, grouped, and compared to finally locate the specific damaged member. The proposed framework provides both accurate damage localization and damage quantification using a limited number of sensors for unknown input excitation. Herein, the case study used is a laboratory steel truss bridge. © The Author(s) 2022.","Damage Identification; FE Model Updating; Finite Element Modeling; Structural Health Monitoring; Transmittance Functions","Cost effectiveness; Damage detection; Finite element method; Trusses; Vibration analysis; Damage Identification; Damage localization; Damage quantification; Detection framework; Element models; FE model; FE model updating; Finite element modeling; Structural systems; Transmittance function; Structural health monitoring",,,,,"European Commission, EC; European Social Fund, ESF: MIS 5050131","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Greece and the European Union (European Social Fund-ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning 2014–2020» in the context of the project “Structural Health Monitoring and Damage Detection of Bridges based in vibrational measurements” (MIS 5050131).",,,,,,,,,,"Cawley, P., Structural health monitoring: closing the gap between research and industrial deployment (2018) Structural Health Monitoring, 17 (5), pp. 1225-1244; Doebling, S.W., Farrar, C.R., Prime, M.B., (1996) Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review, , US, Los Alamos National Lab; Sohn, H., Farrar, C.R., Hemez, F., A review of structural health monitoring literature 1996 – 2001, pp. 1-7. , Third World Conference on Structural Control, December 2002, LA-13976-MS,,. 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Worden, K., Burrows, A.P., Optimal sensor placement for fault detection (2001) Eng Structures, 23 (8), pp. 885-901; (2018), Thessaloniki, Greece: BETA CAE Systems; Hansen, N., (2011) The CMA Evolution Strategy: A Tutorial, , Paris-Saclay, LRI, Research centre Saclay –ˆIle-de-France Universite","Giagopoulos, D.; Department of Mechanical Engineering, Greece; email: dgiagopoulos@uowm.gr",,,"SAGE Publications Ltd",,,,,14759217,,,,"English","Struct. Health Monit.",Article,"Article in Press","",Scopus,2-s2.0-85132691703
"Zhao R., Wu Y., Feng Z.","46061697700;57705261300;57704989100;","Research for Pedestrian Steel Bridge Design of Neural Network in Structural Model Updating",2022,"Shock and Vibration","2022",,"1057422","","",,,"10.1155/2022/1057422","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130609782&doi=10.1155%2f2022%2f1057422&partnerID=40&md5=2907bdcf814be78e8dcdb4a9452ee30d","School of Civil Engineering and Architecture, Xinjiang University, Urumqi, 830047, China; Xin Jiang Key Lab of Building Structure and Earthquake Resistance, Urumqi, 830047, China","Zhao, R., School of Civil Engineering and Architecture, Xinjiang University, Urumqi, 830047, China, Xin Jiang Key Lab of Building Structure and Earthquake Resistance, Urumqi, 830047, China; Wu, Y., School of Civil Engineering and Architecture, Xinjiang University, Urumqi, 830047, China; Feng, Z., School of Civil Engineering and Architecture, Xinjiang University, Urumqi, 830047, China","The application of the neural network method in health monitoring and structural system identification has received extensive attention. A reasonable neural network structure is very important for its performance. This paper takes the pedestrian bridge of the Xingfu intersection in Urumqi, China, as the research object and uses MIDAS/Civil to establish a finite element analysis model. Taking the natural vibration frequency obtained from the dynamic test of the actual bridge as the target, two kinds of neural networks are used to predict the structural material parameters. An appropriate bridge model correction method is selected by comparing the prediction results of the BP neural network and the GRNN. The test results show that the pedestrian bridge model based on MIDAS/Civil has a high accuracy, but it still does not meet the actual needs. The modified model based on the BP neural network is close to the actual measured results, and a more accurate finite element analysis model can be established by this method, which makes the modified model closer to the real stress state of the structure. © 2022 Rui Zhao et al.",,"Finite element method; Footbridges; Steel bridges; Structural health monitoring; BP neural networks; Bridge design; Bridge model; Finite element analysis modeling; Model-based OPC; Modified model; Neural network method; Neural-networks; Pedestrian steel bridges; Structural model updating; Neural networks",,,,,,,,,,,,,,,,"Fang, Z., Zhang, G.G., Tang, S.H., Chen, S.J., Finite element modeling and model updating of concrete cable-stayed bridge (2013) China Journal of Highway and Transport, 26 (3), pp. 77-85; Zhang, Y.G., Tang, J., Cheng, Y.M., Huang, L., Guo, F., Yin, X.J., Li, N., Prediction of landslide displacement with dynamic features using intelligent approaches (2022) International Journal of Mining Science and Technology, 2 (1), p. 111; 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"Li S., Wang D., Lin C., Wang S.","57218879558;57659839200;57659839300;57659523400;","Long-term friction performance monitoring of sliding layer in China railway track system Ⅱ slab track on bridge superstructure",2022,"Structural Health Monitoring",,,,"","",,,"10.1177/14759217221081558","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129264325&doi=10.1177%2f14759217221081558&partnerID=40&md5=f0408b6ef5ac7c4c1974507763f6935f","School of Transportation Science and Engineering, Harbin Institute of Technology, China; China Railway Siyuan Survey and Design Group CO LTD, China","Li, S., School of Transportation Science and Engineering, Harbin Institute of Technology, China; Wang, D., School of Transportation Science and Engineering, Harbin Institute of Technology, China; Lin, C., China Railway Siyuan Survey and Design Group CO LTD, China; Wang, S., China Railway Siyuan Survey and Design Group CO LTD, China","The sliding layer is an important component of the China Railway Track System (CRTS) Ⅱ slab track on bridges, and its friction performance has a significant influence on the intensity of track–bridge interaction (TBI). However, the friction performance deterioration of this sliding layer under actual repeated abrasion and harsh environmental conditions is difficult to grasp since structural health monitoring implementation into in-service high-speed railway tracks is extremely hard to be permitted and only limited valuable monitoring datasets are available at present. In this study, the friction performance degradation of the sliding layer was therefore investigated using long-term monitoring data from a multi-span simply supported box girder bridge. First, the friction-induced strain in the base plate was decoupled using the monitored strain at the fixing point and mid-span based on mechanical properties of TBI. Then, structural health monitoring and finite element analysis both indicated an approximately linear relationship between the decoupled friction-induced strain and the temperature of the investigated bridge under certain circumstances. Furthermore, the correspondence between the slope of this linear relationship and the friction coefficient was modelled. Finally, the friction coefficients of the sliding layers on target spans were identified using 4 years of monitoring data and the established correspondence model to analyse the statistical characteristics and degradation performance of the layers. This investigation of the friction performance degradation of the sliding layer in the CRTS II slab track system provides guidance for future maintenance and replacement decision making. © The Author(s) 2022.","china railway track system Ⅱ slab track; Friction coefficient; long-term monitoring; sliding layer; track–bridge interaction","Box girder bridges; Decision making; Deterioration; Railroad tracks; Railroad transportation; Railroads; Steel bridges; Strain; China railway track system ⅱ slab track; Friction coefficients; Friction performance; Long term monitoring; Performance degradation; Railway track; Slab tracks; Sliding layer; Track systems; Track-bridge interactions; Structural health monitoring",,,,,"2020K026; YQ2019E025; 2021-A03; National Natural Science Foundation of China, NSFC: 51678204, 51922034; Fundamental Research Funds for the Central Universities: FRFCU5710051018","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided by the NSFC [Grant No. 51922034, 51678204], Heilongjiang Natural Science Foundation for Excellent Young Scholars [Grant No. YQ2019E025], China Railway Siyuan Survey and Design Group R&D Program [Grant No. 2020K026], and Major Scientific and Technological R&D Projects of China Railway Construction Co, Ltd [Grant No. 2021-A03], and Fundamental Research Funds for the Central Universities (Grant No. FRFCU5710051018).",,,,,,,,,,"Gautier, P.-E., Slab track: Review of existing systems and optimization potentials including very high speed (2015) Construction Building Mater, 92, pp. 9-15; 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Wang, T., Jia, H., Liu, Z., Experimental study of the gap between track slab and cement asphalt mortar layer in CRTS I slab track (2018) J Mod Transportation, 26, pp. 173-178; Li, Y., Chen, J., Wang, J., Study on the interface damage of CRTS Ⅱ slab track under temperature load (2020) Structures, 26, pp. 224-236; Peng, H., Zhang, Y., Wang, J., Interfacial Bonding Strength between Cement Asphalt Mortar and Concrete in Slab Track (2019) J Mater Civil Eng, 31, p. 04019107; Su, C., Liu, D., Ding, C., Experimental Study on Bond Performances of Track Slab and Mortar Based on DIC Technology (2018) KSCE J Civil Eng, 22, pp. 3546-3555; Zhu, S., Wang, M., Zhai, W., Mechanical property and damage evolution of concrete interface of ballastless track in high-speed railway: Experiment and simulation (2018) Construction Building Mater, 187, pp. 460-473; Su, M., Dai, G.L., Peng, H., Bond-slip constitutive model of concrete to cement-asphalt mortar interface for slab track structure (2020) Struct Eng Mech, 74, pp. 589-600; 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Li, D.S., Niu, B., Hu, S.T., Mechanical properties of CRTS II slab ballastless track on long temperature span concrete bridge (2016) Chin Railw Sci, 37, pp. 22-29. , (in Chinese; Chen, R., Xing, J., Xie, K.Z., The Continuous-Slab-Track Coupling Laws between the Bridge and Track under Temperature Loads (2017) J Railway Eng Soc, 34, pp. 15-21. , (in Chinese; Su, M., A comprehensively overall track-bridge interaction study on multi-span simply supported beam bridges with longitudinal continuous ballastless slab track (2021) Struct Eng Mech, 78, pp. 163-174; Dai, G.L., Ge, H., Liu, W.S., Interaction analysis of Continuous Slab Track (CST) on long-span continuous high-speed rail bridges (2017) Struct Eng Mech, 63, pp. 713-723; Cai, X.P., Gao, L., Sun, H.W., Analysis on the mechanical properties of longitudinally connected ballastless track continuously welded rail on bridge (2011) Chin Railw Sci, 32, pp. 28-33. , (in Chinese; Dai, G.L., Ge, H., Liu, W.S., Analysis of Longitudinally Connected Ballastless Track on the High-speed Railway Long-span Bridge Based on the Actual Measured Temperature (2017) J Railway Eng Soc, 34, pp. 26-31. , (in Chinese","Li, S.; School of Transportation Science and Engineering, China; email: lishunlong@hit.edu.cn",,,"SAGE Publications Ltd",,,,,14759217,,,,"English","Struct. Health Monit.",Article,"Article in Press","",Scopus,2-s2.0-85129264325
"Filograno M.L., Piniotis G., Gikas V., Papavasileiou V., Gantes C.J., Kandyla M., Riziotis C.","36550163600;57173041500;6507877193;57580350000;35610415700;16637066000;6603072535;","Comparative Assessment and Experimental Validation of a Prototype Phase-Optical Time-Domain Reflectometer for Distributed Structural Health Monitoring",2022,"Journal of Sensors","2022",,"6856784","","",,,"10.1155/2022/6856784","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128377686&doi=10.1155%2f2022%2f6856784&partnerID=40&md5=b6455a65beddcdbfbb37e12d8edb6055","National Hellenic Research Foundation, Theoretical and Physical Chemistry Institute, Athens, 11635, Greece; Department of Electrical and Electronic Engineering, University of Jeddah, Jeddah, 23890, Saudi Arabia; School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens Zographos, Athens, 15780, Greece; School of Civil Engineering, National Technical University of Athens Zographos, Athens, 15780, Greece; Defence and Security Research Institute, University of Nicosia, Nicosia, CY-2417, Cyprus","Filograno, M.L., National Hellenic Research Foundation, Theoretical and Physical Chemistry Institute, Athens, 11635, Greece, Department of Electrical and Electronic Engineering, University of Jeddah, Jeddah, 23890, Saudi Arabia; Piniotis, G., School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens Zographos, Athens, 15780, Greece; Gikas, V., School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens Zographos, Athens, 15780, Greece; Papavasileiou, V., School of Civil Engineering, National Technical University of Athens Zographos, Athens, 15780, Greece; Gantes, C.J., School of Civil Engineering, National Technical University of Athens Zographos, Athens, 15780, Greece; Kandyla, M., National Hellenic Research Foundation, Theoretical and Physical Chemistry Institute, Athens, 11635, Greece; Riziotis, C., National Hellenic Research Foundation, Theoretical and Physical Chemistry Institute, Athens, 11635, Greece, Defence and Security Research Institute, University of Nicosia, Nicosia, CY-2417, Cyprus","Dynamic characterization and Structural Health Monitoring (SHM) are crucial tools, of increasing demand, for reliable operation and predictive maintenance of large infrastructures, as the percentage of critically ageing infrastructures is growing steadily. We present a minimally invasive and synchronous fiber optic monitoring system for SHM, based on Phase-Optical Time-Domain Reflectometry (Phase-OTDR), and we assess its applicability and performance on a modular Bailey-type bridge of 1: 2.5 scale. Phase-OTDR systems, along with other fiberoptic-distributed techniques have proven their capabilities in long-range SHM applications, although their complexity and high cost limits drastically their applicability and SHM market penetration. Here, we propose the use of a prototype Phase-OTDR system, featuring customized interrogation instrumentation with a balanced trade-off between performance and cost. Its experimental validation is achieved by comparison with well-established commercial monitoring systems, such as Ground-Based Radar Interferometer (GBRI), laser tracker, and multipoint optical Fiber Bragg Gratings (FBGs), in various excitation conditions and structure-damage scenarios, easily implementable in the model bridge. Finite-element modelling (FEM) and simulations were employed to study the bridge behaviour and provide a reference and comparison framework for the experimental characterization. The Phase-OTDR system successfully detected the structural behaviour in an efficient distributed manner, demonstrating comparable performance to commercial point sensor systems, thus demonstrating its application potential. © 2022 Massimo Leonardo Filograno et al.",,"Economic and social effects; Fiber Bragg gratings; Laser excitation; Reflectometers; Time domain analysis; Ageing infrastructures; Comparative assessment; Dynamic characterization; Experimental validations; Operations maintenance; Optical time domain reflectometer; Optical time domain reflectometry; Performance; Predictive maintenance; Reliable operation; Structural health monitoring",,,,,,"The Marie Sklodowska-Curie grant agreement No. 706221",,,,,,,,,,"Gattulli, V., Chiaramonte, L., Condition assessment by visual inspection for a bridge management system (2005) Computer-Aided Civil and Infrastructure Engineering, 20 (2), pp. 95-107. , 2-s2.0-21644484169; Quirk, L., Matos, J., Murphy, J., Pakrashi, V., Visual inspection and bridge management (2018) Structure and Infrastructure Engineering, 4, pp. 320-332; Piniotis, G., Gikas, V., Mpimis, A., Perakis, H., Deck and cable dynamic testing of a single-span bridge using radar interferometry and videometry measurements (2016) Journal of Applied Geodesy, 10 (1), pp. 87-94. , 2-s2.0-84960983610; Gikas, V., Karydakis, P., Mpimis, A., Piniotis, G., Perakis, H., Structural integrity verification of cable stayed footbridge based on FEM analyses and geodetic surveying techniques (2016) Survey Review, 48 (346), pp. 1-10. , 2-s2.0-84962006665; Chen, Z., Zhou, X., Wang, X., Dong, L., Qian, Y., Deployment of a smart structural health monitoring system for long-span arch bridges: a review and a case study (2017) Sensors, 17 (9), p. 2151. , 2-s2.0-85029802240 28925943; Ye, X., Chen, X., Lei, Y., Fan, J., Mei, L., An integrated machine learning algorithm for separating the long-term deflection data of prestressed concrete bridges (2018) Sensors, 18 (11), p. 4070. , 2-s2.0-85057129534 30469405; Calvi, G.M., Moratti, M., O'Reilly, G.J., Scattarreggia, N., Monteiro, R., Malomo, D., Calvi, P.M., Pinho, R., Once upon a time in Italy: the tale of the Morandi bridge (2019) Structural Engineering International, 29 (2), pp. 198-217. , 2-s2.0-85064954765; Zarikas, V., Gikas, V., Kitsos, C., Evaluation of the optimal design ""cosinor model""for enhancing the potential of robotic theodolite kinematic observations (2010) Measurement, 43 (10), pp. 1416-1424. , 2-s2.0-77957843948; Gikas, V., Smart RTS: monitoring highly dynamic structures (2012) GIM International, 22 (6), pp. 44-45; Psimoulis, P., Stiros, S.C., Measuring deflections of a short-span railway bridge using a robotic total station (2013) Journal of Bridge Engineering, 18 (2), pp. 182-185. , 2-s2.0-84873325506; Moschas, F., Stiros, S.C., Three-dimensional dynamic deflections and natural frequencies of a stiff footbridge based on measurements of collocated sensors (2014) Structural Control and Health Monitoring, 21 (1), pp. 23-42. , 2-s2.0-84890116777; Lienhart, W., Ehrhart, M., Grick, M., High frequent total station measurements for the monitoring of bridge vibrations (2017) Journal of Applied Geodesy, 11 (1), pp. 1-8. , 2-s2.0-85015297181; Gikas, V., Mpimis, A., Piniotis, G., Perakis, H., Papadimitriou, H., Drimeris, K., Sotiriou, P., Long-term monitoring of the tall piers of a girder bridge using a network of digital inclinometers: first results and perspectives for future analyses, pp. 15-17. , Proceedings of the 4th Joint International Symposium on Deformation Monitoring (JISDM) 2019, May Athens, Greece; Zhang, R., Gao, C., Pan, S., Shang, R., Fusion of GNSS and speedometer based on VMD and its application in bridge deformation monitoring (2020) Sensors, 20 (3), p. 694. , 32012752; Yigit, C.O., Dindar, A.A., El-Mowafy, A., Bezcioglu, M., Gikas, V., Investigating the ability of high-rate GNSS-PPP for determining the vibration modes of engineering structures: small scale model experiment, pp. 15-17. , Proceedings of 4th Joint International Symposium on Deformation Monitoring 2019, May Athens, Greece; Yu, J., Meng, X., Yan, B., Xu, B., Fan, Q., Xie, Y., Global navigation satellite system-based positioning technology for structural health monitoring: a review (2020) Structural Control and Health Monitoring, 27 (1), pp. 1-27; Gikas, V., Ambient vibration monitoring of slender structures by microwave interferometer remote sensing (2012) Journal of Applied Geodesy, 6 (3-4), pp. 167-176. , 2-s2.0-84984984530; Lee, J.J., Ho, H.N., Lee, J.H., A vision-based dynamic rotational angle measurement system for large civil structures (2012) Sensors, 12 (6), pp. 7326-7336. , 2-s2.0-84863226499 22969348; Matias, I.R., Ikezawa, S., Corres, J., (2017) Fiber optic sensors: current status and future possibilities, smart sensors, measurement and instrumentation, , Springer; Barrias, A., Casas, J.R., Villalba, S., A review of distributed optical fiber sensors for civil engineering applications (2016) Sensors, 16 (5), p. 748. , 2-s2.0-84969522887 27223289; Wang, H., Xiang, P., Jiang, L., Strain transfer theory of industrialized optical fiber-based sensors in civil engineering: a review on measurement accuracy, design and calibration (2019) Sensors and Actuators A: Physical, 285, pp. 414-426. , 2-s2.0-85058228589; Pruneri, V., Riziotis, C., Smith, P.G.R., Vasilakos, A., Fiber and integrated waveguide-based optical sensors (2009) Journal of Sensors, 2009, p. 3. , 171748 2-s2.0-77649255740; Filograno, M.L., Piniotis, G., Gikas, V., Papavassiliou, V., Gantes, C., Kandyla, M., Riziotis, C., Experimental validation of a prototype photonic phase optical time domain reflectometer for SHM in large-scale infrastructures, , 4th Joint International Symposium on Deformation Monitoring (JISDM) 2019 Athens, Greece; Lu, P., Lalam, N., Badar, M., Liu, B., Chorpening, B.T., Buric, M.P., Ohodnicki, P.R., Distributed optical fiber sensing: review and perspective (2019) Applied Physics Reviews, 6 (4, ARTICLE 041302). , 2-s2.0-85073601860; Xiang, P., Wang, H., Optical fibre-based sensors for distributed strain monitoring of asphalt pavements (2018) International Journal of Pavement Engineering, 19 (9), pp. 842-850. , 2-s2.0-84982812945; Lopez-Higuera, J.M., Rodriguez Cobo, L., Quintela Incera, A., Cobo, A., Fiber optic sensors in structural health monitoring (2011) Journal of Lightwave Technology, 29 (4), pp. 587-608. , 2-s2.0-80052596713; He, Z., Liu, Q., Fan, X., Chen, D., Wang, S., Yang, G., A review on advances in fiber-optic distributed acoustic sensors (DAS), p. 1. , Proceedings CLEO Pacific Rim Conference 2018 Hong Kong, China; Muanenda, Y., Recent advances in distributed acoustic sensing based on phase-sensitive optical time domain reflectometry (2018) Journal of Sensors, 2018, p. 16. , 2-s2.0-85054746950 3897873; Masoudi, A., Newson, T.P., Analysis of distributed optical fibre acoustic sensors through numerical modelling (2017) Optics Express, 25 (25), pp. 32021-32040. , 2-s2.0-85038207670 29245868; Tejedor, J., Macias-Guarasa, J., Martins, H.F., Piote, D., Pastor-Graells, J., Martin-Lopez, S., Corredera, P., Gonzalez-Herraez, M., Towards detection of pipeline integrity threats using a smart fiber-optic surveillance system: pit-stop project blind field test results, , 25th International Conference on Optical Fiber Sensors 2017 Jeju, Korea (South); Shlyagin, M.G., Arias, A., Martinez Manuel, R., Distributed detection and localization of multiple dynamic perturbations using coherent correlation OTDR, , 23rd International Conference on Optical Fibre Sensors. 9157. International Society for Optics and Photonics 2014 Santander, Spain; Pastor-Graells, J., Martins, H.F., Garcia-Ruiz, A., Martin-Lopez, S., Gonzalez-Herraez, M., Single-shot distributed temperature and strain tracking using direct detection phase-sensitive OTDR with chirped pulses (2016) Optics Express, 24 (12), pp. 13121-13133. , 2-s2.0-84977665771 27410330; Martins, H.F., Martin-Lopez, S., Corredera, P., Filograno, M.L., Frazao, O., Gonzalez-Herraez, M., Coherent noise reduction in high visibility phase-sensitive optical time domain reflectometer for distributed sensing of ultrasonic waves (2013) Journal of Lightwave Technology, 31 (23), pp. 3631-3637. , 2-s2.0-84888101317; Filograno, M.L., Riziotis, C., Kandyla, M., A low-cost phase-OTDR system for structural health monitoring: design and instrumentation (2019) Instruments, 3 (3), p. 46; Doa-Department Of The, Army, (1986) Bailey Bridges FM5-277, , Washington, DC, USA Headquarters Department of the Army; Gentile, C., Kouemou, G., (2010) Application of radar technology to deflection measurement and dynamic testing of bridges, radar technology, , InTechOpen; Pieraccini, M., Miccinesi, L., Ground-based radar interferometry: a bibliographic review (2019) Remote Sensing, 11 (9), p. 1029. , 2-s2.0-85065710846; Gocal, J., Ortyl, L., Owerko, T., Kuras, P., Kocierz, R., Cwiakala, P., Puniach, E., Balut, A., (2013) Determination of displacements and vibrations of engineering structures using ground-based radar interferometry, , Wydawnictwa AGH; Werneck, M.M., Allil, R.C.S.B., Ribeiro, B.A., de Nazaré, F.V.B., (2013) A guide to fiber Bragg grating sensors, , IntechOpen; Filograno, M.L., Corredera, P., Rodríguez-Plaza, M., Andrés-Alguacil, A., González-Herráez, M., Wheel flat detection in high-speed railway systems using fiber Bragg gratings (2013) IEEE Sensors Journal, 13 (12), pp. 4808-4816. , 2-s2.0-84885931936; Filograno, M.L., Corredera Guillen, P., Rodriguez-Barrios, A., Martin-Lopez, S., Rodriguez-Plaza, M., Andres-Alguacil, Á., Gonzalez-Herraez, M., Real-time monitoring of railway traffic using fiber Bragg grating sensors (2012) IEEE Sensors Journal, 12 (1), pp. 85-92. , 2-s2.0-82555197037","Kandyla, M.; National Hellenic Research Foundation, Greece; email: kandyla@eie.gr",,,"Hindawi Limited",,,,,1687725X,,,,"English","J. Sensors",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85128377686
"Menon A.M., Tanmayee K.R.R., Verma H., Omprakash P., Haldar A., Swayamjyoti S., Sahu K.K., Featherston C.","57429898400;57429193100;57428835200;57221689394;35787867100;56210917600;26435535400;6601990060;","Deep Learning-based Optimization of Piezoelectric Vibration Energy Harvesters",2022,"AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022",,,"AIAA 2022-2142","","",,,"10.2514/6.2022-2142","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123642839&doi=10.2514%2f6.2022-2142&partnerID=40&md5=7d1f2b3a5e74df5c75ae87b53ea3b6b2","IIT Bhubaneswar, Odisha, Argul, 752050, India; NIT Surathkal, Srinivasanagar, Surathkal, Karnataka, Mangalore, 575025, India; IIT BHU, UP, Varanasi, 221005, India; School of Engineering, Cardiff University, Queen’s Buildings, The Parade, Newport Road, Cardiff, CF24 3AA, United Kingdom","Menon, A.M., IIT Bhubaneswar, Odisha, Argul, 752050, India; Tanmayee, K.R.R., IIT Bhubaneswar, Odisha, Argul, 752050, India; Verma, H., IIT Bhubaneswar, Odisha, Argul, 752050, India; Omprakash, P., NIT Surathkal, Srinivasanagar, Surathkal, Karnataka, Mangalore, 575025, India; Haldar, A., IIT BHU, UP, Varanasi, 221005, India; Swayamjyoti, S., IIT Bhubaneswar, Odisha, Argul, 752050, India; Sahu, K.K., IIT Bhubaneswar, Odisha, Argul, 752050, India; Featherston, C., School of Engineering, Cardiff University, Queen’s Buildings, The Parade, Newport Road, Cardiff, CF24 3AA, United Kingdom","Advances in energy harvesting technologies present prospective concepts to capture and store energy from the environment and use it to power sensors used in Structural Health Monitoring (SHM) systems. Among many others, ambient vibrations are a ubiquitous source of energy that has the potential to charge low-powered sensors attached to aircraft structures. This study aims at designing a vibrational-based energy harvesting system consisting of Macro-Fiber Composite (MFC) patches bonded to a cantilever beam with optimal design parameters. As a base model, an electromechanically coupled Finite Element (FE) model is first developed to predict the open-source voltage when subjected to input excitation, which is validated using previous experimental data. Subsequently, the harvested power is found by simulating an electrical circuit consisting of a full-bridge rectifier and an external capacitor, using Electronic Design Automation (EDA) simulation. A Deep learning-based optimization is proposed to calculate the optimal mechanical and electrical parameters, resulting in the maximum number of resonant peaks within a specified frequency range, and also to maximize the power generated from higher-order resonant peaks. Using the developed FE model, a large number of data is generated to train a Deep Neural Network (DNN), which has the capability to find the optimal design parameters for the specified objective. This approach aims at replacing conventional optimization techniques and to obtain an optimal design of broadband vibrational-based energy harvester in a more computationally efficient manner. © 2022, American Institute of Aeronautics and Astronautics Inc.. All rights reserved.",,"Aircraft manufacture; Airframes; Computer aided design; Deep neural networks; Structural dynamics; Structural health monitoring; Energy; Finite element modelling (FEM); Optimal design parameters; Optimisations; Piezoelectric vibration; Power; Power sensor; Prospectives; Resonant peaks; Vibration energy harvesters; Energy harvesting",,,,,,"AMM, KRRT, HV, SS, KKS thank IIT Bhubaneswar for financial assistance.",,,,,,,,,,"Pearson, M. R., Eaton, M. J., Pullin, R., Featherston, C. A., Holford, K. M., Energy Harvesting for Aerospace Structural Health Monitoring Systems (2012) J. Phys. Conf. 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Signal Process, 160, p. 107876. , Nov; Bagheri, S., Wu, N., Filizadeh, S., Application of artificial intelligence and evolutionary algorithms in simulationbased optimal design of a piezoelectric energy harvester (2020) Smart Mater. Struct, 29 (10), p. 105004. , Oct; Yang, Y., Tang, L., Li, H., Vibration energy harvesting using macro-fiber composites (2009) Smart Mater. Struct, 18 (11), p. 115025. , Nov; MacroFiberCompositeTM, , https://www.smart-material.com/MFC-product-mainV2.html?gclid=Cj0KCQjwna2FBhDPARIsACAEc_USSUurpdOireFnKwGJH_u1edxPzCTg7A68ODOlleIgL1TcJHqcbwaAmA8EALw_wcB; ABAQUS Analysis User’s Manual (v6.5-1), , https://classes.engineering.wustl.edu/2009/spring/mase5513/abaqus/docs/v6.5/books/usb/default.htm?startat=pt07ch20s02aus98.html; MacroFiberCompositeTM P1 Type, , https://www.smart-material.com/MFC-product-P1V2.html; ABAQUS Analysis User’s Manual (v6.6), , https://classes.engineering.wustl.edu/2009/spring/mase5513/abaqus/docs/v6.6/books/usb/default.htm?startat=pt05ch20s01abm43.html; ABAQUS Analysis User’s Manual (v6.6), , https://classes.engineering.wustl.edu/2009/spring/mase5513/abaqus/docs/v6.6/books/usb/default.htm?startat=pt03ch06s03at08.html, (accessed May 24, 2021)",,,,"American Institute of Aeronautics and Astronautics Inc, AIAA","AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022","3 January 2022 through 7 January 2022",,270339,,9781624106316,,,"English","AIAA SCITECH Forum",Conference Paper,"Final","",Scopus,2-s2.0-85123642839
"Aloupis C., Shenton H.W., III, Chajes M.J.","57208238104;7003884575;57202996479;","Using Dead and Thermal Loads to Capture the Behavioral Changes of a Cable-Stayed Bridge",2022,"Conference Proceedings of the Society for Experimental Mechanics Series",,,,"175","177",,,"10.1007/978-3-030-77348-9_21","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122523326&doi=10.1007%2f978-3-030-77348-9_21&partnerID=40&md5=977606b98614686069875fc8f4551d16","Department of Civil and Environmental Engineering, College of Engineering, University of Delaware, Newark, DE, United States","Aloupis, C., Department of Civil and Environmental Engineering, College of Engineering, University of Delaware, Newark, DE, United States; Shenton, H.W., III, Department of Civil and Environmental Engineering, College of Engineering, University of Delaware, Newark, DE, United States; Chajes, M.J., Department of Civil and Environmental Engineering, College of Engineering, University of Delaware, Newark, DE, United States","To enhance the maintenance process of the Indian River Inlet Bridge, the Delaware Department of Transportation worked with the Center for Innovative Bridge Engineering (CIBrE) of the University of Delaware (UD) to install a structural health monitoring (SHM) system on the bridge during construction. The SHM system collects data in real time 24/7. For this research, data collected are transformed into 10-minute average values. These average values represent the response of the bridge to slowly changing thermal loads or to constant loads such as dead loads. This paper presents a method for identifying damage from the structures’ strain vs. temperature response. The methodology is evaluated based on both actual response and response simulated using a calibrated finite element model (FEM). Using data collected over 8 years since the bridge was opened to traffic, a finite element model (FEM) was used to evaluate the ability of the SHM to identify different types and severity of damage. To do this, different levels of severity were simulated, and their effect on the structural response was compared with the observed response, including the variability of that response. Using this approach, the ability to assess various levels of damage has been determined. © 2022, The Society for Experimental Mechanics, Inc.","Cable-stayed bridge; Dead loads; Structural health monitoring (SHM); Temperature-driven SHM; Thermal loads","Cable stayed bridges; Finite element method; Structural health monitoring; Thermal load; Average values; Behavioral changes; Dead loads; Delawares; Finite element modelling (FEM); Indian rivers; Maintenance process; Structural health monitoring; Structural health monitoring systems; Temperature-driven structural health monitoring; Cables",,,,,"Federal Highway Administration, FHWA; Delaware Department of Transportation, DelDOT","Acknowledgements The authors would like to acknowledge the Delaware Department of Transportation (especially Doug Robb, Craig Stevens, Marx Possible, David Gray, Alastair Probert, Jason Arndt, Craig Kursinski, Raymond Eskaros) and the Federal Highway Administration for the financial support to develop and implement the structural monitoring system.",,,,,,,,,,"Al-Khateeb, H.T., Shenton, H.W., Chajes, M.J., Aloupis, C., Structural health monitoring of a cable-stayed bridge using regularly conducted diagnostic load tests (2019) Front. Built Environ., 5 (2019); Shenton, H.W., Al-Khateeb, H.T., Chajes, M.J., Wenczel, G., Indian river inlet bridge (part A): Description of the bridge and the structural health monitoring system (2017) Bridg. Struct., 13, pp. 3-13; Yarnold, M.T., Moon, F.L., Temperature-based structural health monitoring baseline for long-span bridges (2015) Eng. Struct., 86, pp. 157-167","Aloupis, C.; Department of Civil and Environmental Engineering, United States; email: chr.aloup@gmail.com","Mao Z.",,"Springer","39th IMAC, A Conference and Exposition on Structural Dynamics, 2021","8 February 2021 through 11 February 2021",,264509,21915644,9783030773472,,,"English","Conf. Proc. Soc. Exp. Mech. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85122523326
"Li J., Yu Z., Zhang X., Wang Y.","57402753700;57402467800;57402753800;57402753900;","Civil Structure Damage Identification Method Based on Finite Element Model Technology",2022,"Lecture Notes on Data Engineering and Communications Technologies","103",,,"113","121",,,"10.1007/978-981-16-7469-3_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122345064&doi=10.1007%2f978-981-16-7469-3_12&partnerID=40&md5=1d2fa7c354c178fb50ec9df7a5bac4d5","Department of Logistics and Infrastructure Construction, Beijing Institute of Technology, Beijing, China; Beijing Wodong Tianjun Information Technology Co., Ltd., Beijing, China","Li, J., Department of Logistics and Infrastructure Construction, Beijing Institute of Technology, Beijing, China; Yu, Z., Beijing Wodong Tianjun Information Technology Co., Ltd., Beijing, China; Zhang, X., Department of Logistics and Infrastructure Construction, Beijing Institute of Technology, Beijing, China; Wang, Y., Department of Logistics and Infrastructure Construction, Beijing Institute of Technology, Beijing, China","IDamage identification of structures is the key to structural health monitoring. How to develop effective damage identification methods is still a difficult problem. In practical engineering, due to the complexity of the structure, the uncertainty of the environment, the experimental measurement data are not complete and have errors, the damage index is not sensitive enough, and the accuracy of the finite element model is difficult to ensure, there is no universally applicable method for structural damage identification. This paper mainly studies the damage identification method of civil structure based on finite element model technology. The initial finite element model of the bridge is established by using the finite element software, and the temperature response and static load response of the model are calculated. Combined with the existing problems in the modeling process, two steps of model modification are developed to realize the modification of the finite element model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","Abaqus software; Civil structures; Damage identification; Finite element modeling","ABAQUS; Damage detection; Structural health monitoring; Uncertainty analysis; ABAQUS software; Civil structure; Damage Identification; Finite element modelling (FEM); Identification method; Identification of structures; Modeling technology; Practical engineering; Structure damage; Uncertainty; Finite element method",,,,,,,,,,,,,,,,"Astroza, R., Ebrahimian, H., Yong, L., Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures (2017) Mech Syst Sig Process, 93, pp. 661-687; Ebrahimian H, Astroza R, Conte J P et al (2018) Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures. Struct Control Health Monit 4:e2128.1–e2128.32 (in press); Beer, M., Kougioumtzoglou, I.A., Patelli, E., System and damage identification of civil structures (2021) Encyclop Earthq Eng, 70 (1), pp. 1-9. , https://doi.org/10.1007/978-3-642-36197-5; Hua, X., Model study of civil engineering structure damage identification system based on modern intelligent algorithm (2017) Rev Fac Ing, 32 (15), pp. 196-201; Zhi-Xiong, L., Wen-Ping, Z., Bin, Z., Research on performance detection system and damage identification method based on civil engineering structure (2017) Acta Tech CSAV Cesk Akad Ved, 62 (1), pp. 201-213; Pang-Jo, C., Naoya, K., Kouji, K., Damage identification on I-beam member using multipoint acceleration measurement and machine learning (2016) J Jpn Soc Civ Eng Ser A2 Appl Mech (AM), 72 (2), pp. 623-I; Gu, Y., Huang, Z., Wavelet analysis to realize damage identification and earthquake disaster mitigation design of civil engineering building structure by using structural dynamic characteristics (2017) Bol Tecn Tech Bull, 55 (6), pp. 325-334; Pegg, E.C., Gill, H.S., An open source software tool to assign the material properties of bone for ABAQUS finite element simulations (2016) J Biomech, 49 (13), pp. 3116-3121; Alotta, G., Barrera, O., Cocks, A., The finite element implementation of 3D fractional viscoelastic constitutive models (2018) Finite Elem Anal Des, 146, pp. 28-41; Druckrey, A.M., Alshibli, K.A., 3D finite element modeling of sand particle fracture based on in situ X-ray synchrotron imaging: 3D finite element modeling of sand particle fracture (2016) Int J Numer Anal Meth Geomech, 40 (1), pp. 105-116; Ansari, M.A., Samanta, A., Behnagh, R.A., An efficient coupled Eulerian-Lagrangian finite element model for friction stir processing (2019) Int J Adv Manufact Technol, 101 (1), pp. 1-14; Wang, Y., Liu, Y., Tang, W., Parametric finite element modeling and tooth contact analysis of spur and helical gears including profile and lead modifications (2017) Eng Comput, 34 (8), pp. 00-00","Li, J.; Department of Logistics and Infrastructure Construction, China; email: 18337178897@163.com",,,"Springer Science and Business Media Deutschland GmbH",,,,,23674512,,,,"English","Lecture. Notes. Data Eng. Commun. Tech.",Conference Paper,"Final","",Scopus,2-s2.0-85122345064
"Pellegrino C., Zanini M.A., Faleschini F., Andreose F., Toska K., Zampieri P., Hofer L., Feltrin G.","7006716267;55479744800;14522174100;57222614941;57208601116;56353092200;57191293652;57225405946;","Modal Characterization of a Prestressed Reinforced Concrete Bridge Composed by Decks with Different Ages",2022,"Lecture Notes in Civil Engineering","200 LNCE",,,"1405","1415",,,"10.1007/978-3-030-91877-4_160","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121920176&doi=10.1007%2f978-3-030-91877-4_160&partnerID=40&md5=7ab42af3784328a38e1951be575bf591","Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, 35131, Italy; Department of Industrial Engineering, University of Padova, Padova, 35131, Italy","Pellegrino, C., Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, 35131, Italy; Zanini, M.A., Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, 35131, Italy; Faleschini, F., Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, 35131, Italy, Department of Industrial Engineering, University of Padova, Padova, 35131, Italy; Andreose, F., Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, 35131, Italy; Toska, K., Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, 35131, Italy; Zampieri, P., Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, 35131, Italy; Hofer, L., Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, 35131, Italy; Feltrin, G., Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, 35131, Italy","The use of ambient vibration test for modal characterization of structures is becoming a common practice to capture samples of the experimental response of the “real structure”, especially for existing structures where conditions with respect to the “as-built” state can be further different due to the presence of deterioration phenomena. The present work illustrates experimental activities and data process of dynamic tests carried out on a prestressed reinforced concrete bridge, located along a highway in Italy. The structure is characterized by four main decks built in two different ages. Objective of the tests was to determine modal parameters of the two bridges, like vibration frequencies, mode shapes and damping-ratios, to calibrate a FE model. Furthermore, the longitudinal and transversal joints behavior was investigated. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Ambient vibration test; FE model update; Mode shapes; OMA; Static load test; Structural health monitoring","Concrete beams and girders; Concrete bridges; Concrete testing; Deterioration; Finite element method; Load testing; Modal analysis; Railroad bridges; Reinforced concrete; Structural health monitoring; Testing; Vibration analysis; Ambient vibration test; Characterization of structure; Different ages; FE model; FE model update; Mode shapes; Model updates; OMA; Reinforced concrete bridge; Static load tests; Prestressed concrete",,,,,,,,,,,,,,,,"Zanini, M.A., Faleschini, F., Pellegrino, C., Probabilistic seismic risk forecasting of aging bridge networks (2017) Eng Struct, 136, pp. 219-232; Zanini, M.A., Faleschini, F., Pellegrino, C., Cost analysis for maintenance and seismic retrofit of existing bridges (2016) Struct Infrastruct Eng, 12 (11), pp. 1411-1427; Morbin, R., Zanini, M.A., Pellegrino, C., Zhang, H., Modena, C., A probabilistic strategy for seismic assessment and FRP retrofitting of existing bridges (2015) Bull Earthq Eng, 13 (8), pp. 2411-2428. , https://doi.org/10.1007/s10518-015-9725-2; Pellegrino, C., Zanini, M.A., Zampieri, P., Modena, C., Contribution of in situ and laboratory investigations for assessing seismic vulnerability of existing bridges (2015) Struct Infrastruct Eng, 11 (9), pp. 1147-1162; Zampieri, P., Zanini, M.A., Faleschini, F., Derivation of analytical seismic fragility functions for common masonry bridge types: Methodology and application to real cases (2016) Eng Fail Anal, 68, pp. 275-291; Prendergast, L.J., Limongelli, M.P., Ademovic, N., Anzlin, A., Gavin, K., Zanini, M.A., Structural health monitoring for performance assessment of bridges under flooding and seismic actions (2018) Struct Eng Int, 28 (3), pp. 296-307; Zanini, M.A., Hofer, L., Faleschini, F., Pellegrino, C., The influence of record selection in assessing uncertainty of failure rates (2017) Ingegneria Sismica, 34 (4), pp. 30-40; Carturan, F., Zanini, M.A., Pellegrino, C., Modena, C., A unified framework for earthquake risk assessment of transportation networks and gross regional product (2013) Bull Earthq Eng, 12 (2), pp. 795-806. , https://doi.org/10.1007/s10518-013-9530-8; Brincker, R., Ventura, C.E., (2015) Introduction to Operational Modal Analysis, , Wiley, Hoboken; Rainieri, C., Fabbrocino, G., (2014) Operational Modal Analysis of Civil Engineering Structures-An Introduction and Guide for Applications, , Springer Science+Business Media, New York; Batel, M., Operational modal analysis – another way of doing modal testing (2002) Sound Vib, 36, pp. 22-27; Brincker, R., Andersen, P., Jacobsen, N.-J., Automated frequency domain decomposition for operational modal analysis (2007) Proceedings of the 25Th SEM International Modal Analysis Conference, , Orlando; Brincker, R., Andersen, P., Møller, N., An indicator for separation of structural and harmonic modes in output-only modal testing (2000) Proceedings of the European COST F3 Conference on System Identification and Structural Health Monitoring, , Madrid; Brownjohn, J.M.W., Moyo, P., Omenzetter, P., Lu, Y., Assessment of highway bridge upgrading by dynamic testing and finite-element model updating (2003) J Bridge Eng, 8, pp. 162-172; Pellegrino, C., Zanini, M.A., Faleschini, F., Andreose, F., Mancassola, L., Frizzarin, M., (2020) Ambient Vibration Tests for Modal Characterization of an Exixting Steel-Concrete Composite Bridge. In: Proceeding of IABSE Symposium; Synergy of Culture and Civil Engineering – History and Challenges, , Wroclaw, Poland","Pellegrino, C.; Department of Civil, Italy; email: carlo.pellegrino@unipd.it","Pellegrino C.Faleschini F.Zanini M.A.Matos J.C.Casas J.R.Strauss A.",,"Springer Science and Business Media Deutschland GmbH","1st Conference of the European Association on Quality Control of Bridges and Structures, EUROSTRUCT 2021","29 August 2021 through 1 September 2021",,269849,23662557,9783030918767,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85121920176
"Gibbs D., Jankowski K., Rees B., Farrar C., Flynn G.","57302663000;57302506600;57302828100;7006572457;56201363100;","Identifying Environmental- and Operational-Insensitive Damage Features",2022,"Conference Proceedings of the Society for Experimental Mechanics Series",,,,"105","121",,,"10.1007/978-3-030-76004-5_13","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117495612&doi=10.1007%2f978-3-030-76004-5_13&partnerID=40&md5=3fdc9cd7de0bdd7dcea5ebbbe410ffa4","Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States; Department of Mechanical and Aerospace Engineering, New Mexico State University, Las Cruces, NM, United States; Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, United States; Los Alamos National Laboratory, Los Alamos, NM, United States","Gibbs, D., Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States; Jankowski, K., Department of Mechanical and Aerospace Engineering, New Mexico State University, Las Cruces, NM, United States; Rees, B., Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, United States; Farrar, C., Los Alamos National Laboratory, Los Alamos, NM, United States; Flynn, G., Los Alamos National Laboratory, Los Alamos, NM, United States","Structural health monitoring (SHM) detects damage in structures using online, in situ, monitoring. In practice, structures are affected by factors that make it difficult to discern damage from environmental and operational (E&O) variability. Therefore, an improved process for identifying features that are sensitive to damage while insensitive to E&O effects is needed. In this study a SHM approach that utilizes causality metrics is proposed. The assumption is made that under E&O variability, the structure will remain linear while in its undamaged state, and linear changes in the structural properties will not affect the causal relations between sensor readings. Furthermore, the structure will exhibit an observable nonlinear response when damage is introduced to the system and that response will cause changes detected by the three proposed measures of causality: granger causality, mutual information, and coherence. This paper aims to evaluate the candidacy of causality measures in detecting nonlinearities introduced by damage while remaining insensitive to linear changes to the structural properties caused by E&O variability. This approach is evaluated using a numerical model simulating damage and E&O variability, an experimental dataset looking at the vibrational response of a concrete column with the introduction of damage, and an experimental dataset looking at the vibrational response of a bridge in different environmental conditions. Each measure was found to suggest an ability to detect damage and remain insensitive to E&O variability. © 2022, The Society for Experimental Mechanics, Inc.","Damage detection; Environmental and operational variability; Finite element analysis modeling; Granger causality; Structural health monitoring","Finite element method; Statistical tests; Structural dynamics; Structural health monitoring; Structural properties; Causal relations; Damage features; Environmental and operational variability; Finite element analysis modeling; Granger Causality; Improved process; In-situ monitoring; Monitoring approach; Operational effects; Vibrational response; Damage detection",,,,,"University of California, San Diego, UCSD; Los Alamos National Laboratory, LANL","Acknowledgments This research was funded by Los Alamos National Laboratory (LANL) through the Engineering Institute’s Los Alamos Dynamics Summer School. The Engineering Institute is a research and education collaboration between LANL and the University of California San Diego’s Jacobs School of Engineering. This collaboration seeks to promote multidisciplinary engineering research that develops and integrates advanced predictive modeling, novel sensing systems, and new developments in information technology to address LANL mission-relevant problems.","This research was funded by Los Alamos National Laboratory (LANL) through the Engineering Institute’s Los Alamos Dynamics Summer School. The Engineering Institute is a research and education collaboration between LANL and the University of California San Diego’s Jacobs School of Engineering. This collaboration seeks to promote multidisciplinary engineering research that develops and integrates advanced predictive modeling, novel sensing systems, and new developments in information technology to address LANL mission-relevant problems.",,,,,,,,,"Farrar, C.R., Worden, K., (2012) Structural Health Monitoring: A Machine Learning Perspective, , Wiley, Hoboken; Sohn, H., Effects of Environmental and Operational Variability on Structural Health Monitoring (2007) Philos. Trans. R. Soc. a Math. Phys. Eng. Sci., 365 (1851), pp. 539-560; Ugalde, U., Anduaga, J., Martinez, F., Iturrospe, A., (2016) A SHM Method for Detecting Damage with Incomplete Observations Based on VARX Modelling and Granger Causality, , arXiv preprint arXiv; Zheng, W., Wu, C., A bio-inspired memory model embedded with a causality reasoning function for structural fault location (2015) Plos ONE, 10 (3); Liu, C., Gong, Y., Laflamme, S., Phares, B., Sarkar, S., Bridge damage detection using spatiotemporal patterns extracted from dense sensor network (2017) Meas. Sci. Technol., 28; Thomas, C., Coherence function in noisy linear system (2015) Int. J. Biomed. Sci. Eng., 3 (2), pp. 25-33; Cadzow, J., Solomon, O., Linear modeling and the coherence function (1987) IEEE Trans. Acoust. Speech Signal Process., 35 (1), pp. 19-28; Granger, C.W., Investigating causal relations by econometric models and cross-spectral methods (1969) Econometrica, 37, pp. 424-438; Shannon, C.E., A mathematical theory of communication (1948) Bell Syst. Tech. J., 27 (3), pp. 379-423; Kraskov, A., Stögbauer, H., Grassberger, P., Estimating mutual information (2004) Phys. Rev. E., 69 (6), pp. 066138-66141; Farrar, C.R., Worden, K., Ch. 5.2 The concrete column (2013) Structural Health Monitoring: A Machine Learning Approach. Wiley, Chichester, UK; Farrar, C.R., Cornwell, P.J., Doebling, S.W., Prime, M.B., Structural health monitoring studies of the Alamosa Canyon and I-40 bridges. Los Alamos National Laboratory report (2000) LA-13635-MS","Farrar, C.; Los Alamos National LaboratoryUnited States; email: farrar@lanl.gov","Madarshahian R.Hemez F.",,"Springer","39th IMAC, A Conference and Exposition on Structural Dynamics, 2021","8 February 2021 through 11 February 2021",,264509,21915644,9783030760038,,,"English","Conf. Proc. Soc. Exp. Mech. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85117495612
"Abouelleil A., Rasheed H.A., Fletcher E.","57194243473;7004021000;57928284400;","Damage Detection in Concrete Bridge T girders using 3D Finite Element Simulations Trained by Artificial Neural Network",2021,"American Concrete Institute, ACI Special Publication","SP-350",,,"1","15",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139876415&partnerID=40&md5=5b8cb34fe4a05e0acf06792a4f65cd08","Advanced Engineering Design Apps (AEDA, LLC), United States; Kansas State University, United States; BSE Structural Engineers LLC, Lenexa, KS, United States","Abouelleil, A., Advanced Engineering Design Apps (AEDA, LLC), United States; Rasheed, H.A., Kansas State University, United States; Fletcher, E., BSE Structural Engineers LLC, Lenexa, KS, United States","The structural deterioration of aging infrastructure systems is becoming an increasingly important issue worldwide. To compound the problem, economic strains limit the resources available for repair or replacement of such systems. Over the past several decades, structural health monitoring (SHM) has proven to be a cost-effective method for the detection and evaluation of damage in structures. Visual inspection and condition rating is one of the most commonly applied SHM techniques, but the effectiveness of SHM varies depending on the availability and experience of qualified personnel and largely qualitative damage evaluations. Simply supported three-dimensional reinforced concrete T-beams with varying geometric, material, and cracking properties were modeled using Abaqus finite element (FE) analysis software. Up to five cracks were considered in each beam, and the ratios of stiffness between cracked and healthy beams with the same geometric and material parameters were measured at nine equidistant nodes along the beam. A feedforward ANN utilizing backpropagation learning algorithms was then trained on the FE model database with beam properties and nodal stiffness ratios serving as inputs for the neural network model. The outputs consisted of the predicted parameters of location, depth and width of up to five cracks. This inverse problem is very difficult or impossible to solve with the training done by the Artificial Neural Network. One ANN was trained to predict the parameters of the cracks using the full database of FE simulations. The damage prediction ANN achieved fair prediction accuracies, with coefficients of determination (R2) equal to 0.42. This result was the outcome of the no uniqueness in the prediction of this inverse analysis. Nevertheless, this ANN model provides a rough estimate of the cracking type and damage content in bridge girders once the nodal stiffness ratios are measured by applying a field vehicle loading and measuring the deflection using a theodolite. A touch-enabled user interface was developed to allow the ANN model to predict the crack configurations. The application was given the acronym DRY BEAM, for Damage Recognition Yielding Bridge Evaluation After Monitoring. © 2021 American Concrete Institute. All rights reserved.","artificial neural network; damage detection; finite element; girders","ABAQUS; Concrete beams and girders; Cost effectiveness; Damage detection; Deterioration; Finite element method; Forecasting; Inverse problems; Reinforced concrete; Steel beams and girders; Stiffness; Structural health monitoring; 3D finite-element simulation; Ageing infrastructures; Cost-effective methods; Finite element; Infrastructure systems; Stiffness ratios; Strain limit; Structural deterioration; T-girders; Visual condition; Neural networks",,,,,"Office of the Assistant Secretary for Research and Technology, OST-R; Kansas Department of Transportation, KDOT","The authors would like to thank the Kansas Department of Transportation, the Midwest Transportation Center at Iowa State University, and the U.S. Department of Transportation Office of the Assistant Secretary for Research and Technology for sponsoring this research.",,,,,,,,,,"(2013) Abaqus 6.13 [computer software], , Providence, RI: Dassault Systèmes Simulia Corp; Al-Rahmani, A., (2012) A combined soft computing-mechanics approach to damage evaluation and detection in reinforced concrete beams, , (Master's thesis). Kansas State University, Manhattan, Kansas; Al-Rahmani, A., Rasheed, H., Najjar, Y., Intelligent damage detection in bridge girders: Hybrid approach (2013) Journal of Engineering Mechanics, 139 (3), pp. 296-304; Al-Rahmani, A., Rasheed, H., Najjar, Y., An artificial intelligence approach to objective health monitoring and damage detection in concrete bridge girders (2014) American Concrete Institute Special Publication, 298 (6), pp. 73-89; (2014) Building code requirements for structural concrete and commentary, , (ACI 318-14). Farmington Hills, MI: American Concrete Institute; Beer, F. P., Johnston, E. R., Dewolf, J. T., Mazurek, D. F., (2012) Mechanics of Materials, , (6th ed). New York, NY: McGraw-Hill Companies, Inc; Chen, S., Liu, W., Bian, H., Smith, B., 3D LiDAR scans for bridge damage evaluation (2012) Paper presented at the 6th Congress on Forensic Engineering: Gateway to a Better Tomorrow, , San Francisco, CA, United States; Ghods, A. S., Esfahani, M. R., Damage assessment of reinforced concrete beams by modal test (2009), (July 22-24). Paper presented at the 4th International Conference on Structural Health Monitoring of Intelligent Infrastructure. Zurich, Switzerland. Retrieved from Compendex database; Hasançebi, O., Dumlupinar, T., Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks (2013) Computers & Structures, 119, pp. 1-11; Jeyasehar, C. A., Sumangala, K., Damage assessment of prestressed concrete beams using artificial neural network (ANN) approach (2006) Computers & Structures, 84 (26), pp. 1709-1718; Li, L., Ghrib, S., Lee, S., Damage identification of reinforced concrete beams by digital image correlation and FE-updating (2008) the Canadian Society for Civil Engineering Annual Conference: Partnership for Innovation, , (Jun 10-13). Paper presented at Quebec City, QC, Canada. Retrieved from Compendex database; Ndambi, J., Vantomme, J., Harri, K., Damage assessment in reinforced concrete beams using eigenfrequencies and mode shape derivatives (2002) Engineering Structures, 24 (4), pp. 501-515; Ongpeng, J. M. C., Oreta, A. W. C., Hirose, S., Monitoring damage using acoustic emission source location and computational geometry in reinforced concrete beams (2018) Applied Sciences, 8 (2), p. 189; Reynders, E., De Roeck, G., A local flexibility method for vibration-based damage localization and quantification (2009) Journal of Sound and Vibration, 329 (12), pp. 2367-2383; Sagar, R. V., Prasad, B. K. R., Sharma, R., Evaluation of damage in reinforced concrete bridge beams using acoustic emission technique (2012) Nondestructive Testing and Evaluation, 27 (2), pp. 95-108; Shiotani, T., Ohtsu, H., Momoki, S., Chai, H. K., Onishi, H., Kamada, T., Damage evaluation for concrete bridge deck by means of stress wave techniques (2012) Journal of Bridge Engineering, 17 (6), pp. 847-856; Tan, Z. X., Thambiratnam, D. P., Chan, T. H., Gordan, M., Abdul Razak, H., Damage detection in steel-concrete composite bridge using vibration characteristics and artificial neural network (2020) Structure and Infrastructure Engineering, 16 (9), pp. 1247-1261; Teughels, A., Maeck, J., De Roeck, G., Damage assessment by FE model updating using damage functions (2002) Computers & Structures, 80 (25), pp. 1869-1879",,"Naser M.Z.Mueller K.","ACI Committee 216;ACI Committee 444;ACI Committee 544","American Concrete Institute","The Concrete Industry in the Era of Artificial Intelligence 2020 - ACI Spring Concrete Convention 2020","29 March 2020 through 2 April 2020",,183072,01932527,9781641951623,,,"English","Am. Concr. Inst. ACI Spec. Publ.",Conference Paper,"Final","",Scopus,2-s2.0-85139876415
"Zhang W., Li Y.X., Sun L.M.","56646249600;57211568199;7403956279;","SHM-Oriented Hybrid Modeling for Stress Analysis of Steel Girder Bridge",2021,"Journal of Bridge Engineering","26","6","05021002","","",,,"10.1061/(ASCE)BE.1943-5592.0001710","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102865167&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001710&partnerID=40&md5=8c5fd338fd605aba1f173338a195b75c","Fujian Academy of Building Research, Fujian Key Laboratory of Green Building Technology, Fujian, 350000, China; Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China; Dept. of Bridge Engineering, State Key Laboratory of Disaster Reduction in Civil Engineering, College of Civil Engineering, Tongji Univ., Shanghai Qi Zhi Institute, Shanghai, 200092, China","Zhang, W., Fujian Academy of Building Research, Fujian Key Laboratory of Green Building Technology, Fujian, 350000, China; Li, Y.X., Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai, 200092, China; Sun, L.M., Dept. of Bridge Engineering, State Key Laboratory of Disaster Reduction in Civil Engineering, College of Civil Engineering, Tongji Univ., Shanghai Qi Zhi Institute, Shanghai, 200092, China","Stress monitoring is always a challenging task in bridge structural health monitoring (SHM) since the measured pointwise stress is not enough for fully reflecting structural conditions. Therefore, a novel hybrid modeling technique was proposed in this paper, which calculates stress distribution with the aid of finite-element (FE) submodels from limited measured data. However, unlike common FE analyses, the requirement of complete input information is avoided by an FE model-based partial least-squares regression (FEM-PLSR) method. First, the regression equations among the FE model, unknown structural input, and output were set up, into which measured displacements, rotations, and strains were fused simultaneously. By solving the regression equations, the boundary conditions of the FE submodel can be precisely estimated. Then, the stress distribution can be calculated through FE analyses under the assumption of known local vehicular loads. Numerical simulations of a continuous steel box girder bridge were carried out for verification. Corresponding results indicated that the accuracy of the calculated stress distributions was competitive to the widely used multiscale FE model, even if the structural input information outside the submodel was not directly measured. Furthermore, the proposed method was also proved insensitive to random measurement errors. Finally, a large-scale experiment was designed to validate the accuracy of the hybrid model under three loading conditions. The strain distribution over both space and time accorded well with the measurement. Thus, the present hybrid modeling offered a novel way to obtain unmeasured structural responses, revealing great potential in the field of bridge SHM. © 2021 American Society of Civil Engineers.","Hybrid modeling; Partial least square regression; Stress monitoring; Substructure","Box girder bridges; Least squares approximations; Random errors; Strain; Stress analysis; Stress concentration; Structural health monitoring; Bridge structural health monitoring; Large scale experiments; Multi-scale FE modeling; Partial least squares regression; Random measurement errors; Strain distributions; Structural condition; Structural response; Steel bridges",,,,,"SLDRCE15-A-02; National Natural Science Foundation of China, NSFC: 51878482","The authors acknowledge support for the work reported in this paper from the National Natural Science Foundation of China (Grant No. 51878482) and State Key Laboratory of Disaster Reduction in Civil Engineering, Tonging Univ. (Grant No. SLDRCE15-A-02).",,,,,,,,,,"Cardini, A.J., Dewolf, J.T., Long-term structural health monitoring of a multi-girder steel composite bridge using strain data (2009) Struct. Health Monit., 8 (1), pp. 47-58. , https://doi.org/10.1177/1475921708094789; Chan, T.H.T., Guo, L., Li, Z.X., Finite element modelling for fatigue stress analysis of large suspension bridges (2003) J. Sound Vib., 261 (3), pp. 443-464. , https://doi.org/10.1016/S0022-460X(02)01086-6; Chan, T.H.T., Zhou, T.Q., Li, Z.X., Guo, L., Hot spot stress approach for Tsing Ma Bridge fatigue evaluation under traffic using finite element method (2005) Struct. Eng. Mech., 19 (3), pp. 261-279. , https://doi.org/10.12989/sem.2005.19.3.261; Coifman, B., Beymer, D., McLauchlan, P., Malik, J., A real-time computer vision system for vehicle tracking and traffic surveillance (1998) Transp. Res. 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Dyn., 11 (2), pp. 313-344. , https://doi.org/10.1142/S0219455411004117; Fraser, M.S., (2006) Development and Implementation of An Integrated Framework for Structural Health Monitoring, , San Diego: Unive. of California; Guoping, L., (2007) Research about Low Frequency Dynamic Characteristics of Deflection Testing System for LianTongGuan Type Bridge, , Degree of master, Dept. of Civil Engineering, Chongqing Univ; Hou, X., Yang, X., Huang, Q., Using inclinometers to measure bridge deflection (2005) J. Bridge Eng., 10 (5), pp. 564-569. , https://doi.org/10.1061/(ASCE)1084-0702(2005)10:5(564); Jian, X., Xia, Y., Lozano-Galant, J.A., Sun, L., Traffic sensing methodology combining influence line theory and computer vision techniques for girder bridges (2019) J. Sens., 2019, p. 3409525. , https://doi.org/10.1155/2019/3409525; Li, Y.Y., Hypersensitivity of strain-based indicators for structural damage identification: A review (2010) Mech. Syst. Sig. 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Bouteldja, Chichester, UK: Wiley; Pan, B., Qian, K., Xie, H., Asundi, A., Two-dimensional digital image correlation for in-plane displacement and strain measurement: A review (2009) Meas. Sci. Technol., 20 (6), p. 062001. , https://doi.org/10.1088/0957-0233/20/6/062001; Ren, W.-X., Peng, X.-L., Baseline finite element modeling of a large span cable-stayed bridge through field ambient vibration tests (2005) Comput. Struct., 83 (89), pp. 536-550. , https://doi.org/10.1016/j.compstruc.2004.11.013; Sanli, A.K., Uzgider, E.A., Caglayan, O.B., Ozakgul, K., Bien, J., Testing bridges by using tiltmeter measurements (2000) Transp. Res. Rec., 1696, pp. A111-A117. , https://doi.org/10.3141/1696-51; Sousa, H., Cavadas, F., Henriques, A., Bento, J., Figueiras, J., Bridge deflection evaluation using strain and rotation measurements (2013) Smart Struct. Syst., 11 (4), pp. 365-386. , https://doi.org/10.12989/sss.2013.11.4.365; Sun, L., Li, Y., Zhang, W., Experimental study on continuous bridge-deflection estimation through inclination and strain (2020) J. Bridge Eng., 25 (5), p. 04020020. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0001543; Sun, P., Bachilo, S.M., Weisman, R.B., Nagarajaiah, S., Carbon nanotubes as non-contact optical strain sensors in smart skins (2015) J. Strain Anal. Eng. Des., 50 (7), pp. 505-512. , https://doi.org/10.1177/0309324715597414; Sun, S., Sun, L., Chen, L., Damage detection based on structural responses induced by traffic load: Methodology and application (2016) Int. J. Struct. Stab. Dyn., 16 (4), p. 1640026. , https://doi.org/10.1142/S0219455416400265; Tang, Y., Wu, Z., Distributed long-gauge optical fiber sensors based self-sensing FRP bar for concrete structure (2016) Sensors, 16 (3), p. 286. , https://doi.org/10.3390/s16030286; Tobias, R.D., An introduction to partial least squares regression (1995) Proc. Of the 20th Annual SAS Users Group Int. Conf., 20, pp. 1-8. , Cary, NC: SAS Institute; Wang, H., Li, A.Q., Hu, R.M., Li, J.A., Accurate stress analysis on steel Box girder of long span suspension bridges based on multi-scale submodeling method (2010) Adv. Struct. Eng., 13 (4), pp. 727-740. , https://doi.org/10.1260/1369-4332.13.4.727; Wang, J., Wu, M., An overview of research on weigh-in-motion system (2004) Proc. 5th World Congress on Intelligent Control and Automation, 6, pp. 5241-5244. , IEEE Cat. No. 04EX788. New York: IEEE; Wold, H., Path models with latent variables: The NIPALS approach (1975) Quantitative Sociology: International Perspectives on Mathematical and Statistical Modeling, pp. 307-357. , edited by H. M. Blalock, Cambridge, MA: Academic Press; Zaurin, R., Catbas, F.N., Structural health monitoring using video stream, influence lines, and statistical analysis (2011) Struct. Health Monit., 10 (3), pp. 309-332. , https://doi.org/10.1177/1475921710373290; Zhang, W., Sun, L., Sun, S., Bridge-deflection estimation through inclinometer data considering structural damages (2016) J. Bridge Eng., 22 (2), p. 04016117. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000979; Zhou, L.R., Yan, G.R., Wang, L., Ou, J.P., Review of benchmark studies and guidelines for structural health monitoring (2013) Adv. Struct. Eng., 16 (7), pp. 1187-1206. , https://doi.org/10.1260/1369-4332.16.7.1187; Zhu, Q., Xu, Y.L., Xiao, X., Multiscale modeling and model updating of a cable-stayed bridge. I: Modeling and influence line analysis (2015) J. Bridge Eng., 20 (10), p. 04014112. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000722","Sun, L.M.; Dept. of Bridge Engineering, China; email: lmsun@tongji.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85102865167
"Avendano J.C., Otero L.D., Otero C.","55835677700;8507380900;25634677300;","Optimization of Sensor Placement in a Bridge Structural Health Monitoring System",2021,"15th Annual IEEE International Systems Conference, SysCon 2021 - Proceedings",,,"9447077","","",,,"10.1109/SysCon48628.2021.9447077","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111432938&doi=10.1109%2fSysCon48628.2021.9447077&partnerID=40&md5=17a1b3a36d7410ac19cc46da75803899","Florida Institute of Technology, Department of Computer Engineering and Sciences, Melbourne, FL  32901, United States","Avendano, J.C., Florida Institute of Technology, Department of Computer Engineering and Sciences, Melbourne, FL  32901, United States; Otero, L.D., Florida Institute of Technology, Department of Computer Engineering and Sciences, Melbourne, FL  32901, United States; Otero, C., Florida Institute of Technology, Department of Computer Engineering and Sciences, Melbourne, FL  32901, United States","This paper presents an optimal sensor placement (OSP) technique designed to be implemented on Structural Health Monitoring (SHM) systems. A steel bridge was modeled in ANSYS environment and four load values were applied at pre-identified locations to generate data. Each experiment yielded an array of data that contains the location, as well as corresponding deformation and safety factors. Measurements were taken at 1,000,000 positions on the bridge and a library of a similar number of failure modes was created for each experiment. Each data library was processed as a multi-dimensional matrix by applying the average filtering algorithm. Local extrema were identified in terms of the corresponding deformation and safety factors by removing repeated values at nearby locations. The results provided a list of 100 locations with maximum deformation or minimum safety factors, containing the optimized positions on the bridge for placement of sensors. The final developed system that includes this placement algorithm capable of simulating multiple load conditions on structures, identifying possible failure points, and detecting and predicting failure scenarios. Both hardware and software implementations of a model of a bridge were performed as a pilot project to validate the proposed system. © 2021 IEEE.","average filtering algorithm; deformation; finite element analysis; optimal sensor placement; structural health monitoring","Deformation; Location; Safety factor; Steel bridges; Structural health monitoring; Bridge structural health monitoring; Hardware and software implementations; Minimum safety factor; Multi-dimensional matrices; Optimal sensor placement; Placement algorithm; Placement of sensors; Structural health monitoring (SHM); Monitoring",,,,,,,,,,,,,,,,"Yang, J., Peng, Z., Beetle-swarm evolution competitive algorithm for bridge sensor optimal placement in shm (2019) Ieee Sensors Journal; Yang, Z.C., Lu, Z., Yang, Z., Robust optimal sensor placement for uncertain structures with intervt] parameters (2018) Ieee Sensors.Journal, 18, pp. 2031-2041; Li, D., Li, H., Fritzen, C., The connection between effective independence and modal kinetic energy methods for sensor placement (2007) Journal of Sound and Vibration, 305, pp. 945-955; Yi, T.I.I., Wang, X., Li, I.I.N., Optimal placement of triaxial accelerometers using modal kinetic energy method (2012) Applied Mechanics and Materials, pp. 1583-1586; Came, T.G., Dohrmann, C.R., (1994) A Modal Test Design Strategy for Model Correlation, , Sandia National Labs., Albuquerque, NM (United States); Yuan, A., Dai, I.I., Sun, D., Optimal sensor placement of cable-stayed bridge using a mixed algorithm based on effective independence and modal assurance criterion methods (2009) J. i'Ib. Meas. Diagn, 29, pp. 55-59; Ostachowicz, V., Soman, R., Malinowski, P., Optimization of sensor placement for structural health monitoring: A review (2019) Structural Health Monitoring, 18, pp. 963-988; Tan, Y., Zhang, L., Computational methodologies for optimal sensor placement in structural health monitoring: A review (2020) Structural Health Monitoring, 19, pp. 1287-1308. , 119, pp. 48-59, 2018; Flynn, E.B., Todd, M.D., A bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing (2010) Mechanical Systems and Signal Processing, 24, pp. 891-903; Surya, S., Ravi, R., Deployment of backup sensors in wireless sensor networks for structural health monitoring (2018) 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1526-1533; Ismail, Z., Mustapha, S., Fakih, M.A., Tarhini, H., Sensor placement optimization on complex and large metallic and composite structures (2020) Structural Health Monitoring, 19, pp. 262-280; Gomes, G.F., Da Cunha, S.S., Alexandrino, P.D.S.L., De Sousa, B.S., Ancelotti, A.C., Sensor placement optimization applied to laminated composite plates under vibration (2018) Structural and Multidisciplmary Optimization, 58, pp. 2099-2118; Gomes, G.F., De Almeida, F.A., Alexandrino, P.D.S.L., Da Cunha, S.S., De Sousa, B.S., Ancelotti, A.C., A multi-objective sensor placement optimization for shm systems considering fisher information matrix and mode shape interpolation (2019) Engineering with Computers, 35, pp. 519-535; Barthorpe, R.J., Worden, K., Emerging trends in optimal structural health monitoring system design: From sensor placement to system evaluation (2020) Journal of Sensor and Actuator Networks, 9, p. 31; Pachón, P., Castro, R., García-Macías, E., Compan, V., Puertas, E., Torroja's, E., Bridge: Tailored experimental setup for shm of a historic bridge with a reduced number of sensors (2018) Engineering Structures, 162, pp. 11-21; Downey, A., Hu, C., Aflamme, S.I., Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool (2018) Structural Health Monitoring, 17, pp. 450-460; Chen, Z., Zhou, X., Wang, X., Dong, L., Qian, Y., Deployment of a smart structural health monitoring system for long-span arch bridges: A review and a case study (2017) Sensors, 17, p. 2151; Li, L., Liu, G., Zhang, L., Li, Q., Sensorfault detection with generalized likelihood ratio and correlation coefficient for bridge shm (2019) Journal of Sound and Vibration, 442, pp. 445-458; Valinejadshoubi, M., Bagchi, A., Moselhi, O., Managing structural health monitoring data using building information modeling (2017) Proceedings of the 2nd World Congress and Exhibition on Construction and Steel Structure, , Las Vegas, A**'. USA; Otero, E.C.E., Otero, L.D., A simulation-based fuzzy multi-Attribute decision making for prioritizing software requirements (2012) RIFF' 12-Proceedings of the Acm Research in Information Technology",,,"IEEE Systems Council","Institute of Electrical and Electronics Engineers Inc.","15th Annual IEEE International Systems Conference, SysCon 2021","15 April 2021 through 15 May 2021",,170481,,9781665444392,,,"English","Annual IEEE Int. Syst. Conf., SysCon - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85111432938
"Yu K., Shu J., Zandi K.","57286237500;55654267000;57433878000;","Incorporating Pre-Existing Cracks in Structural Assessment of RC Structures",2021,"Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021",,,,"737","745",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139265335&partnerID=40&md5=e8929377e9d13df6498ca5170c0be7a0","Stanford University, 781 Escondido rd, Stanford, CA  94305, United States; Zhejiang University, 866 Yuhangtang rd, Zhejiang, Hangzhou, 310058, China; Timezyx Inc., Vancouver, BC  V6N 2R2, Canada","Yu, K., Stanford University, 781 Escondido rd, Stanford, CA  94305, United States; Shu, J., Zhejiang University, 866 Yuhangtang rd, Zhejiang, Hangzhou, 310058, China; Zandi, K., Timezyx Inc., Vancouver, BC  V6N 2R2, Canada","Cracks in reinforced concrete (RC) structures can be detrimental as they grow beyond the limits. Cracks should be included in structural assessment methods to ensure the durability and load capacity of existing structures. However, conventional models used in the assessment of existing structures do not reflect the real cracking condition which implies that advanced assessment methods are required. In this study, pre-existing cracks were introduced into finite element analysis to identify the ductility, failure characteristics, and ultimate capacity of cracked structures. A beam specimen taken from the edge beams of an existing bridge had been subjected to a four-point bending test, and the results are used in this study for validation purposes. The specimens showed varying levels of cracking due to loading as well as reinforcement corrosion during the service life. Five different analyses were carried out to account for the effect of loading cracks and corrosion cracks based on two crack modeling approaches, namely weakened element approach and weakened bond-slip relation approach. The results showed that the failure of the beams was caused by anchorage failure. The differences in the load capacity predicated by different models are discussed. It was observed that incorporating pre-existing cracks by using weakened elements and weakened bond-slip relation approaches can be a practical method to model and assess cracked RC beams. © 2021 Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021. All rights reserved.",,"Concrete beams and girders; Embedded systems; Life cycle; Reinforced concrete; Structural health monitoring; Bond slips; Conventional modeling; Cracking condition; Existing structure; Failure characteristics; Finite element analyse; Load capacity; Pre-existing crack; Reinforced concrete structures; Structural assessments; Cyber Physical System",,,,,,,,,,,,,,,,"Tahershamsi, M., Lundgren, K., Plos, M., Zandi, K., Anchorage of naturally corroded bars in reinforced concrete structures (2014) Mag. Concr. Res, 66 (14), pp. 729-744; Lundgren, K., Tahershamsi, M., Zandi, K., Plos, M., Tests on anchorage of naturally corroded reinforcement in concrete (2015) Mater. Struct. Constr, 48 (7), pp. 2009-2022; Blomfors, M., Berrocal, C. G., Lundgren, K., Zandi, K., Incorporation of pre-existing cracks in finite element analyses of reinforced concrete beams without transverse reinforcement (2020) Eng. Struct, 229, p. 2021. , May; Wittmann, F. H., Rokugo, K., Brühwiler, E., Mihashi, H., Simonin, P., Fracture energy and strain softening of concrete as determined by means of compact tension specimens (1988) Mater. Struct; (2010) fib Model Code for Concrete Structures 2010, , FIB, Model Code Lausanne, Switzerland, 2013; Lundgren, K., Kettil, P., Hanjari, K. Z., Schlune, H., Roman, A. S. S., Analytical model for the bond-slip behaviour of corroded ribbed reinforcement (2012) Struct. Infrastruct. Eng, 8 (2), pp. 157-169; Blomfors, M., Zandi, K., Lundgren, K., Coronelli, D., Engineering bond model for corroded reinforcement (2018) Eng. Struct, 156 (November 2017), pp. 394-410; Tahershamsi, M., Fernandez, I., Zandi, K., Lundgren, K., Four levels to assess anchorage capacity of corroded reinforcement in concrete (2017) Eng. Struct, 147, pp. 434-447; (2015) User’s manual – release 9.6, , DIANA FEA BV; Thorenfeldt, E., Tomaszewicz, A., Jensen, J. J., Mechanical Properties of High Strength Concrete and Application to Design (1987) Proc. Symp. Util. High-Strength Concr. Stavanger, pp. 149-159; Hordijk, D., (1991) Local approach to fatigue of concrete, , Delft University of Technology",,"Farhangdoust S.Guemes A.Chang F.-K.","Air Force Office of Scientific Research, United States Air Force;Boeing;et al.;Office of Naval Research Science and Technology;Transportation Research Board;U.S. Department of Transportation","DEStech Publications Inc.","13th International Workshop on Structural Health Monitoring: Enabling Next-Generation SHM for Cyber-Physical Systems, IWSHM 2021","15 March 2022 through 17 March 2022",,182782,,9781605956879,,,"English","Struct. Health Monit.: Enabling Next-Gener. SHM Cyber-Phys. Syst. - Proc. Int. Workshop Struct. Health Monit., SHM",Conference Paper,"Final","",Scopus,2-s2.0-85139265335
"Zeng J., Kim Y.H., Qin S.","57219454011;55699610400;36774943300;","Bayesian Model Updating for a Cable-Stayed Pedestrian Bridge Using DREAM and Kriging Model",2021,"Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021",,,,"188","195",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139235909&partnerID=40&md5=d5c8a955a4467e0d7db248dae0054a64","Department of Civil and Environment Engineering, University of Louisville, Louisville, KY  40292, United States; School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan, 430070, China","Zeng, J., Department of Civil and Environment Engineering, University of Louisville, Louisville, KY  40292, United States; Kim, Y.H., Department of Civil and Environment Engineering, University of Louisville, Louisville, KY  40292, United States; Qin, S., School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan, 430070, China","Modeling error and measurement noise are inevitable and lead to a significant discrepancy between Finite element model (FEM) and a real structure. Finite element model updating (FEMU) is, therefore, necessary to match the measured data with a predicted response from FEM for advancing structural health monitoring (SHM). Bayesian approach has been proposed to identify the most probable values (MPVs) of physical parameters and provide parameters’ uncertainties. However, the current Bayesian approach has challenges in high-dimensional problems and requires high computational costs in the complex structure. In this study, a new Bayesian updating framework is proposed using Differential Evolution Adaptive Metropolis (DREAM) sampling method with a variance-based global sensitivity analysis (GSA) and Kriging model to enhance the Bayesian approach’s performance and computational efficiency. Firstly, variance-based GSA is used to eliminate insignificant parameters to measured responses and reduce model dimensionality. Secondly, a Kriging model is employed as a surrogate of the time-consuming FE model for reducing the computational burden. DREAM is essentially a multi-chain sampling method, which parallelly runs different paths for all possible solutions and accurately approximates the posterior distribution density function (PDF) for the Bayesian approach. The demonstration of the proposed updating framework of a real-world cable-stayed pedestrian bridge is presented. © 2021 Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021. All rights reserved.",,"Bayesian networks; Cables; Cyber Physical System; Distribution functions; Embedded systems; Evolutionary algorithms; Finite element method; Footbridges; Optimization; Probability density function; Sensitivity analysis; Structural health monitoring; Adaptive metropolis; Bayesian approaches; Bayesian model updating; Differential Evolution; Finite element modelling (FEM); Kriging model; Measurement Noise; Model errors; Model measurements; Variance-based global sensitivity analysis; Computational efficiency",,,,,,,,,,,,,,,,"Beck, J.L., Katafygiotis, L.S., Updating models and their uncertainties. I: Bayesian statistical framework (1998) J. of Eng. Mech, 124 (4), pp. 455-461; Mao, J., Wang, H., Li, J., Bayesian Finite Element Model Updating of a Long-Span Suspension Bridge Utilizing Hybrid Monte Carlo Simulation and Kriging Predictor (2020) KSCE J. of Civil Eng, 24 (2), pp. 569-579; Simoen, E., Uncertainty Quantification in the Assessment of Progressive Damage in a 7-Story Full-Scale Building Slice (2013) J. of Eng. Mech, 139 (12), pp. 1818-1830; Vrugt, J.A., Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016) Environ. Model. Softw, 75, pp. 273-316; Vrugt, J.A., Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling (2009) Int. J. Nonlinear Sci. Numer. Simul, 10 (3), pp. 273-290; Saltelli, A., (2004) Sensitivity analysis in practice: a guide to assessing scientific models, 1. , Wiley Online Library; Simpson, T.W., Kriging models for global approximation in simulation-based multidisciplinary design optimization (2001) AIAA J, 39 (12), pp. 2233-2241; Zeng, J., Kim, Y.H., A two-stage framework for automated operational modal identification (2021) Struct. Infrastruct. Eng, pp. 1-20","Kim, Y.H.; Department of Civil and Environment Engineering, United States; email: young.kim@louisville.edu","Farhangdoust S.Guemes A.Chang F.-K.","Air Force Office of Scientific Research, United States Air Force;Boeing;et al.;Office of Naval Research Science and Technology;Transportation Research Board;U.S. Department of Transportation","DEStech Publications Inc.","13th International Workshop on Structural Health Monitoring: Enabling Next-Generation SHM for Cyber-Physical Systems, IWSHM 2021","15 March 2022 through 17 March 2022",,182782,,9781605956879,,,"English","Struct. Health Monit.: Enabling Next-Gener. SHM Cyber-Phys. Syst. - Proc. Int. Workshop Struct. Health Monit., SHM",Conference Paper,"Final","",Scopus,2-s2.0-85139235909
"Bud M.A., Nedelcu M., Moldovan I., Figueiredo E.","57194503406;35786491000;26321771600;35619844900;","Hybrid Supervised Machine Learning Approach for Damage Identification in Bridges",2021,"Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021",,,,"499","506",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139211071&partnerID=40&md5=bb1627f4a1eed3944ddf1508513f6327","Faculty of Civil Eng., Technical University of Cluj-Napoca, Memorandumului 28, Cluj-Napoca, 400114, Romania; Faculty of Eng., Lusófona University, Campo Grande 376, Lisboa, 1749-024, Portugal; CERIS, IST, Universidade de Lisboa, Av. Rovisco Pais, Lisboa, 1049-001, Portugal; CONSTRUCT, Faculty of Eng., R. Dr. Roberto Frias s/n, Porto, 4200-465, Portugal","Bud, M.A., Faculty of Civil Eng., Technical University of Cluj-Napoca, Memorandumului 28, Cluj-Napoca, 400114, Romania; Nedelcu, M., Faculty of Eng., Lusófona University, Campo Grande 376, Lisboa, 1749-024, Portugal; Moldovan, I., CERIS, IST, Universidade de Lisboa, Av. Rovisco Pais, Lisboa, 1049-001, Portugal; Figueiredo, E., CONSTRUCT, Faculty of Eng., R. Dr. Roberto Frias s/n, Porto, 4200-465, Portugal","Structural health monitoring (SHM) of bridges often involves machine learning algorithms, trained based on two independent learning strategies, namely unsupervised and supervised learning, depending on the type of training data available. When unsupervised learning strategy is employed, the algorithms are normally trained with data gathered from monitoring systems, corresponding to normal operational and environmental conditions. The lack of information regarding the dynamic response of the structure under extreme environmental and operational conditions, as well as under damage scenarios, may lead to flaws in the damage detection process, namely the rise of false indications of damage. In order to overcome this drawback, finite element models can be used as structural proxies to generate data that correspond to scenarios unlikely to be recorded by the monitoring systems, such as extreme temperatures or structural damage. The use of both monitoring and numerical data in the framework of a hybrid approach greatly improves the quality of the training process, as recently shown by the authors. The hybrid approach also enables the use of the supervised learning strategy if numerical data corresponding to damage scenarios are available. Therefore, this paper assesses the reliability of a hybrid approach for the supervised training of machine learning algorithms using numerical data corresponding to extreme temperatures and several damage scenarios. The damage scenarios comprise various degrees of settlement of a bridge pier and a landslide near the same pier. Monitoring data are used for the testing of the algorithms and for the initial calibration of the finite element model, which does not need to be exceedingly detailed, as the probabilistic variation of the uncertain parameters is taken into account. The procedure was applied to the Z-24 Bridge, a well-known benchmark consisting of one year of continuous monitoring and including progressive damage readings. © 2021 Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021. All rights reserved.",,"Chemical detection; Cyber Physical System; Damage detection; Embedded systems; Finite element method; Internet of things; Learning algorithms; Piers; Safety engineering; Supervised learning; Uncertainty analysis; Damage scenarios; Environmental conditions; Extreme temperatures; Finite element modelling (FEM); Hybrid approach; Learning strategy; Machine learning algorithms; Monitoring system; Numerical data; Operational conditions; Structural health monitoring",,,,,,,,,,,,,,,,"Rytter, A., (1993) Vibrational based inspection of civil engineering structures, , PhD Thesis, Department of Building Technology and Structural Engineering, Aalborg University, Aalborg; Figueiredo, E., Moldovan, I., Marques, M. B., (2013) Condition Assessment of Bridges: Past, Present, and Future - A Complementary Approach, , Universidade Católica Editora, Portugal; Figueiredo, E., Cross, E., Linear approaches to modeling nonlinearities in long-term monitoring of bridges (2013) Journal of Civil Structural Health Monitoring, 3 (3), pp. 187-194; Barthorpe, R. J., (2010) On model- and data-based approaches to structural health monitoring, , PhD Thesis, Univ. of Sheffield, Sheffield; Figueiredo, E., Moldovan, I., Santos, A., Campos, P., Costa, J. C. W. A., Finite element-based machine learning approach to detect damage in bridges under operational and environmental variations (2019) Journal of Bridge Engineering, 24 (7), p. 04019061; Bud, M., Nedelcu, M., Radu, L., Moldovan, I., Figueiredo, E., On the reliability of finite element models for training machine learning algorithms for damage detection in bridges (2019) the 12th International Workshop on Structural Health Monitoring (IWSHM), , presented at Stanford, CA, September 10-12; Peeters, B., de Roeck, G., One-year monitoring of the Z-24 Bridge: Environmental effects versus damage events (2001) Earthquake Eng. Struct. Dyn, 30 (2), pp. 149-171; Peeters, B., de Roeck, G., Reference-based stochastic subspace identification for output-only modal analysis (1999) Mech. Syst. Signal Process, 13 (6), pp. 855-878; Reynders, E., Teughels, A., de Roeck, G., Finite element model updating and structural damage identification using OMAX data (2010) Mech. Syst. Signal Process, 24, pp. 1306-1323; Bud, M., Moldovan, I., Radu, L., Nedelcu, M., Figueiredo, E., Reliability of probabilistic numerical data for training machine learning algorithms to detect damage in bridges (2022) Struct. Control Hlth, p. e2950; Figueiredo, E, Radu, L., Worden, K., Farrar, C.R., A Bayesian approach based on a Markov-chain Monte Carlo method for damage detection under unknown sources of variability (2014) Engineering Structures, 80, pp. 1-10",,"Farhangdoust S.Guemes A.Chang F.-K.","Air Force Office of Scientific Research, United States Air Force;Boeing;et al.;Office of Naval Research Science and Technology;Transportation Research Board;U.S. Department of Transportation","DEStech Publications Inc.","13th International Workshop on Structural Health Monitoring: Enabling Next-Generation SHM for Cyber-Physical Systems, IWSHM 2021","15 March 2022 through 17 March 2022",,182782,,9781605956879,,,"English","Struct. Health Monit.: Enabling Next-Gener. SHM Cyber-Phys. Syst. - Proc. Int. Workshop Struct. Health Monit., SHM",Conference Paper,"Final","",Scopus,2-s2.0-85139211071
"Zhang Q., Zhang J.","56416729300;56014789300;","Reconstruction of full-bridge response using finite elements and inspection and monitoring data",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1747","1754",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130746045&partnerID=40&md5=21f8c8c023caba1434f3bc0bfe290d2c","School of Civil Engineering, Sichuan Agricultural University, Dujiangyan611830, China; Jiangsu Key Laboratory of Engineering Mechanics, Southeast Univerisity, Southeast Univerisity., Nanjing, 210096, China","Zhang, Q., School of Civil Engineering, Sichuan Agricultural University, Dujiangyan611830, China, Jiangsu Key Laboratory of Engineering Mechanics, Southeast Univerisity, Southeast Univerisity., Nanjing, 210096, China; Zhang, J., Jiangsu Key Laboratory of Engineering Mechanics, Southeast Univerisity, Southeast Univerisity., Nanjing, 210096, China","The need to perform dynamic response reconstruction of full-bridge structures always arises as the evaluation effect of one-dimensional bridge inspection and monitoring (IM) data is limited and it is difficult to realize the accurate identification of structural parameters. This study presents a hybrid methodology to reconstruct the full-bridge response through fusion inspection and monitoring data and finite element simulation. With the help of proper orthogonal decomposition (POD) technique, the distributed sensing IM data is decomposed into proper modes and time random function in time domain. In order to reconstruct the dynamic response of the predicted element, the separated proper modes are substituted into an identification network established by the multi-level finite element model to extract structure features, which are taken as the input vectors in the network used for proper model estimation of the predicted elements. Subsequently, the identified proper mode from IM data and the estimated proper mode from multi-level simulation are employed to obtain dynamic responses at the remaining elements where direct sensor measurements are not available. The full-bridge response reconstruction can provide a new basis for the structural state assessment by using the convergence of different analysis methods. A numerical beam example is used to demonstrate the overall reconstruction procedure and validate the effectiveness and accuracy of the proposed methodology. Effects of sensor layout, the number of sensors, damage conditions, and the noise level in measurement are studied in detail. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","finite element simulation; full-bridge response; Inspection; monitoring data; POD technique; strain identification","Dynamic response; Inspection; Orthogonal functions; Principal component analysis; Structural health monitoring; Time domain analysis; Bridge response; Bridge structures; Dynamic response reconstruction; Finite elements simulation; Full bridge; Full-bridge response; Monitoring data; Multilevels; Proper orthogonal decomposition techniques; Strain identification; Finite element method",,,,,"National Natural Science Foundation of China, NSFC: 51908386","This research was supported by the National Natural Science Foundation of China (Grant No. 51908386).",,,,,,,,,,"Li, H., Ou, J., Zhang, X., Pei, M., Li, N., Research and practice of health monitoring for long-span bridges in the mainland of china (2015) Smart Structures & Systems, 15 (3), pp. 555-576; Ni, F. T., Zhang, J., Noori, M. N., Deep learning for data anomaly detection and data compression of a long-span suspension bridge (2019) Computer-Aided Civil and Infrastructure Engineering, pp. 1-17. , https://doi.org/10.1111/mice.12528; Tian, Y. D., Zhang, C., Jiang, S., Zhang, J., Duan, W. H., Noncontact cable force measurement with unmanned aerial vehicle and computer vision (2020) Computer-Aided Civil and Infrastructure Engineering, , https://doi.org/10.1111/mice.12567; Park, S. W., Park, H. S., Kim, J. H., Adeli, H., 3D displacement measurement model for health monitoring of structures using a motion capture system (2015) Measurement, 59, pp. 352-362; Zhang, Q., Zhang, J., Internal force monitoring and estimation of a long-span ring beam using long-gauge strain sensing (2020) Computer-Aided Civil and Infrastructure Engineering, , https://doi.org/10.1111/mice.12569; Law, S. S., Li, J., Ding, Y., Structural response reconstruction with transmissibility concept in frequency domain (2011) Mechanical Systems & Signal Processing, 25 (3), pp. 952-968; Jie, L., Bing, L., A novel strategy for response and force reconstruction under impact excitation (2018) Journal of Mechanical Science & Technology, 32 (8), pp. 3581-3596; Wang, J., Law, S. S., Yang, Q. S., Sensor placement method for dynamic response reconstruction (2014) Journal of Sound & Vibration, 333 (9), pp. 2469-2482; Zhang, Xiao-Hua, Xu, You-Lin, Zhu, Songye, Zhan, Sheng, Dual-type sensor placement for multi-scale response reconstruction (2014) Mechatronics: The Science of Intelligent Machines, 24, pp. 376-384; Li, J., Hao, H., Substructure damage identification based on wavelet-domain response reconstruction (2014) Structural Health Monitoring, 13 (4), pp. 389-405; Li, J., Law, S. S., Ding, Y., Substructure damage identification based on response reconstruction in frequency domain and model updating (2012) Engineering Structures, 41 (3), pp. 270-284; Zhang, S., Wang, Z., Jian, Z., Liu, G., Liu, X., A two-step method for beam bridge damage identification based on strain response reconstruction and statistical theory (2020) Mwas. Sci. Technol, pp. 1-15. , https://doi.org/10.1088/1361-6501/ab825d; Chierichetti, M., Ruzzene, M., Dynamic displacement field reconstruction through a limited set of measurements: application to plates (2012) Journal of Sound & Vibration, 331 (21), pp. 4713-4728; Wan, Z., Li, S., Huang, Q., Wang, T., Structural response reconstruction based on the modal superposition method in the presence of closely spaced modes (2014) Mechanical Systems & Signal Processing, 42 (1-2), pp. 14-30; Jingjing, H., Yibin, Z., Xuefei, G., Wei, Z., Weifang, Z., Yongming, L., Time domain strain/stress reconstruction based on empirical mode decomposition: numerical study and experimental validation (2016) Sensors (Basel, Switzerland), 16 (8); Pan, S., Xiao, D., Xing, S., Law, S. S., Du, P., Li, Y., A general extended kalman filter for simultaneous estimation of system and unknown inputs (2016) Engineering Structures, 109, pp. 85-98. , (Feb.15); Hsieh, C-S, Chen, F-C., Optimal solution of the two-stage Kalman estimator (1999) IEEE Trans Autom Control, 44, pp. 194-199; Zhang, C. D., Xu, Y. L., Optimal multi-type sensor placement for response and excitation reconstruction (2016) Journal of Sound & Vibration, 360, pp. 112-128; Sun, L., Li, Y., Zhu, W., Zhang, W., Structural response reconstruction in physical coordinate from deficient measurements (2020) Engineering Structures, 212, p. 110484; Peng, Z., Dong, K., Yin, H., A modal-based kalman filter approach and osp method for structural response reconstruction (2019) Shock and Vibration, (1), pp. 1-15; Zhang, X. H., Wu, Z. B., Dual-type structural response reconstruction based on moving-window kalman filter with unknown measurement noise (2019) Journal of Aerospace Engineering, 32 (4), pp. 040190291-0401902914; Hu, R. P., Xu, Y. L., Zhan, S., Multi-type sensor placement and response reconstruction for building structures: experimental investigations (2018) Earthquake Engineering and Engineering Vibration, 17, pp. 29-46. , (001); Zhang, C. D., Xu, Y. L., Structural damage identification via multi-type sensors and response reconstruction (2016) Structural Health Monitoring, 15 (6), pp. 705-719; Friswell, M. I., Mottershead, J. E., Inverse methods in structural health monitoring (2001) Key Engineering Materials, 204-205, pp. 201-210; Katafygiotis, L., Lam, H. F., Papadimitriou, C., Treatment of unidentifiability in structural model updating (2000) Advances in Structural Engineering, 3 (1), pp. 19-39; Kuok, S. C., Yuen, K. V., Investigation of modal identification and modal identifiability of a cable-stayed bridge with bayesian framework (2016) SMART STRUCTURES AND SYSTEMS, 17 (3), pp. 445-470; Lei, Y., Jiang, Y., Xu, Z., Structural damage detection with limited input and output measurement signals (2012) Journal of Vibration Measurement & Diagnosis, 28 (5), pp. 229-243; Candes, E. J., Romberg, J., Tao, T., Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information (2006) IEEE Transactions on Information Theory, 52 (2), pp. 489-509; Donoho, D.L., Compressed sensing (2006) IEEE Trans. Inf. Theory, 52, pp. 1289-1306; Bao, Y., Li, H., Sun, X., Yu, Y., Ou, J., Compressive sampling-based data loss recovery for wireless sensor networks used in civil structural health monitoring (2013) Structural Health Monitoring, 12, pp. 78-95; Zou, Z., Bao, Y., Li, H., Spencer, B. F., Ou, J., Embedding compressive sensing-based data loss recovery algorithm into wireless smart sensors for structural health monitoring (2015) Sensors Journal IEEE, 15 (2), pp. 797-808; Thadikemalla, V. S. G., Gandhi, A. S., A data loss recovery technique using compressive sensing for structural health monitoring applications (2018) KSCE Journal of Civil Engineering, 22, pp. 5084-5093; Bao, Y., Tang, Z., Li, H., Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach (2019) Structural Health Monitoring; Huang, Y., Shao, C., Wu, S., Li, H., Diagnosis and accuracy enhancement of compressive-sensing signal reconstruction in structural health monitoring using multi-task sparse bayesian learning (2018) Smart Materials and Structures, p. 28; Hua-Ping, W., Yi-Qing, N., Bayesian multi-task learning methodology for reconstruction of structural health monitoring data (2018) Structural Health Monitoring, 18, p. 147592171879495; Kuok, S. C., Yuen, K. V., Model-free data reconstruction of structural response and excitation via sequential broad learning (2020) Mechanical Systems and Signal Processing, 141, p. 106738; Wei, L., Jun, T., Chao, L., Yan, C., Reconstruction to sensor measurements based on a correlation model of monitoring data (2017) Applied Sciences, 7 (3), p. 243",,,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130746045
"Pereira M., Glisic B.","57202815487;57200346944;","Long-term prediction of rheological effects in concrete structures using probabilistic neural networks",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"921","928",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130736398&partnerID=40&md5=f1259bfe7d7065ad523330409520adfb","Department of Civil Eng., Princeton University, PrincetonNJ, United States","Pereira, M., Department of Civil Eng., Princeton University, PrincetonNJ, United States; Glisic, B., Department of Civil Eng., Princeton University, PrincetonNJ, United States","Concrete structures, such as long-span bridges, dams, nuclear containments, etc., display complex long-term behavior due to creep and shrinkage that must be accurately evaluated for safe service. Incorrect assessment of long-term rheological effects can reduce serviceability, adversely affect prestressing forces (if any), and require costly retrofitting measures. However, even under laboratory conditions, it is difficult to accurately predict long-term rheological effects, and only few decade-long experiments are available for thorough validation of proposed rheological models. Prediction in real-life structures is an even more challenging task due to the spatiotemporal variations in concrete properties, dependence on uncontrolled environment condition and load history, and complicated internal strain evolution in indeterminate structures. Numerous aging structures have been designed worldwide under codes that underestimate rheological effects, and, as a consequence, excessive deflections have been observed in these structures. The state-of-the-art methods employ FEM stochastic analysis and Bayesian approaches to reduce uncertainty by incorporating information stemming from laboratory specimen testing and scarce in-situ measurements throughout the structure's life. This requires the definition of a good numerical model, which entails specification of complex geometry and appropriate boundary conditions, and simplifying assumptions regarding environmental conditions. Relatively recently, data from structures that are equipped with embedded SHM systems since pouring of concrete are becoming available and they open doors for novel data for data-driven or hybrid methods for prediction of long-term rheological effects. In this work, a hybrid method employing a probabilistic neural network and reduced-order analytical model is proposed, and its performance assessed using data collected over multiple years from a pedestrian bridge equipped with strain and temperature fiber optic sensors. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Concrete Structures; Creep and Shrinkage; Data-Driven Analysis; Fiber-Optic Sensors; Long-Term Strain Monitoring; Probabilistic Neural Networks; Reduced Order Modelling; Rheological Effects; Structural Health Monitoring","Bayesian networks; Complex networks; Concrete beams and girders; Concrete buildings; Concrete construction; Creep; Data reduction; Fiber optic sensors; Footbridges; Forecasting; Prestressed concrete; Shrinkage; Statistical tests; Stochastic systems; Structural health monitoring; Uncertainty analysis; Concrete structure; Creep and shrinkages; Data-driven analysis; Fibre-optic sensor; Long-term strain monitoring; Neural-networks; Probabilistic neural network; Probabilistics; Reduced order modelling; Reduced-order model; Rheological effect; Strain monitoring; Neural networks",,,,,"Princeton University","The authors would like to thank Vivek Kumar for the aid with data preparation, and Princeton University for supporting this work.",,,,,,,,,,"Farrar, C. R., Worden, K., (2013) Structural Health Monitoring: A Machine LearningPerspective, , John Wiley & Sons, West Sussex, UK; Abdel-Jaber, H, Glisic, B, (2016) Smart Mater. Struct, 25, p. 125025. , Systematic method for the validation of long-term temperature measurements; Bažant, Z. P., Jirásek, M., (2018) Creep and Hygrothermal Effects in Concrete Structures, , Springer, Dordrecht, The Netherlands; Sellier, A., Multon, S., Buffo-Lacarriere, L., Vidal, T., Bourbon, X., Camps, G., Concrete creep modelling for structural applications: non-linearity, multi-axiality, hydration, temperature and drying effects (2016) Cement and Concrete Research, 79, pp. 301-315. , http://www.sciencedirect.com/science/article/pii/S0008884615002616, [Online]. Available; Bažant, Z., Hubler, M., Yu, Q., Pervasiveness of excessive segmental bridge deflections: Wake-up call for creep (2011) ACI Structural Journal, 108 (6), pp. 766-774. , Nov; Bažant, Z. P., Yu, Q., Li, G.-H., Excessive long-time deflections of prestressed box girders.i: Record-span bridge in Palau and other paradigms (2012) Journal of Structural Engineering, 138 (6), pp. 676-686; Abdellatef, M., Vorel, J., Wan-Wendner, R., Alnaggar, M., Predicting time-dependent behavior of post-tensioned concrete beams: Discrete multiscale multiphysics formulation (2019) J.Struct. Eng; Sousa, H., Santos, L. O., Chryssanthopoulous, M., Quantifying monitoring requirements for predicting creep deformations through bayesian updating methods (2019) Structural Safety; Han, B., Xiang, T.-Y., Xie, H.-B., A bayesian inference framework for predicting the long-term deflection of concrete structures caused by creep and shrinkage (2017) Engineering Structures, 142 (C), pp. 46-55; Strauss, A, Wan-Wendner, R., Vidovic, A., Zambon, I., Yu, Q, Frangopol, D. 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Available; Suo, Q., Yao, L., Xun, G, Sun, J., Zhang, A., Recurrent imputation for multivariate timeseries with missing values (2019) 2019 IEEE International Conference on Healthcare Informatics(ICHI), pp. 1-3; Hu, W H, Cunha, Á, Caetano, E, Rohrmann, RG, Said, S, Teng, J., Comparison of different statistical approaches for removing environmental/operational effects for massive data continuously collected from footbridges (2017) Struct Control Health Monit, 24, p. e1955. , https://doi.org/10.1002/stc.1955; Sigurdardottir, D H, Glisic, B, Neutral axis as damage sensitive feature (2013) Smart Mater. Struct, 22, p. 075030; Reilly, J., Glisic, B., Identifying Time Periods of Minimal Thermal Gradient for Temperature-Driven Structural Health Monitoring (2018) Sensors, 18, p. 734; (2008) Guide for modeling and calculating shrinkage and creep in hardened concrete, , Farmington Hills; (1993) CEB-FIP model code 1990, , CEB-FIP. Committee Euro-International du Béton; Abdel-Jaber, Hiba, Glisic, Branko, Monitoring of long-term prestress losses in prestressed concrete structures using fiber optic sensors (2019) Structural Health Monitoring, 18 (1), pp. 254-269; Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G S., Zheng, X., (2015), https://www.tensorflow.org/, s software tensorflow.org. [Online].. Available; Bažant, Z P, Baweja, S, 995 Creep and shrinkage prediction model for analysis and design of concrete structures - model B3 Matériaux et Constructions, 28, pp. 357-365",,,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130736398
"Yan Q., Zhao Y.","7201665384;57211810443;","Parameters identification of a steel arch truss bridge with modified artificial bee colony algorithm",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"557","562",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130729057&partnerID=40&md5=5ead8ea4d414d36a60ea74f157259bc4","School of Civil Engineering & Transportation, South China University of Technology, Guangzhou, 510640, China; Guangzhou Institute of Building Science Co., Ltd., Guangzhou, 510440, China","Yan, Q., School of Civil Engineering & Transportation, South China University of Technology, Guangzhou, 510640, China; Zhao, Y., School of Civil Engineering & Transportation, South China University of Technology, Guangzhou, 510640, China, Guangzhou Institute of Building Science Co., Ltd., Guangzhou, 510440, China","In bridge engineering, the evaluation of bridge design, seismic response and structural health monitoring are crucial to both the government and the society. To achieve these goals, conducting numerical analyses is essential to the related finite element (FE) model. Due to many effective factors such as structure geometric errors, material uncertainties, construction errors etc., it is very difficult to establish an accurate FE model of the existing structures. In this paper, two methods based on different foundations are adopted to identify the parameters of a steel arch truss bridge with the span of 100m+400m+100m; large quantities of frequencies and modals obtained from test data are used during the process. The first method is an artificial intelligence (AI) method and is named as the Artificial Bee Colony (ABC) algorithm. With the ABC algorithm, the parameter identification problem is transferred to find the extreme values of the corresponding objective function. The second method is the Bayesian improved Markov Chain Monte Carlo (MCMC). The numerical results show that both methods can achieve promising results, and the ABC algorithm can verify the status of the steel arch truss bridge even when the test data are limited. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Artificial Bee Colony algorithm; Parameter Identification; Steel Truss Bridge","Arches; Markov processes; Monte Carlo methods; Numerical methods; Optimization; Parameter estimation; Seismic design; Statistical tests; Steel bridges; Structural health monitoring; Trusses; Artificial bee colony algorithm; Bee colony algorithms; Bridge design; Bridge engineering; Finite element modelling (FEM); Geometric errors; Parameters identification; Steel arches; Steel truss bridge; Test data; Arch bridges",,,,,,,,,,,,,,,,"Li, J., Hao, H., A review of recent research advances on structural health monitoring in Western Australia (2016) Structural Monitoring and Maintenance, 3 (1), pp. 33-49; Liu, K., Yan, J., Alam, M., Zou, C., Seismic fragility analysis of deteriorating recycled aggregate concrete bridge columns subjected to freeze-thaw cycles (2019) Engineering Structures, 187, pp. 1-15; Choudhury, T., Kaushik, H., Treatment of uncertainties in seismic fragility assessment of RC frames with masonry infill walls (2019) Soil Dynamics and Earthquake Engineering, 126, p. 105771; Jia, B., Yu, X., Yan, Q., Yang, Z., Analysis of bridge time-dependent performance based on dynamic Bayesian networks (2016) International Journal of Earth Sciences and Engineering, 9 (6), pp. 2427-2436; Karaboga, D., (2005) An Idea Based on Honey Bee Swarm for Numerical Optimization, , Technical Report-TR06, Department of Computer Engineering, Engineering Faculty, Erciyes University; Ewins, D., (2000) Modal testing, , Research Studies Press, Baldock, England; Glover, F., Future paths for integer programming and links to artificial intelligence (1986) Computers & Operations Research, 13 (5), pp. 533-549; Jiang, B., Optimizing complex functions by chaos search (1998) Cybernetics and Systems, 29 (4), pp. 409-419; Zhao, Y., Yan, Q., Yang, Z., A Novel Artificial Bee Colony Algorithm for Structural Damage Detection (2020) Advances in Civil Engineering, 29, pp. 1-21; Beck, J., Au, S., Bayesian Updating of Structural Model Sand Reliability using Markov Chain Monte Carlo Simulation (2002) Journal of Engineering Mechanics, 128 (4), pp. 380-391; Haario, H., Laine, M., Mira, A., DRAM: Efficient Adaptive MCMC (2006) Statistics and Computing, 16 (4), pp. 339-354; Wan, H., Ren, W., Parameter Selection in Finite-Element-Model Updating by Global Sensitivity Analysis Using Gaussian Process Metamodel (2015) Journal of Structural Engineering, 141 (6), p. 04014164",,,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130729057
"Fontan M., Guerineau L.","57711637600;57205293301;","A new SHM strategy using in situ modal analysis processing to monitor civil engineering structures",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1511","1516",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130725135&partnerID=40&md5=f391d6d00921d263e3d6343adfebab0b","Apave SA, Immeuble Canopy, 6 rue du Général Audran, COURBEVOIE Cedex92 412, France; SERCEL, 16 Rue de bel air, Carquefou44470, France","Fontan, M., Apave SA, Immeuble Canopy, 6 rue du Général Audran, COURBEVOIE Cedex92 412, France; Guerineau, L., SERCEL, 16 Rue de bel air, Carquefou44470, France","This article introduces an application of the SHM solution 'AP'Structure' which has been jointly developed by Apave and Sercel. An ageing and decommissioned concrete bridge built more than 70 years ago was monitored using both new and standard sensors, in order to perform an Operational Modal Analysis (OMA) on site. The collected raw data were compared and analyzed in the frequency domain, using several algorithms such as Stochastic Subspace Identification or Enhanced Frequency Domain Decomposition. The comparison of the results not only highlights the efficiency of the sensors in terms of accuracy of the identification of the dynamics properties, but also their operational effectiveness on site in order to improve the measurement ratio per day using an improved communication system. Then the collected data were used to calibrate a Finite Element model of the bridge using the MAC matrix, so as to estimate the feasibility of using this structure as a pedestrian bridge. The comparison of the final results between two FE models developed either with and/or without OMA data highlights that the use of dynamics properties improves the knowledge of the structure and avoids inappropriate conclusions in terms of the use of the ageing bridge. Indeed, the application of the French regulations code to verify a pedestrian bridge has been applied and conclusions may be quite different according to whether the FE model is calibrated on modal basis or not. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","asset-management; Calibration on modal basis; decision making; FE model; frequency; Operational Modal Analysis; pedestrian bridge","Domain decomposition methods; Finite element method; Footbridges; Frequency domain analysis; Modal analysis; Stochastic systems; Structural health monitoring; Assets management; Calibration on modal base; Civil engineering structures; Decisions makings; Dynamics properties; FE model; Frequency; Frequency domains; Modal basis; Operational modal analysis; Decision making",,,,,,"The authors acknowledge both the Grand port maritime de Nantes-Saint-Nazaire (GPMNSN) and the Pôle Mer Bretagne Atlantique for their support and authorization access to the Brivet bridge in order to carry out the study presented in this paper.",,,,,,,,,,"Cremona, C., (2008) Evaluation structurale des ponts - Rapport de synthèse de l'opération de recherche (2004-2007), , LCPC-OA61; Hurt, M., Schrock, S. D., (2016) Highway Bridge Maintenance Planning and Scheduling, , ISBN 978-0-12-802069-2; Brincker, R., Ventura, C., (2015) Introduction to Operational Modal Analysis, , Wiley; Reynders, E., System identification methods for (operational) modal analysis: review and comparison (2012) Archives of Computational Methods in Engineering, 19 (1), pp. 51-124; Avitabile, P., (2018) Modal Testing A practitioners' guide, , Wiley; Döhler, M., Hille, F., Lam, X.B., Mevel, L., Rücker, W., Structural health monitoring with statistical methods during progressive damage test of S101 Bridge (2014) Engineering Structure, 69, pp. p183-p193; Cabboi, A., Gentile, C., Saisi, A., Vibration-based SHM of a centenary bridge: a comparative study between two different automated OMA techniques (2014) Proceedings of the 9th International Conference on Structural Dynamics, EURODYN 2014; Döhler, M., Andersen, P., Mevel, L., Operational Modal Analysis Using a Fast Stochastic Subspace Identification Method (2012) 30th International Modal Analysis Conference, , SEM, Jan Jacksonville, United States; Sercel, , https://www.sercel.com; Dallard, P., Fitzpatrick, A., Flint, A., Le Bourva, S., Low, A., Ridsdill Smith, R., The London Millennium Footbridge (2001) The Structural Engineer, 79 (22), pp. 17-33; Blekherman, A., Autoparametric Resonance in a Pedestrian Steel Arch Bridge: Solferino Bridge, Paris (2007) Journal of Bridge Engineering, 12 (6). , November; Magalhaes, F., Cunha, A., Caetano, E., Online automatic identification of the modal parameters of a long span arch bridge (2009) Mechanical Systems and Signal Processing, 23; Magalhães, F., Cunha, A. F., Caetano, E., Fonseca, A. A. D., Bastos, R, Evaluation of dynamic properties of the Infante Dom Henrique Bridge (2006) The Third International Conference on Bridge Maintenance, Safety and Management; Allahdadian, S., Döhler, M., Ventura, C., Mevel, L., Towards robust statistical damage localization via model-based sensitivity clustering (2019) Mechanical Systems and Signal Processing, 134 (1); ARTeMIS, , https://svibs.com/; Overschee, P, De Moor, B., (1996) Subspace identification for linear system, theory, implementation, applications, , Klwer Academic Publishers; (2006) Passerelles piétonnes - évaluation du comportement vibratoire sous l'action des piétons; (2020) Internal visual inspection reports, , GPMNSN; Robot Structural Analysis, , https://www.autodesk.com/; Tromino'sensors, , https://moho.world/en/",,,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130725135
"Morgan C.J., Sparling B.F., Wegner L.D.","57215412595;15037238700;13805490300;","Use of Structural Health Monitoring to Extend the Service Life of the Diefenbaker Bridge",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1073","1080",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130720347&partnerID=40&md5=75a23b9f9efcb170c6fa1b043b07c0ac","College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada; WSP Canada Inc., Saskatoon, SK, Canada","Morgan, C.J., College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada, WSP Canada Inc., Saskatoon, SK, Canada; Sparling, B.F., College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada; Wegner, L.D., College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada","A structural health monitoring system was installed on the 60-year-old Diefenbaker Bridge, located in Prince Albert, Saskatchewan, Canada, to investigate in-situ bridge behaviours such as the degree of composite action, lateral load distribution, and dynamic load influence. With an enhanced understanding of the bridge's behaviour, an estimate of the remaining fatigue life was refined. The 304 metre long, seven span bridge consists of two separate fracture critical superstructures, each comprising a cast-in-place concrete deck supported by two non-composite welded wide flange girders. The bridge is of vital importance to the economy of the province of Saskatchewan, as it lies along a major corridor that services the northern half of the province. Previous studies, based solely on a structural analysis, concluded that the connection of the lateral bracing to the girder web had less than five years of remaining fatigue life. Due to the uncertainty involved in this calculation, the data acquired from six months of field monitoring were used to define the structure's response to live loading, and to calibrate a finite element model that was used to characterize the three-dimensional stress state at that connection. It was found that unexpected composite action, increased load sharing between the girders, and minimal dynamic load influence exist on the bridge. Results were compared with those obtained using the Canadian Highway Bridge Design Code (CAN/CSA S6-14) and it was concluded that costly improvements to the connection detail were not required since the remaining fatigue life was estimated to be at least 52 years. In addition, it was found that the exterior girders are more heavily loaded than the interior girders, and the northbound structure is more heavily loaded that the southbound, permitting the location of the most critical connection for fatigue life to be identified. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Bridge Dynamics; Bridge Evaluation; Bridge Inspection; Fatigue Life Evaluation; Finite Element Modelling; Structural Health Monitoring","Beams and girders; Dynamic loads; Fatigue of materials; Highway bridges; Highway planning; Stress analysis; Structural health monitoring; Bridge dynamics; Bridge evaluation; Bridge inspection; Composite action; Fatigue life evaluation; Lateral load distributions; Load dynamics; Remaining fatigue life; Saskatchewan; Structural health monitoring systems; Finite element method",,,,,,"The support of the Saskatchewan Ministry of Highways and Infrastructure and the City of Prince Albert for this project are gratefully acknowledged. Also, the ISL Engineering and Land Services team (including Bridge Diagnostics Inc.) are gratefully acknowledged for their financial and technical support.",,,,,,,,,,"(2016) Diefenbaker Assessment and Evaluation Report, , ISL Engineering and Land Services, Saskatchewan Ministry of Highways and Infrastructure, Saskatoon, Saskatchewan, Canada; (2014) Canadian Highway Bridge Design Code, , CAN/CSA S6-14, Canadian Standards Association, Rexdale, Ontario, Canada; (1990) Distortion Induced Fatigue Cracking in Steel Bridges, , National Cooperative Highway Research Program, NCHRP Report 336, Transportation Research Board, National Research Council, Washington, DC, USA; Morgan, C., Sparling, B., Wegner, L., Fatigue Life Evaluation of the Diefenbaker Bridge using Structural Health Monitoring (2019) Proceedings of the 2019 CSCE Annual Conference, , Laval, Canada; Beer, F., Johnston, E., Dewolf, J., Mazurek, D., (2009) Mechanics of Materials, , 5th Edition, McGraw-Hill, New York, NY, USA; (2011) Standard Practices for Cycle Counting in Fatigue Analysis, , ASTM E1049-85: ASTM International, West Conshohocken, PA, USA; (2019) SAP 2000 Integrated Software for Structural Analysis and Design, , CSI. Computers and Structures Inc., Berkeley, California, USA; Castillo, J., (2019) An Investigation into the Effects of Damage on the Diefenbaker Bridge, , M.Eng. Report, University of Saskatchewan, Saskatoon, Saskatchewan, Canada; (2019) ANSYS Simulation Software, , ANSYS Inc., Version 19, Delaware, USA; Miner, M., Cumulative Damage in Fatigue (1945) Journal of Applied Mechanics, 12, pp. A159-A164; Fasl, J., (2013) Estimating the Remaining Fatigue Life of Steel Bridges Using Field Measurements, , Ph.D. Dissertation, The University of Texas at Austin; Fisher, J., Mertz, D., Zhong, A., (1983) Steel Bridge Members Under Variable Amplitude Long Life Fatigue Loading, , Transportation Research Board, National Research Council, Washington, D.C. USA; (2011) The Manual for Bridge Evaluation, , 2nd Edition, American Association of State Highway and Transportation Officials, Washington, D.C., USA; Moses, F., Schilling, C., Raju, K., (1987) Fatigue Evaluation Procedures for Steel Bridges, , Transportation Research Board, National Research Council, Washington, D.C. USA; Bakht, B., Mufti, A., (2015) Bridges - Analysis, Design, Structural Health Monitoring, and Rehabilitation, , 2nd Edition, Springer International Publishing, Switzerland; Feldman, L., Jackson, K., Sparling, B., Sparks, G., Comparison of Load Rating Techniques for the Red Deer River Bridge (2011) Canadian Journal of Civil Engineering, 38 (10), pp. 1072-1081",,,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130720347
"Anastasopoulos D., Reynders E.P.B., François S., De Roeck G., Van Lysebetten G., Van Itterbeeck P., Huybrechts N.","57191913184;15830150900;14068350300;7007019763;55631367800;12240697100;6504428215;","Investigation of the influence of damage on modal strains of a fiber reinforced polymer footbridge using model updating for SHM purposes",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"571","579",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130718488&partnerID=40&md5=0daf6c309a09a8a67aa5eb9bb3689d56","Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, Leuven, 3001, Belgium; Belgian Building Research Institute (BBRI), Lozenberg 7, Sint-Stevens-Woluwe1932, Belgium","Anastasopoulos, D., Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, Leuven, 3001, Belgium; Reynders, E.P.B., Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, Leuven, 3001, Belgium; François, S., Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, Leuven, 3001, Belgium; De Roeck, G., Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, Leuven, 3001, Belgium; Van Lysebetten, G., Belgian Building Research Institute (BBRI), Lozenberg 7, Sint-Stevens-Woluwe1932, Belgium; Van Itterbeeck, P., Belgian Building Research Institute (BBRI), Lozenberg 7, Sint-Stevens-Woluwe1932, Belgium; Huybrechts, N., Belgian Building Research Institute (BBRI), Lozenberg 7, Sint-Stevens-Woluwe1932, Belgium","Vibration-Based Monitoring (VBM) can constitute a successful approach for Structural Health Monitoring (SHM) of civil structures. The idea behind VBM is to identify structural damage by detecting damage-related changes of the modal characteristics of a structure. However, the most commonly used modal characteristics in VBM applications, natural frequencies, can exhibit a low sensitivity to certain types of damage, especially when compared to their sensitivity to environmental influences such as temperature. Modal strains are another modal characteristic which is obtained from dynamic strain measurements. Modal strains have been proved to be more sensitive to local damage, while less sensitive to temperature than natural frequencies. In the context of the present work, a footbridge whose Fiber Reinforced Polymer (FRP) deck is made out of one piece by vacuum infusion, is subjected to experimental modal analyses, where hammer impacts are used to dynamically excite the bridge. The dynamic strains of the bridge are monitored with embedded Fiber-optic Bragg Grating (FBG) strain sensors. In-plane bending modes are accurately identified from dynamic strains with a typical measured root mean square (RMS) strain value of the order of 0.5 micro-strains. The identified modes are used for updating a finite element model (FEM) of the bridge that is built in ANSYS. Possible damage scenarios that can occur on FRP structures are simulated in the FEM and the influence of these on natural frequencies and modal strains of the bridge is investigated. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Experimental modal analysis; fiber Bragg gratings; fiber reinforced polymer; footbridge; modal strains","Damage detection; Fiber Bragg gratings; Fiber reinforced plastics; Footbridges; Modal analysis; Natural frequencies; Reinforcement; Structural health monitoring; A.Fibres; Dynamic strain; Experimental modal analysis; Fiber-reinforced polymers; Fibre reinforced polymers; Finite element modelling (FEM); Modal characteristics; Modal strain; Model updating; Vibration-based monitoring; Strain",,,,,,"The research presented in this paper has been performed within the framework of the project COOCK HBC.2019.2505”Monitoring of structures and systems with fiber optic sensors”, funded by Flanders Innovation & Entrepreneurship (Vlaio), Belgium. The financial support of Vlaio is gratefully acknowledged. The authors wish also to express their gratitude to the Flemish Department of Mobility and Public Works (MOW-EBS) and De Vlaamse Waterweg, for providing access to the Canada footbridge and facilitating with the testing and also to FiberCore Europe for providing the blueprints and the material properties of the upstream Canada bridge.","The research presented in this paper has been performed within the framework of the project COOCK HBC.2019.2505 ”Monitoring of structures and systems with fiber optic sensors”, funded by Flanders Innovation & Entrepreneurship (Vlaio), Belgium. The financial support of Vlaio is gratefully acknowledged. The authors wish also to express their gratitude to the Flemish Department of Mobility and Public Works (MOW-EBS) and De Vlaamse Waterweg, for providing access to the Canada footbridge and facilitating with the testing and also to FiberCore Europe for providing the blueprints and the material properties of the upstream Canada bridge.",,,,,,,,,"Brownjohn, J.M.W., De Stefano, A., Xu, Y.-L., Wenzel, H., Aktan, A.E., Vibration-based monitoring of civil infrastructure: challenges and successes (2011) Journal of Civil Structural Health Monitoring, 1 (3-4), pp. 79-95; Fan, W., Qiao, P., Vibration-based damage identification methods: a review and comparative study (2010) Structural Health Monitoring, 10 (1), pp. 83-111; Anastasopoulos, D., De Smedt, M., Vandewalle, L., De Roeck, G., Reynders, E., Damage identification using modal strains identified from operational fiber-optic Bragg grating data (2018) Structural Health Monitoring, 17 (6), pp. 1441-1459; Anastasopoulos, D., De Roeck, G., Reynders, E. P. B., Influence of damage versus temperature on modal strains and neutral axis positions of beam-like structures (2019) Mechanical Systems and Signal Processing, 134, p. 106311; Glisic, B., Inaudi, D., (2007) Fibre Optic Methods for Structural Health Monitoring, , John Willey & Sons, West Sussex, U.K; Othonos, A., Kalli, K., (1999) Fiber Bragg Gratings: Fundamentals and Applications in Telecommunications and Sensing, , Artech House, Boston, MA; Chan, T. H. T., Yu, L., Tam, H. Y., Ni, Y. Q., Liu, S. Y., Chung, W. H., Cheng, L. K., Fiber Bragg grating sensors for structural health monitoring of Tsing Ma bridge: Background and experimental observation (2006) Engineering Structures, 28 (5), pp. 648-659; Anastasopoulos, D., (2020) Structural health monitoring based on operational modal analysis from long-gauge dynamic strain measurements, , Ph.D. thesis, Department of Civil Engineering, KU Leuven; Anastasopoulos, D., Moretti, P., Geernaert, T., De Pauw, B., Nawrot, U., De Roeck, G., Berghmans, F., Reynders, E., Identification of modal strains using sub-microstrain FBG data and a novel wavelength-shift detection algorithm (2017) Mechanical Systems and Signal Processing, 86A, pp. 58-74; Anastasopoulos, D., De Roeck, G., Reynders, E., Automated operational modal analysis of a steel arch bridge from dynamic sub-microstrain fiber Bragg grating data (2020) Proceedings of the 11th International Conference On Structural Dynamics, EURODYN 2020, pp. 1096-1108. , M. Papadrakakis, M. Fragiadakis, and C. Papadimitriou, Eds., Athens, Greece, September; (2006) Field inspection of in-service FRP bridge decks, , National Academies of Sciences Engineering and Medicine, Report, The National Academies Press, Washington, D.C; Reynders, E., Schevenels, M., De Roeck, G., (2014) MACEC 3.3: a Matlab toolbox for experimental and operational modal analysis, , Report BWM-2014-06, Department of Civil Engineering, KU Leuven, July; Peeters, B., De Roeck, G., Reference-based stochastic subspace identification for output-only modal analysis (1999) Mechanical Systems and Signal Processing, 13 (6), pp. 855-878; Reynders, E., Maes, K., Lombaert, G., De Roeck, G., Uncertainty quantification in operational modal analysis with stochastic subspace identification: validation and applications (2016) Mechanical Systems and Signal Processing, 66-67, pp. 13-30; Maeda, M., Tanaka, K., Hibi, S., Kakei, K., Mechanical properties of F.R.P., Part I: Young's modulus of polyester resin filled with glass particles and that reinforced with glass fiber clothes (1967) Journal of the Textile Machinery Society of Japan, 2 (13)","Anastasopoulos, D.; Department of Civil Engineering, Kasteelpark Arenberg 40, Belgium; email: dimitrios.anastasopoulos@kuleuven.be",,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130718488
"Jinghui J., Chaoyi X., Kunpeng C., He X., Qikai S.","57710549300;26421550100;57711313200;57198911495;57302831200;","Study on damage assessment method of HSR Bridge under near-field explosion",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1467","1474",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130715813&partnerID=40&md5=40b28ce02ac9567d2938127e93d6595c","Beijing JiaoTong University, School of Civil Engineering, Beijing, 100044, China; China Fire and Rescue Institute, Beijing, 102201, China","Jinghui, J., Beijing JiaoTong University, School of Civil Engineering, Beijing, 100044, China; Chaoyi, X., Beijing JiaoTong University, School of Civil Engineering, Beijing, 100044, China; Kunpeng, C., China Fire and Rescue Institute, Beijing, 102201, China; He, X., Beijing JiaoTong University, School of Civil Engineering, Beijing, 100044, China; Qikai, S., Beijing JiaoTong University, School of Civil Engineering, Beijing, 100044, China","The damage assessment of a high-speed railway (HSR) bridge is realized after it is attacked by a near-field explosion, considering the damage level of the pier, beam and pot bearing comprehensively. Firstly, an ""explosive-air-ground"" coupling analysis model is established by the ANSYS/LS-DYNA software and compared with the experimental results, which verifies the effectiveness of the Arbitrary Lagrangian-Eulerian (ALE) algorithm and the accuracy of the mesh size and material properties. Then, a 3D model of a 2×32m prestressed concrete simply supported beam bridge on the Beijing-Shanghai HSR line is constructed by the finite element (FE) method. The damage of each component of the bridge under three types of car-bombs is evaluated, by using the residual axial bearing capacity of damaged pier, the residual bending capacity of damaged beam and the relative shear displacement of pot bearing as the assessment indices. The study provides a reference for exploring a safety assessment method of post-explosion bridges. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Bridge engineering; Damage assessment; Finite element model; High-speed railway bridge; Near-field explosion","3D modeling; Concrete beams and girders; Damage detection; Explosions; Piers; Prestressed concrete; Railroad bridges; Railroad transportation; Railroads; Structural health monitoring; Air grounds; Analysis models; Bridge engineering; Coupling analysis; Damage assessments; Damage level; Finite element modelling (FEM); High-speed railway bridges; Near fields; Near-field explosion; Finite element method",,,,,"B13002; Fundamental Research Funds for the Central Universities: 2020JBM042","The paper is supported by the Fundamental Research Funds for the Central Universities of China (2020JBM042) and Introducing Talents Base of Mitigating Wind-induced Disaster of Wind-sensitive Infrastructure (B13002).",,,,,,,,,,"Garlock, M, Paya-Zaforteza, I., Fire hazard in bridges: Review, assessment and repair strategies (2012) Engineering Structures, 35, pp. 89-98; Gao, Y., The main bridge of Yichang Bridge in Henan Province collapsed due to truck explosion and several vehicles fell off (2015) CCTV network, , http://news.cntv.cn/2013/02/01/ARTI1359689498633464.shtml; Wu, J, Zhou, Y., Numerical simulation of reinforced concrete slab subjected to blast loading and the structural damage assessment (2020) Engineering Failure Analysis, 118, p. 104926; Zhang, J, Jiang, S., Numerical study of damage modes and damage assessment of CFST columns under blast loading (2016) Shock and Vibration, 2016, pp. 1-12; Bao, X, Li, B., Residual strength of blast damaged reinforced concrete columns (2010) International Journal of Impact Engineering, 37 (3), pp. 295-308; Yan, D M, Chen, G D, Baird, J, Blast tests of full-size wall barriers reinforced with enamel-coated steel rebar (2011) Structures Congress, pp. 1538-1551; Gao, C, Zong, Z H, Wu, J., Experimental study on progressive collapse failure of RC frame structures under blast loading (2013) China Civil Engineering Journal, 46 (7), pp. 9-20; Shi, Y, Hao, H, Li, Z X., Numerical derivation of pressure-impulse diagrams for prediction of RC column damage to blast loads (2008) International Journal of Impact Engineering, 35 (11), pp. 1213-1227; Li, N X., (2019) Analysis of seismic vulnerability on curved continuous beam bridge under near and far field earthquake of different bearing arrangements, , Beijing JiaoTong University; Peng, Y, Fu, Y., Analysis and numerical calculation of damaged reinforced concrete beam (2016) Journal of Southwest University of Science and Technology, 31 (1), pp. 30-34; Chen, W, Hao, H., Numerical analysis of prestressed reinforced concrete beam subjected to blast loading (2015) Materials & Design (1980-2015), 65, pp. 662-674; Dutta, A, Mander, J., Rapid and detailed seismic fragility analysis of highway bridges (2001), pp. 1-152. , Report, New York: Multidisciplinary Center for Earthquake Engineering Research; (2009) Code for design of high speed railway, , Beijing, China: National Railway Administration of the PRC; Wang, H., (2013) Experimental study and numerical analysis of fatigue residual bearing capacity of prestreesed concrete beam, , Beijing: Beijing Jiaotong University; (2017) The China railway bridge and culvert design specification, , TB 10002 Beijing, China: National Railway Administration of the PRC","Chaoyi, X.; Beijing JiaoTong University, China; email: cyxia@bjtu.edu.cn",,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130715813
"Mendler A., Cadoret A., Freyssinet C., Dohler M., Mevel Y.L.L., Ventura C.","57209199058;57710565900;57710819600;39361406400;57711076700;7101926223;","Minimum Localizable Damage for Stochastic Subspace-based Damage Diagnosis",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1553","1560",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130712612&partnerID=40&md5=870e04498f1acbd905f78ed611ff2f4d","Dept. of Civil Engineering, University of British Columbia, Vancouver, BC  V6T 1Z4, Canada; GeM, Universite de Nantes, Nantes, 44000, France; Univ. Gustave Eiffel, Inria, COSYS/SII, I4S, Rennes, 35042, France","Mendler, A., Dept. of Civil Engineering, University of British Columbia, Vancouver, BC  V6T 1Z4, Canada; Cadoret, A., GeM, Universite de Nantes, Nantes, 44000, France; Freyssinet, C., GeM, Universite de Nantes, Nantes, 44000, France; Dohler, M., Univ. Gustave Eiffel, Inria, COSYS/SII, I4S, Rennes, 35042, France; Mevel, Y.L.L., Univ. Gustave Eiffel, Inria, COSYS/SII, I4S, Rennes, 35042, France; Ventura, C., Dept. of Civil Engineering, University of British Columbia, Vancouver, BC  V6T 1Z4, Canada","This article describes an approach to evaluate the minimum localizable damage for stochastic subspace-based damage diagnosis. Localizability is defined as the sensitivity to small and local damages (detectability), the ability to narrow down the exact damage location (localization resolution), and the test response of undamaged parameters (false localization alarms). For the analysis, damage is defined as a change in model-based design parameters, for example, material constants or cross-sectional values in a finite element model. Subsequently, the parameter changes are linked to changes in the global damage-sensitive features using sensitivity vectors, and inherent uncertainties (due to stochastic loads and measurement noise) are quantified. This way, local structural parameters can be tested for changes using statistical hypothesis tests, such as the general likelihood ratio and the statistical minmax localization test. Due to the numerical conditioning of the damage localization problem, the sensitivity vectors have to be clustered before damage can be localized. Sensitivity clustering corresponds to a substructuring of the finite element model, where the number of clusters (the localization resolution) is a user-defined input parameter. The main results of this paper are mathematical criteria to calculate the damage detectability and the false alarm susceptibility for different localization resolutions. Moreover, an automated substructuring routine is described that finds the optimal substructure arrangement as a compromise between high damage detectability, high localization resolution, and low false alarm susceptibility. For proof of concept, a numerical case study is presented, where the damage localizability is determined and validated for a cable-stayed bridge. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Ambient vibrations; detectability; false localization alarms; localization resolution; statistical test","Alarm systems; Damage detection; Errors; Life cycle; Statistical tests; Stochastic systems; Structural health monitoring; Uncertainty analysis; Vectors; Ambient vibrations; Damage detectability; Damage diagnosis; Detectability; False localization alarm; Localisation; Localization resolution; Stochastic subspace; Subspace based; Finite element method",,,,,,,,,,,,,,,,"Farrar, C., Worden, K., (2012) Structural health monitoring: A machine learning perspective, , Wiley, Oxford, United Kingdom; Rytter, A., (1993) Vibrational based inspection of civil engineering structures, , Ph.D. Thesis, Aalborg University, Aalborg; Brun, R., Reichert, P., Ku¨nsch, H. R., Practical identifiability analysis of large environmental simulation models (2001) Water Resources Research, (37), pp. 1015-1030; Velez-Reyes, M., Verghese, G. C., Subset selection in identification, and application to speed and parameter estimation for induction machines (1995) Proceedings of International Conference on Control Applications, pp. 991-997. , IEEE, Albany, United States; Yao, K. Z., Shaw, B. M., Kou, B., McAuley, K. B., Bacon, D. W., Modeling Ethylene/Butene Copolymerization with Multi-site Catalysts: Parameter Estimability and Experimental Design (2003) Polymer Reaction Engineering, 11 (3), pp. 563-588; Li, R., Henson, M. A., Kurtz, M. J., Selection of Model Parameters for Off-Line Parameter Estimation (2004) IEEE Transactions on Control Systems Technology, 12 (3), pp. 402-412; Basseville, M., Abdelghani, M., Benveniste, A., Subspace-based fault detection algorithms for vibration monitoring (2000) Automatica, 36 (1), pp. 101-109; Basseville, M., Mevel, L., Goursat, M., Statistical model-based damage detection and localization: Subspace-based residuals and damage-to-noise sensitivity ratios (2004) Journal of Sound and Vibration, 275 (3-5), pp. 769-794; Balmes, E., Basseville, M., Mevel, L., Nasser, H., Zhou, W., Statistical model-based damage localization: A combined subspace-based and substructuring approach (2008) Structural Control and Health Monitoring, 15 (6), pp. 857-875; Allahdadian, S., D¨ohler, M., Ventura, C., Mevel, L., Towards robust statistical damage localization via model-based sensitivity clustering (2019) Mechanical Systems and Signal Processing, 134, p. 106341; Mendler, A., D¨ohler, M., Ventura, C. E., A reliability-based approach to determine the minimum dectable damage for statistical damage detection (2021) Mechanical Systems and Signal Processing, , (under review); Mendler, A., D¨ohler, M., Ventura, C., Mevel, L., Clustering of Redundant Parameters for Fault Isolation with Gaussian Residuals (2020) Proceedings of the IFAC - 21st World Congress of the International Federation of Automatic Control, , Berlin, Germany; Benveniste, A., Basseville, M., Moustakides, G., The asymptotic local approach to change detection and model validation (1987) IEEE Transactions on Automatic Control, 32 (7), pp. 583-592; van Overschee, P., de Moor, B., (1995) Subspace identification for linear systems: Theory, Implementation, Application, , Kluwer Academic Publishers, Boston/London/Dordrecht; Peeters, B., de Roeck, G., Reference-based Stochastic Subspace Identification for Output-only Analysis (1999) Mechanical Systems and Signal Processing, 13 (6), pp. 855-878; Allahdadian, S., D¨ohler, M., Ventura, C., Mevel, L., Towards robust statistical damage localization via model-based sensitivity clustering (2019) Mechanical Systems and Signal Processing, 134, p. 106341; Duda, R. O., Hart, P. E., Stork, D. G., (2012) Pattern classification, , John Wiley & Sons, New York, United States; D¨ohler, M., Mevel, L., Zhang, Q., Fault detection, isolation and quantification from Gaussian residuals with application to structural damage diagnosis (2016) Annual Reviews in Control, 42, pp. 244-256; Mendler, A., (2020) Minimum diagnosable damage and optimal sensor placement for structural health monitoring, , Ph.D. thesis, University of British Columbia, Vancouver, Canada; Cadoret, A., Freyssinet, C., Lecieux, Y., (2020) Fault detection using modal analysis (in French), , Tech. rep., University of Nantes, Nantes; Sutter, T. R., Camarda, C. J., Walsh, J. L., Adelman, H. M., Comparison of several methods for calculating vibration mode shape derivatives (1988) AIAA journal, 26 (12), pp. 1506-1511","Mendler, A.; Dept. of Civil Engineering, Canada
Ventura, C.; Dept. of Civil Engineering, Canada",,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130712612
"Ramancha M.K., Conte J.P.","56926718100;7101953827;","Some Recent Work in Bayesian FE Model Updating of Civil Structures",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"107","112",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130696159&partnerID=40&md5=420eb8b8d5119e407c587fb0f8357f39","Department of Structural Eng., University of California, 9500 Gilman Drive, La Jolla, San Diego, CA  92093, United States","Ramancha, M.K., Department of Structural Eng., University of California, 9500 Gilman Drive, La Jolla, San Diego, CA  92093, United States; Conte, J.P., Department of Structural Eng., University of California, 9500 Gilman Drive, La Jolla, San Diego, CA  92093, United States","Some recent work on Bayesian finite element (FE) model updating of civil/structural systems performed in our lab at UC San Diego is summarized in this paper. The FE model updating problem is formulated and its application is illustrated for various civil structures (concrete gravity dam, reinforced concrete bridge column, steel miter gate). (1) The topics of Bayesian model updating, and identifiability analysis of nonlinear FE models are discussed with the Pine Flat concrete gravity dam as illustration example. The non-identifiability of FE model parameters poses challenges in the model updating process. This is demonstrated using the dam model where the sensitivity and identifiability analysis results are used to eliminate the non-identifiable parameters in the model updating process. (2) FE model updating of a full-scale reinforced-concrete bridge column subjected to seismic tests at the UC San Diego large outdoor shake table is performed and the results obtained are summarized. The material and damping parameters of the bridge column FE model are estimated/updated using the input-output data collected during the shake table tests. (3) FE model updating of a miter gate system subjected to hydrostatic loading is performed using numerically simulated strain measurement data to estimate the loss of contact between the gate and the wall quoin blocks, a primary damage mode in miter gate systems. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Bayesian inference; Bridge column; Finite element; Gravity dam; Identifiability; Miter gate; Model updating; Sensitivity; Shake table; Structural health monitoring","Bayesian networks; Concrete bridges; Concrete dams; Gravity dams; Inference engines; Railroad bridges; Reinforced concrete; Structural health monitoring; Bayesian; Bayesian inference; Bridge columns; Civil structure; Finite-element model updating; Identifiability; Miter gates; Model updating; Sensitivity; Shake table; Finite element method",,,,,"Engineer Research and Development Center, ERDC: W912HZ-17-2-0024; U.S. Army Corps of Engineers, USACE","ACKNOWLEDGEMENTS Funding for this work by the U.S. Army Corps of Engineers through the U.S. Army Engineer Research and Development Center Research Cooperative Agreement W912HZ-17-2-0024 is gratefully acknowledged. The authors would like to thank Prof. Michael Todd and Dr. Manuel Vega in the Department of the Structural Engineering at UC San Diego for their collaboration in the FE model updating of the miter gate.","Funding for this work by the U.S. Army Corps of Engineers through the U.S. Army Engineer Research and Development Center Research Cooperative Agreement W912HZ-17-2-0024 is gratefully acknowledged. The authors would like to thank Prof. Michael Todd and Dr. Manuel Vega in the Department of the Structural Engineering at UC San Diego for their collaboration in the FE model updating of the miter gate.",,,,,,,,,"Yuen, K. V., (2010) Bayesian methods for structural dynamics and civil engineering, , John Wiley & Sons, Ltd; Moaveni, B., He, X., Conte, J. P., Restrepo, J. I., Panagiotou, M., System identification study of a 7-story full-scale building slice tested on the UCSD-NEES shake table (2011) Journal of Structural Engineering, ASCE, 137 (6), pp. 705-717. , June; Moaveni, B., He, X., Conte, J. P., Restrepo, J. I., Damage identification study of a seven-story full-scale building slice tested on the UCSD-NEES shake table (2010) Structural Safety, 32 (5), pp. 347-356. , September; Ramancha, M. K., Astroza, R., Madarshahian, R., Conte, J. P., Bayesian updating and identifiability assessment of nonlinear finite element models (2021) Mechanical Systems and Signal Processing, , under review/revision; Astroza, R., Ebrahimian, H., Conte, J. P., Material parameter identification in distributed plasticity FE models of frame-type structures using nonlinear stochastic filtering (2015) Journal of Engineering Mechanics, ASCE, 141 (5). , May; Ramancha, M. K., Astroza, R., Conte, J. P., Restrepo, J. I., Todd, M. D., Bayesian nonlinear finite element model updating of a full-scale bridge-column using sequential Monte Carlo (2020) Model Validation and Uncertainty Quantification, 3, pp. 389-397; Vega, M. A., Ramancha, M. K., Conte, J. P., Todd, M. D., Efficient Bayesian inference of miter gates using high-fidelity models (2020) Model Validation and Uncertainty Quantification, 3, pp. 375-382; Sarkka, S., (2013) Bayesian filtering and smoothing, , Cambridge University Press; Ching, J., Chen, Y. C., Transitional Markov Chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging (2007) Journal ofEngineering Mechanics, ASCE, 133 (7), pp. 816-832. , July; Minson, S. E., Simons, M., Beck, J. L., Bayesian inversion for finite fault earthquake source models I - theory and algorithm (2013) Geophysical Journal International, 194 (3), pp. 1701-1726; van der Merwe, R., Wan, E., (2004) Sigma-point Kalman filters for probabilistic inference in dynamic state-space models, , Ph.D. Dissertation, OGI School of Science & Engineering, Oregon Health & Science University; Simo, J. C., Ju, J., Pister, K. S., Taylor, R. L., Assessment of cap model: consistent return algorithms and rate-dependent extension (1988) Journal of Engineering Mechanics, ASCE, 114 (2), pp. 191-218; Schoettler, M. J., Restrepo, J. I., Guerrini, G., Duck, D. E., Carrea, F., (2015) A full-scale, single-column bridge bent tested by shake-table excitation, , PEER Report No. 2015/02, Pacific Earthquake Engineering Research Center, University of California, Berkeley, March; Eick, B. A., Smith, M. D., Fillmore, T. B., (2019) Feasibility of Discontinuous Quoin Blocks for USACE Miter Gates, , Technical Report No. ERDC TR-19-16, Engineer Research and Development Center, US Army Corps of Engineers, July",,,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130696159
"Sharry T., Guan H., Hoang N., Nguyen A., Oh E.","57449367300;7202612804;57449585500;57222629709;56654341700;","Finite element model updating of a cable-stayed bridge using structural health monitoring data",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1673","1679",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130685141&partnerID=40&md5=dde9d3cfba045cb8b87d1067a19ca47d","School of Engineering and Built Environment, Griffith University, Australia; University of Management and Technology, HCMC, Viet Nam; School of Civil Engineering and Surveying, University of Southern Queensland, Australia","Sharry, T., School of Engineering and Built Environment, Griffith University, Australia; Guan, H., School of Engineering and Built Environment, Griffith University, Australia; Hoang, N., University of Management and Technology, HCMC, Viet Nam; Nguyen, A., School of Civil Engineering and Surveying, University of Southern Queensland, Australia; Oh, E., School of Engineering and Built Environment, Griffith University, Australia","This paper presents a finite-element model of the Phu My Bridge, a 380m-main span reinforced concrete cable-stayed bridge in Ho Chi Minh City, Vietnam. The model is also updated based on accelerometer data from the on-structure sensing system for structural health monitoring (SHM). A comprehensive sensitivity study is undertaken to examine the effects of various structural parameters on the modal properties, according to which a set of structural parameters are then selected for model updating. The finite-element model is updated in an iterative procedure to minimise the differences between the analytical and measured natural frequencies. The model updating process converges after a small number of four iterations, due to the accuracy of the initial model which was achieved through careful consideration of the structural parameter values for the model, optimal element discretisation for mesh convergence, and the most sensitive parameters for updating. The updated finite-element model for the Phu My Bridge is able to reproduce natural frequencies in good agreement with measured ones and can be helpful for long-term monitoring efforts. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Operational Modal Analysis; Sensitivity-Based Model Updating; Structural Health Monitoring","Cable stayed bridges; Cables; Modal analysis; Natural frequencies; Reinforced concrete; Sensitivity analysis; Structural health monitoring; Accelerometer data; Finite element modelling (FEM); Finite-element model updating; Model updating; Operational modal analysis; Sensing systems; Sensitivity studies; Sensitivity-based model updating; Structural parameter; Viet Nam; Finite element method",,,,,,,,,,,,,,,,"Ernst, J., Der E-Modul von Seilen unter berucksichtigung des Durchhanges (1965) Der bauingerieur, 40 (2), pp. 53-55; Mottershead, J.E., Friswell, M., Model updating in structural dynamics: a survey (1993) Journal of Sound and Vibration, 167 (2), pp. 347-375; Sehgal, S., Kumar, H., Structural dynamic model updating techniques: A start of the art review (2016) Archives of Computational Methods in Engineering, 23 (3), pp. 515-533; Marwala, T., (2010) Finite element model updating using computational intelligence techniques: applications to structural dynamics, , Springer Science & Business Media; Friswell, M., Mottershead, J.E., (2013) Finite element model updating in structural dynamics, 38. , Springer Science & Business Media; Brownjohn, J.M., Xia, P.Q., Hao, H., Xia, Y., Dynamic assessment of curved cable-stayed bridge by model updating (2001) Journal of structural engineering, 126 (2), pp. 253-260; Zhang, Q., Chang, T.Y.P., Chang, C.C., Finite-element model updating for the Kap Shui Mun cable-stayed bridge (2001) Journal of bridge engineering, 6 (4), pp. 285-293; Brownjohn, J.M., Xia, P.Q., Dynamic assessment of curved cable-stayed bridge by model updating (2000) Journal of structural engineering, 126 (2), pp. 252-260; Link, M., Qian, Z., Updating substructure models with dynamic boundary conditions (1995) the 1995 Des. Engrg. Tech. Conf, , Paper presented at",,,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130685141
"Wang L., Chen H.-P., Ye L., Lu S., Wu W.","57710313400;7501622980;56032698200;57711330100;57711076900;","Structural dynamic analysis and modal parameter identification of the cable-stayed Poyang Lake Second Bridge",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"477","483",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130680754&partnerID=40&md5=b281765429be60254890479438ccccb3","Institute for Smart Transportation Infrastructure, East China Jiaotong University, Jiangxi, 330013, China; Institute for Transportation Science of Jiangxi Province, Jiangxi, 330200, China","Wang, L., Institute for Smart Transportation Infrastructure, East China Jiaotong University, Jiangxi, 330013, China; Chen, H.-P., Institute for Smart Transportation Infrastructure, East China Jiaotong University, Jiangxi, 330013, China; Ye, L., Institute for Smart Transportation Infrastructure, East China Jiaotong University, Jiangxi, 330013, China; Lu, S., Institute for Smart Transportation Infrastructure, East China Jiaotong University, Jiangxi, 330013, China; Wu, W., Institute for Transportation Science of Jiangxi Province, Jiangxi, 330200, China","Modal parameters of cable-stayed bridges such as natural frequency and mode shape are of great significance for dynamic analysis, finite element model updating and structural damage identification of the structures. In this paper, the main bridge of the Poyang Lake Second Bridge is adopted to investigate the dynamic characteristics of the cable-stayed bridge. The lower order natural frequencies and mode shapes are obtained by using the finite element dynamic modelling. Meanwhile, from the measured acceleration data, the modal data of the bridge such as natural frequencies and mode shapes, are identified by the modal analysis methods. By comparing the finite element numerical dynamic parameters with the modal data from ambient vibration test, the results show that the actual dynamic modes of the cable-stayed bridge are complicated, and the calculated frequencies are generally consistent with the experimental data, indicating that the finite element model can well reflect the dynamic behavior of the actual cable-stayed bridge. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Cable-stayed bridge; finite element modeling; modal analysis; model updating; structural dynamic analysis","Cable stayed bridges; Cables; Damage detection; Lakes; Modal analysis; Natural frequencies; Parameter estimation; Structural health monitoring; Dynamics analysis; Finite-element model updating; Modal data; Modal parameters; Modal-parameter identifications; Mode shapes; Model updating; Natural frequencies and modes; Poyang Lake; Structural dynamic analysis; Finite element method",,,,,"National Natural Science Foundation of China, NSFC: 51978263, 52008168; Natural Science Foundation of Jiangxi Province: 20192ACBL20008; National Key Research and Development Program of China, NKRDPC: SQ2019YFE012159","The authors are very grateful for the financial supports received from the National Key Research and Development Program (Grant No. SQ2019YFE012159), the National Natural Science Foundation of China (Grant No. 51978263, No. 52008168) and the Natural Science Key Foundation of Jiangxi Province (Grant No. 20192ACBL20008).",,,,,,,,,,"Shao, XD., (2014) Bridge Engineering, , China Communications Press; Lin, YP., (2004) Cable-stayed Bridge, , China Communications Press; Balageas, D, Fritzen, CP, Güemes, A., (2006) Structural Health Monitoring, , ISTE Ltd; Chen, HP., (2018) Structural Health Monitoring of Large Civil Engineering Structures, , John Wiley & Sons Limited, Oxford, UK; Zong, ZH., Damage and safety prognosis of bridge structures based on structural health monitoring: Progress and Challenges (2014) China Journal of Highway and Transport, 27 (12), pp. 46-57; Zhu, HP., A Three dimensional finite element model of cable-stayed bridges for dynamic analysis (1998) Journal o f Vibration Engineering, 11 (1), pp. 121-126; Su, C., The establishment of 3-D finite element dynamic models for long-span cable-stayed bridges (1999) Journal of South China University of Technology (Natural Science), 27 (11), pp. 51-56; Yao, ZY., Method of identification of a structural physical parameters-based continuous time model (2003) Journal of southeast university (Natural Science Edition), 33 (5), pp. 617-620; Ren, WX., Baseline finite element modeling of a large span cable-stayed bridge through field ambient vibration tests (2005) Computers & Structures, 83 (8-9), pp. 536-550; Gorski, P., Variability evaluation of dynamic characteristics of highway steel bridge based on daily traffic-induced vibrations (2020) Measurement, 164, p. 108074; Zhang, GW., Automated eigensystem realisation algorithm for operational modal analysis (2014) Journal of Sound and Vibration, 333 (15), pp. 3550-3563; Liu, YF., A review of structure modal identification methods through ambient excitation (2014) Engineering Mechanics, 31 (4), pp. 46-53; Fu, ZF, Hua, HX., (2000) Theory and application of modal analysis, , Shanghai: Shanghai Jiao Tong University Press; Juang, JN, Pappa, RS., An eigensystem realization algorithm for modal parameter identification and model reduction (1985) Journal of Guidance, 8 (5), pp. 620-627; Li, LH., A study of eigensystem realization algorithm and its generalization (2002) Engineering Mechanics, 19 (1), pp. 109-114; Huang, TL, Chen, HP., Mode identifiability of a cable-stayed bridge using modal contribution index (2017) Smart Structures and Systems, 20 (2), pp. 115-126; Chen, HP, Maung, TS., Regularised finite element model updating using measured incomplete modal data (2014) Journal of Sound and Vibration, 333 (21), pp. 5566-5582","Chen, H.-P.; Institute for Smart Transportation Infrastructure, China; email: hp.chen@ecjtu.edu.cn",,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130680754
"Hectors K., de Backer H., Saelens L., de Waele W.","57212460698;16836127400;57337898900;6602199404;","Fatigue assessment of a steel truss bridge based on multi-dimensional finite element modelling",2021,"IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs",,,,"986","994",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119063370&partnerID=40&md5=eb4d60c6c18a728cbce7a1cea1114e23","SIM Vzw, Tech Lane Ghent Science, Park/Campus A 48, Zwijnaarde, BE-9052, Belgium; Ghent University, Faculty of Engineering and Architecture, Department of Electromechanical, Systems and Metal Engineering, Laboratory Soete, Belgium; Ghent University, Faculty of Engineering and Architecture, Department of Civil Engineering, Coastal Engineering, Bridges and Roads, Belgium","Hectors, K., SIM Vzw, Tech Lane Ghent Science, Park/Campus A 48, Zwijnaarde, BE-9052, Belgium, Ghent University, Faculty of Engineering and Architecture, Department of Electromechanical, Systems and Metal Engineering, Laboratory Soete, Belgium; de Backer, H.; Saelens, L., Ghent University, Faculty of Engineering and Architecture, Department of Civil Engineering, Coastal Engineering, Bridges and Roads, Belgium; de Waele, W., Ghent University, Faculty of Engineering and Architecture, Department of Electromechanical, Systems and Metal Engineering, Laboratory Soete, Belgium","This paper presents a multidimensional finite element modelling approach for the fatigue assessment of welded railway bridges based on a case study of a railway bridge in Belgium. The nominal stress approach of Eurocode 3 is compared to a hot spot stress based fatigue life calculation for the standardized fatigue load models for railway traffic. Hot spot stresses are calculated with an in-house developed framework that allows automated determination of hot spot stresses. It is discussed how this work can fit in a larger decision support system in the scope of structural health monitoring. The presented approach proves to be better for decision support compared to the conventional approach in the Eurocode. © 2021 IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs. All rights reserved.","Fatigue assessment; Hot spot stress; Multidimensional finite element modelling; Nominal stress; Steel truss railway bridge","Artificial intelligence; Decision support systems; Fatigue of materials; Railroad bridges; Railroads; Steel bridges; Structural health monitoring; Trusses; Fatigue assessments; Hot-spot stress; Modeling approach; Multi dimensional; Multidimensional finite element modeling; Nominal stress; Railway bridges; Steel truss; Steel truss bridge; Steel truss railway bridge; Finite element method",,,,,,"The authors acknowledge the financial support of Vlaio through the SafeLife project (project number 179P04718W) and also the support of SIM (Strategic Initiative Materials in Flanders) and IBN Offshore Energy.",,,,,,,,,,"Haghani, R., (2013) PANTURA - D5.3 Needs for maintenance and refurbishment of bridges in urban environments, , Report; Bell, B., (2004) Sustainable Bridges - D 1.2 report on the age profile and condition of existing European railway bridges, , London, UK; Woodward, RJ, Cullington, DW, Daly, AF, Vassie, P., Haardt, P, Kashner, R, (2001) BRIME Final Report, , Europe; Olofsson, I, Elfgren, L, Bell, B, Paulsson, B, Niederleithinger, E, Sandager Jensen, J, Assessment of European railway bridges for future traffic demands and longer lives - EC project “Sustainable Bridges (2005) Struct Infrastruct Eng, 1 (2), pp. 93-100; Zhang, QH, Bu, YZ, Li, Q., Review on fatigue problems of orthotropic steel bridge deck (2017) Zhongguo Gonglu Xuebao/China J Highw Transp, 30 (3); Aygül, M, Bokesjö, M, Heshmati, M, Al-Emrani, M., A comparative study of different fatigue failure assessments of welded bridge details (2013) Int J Fatigue, 49, pp. 62-72; Alencar, G, de Jesus, A, da Silva, JGS, Calçada, R., Fatigue cracking of welded railway bridges: A review (2019) Eng Fail Anal, 104, pp. 154-176. , [Internet]. ;(September 2018); Ye, XW, Su, YH, Han, JP., A state-of-the-art review on fatigue life assessment of steel bridges (2014) Math Probl Eng, 2014; (2005) Design of steel structures - Part 1-9: Fatigue, , Eurocode 3; Niemi, E, Fricke, W, Maddox, SJ., (2018) Structural Hot-Spot Stress Approach to Fatigue Analysis of Welded Components, p. 85. , 2nd ed. International institute of Welding, editor. Springer; Li, ZX, Zhou, TQ, Chan, THT, Yu, Y., Multi-scale numerical analysis on dynamic response and local damage in long-span bridges (2007) Eng Struct, 29 (7), pp. 1507-1524; Wang, H, Li, A, Guo, T, Ma, S., Accurate stress analysis on rigid central buckle of long-span suspension bridges based on submodel method (2009) Sci China, Ser E Technol Sci, 52 (4), pp. 1019-1026; Wang, H, Li, A, Hu, R, Li, J., Accurate stress analysis on steel box girder of long span suspension bridges based on multi-scale submodeling method (2010) Adv Struct Eng, 13 (4), pp. 727-740; Albuquerque, C, Silva, ALL, De Jesus, AMP, Calçada, R., An efficient methodology for fatigue damage assessment of bridge details using modal superposition of stress intensity factors (2015) Int J Fatigue, 81, pp. 61-77. , [Internet]; Horas, CS, Alencar, G, De Jesus, AMP, Calçada, R., Development of an efficient approach for fatigue crack initiation and propagation analysis of bridge critical details using the modal superposition technique (2018) Eng Fail Anal, 89, pp. 118-137. , (March); Liu, Z, Correia, J, Carvalho, H, Mourão, A, de Jesus, A, Calçada, R, Global-local fatigue assessment of an ancient riveted metallic bridge based on submodelling of the critical detail (2018) Fatigue Fract Eng Mater Struct, pp. 546-560. , (June 2018); Alencar, G, de Jesus, AMP, Calçada, RAB, Silva, JGS d., Fatigue life evaluation of a composite steel-concrete roadway bridge through the hot-spot stress method considering progressive pavement deterioration (2018) Eng Struct, 166, pp. 46-61. , [Internet]. ;(January); Zhu, Z, Xiang, Z, Zhou, YE., Fatigue behavior of orthotropic steel bridge stiffened with ultrahigh performance concrete layer (2019) J Constr Steel Res, 157, pp. 132-142. , [Internet]; Mashayekhi, M, Santini-Bell, E., Fatigue assessment of a complex welded steel bridge connection utilizing a three-dimensional multi-scale finite element model and hotspot stress method (2020) Eng Struct, 214, p. 110624. , (September 2019); Hectors, K, De Backer, H, Loccufier, M, De Waele, W., (2019) Numerical framework for fatigue lifetime prediction of complex welded structures; (2003) Eurocode 1: Actions on structures - Part 2: Traffic loads on bridges, , European committee for standardization; Yan, F, Lin, Z, Huang, Y., Numerical simulation of fatigue behavior for cable-stayed orthotropic steel deck bridges using mixed-dimensional coupling method (2017) KSCE J Civ Eng, 21 (6), pp. 2338-2350","Hectors, K.; SIM Vzw, Tech Lane Ghent Science, Park/Campus A 48, Belgium; email: kris.hectors@ugent.be","Snijder H.H.De Pauw B.De Pauw B.van Alphen S.F.C.Mengeot P.","Allplan;et al.;Greisch;Infrabel;Royal HaskoningDHV;TUC RAIL","International Association for Bridge and Structural Engineering (IABSE)","IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs","22 September 2021 through 24 September 2021",,172892,,,,,"English","IABSE Congr., Ghent: Struct. Eng. Future Soc. Needs",Conference Paper,"Final","",Scopus,2-s2.0-85119063370
"Iakovidis I., Morfidis K.","57204789676;57695573800;","A finite element model updated by artificial neural networks to explain the behaviour of the Z24 Swiss bridge in different temperature states.",2021,"IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs",,,,"366","375",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119037847&partnerID=40&md5=97ce4c0acf2ae8a58ccdea2e6725dcf2","Ramboll UK Birmingham, Cornerblock, Two Cornwall Street, Birmingham, B3 2DX, United Kingdom; Institute of Engineering, Seismology and Earthquake Engineering, Research and Technical Institute, End of Dassylioy Street, Eleones Pylaia, Thessaloniki, 55535, Greece","Iakovidis, I., Ramboll UK Birmingham, Cornerblock, Two Cornwall Street, Birmingham, B3 2DX, United Kingdom; Morfidis, K., Institute of Engineering, Seismology and Earthquake Engineering, Research and Technical Institute, End of Dassylioy Street, Eleones Pylaia, Thessaloniki, 55535, Greece","A Finite Element (FE) model of bridge Z24 was developed to reflect its dynamic response and investigate the physical reasons behind the large variations observed on its natural modal properties during a 7-month continuous monitoring campaign conducted before its demolition in 1997. A significant increase in natural frequencies was observed especially during the winter period, something which was explained as a consequence of deck stiffness increase and boundary conditions change, due to the formation of ice layers on the deck and supports. The paper concentrates on the procedure of developing a FE model update process, which employs Artificial Neural Networks (ANNs), which are trained using data generated through the Monte Carlo process and analysed within the FE model of the bridge. The aim of this procedure is to calibrate the FE update sensitivity parameters in such a way as to replicate the dynamic behaviour of the bridge based on real-time measured eigenvalues obtained during monitoring for five different temperature states at -10 oC, -5 oC, 0 oC, 5 oC and 10oC. © 2021 IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs. All rights reserved.","Artificial neural networks (ANNs); Bridges; Finite element model update; Monte Carlo analysis; Reinforced concrete; Structural health monitoring","Dynamic response; Eigenvalues and eigenfunctions; Monte Carlo methods; Neural networks; Reinforced concrete; Structural health monitoring; Artificial neural network; Continuous monitoring; Finite element model update; Finite element modelling (FEM); Ice layers; Modal properties; Model updates; Monte carlo analysis; Sensitivity parameters; Stiffness increase; Finite element method",,,,,"National Institutes of Health, NIH: UL1 TR000445; U.S. Department of Veterans Affairs, VA; National Center for Advancing Translational Sciences, NCATS; Office of Research and Development, ORD; Health Services Research and Development, HSR&D; Biomedical Laboratory Research and Development, VA Office of Research and Development, BLR&D, ORD","M.J.B. is a program manager for the Department of Veterans Affairs biorepositories and biobanks and is a member of the International Society for Biological and Environmental Repositories (ISBER). N.Z. operates a cancer biobank at St. John of God HealthCare within its Pathology Practice in the Bendat Family Comprehensive Cancer Centre. He is a member of ISBER and the Australasian Biospecimen Network Association. W.E.G. operates tumor banks as part of the Breast, Pancreatic, and Cervical Specialized Programs of Research Excellence at the University of Alabama at Birmingham and the Pulmonary Hypertension Breakthrough Initiative and prospective tissue repositories as part of the Cooperative Human Tissue Network and the Comprehensive Cancer Center and is a member of ISBER. He is a member of the ethics committee of the U54 grant, U54 MSM/TU/UAB Comprehensive Cancer Center Partnership. E.W.C. has long been involved in the creation, maintenance, and assessment of BioVu and has been studying ethical issues in genetics/genomics research for many years. She was part of the working group on biobanks convened by Professor Wolf but is not an author of its final document because she did not endorse its analysis and conclusions. A.L.M. and P.P.O. declared no conflict of interest. The authors are funded by their affiliated institutions.",,,,,,,,,,"Farrar, C.R., Worden, K., (2012) Structural Health Monitoring: A Machine Learning Perspective, , John Wiley & Sons; Barthorpe, R.J., (2010) On Model-and Data-based Approaches to Structural Health Monitoring, , PhD thesis, University of Sheffield; Iakovidis, I., (2018) On Nonstationarity from Operational and Environmental Effects in Structural Health Monitoring Bridge Data PhD thesis, , University of Sheffield; Mottershead, J. E., Link, M., Friswell, M. I., The sensitivity method in finite element model updating: A tutorial (2011) Mechanical systems and signal processing, 25 (7), pp. 2275-2296; Marwala, T., Finite element model updating using computational intelligence techniques: applications to structural dynamics, 210. , Springer Science & Business Media; Peeters, B., De Roeck, G., One-year Monitoring of the Z24-Bridge: environmental effects versus damage events (2001) Earthquake Engineering and Structural Dynamics, 30, pp. 149-171; Peeters, B., Maeck, J., De Roeck, G., Vibration-based damage detection in civil engineering: excitation sources and temperature effects (2001) Smart Materials and Structures, 10 (3), pp. 518-527; Masciotta, M.G., Ramos, L.F., Lourenço, P.B., Vasta, M., Damage Detection on the Z24 Bridge by a Spectral-Based Dynamic Identification Technique (2014) Dynamics of Civil Structures: Proceedings of the 32nd IMAC, A Conference and Exposition on Structural Dynamics, 4, pp. 197-206; Haykin, S, (2009) Neural Networks and Learning Machines, , 3rd Edition, Prentice Hall; Marquardt, DW., An algorithm for least squares estimation of non-linear parameters (1963) Journal of Society for Industrial and Applied Mathematics, 11 (2), pp. 431-441; Winkler, E., (1987) Die Lehre von der Elastizitat und Festigkeit, , Dominicus; Kezdi, A., Rethati, L., (1988) Soil Mechanics of Earthworks, Foundations and Highway Engineering, Handbook of Soil Mechanics, , Elsevier; Bowles, J.E., (1988) Foundation Analysis and Design, , New York: McGraw-Hill Book Co; Hassan, M., Burdet, O., Favre, R., Analysis and evaluation of bridge behavior under static load testing leading to better design and judgment criteria (1995) Fourth Bridge Engineering Conference, , Transportation Research Board, San Francisco, USA; Stevens, H.W., Viscoelastic properties of frozen soil under vibratory loads (1973) North Am. Conf. Permafrost, pp. 400-409. , Yakutsk, U.S.S.R.: National Academy of Sciences, Washington; Gonzales, I., Ülker-Kaustell, M., Karoumi, R., Seasonal effects on the stiffness properties of a ballasted railway bridge (2013) Engineering Structures, 57, pp. 63-72; Rubinstein, R.Y., Kroese, D.P., (2016) Simulation and the Monte Carlo method, , John Wiley & Sons","Iakovidis, I.; Ramboll UK Birmingham, Cornerblock, Two Cornwall Street, United Kingdom; email: iiakovidis1a@gmail.com","Snijder H.H.De Pauw B.De Pauw B.van Alphen S.F.C.Mengeot P.","Allplan;et al.;Greisch;Infrabel;Royal HaskoningDHV;TUC RAIL","International Association for Bridge and Structural Engineering (IABSE)","IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs","22 September 2021 through 24 September 2021",,172892,,,,,"English","IABSE Congr., Ghent: Struct. Eng. Future Soc. Needs",Conference Paper,"Final","",Scopus,2-s2.0-85119037847
"Jayawickrema U.M.N., Kumar A.S., Herath H.M.C.M., Hettiarachchi N.K., Sooriyaarachchi H.P., Epaarachchi J.A.","57314685400;57741103000;57203285245;57203838994;57226501657;8616849000;","Surface-mounted distributed fiber optic sensor measurements, and concrete damaged plasticity modeling for damage analysis of reinforced concrete beams",2021,"Proceedings of ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2021",,,"V001T08A002","","",,,"10.1115/SMASIS2021-67524","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118107958&doi=10.1115%2fSMASIS2021-67524&partnerID=40&md5=058af3c0cada76048c1452d2eef8aff1","Centre for Future Materials, School of Mechanical and Electrical Engineering, University of Southern Queensland, Toowoomba, QLD  4350, Australia; Department of Engineering Technology, Faculty of Technological Studies, Uva Wellassa University, Badulla, Sri Lanka; Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University of Ruhuna, Hapugala, Sri Lanka; Department of Civil and Environmental Engineering, Faculty of Engineering, University of Ruhuna, Hapugala, Sri Lanka","Jayawickrema, U.M.N., Centre for Future Materials, School of Mechanical and Electrical Engineering, University of Southern Queensland, Toowoomba, QLD  4350, Australia, Department of Engineering Technology, Faculty of Technological Studies, Uva Wellassa University, Badulla, Sri Lanka; Kumar, A.S., Centre for Future Materials, School of Mechanical and Electrical Engineering, University of Southern Queensland, Toowoomba, QLD  4350, Australia; Herath, H.M.C.M., Centre for Future Materials, School of Mechanical and Electrical Engineering, University of Southern Queensland, Toowoomba, QLD  4350, Australia, Department of Engineering Technology, Faculty of Technological Studies, Uva Wellassa University, Badulla, Sri Lanka; Hettiarachchi, N.K., Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University of Ruhuna, Hapugala, Sri Lanka; Sooriyaarachchi, H.P., Department of Civil and Environmental Engineering, Faculty of Engineering, University of Ruhuna, Hapugala, Sri Lanka; Epaarachchi, J.A., Centre for Future Materials, School of Mechanical and Electrical Engineering, University of Southern Queensland, Toowoomba, QLD  4350, Australia","Structural health monitoring (SHM) has become an integral part of essential and costly to replace infrastructures such as bridges and buildings which degrade during their lifetime. Understanding the structural behaviour of these infrastructures is critical for assuring their structural integrity and safe operating conditions. In this study, the structural performance of a reinforced concrete beam was examined under flexural loading. Distributed optical fibre sensor (DOFS) was attached to the beam's bottom surface, and Optical Backscattered Reflectometry (OBR) technology was used to measure the surface strain. A three-point bending test was performed, and the sensor responses were acquired. Unusual high strain peaks were observed at the bottom surface of the beam due to the formation of hairline cracks. Subsequently, the Concrete Damaged Plasticity (CDP) based Finite Element Analysis (FEA) was performed to simulate the plastic behaviour of concrete beam using ABAQUS 2019 commercial software. The FEA results have strong agreement with the strain pattern observed from the DOFS. Therefore, DOFS and CDP technique based FEA can be successfully used to investigate the plastic damage pattern inside the concrete beam and distributed sensing demonstrates a greater capacity for long-term monitoring of the structural health of concrete structures. © 2021 by ASME.","Concrete damaged plasticity; Damage prediction; Distributed optical fibre sensing; Reinforced concrete structures; Strain measurement; Structural health monitoring","ABAQUS; Composite structures; Concrete beams and girders; Concrete buildings; Concrete construction; Fiber optic sensors; Optical fibers; Reinforced concrete; Strain measurement; Bottom surfaces; Concrete damaged plasticity; Damage prediction; Damaged plasticities; Distributed optical fiber sensing; Distributed optical fibers sensor; Finite element analyse; Reinforced concrete beams; Reinforced concrete structures; Strains measurements; Structural health monitoring",,,,,,,,,,,,,,,,"Majumder, M., Gangopadhyay, T. K., Chakraborty, A. K., Dasgupta, K., Bhattacharya, D. K., Fibre Bragg gratings in structural health monitoring-Present status and applications (2008) Sensors Actuators, A Phys, 147 (1), pp. 150-164; Bin Afzal, M. H., Kabir, S., Sidek, O., Fiber optic sensor-based concrete structural health monitoring (2011) Saudi Int. Electron. Commun. Photonics Conf. 2011, SIECPC 2011; Barrias, A., Casas, J. R., Villalba, S., Fatigue performance of distributed optical fiber sensors in reinforced concrete elements (2019) Constr. Build. Mater, 218, pp. 214-223; Barrias, A., Casas, J. R., Villalba, S., A Review of Distributed Optical Fiber Sensors for Civil Engineering Applications (2016) Sensors (Basel), 16 (5); Glisic, B., Inaudi, D., (2012) Structural Health Monitoring; López-Higuera, J. M., Cobo, L. R., Incera, A. Q., Cobo, A., Fiber optic sensors in structural health monitoring (2011) J. Light. Technol, 29 (4), pp. 587-608; Du, C., Dutta, S., Kurup, P., Yu, T., Wang, X., A review of railway infrastructure monitoring using fiber optic sensors (2020) Sensors Actuators, A Phys, 303, p. 111728; Wang, H., Sun, Q., Li, X., Wo, J., Shum, P. P., Liu, D., Improved location algorithm for multiple intrusions in distributed Sagnac fiber sensing system (2014) Opt. Express, 22 (7), p. 7587; Liang, S., Sheng, X., Lou, S., Wang, P., Zhang, Y., Novel Lissajous figure based location method for fiberoptic distributed disturbance sensor (2015) Optik (Stuttg), 126 (23), pp. 4362-4366; Henault, J., (2010) Truly Distributed Optical Fiber Sensors for Structural Health Monitoring : From the Telecommunication Optical Fiber Drawling Tower to Water Leakage Detection in Dikes and Concrete Structure Strain Monitoring, 2010; Villalba, S., Casas, J. R., Application of optical fiber distributed sensing to health monitoring of concrete structures (2013) Mech. Syst. Signal Process, 39 (1-2), pp. 441-451; Berrocal, C. G., Fernandez, I., Rempling, R., Crack monitoring in reinforced concrete beams by distributed optical fiber sensors (2020) Struct. Infrastruct. Eng, pp. 1-16. , 0 0; Revanna, N., Moy, C. K. S., Krevaikas, T., Verifying a Finite Element Analysis Methodology with Reinforced Concrete Beam Experiments (2020) J. Appl. Math. Phys, (11), pp. 2549-2556. , 08; The fib Model Code for Concrete Structures (2010) fib Journal Structural Concrete, , CEB-FIP; Genikomsou, A. S., Polak, M. A., Finite element analysis of punching shear of concrete slabs using damaged plasticity model in ABAQUS (2015) Eng. Struct, 98, pp. 38-48; Youssf, O., ElGawady, M. A., Mills, J. E., Ma, X., Finite element modelling and dilation of FRP-confined concrete columns (2014) Eng. Struct, 79, pp. 70-85; Raza, A., Khan, Q. U. Z., Ahmad, A., Numerical investigation of load-carrying capacity of GFRPreinforced rectangular concrete members using CDP model in abaqus (2019) Adv. Civ. Eng, p. 2019; Rodríguez, G., Casas, J. R., Villaba, S., Cracking assessment in concrete structures by distributed optical fiber (2015) Smart Mater. Struct, 24, p. 035005",,,,"American Society of Mechanical Engineers (ASME)","ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2021","14 September 2021 through 15 September 2021",,172826,,9780791885499,,,"English","Proc. ASME Conf. Smart Mater., Adapt. Struct. Intel. Syst., SMASIS",Conference Paper,"Final","",Scopus,2-s2.0-85118107958
"Ge C.X.","57303643700;","Investigation of the structural system conversion under transverse wind load based on the long-term monitoring lateral response of Sutong Bridge",2021,"Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations - Proceedings of the 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020",,,,"2517","2524",,,"10.1201/9780429279119-344","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117620853&doi=10.1201%2f9780429279119-344&partnerID=40&md5=ffcf152a2936461f2a9f3ea5a813b4f9","STEC Shanghai Road and Bridge (Group) Co., Ltd., Shanghai, China","Ge, C.X., STEC Shanghai Road and Bridge (Group) Co., Ltd., Shanghai, China","Lateral response under transverse wind loads is one of the significant factors in design of long-span cable-stayed bridges. In view of the great potential of the structure health monitoring system (SHMS) is gradually being explored, the lateral displacement and vibration data of Sutong Bridge have been analyzed detailedly in this paper. Unlike the linear shape of pylon which is mainly influenced by in-site temperature, the principle component analysis and continuous wavelet transform (CWT) of the raw data shows the displacement and vibration trends of main girder have three unique phases as the transverse wind load increases. The monitoring data during Typhoon Haikui are consistent with the fitted results taking the structural conversion into consideration and the FEM analysis results with the aerodynamic coefficient modified, indicating that the actual lateral response of the bridge can be obtained more scientifically and accurately with the three phases identified. © 2021 Taylor & Francis Group, London",,"Aerodynamic loads; Cable stayed bridges; Maintenance; Principal component analysis; Structural health monitoring; Vibration analysis; Wavelet transforms; Health monitoring system; Lateral displacements; Lateral response; Lateral vibrations; Long span; Long term monitoring; Structural systems; Structure health monitoring; Su-Tong Bridge; Wind load; Wind stress",,,,,,,,,,,,,,,,"Ni, Y. Q., Xia, H. W., Wong, K. Y., Ko, J. M., In-Service Condition Assessment of Bridge Deck Using Long-Term Monitoring Data of Strain Response (2012) Journal of Bridge Engineering, 17 (6SI), pp. 876-885; Cross, E. J., Koo, K. Y., Brownjohn, J. M. W., Worden, K., Long-term monitoring and data analysis of the Tamar Bridge (2013) Mechanical Systems and Signal Processing, 35 (1-2), pp. 16-34; Wang, H., Wu, T., Tao, T., Li, A., Kareem, A., Measurements and analysis of non-stationary wind characteristics at Sutong Bridge in Typhoon Damrey (2016) Journal of Wind Engineering & Industrial Aerodynamics, 151, pp. 100-106; Wang, H., Li, A., Niu, J., Zong, Z., Li, J., Long-term monitoring of wind characteristics at Sutong Bridge site (2013) Journal of Wind Engineering and Industrial Aerodynamics, 115, pp. 39-47; Mao, J., Wang, H., Feng, D., Tao, T., Zheng, W., Investigation of dynamic properties of long-span cable-stayed bridges based on one-year monitoring data under normal operating condition (2018) Structural Control & Health Monitoring, 25, p. e21465","Ge, C.X.; STEC Shanghai Road and Bridge (Group) Co., China","Yokota H.Frangopol D.M.",,"CRC Press/Balkema","10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020","11 April 2021 through 15 April 2021",,172353,,9780429279119; 9780367232788,,,"English","Bridge Maint., Saf., Manag., Life-Cycle Sustain. Innov. - Proc. Int. Conf. Bridge Maint., Saf. Manag., IABMAS",Conference Paper,"Final","",Scopus,2-s2.0-85117620853
"Amiri E., Setunge S., Mahmoodian M., Tran H.D.","57304088600;57517380600;57195293461;57208870988;","An integrated data-driven approach for deterioration modelling of flexural cracking in concrete bridges",2021,"Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations - Proceedings of the 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020",,,,"2205","2212",,,"10.1201/9780429279119-299","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117588791&doi=10.1201%2f9780429279119-299&partnerID=40&md5=12149427a82b1e707a8454fafbf3780f","RMIT University, Melbourne, Australia","Amiri, E., RMIT University, Melbourne, Australia; Setunge, S., RMIT University, Melbourne, Australia; Mahmoodian, M., RMIT University, Melbourne, Australia; Tran, H.D., RMIT University, Melbourne, Australia","The network level visual inspection of bridges conducted by road agencies in Australia and internationally, generally consists only of discrete condition data. The quantitative links between condition state ratings and various deterioration mechanisms are missing. In this paper, an integrated model is developed in order to link a condition based data-driven model using processed level 2 inspection data, with flexural cracking behavior. The probabilistic Markov Chain process is used to model the discrete data, and is applied on reinforced concrete u-slab bridges with crack defects. A Finite Element Model (FEM) based on a case study is developed and validated with results from Structural Health Monitoring (SHM) of the bridge, with a superload passing through. This FEM is then used to model cracking behavior by increasing levels of superload, through push-down static testing. The cracking model and crack patterns are used to then extract values in order to calculate crack widths according to condition states. These are subsequently integrated with the condition based data-driven model, in order to determine cracking of u-slab bridges over time, thus creating a durability model. The degrees of cracking predicted in this case have more relevance to durability and serviceability rather than load rating. The outcomes of this research can be used for optimizing maintenance and repair activities performed by road authorities, particularly crack repair planning and budgeting, given a bridges age and environmental exposure. © 2021 Taylor & Francis Group, London",,"Deterioration; Durability; Highway bridges; Life cycle; Markov processes; Reinforced concrete; Repair; Roads and streets; Safety engineering; Structural health monitoring; Condition; Condition state; Cracking behavior; Data-driven approach; Data-driven model; Finite element modelling (FEM); Flexural cracking; Integrated data; Slab bridges; Superloads; Budget control",,,,,,,,,,,,,,,,"Adams, V., Askenazi, A., (1998) Building Better Products with Finite Element Analysis, , OnWord Press; Agrawal, A.K., Kawaguchi, A., (2009) Bridge element deterio-ration rates: final report, project C-01-51, , New York: New York State Department of Transportation; Birtel, P., Mark, P., Parameterised Finite Element Modelling of RC Beam Shear Failure (2006) ABAQUS User's Conference; Caprani, C., (2018) Case Study - Health Monitoring Of 6 Bridges, Structural Health Monitoring Workshop Program - Vicroads, , Monash University; (2004) Eurocode 2: Design of concrete structures - Part 1-1: General rules and rules for buildings, , EN 1992-1-1 (English): Eurocode; Estes, A.C., Frangopol, D.M., Repair optimization of highway bridges using system reliability approach (1999) Journal of Structural Engineering, 125 (7), pp. 766-775; Fraden, J., (2010) Handbook of Modern Sensors: Physics, De-signs, and Applications, , New York: Springer; Hafezolghorani, M., Hejazi, F., Vaghei, R., Bin Jaafar, M.S., Karimzade, K., Simplified Damage Plasticity Model for Concrete (2017) Structural Engineering International, 27 (1), pp. 68-78; Lu, C., Liu, R., A model for predicting time to cor-rosion-induced cover cracking in reinforced concrete structures (2010) J. of Fracture Mechanics of Concrete, 53 (4), pp. 967-976; Mahmoodian, M., Alani, A., A gamma distributed degradation rate model for time dependent structural reliability analysis of concrete pipes subject to sulphide corrosion (2014) International Journal of Reliability and Safety, 8 (1), pp. 19-32; Moomen, M., (2016) Deterioration Modeling Of Highway Bridge Components Using Deterministic And Stochastic Methods, , Master Thesis, Purdue University; Nassif, H., Gindy, M., Davis, J., Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration (2005) NDT & E International, 38, pp. 213-218; (2014) Road structures inspection manual, , VicRoads Melbourne: VicRoads; (2017) VicRoads Standard Specification Section 610 - Structural Concrete, , VicRoads Melbourne: VicRoads; Wahalathantri, B.L., Thambiratnam, D.P., Chan, T.H.T., Fawzia, S., (2011) A Material Model for Flexural Crack Simulation in Reinforced Concrete Elements Using ABAQUS, , Brisbane: Queensland University of Technology; Wight, J. K., Macgregor, J. G., (2012) Reinforced Concrete Mechanics & Design, , Pearson Education, Inc",,"Yokota H.Frangopol D.M.",,"CRC Press/Balkema","10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020","11 April 2021 through 15 April 2021",,172353,,9780429279119; 9780367232788,,,"English","Bridge Maint., Saf., Manag., Life-Cycle Sustain. Innov. - Proc. Int. Conf. Bridge Maint., Saf. Manag., IABMAS",Conference Paper,"Final","",Scopus,2-s2.0-85117588791
"Zhu Y., Hou K., Fu C.C., Li N.","57215300957;57215295584;7402803243;57223107918;","Dynamic performance assessment on a MDTA overpass steel bridge with newly constructed link slabs",2021,"Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations - Proceedings of the 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020",,,,"2734","2740",,,"10.1201/9780429279119-373","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117560952&doi=10.1201%2f9780429279119-373&partnerID=40&md5=828a56a91f624b2ca160ca7ae5fb23e0","The Bridge Engineering Software and Technology (BEST) Center, Department of Civil and Environmental Engineering, University of Maryland, College Park, United States","Zhu, Y., The Bridge Engineering Software and Technology (BEST) Center, Department of Civil and Environmental Engineering, University of Maryland, College Park, United States; Hou, K., The Bridge Engineering Software and Technology (BEST) Center, Department of Civil and Environmental Engineering, University of Maryland, College Park, United States; Fu, C.C., The Bridge Engineering Software and Technology (BEST) Center, Department of Civil and Environmental Engineering, University of Maryland, College Park, United States; Li, N., The Bridge Engineering Software and Technology (BEST) Center, Department of Civil and Environmental Engineering, University of Maryland, College Park, United States","Maryland Transportation Authority (MDTA) plans to upgrade their overpass bridges with link slab connections since joint failures are common problems of bridge deterioration in the United States. In the link slab connection pilot study, bearings under the steel girders are modified to allow both rotational and longitudinal movements, thus minimizing the negative moments transferred between spans and the forces imparted to the connection. This paper proposed and performed a retrofitted bridge condition assessment method using wireless sensors. In order to evaluate the performance and detect any occurring cracks of ductile link slab material (UHPC and ECC), wireless accelerometers, strain gauges, and displacement sensors are installed on site during the field test and structural health monitoring. In this study, strain gauges are improved by attaching them on thin aluminum plates and then installed before concrete pouring. By calibrating with several types of modified strain gauges which directly attached to the concrete surface in the lab tests, strains measured by the proposed embedded strain gauges attached to aluminum plates could closely represent the true strains. In addition, with these tests and experiments, finite element analyses of the pilot bridge have also been conducted. These results are critical for calibrating measured data from the field test and monitoring. Finally, the real-time/dynamic performance of the link slab system and the entire bridge would be evaluated, and recommendations would be provided. © 2021 Taylor & Francis Group, London",,"Aluminum; Concretes; Deterioration; Electric measuring bridges; Life cycle; Maintenance; Plates (structural components); Steel bridges; Strain; Strain gages; Structural health monitoring; Aluminium plates; Bridge deterioration; Dynamic performance assessment; Field test; Joint failure; Link slabs; Maryland; Pilot studies; Slab connections; Strain-gages; Aluminum plating",,,,,,"The research for this paper was conducted as part of a BEST Center project for the Maryland Transportation Authority (MDTA), the experiment was conducted in the lab of National Ready-Mix Concrete Association (NRMCA), and sensors adjustment was technically supported by Rensys. We thank all those who provided assistance in obtaining design and field data for this study.",,,,,,,,,,"(2017) AASHTO LRFD Bridge Design Specifications, , American Association of State Highway and Transportation Officials. eighth edition, Customary U.S. Units. Washington, DC: American Association of State Highway and Transportation Officials; (2017) The Manual for Bridge Evaluation AASHTO, , American Association of State Highway and Transportation Officials Washington D.C; (2006) Standard test method for flexural performance of fiber reinforced concrete (using beam with third-point loading), , ASTM C 1609/C 1690M-18, American Society of Testing and Materials; Caner, A., Zia, P., Behavior and Design of Link Slabs for Jointless Bridge Decks (1998) PCI Journal, 43 (3), pp. 68-80; (2017) CSiBridge®, Computer and Structures, , Inc. Berkeley; FRANGOPOL, DAN M., Primer on Bridge Load Testing (2019) Transportation Research Circular, , November; Graybeal, Ben, (2014) Design and Construction of Field-Cast UHPC Connections, , FHWA Publicatio FHWA-HRT-14-084, Oct; Au, A., Lam, C., Au, J., Tharmabala, B., Eliminating Deck Joints Using Debonded Link Slabs: Research and Field Tests in Ontario (2013) Journal of Bridge Engineering, 18 (8), pp. 768-778; Li, V. C., Lepech, M. D., Li, M., (2005) Field Demonstration of Durable Link Slabs for Jointless Bridge Decks Based on Strain-Hardening Cementitious Composites, , University of Michigan, Ann Arbor MI; Okeil, A. M., El-Safty, A., Partial continuity in Bridge Girders with Jointless Decks (2005) Practice Periodical on Structurla Design and Construction, 10 (4), pp. 229-238; Wang, N., (2009) Condition Assessment of Existing Bridge Structures, , GDOT Project No. RP05 01; Zia, P, Caner, A, El-Safte, AK, (1995) Jointless bridge decks, pp. 1-117. , Research project 23241-94-4. Center for Transportation Engineering Studies, North Carolina State","Fu, C.C.; The Bridge Engineering Software and Technology (BEST) Center, United States","Yokota H.Frangopol D.M.",,"CRC Press/Balkema","10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020","11 April 2021 through 15 April 2021",,172353,,9780429279119; 9780367232788,,,"English","Bridge Maint., Saf., Manag., Life-Cycle Sustain. Innov. - Proc. Int. Conf. Bridge Maint., Saf. Manag., IABMAS",Conference Paper,"Final","",Scopus,2-s2.0-85117560952
"Iannacone L., Gardoni P.","57208145743;12644936300;","Time-varying fragility functions for bridges subject to main shock-aftershock sequences including damage accumulation during the events and calibration based on available data",2021,"Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations - Proceedings of the 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020",,,,"2787","2794",,,"10.1201/9780429279119-380","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117560824&doi=10.1201%2f9780429279119-380&partnerID=40&md5=a9a6b2a84fd71a1dc6c524c679c86e8d","Department of Civil and Environmental Engineering, MAE Center, University of Illinois at Urbana-Champaign (UIUC), Urbana, IL, United States","Iannacone, L., Department of Civil and Environmental Engineering, MAE Center, University of Illinois at Urbana-Champaign (UIUC), Urbana, IL, United States; Gardoni, P., Department of Civil and Environmental Engineering, MAE Center, University of Illinois at Urbana-Champaign (UIUC), Urbana, IL, United States","Most structures can experience several earthquakes throughout their service life. The occurrence of multiple seismic events can lead to incremental structural damage that can increase the structural vulnerability of the built environment. Therefore, aftershocks could result in significant damage for structures that have been affected by previous seismic excitations. The current literature addresses this problem by modeling the consequences of earthquake occurrences as shock deterioration processes affecting structures over time and ultimately impairing their ability to sustain given demands. Due to the relatively short time over which earthquakes act on structures, the real-time damage accumulation within the event is usually disregarded in favor of simplified procedures that model the total effect of each earthquake as a whole. However, this could lead to overly simplified models that use aggregated measures of intensity of the earthquakes, as opposed to their specific ground motions, and underestimate the actual probabilities of failure. Recently, formulations that use Stochastic Differential Equations (SDEs) to model the effect of deterioration processes have been proposed. Being continuous in nature, these formulations can be used to provide a more accurate analysis of the effect of an earthquake within the seismic event itself. This work uses the aforementioned formulations of deterioration based on SDEs to accurately analyze the real-time damage accumulation within the occurrence of sequences of earthquakes (i.e., a main shock and the following aftershocks.) Models are calibrated based on results from Structural Health Monitoring and Finite Element Analyses. The performance over time of an example structure (expressed in terms of fragility functions) is then estimated using predictions for the main shock-aftershock sequences for a site of interest. © 2021 Taylor & Francis Group, London",,"Deterioration; Earthquakes; Life cycle; Maintenance; Stochastic models; Stochastic systems; Structural health monitoring; Aftershock sequence; Damages accumulation; Deterioration process; Fragility function; Main shock; Real- time; Seismic event; Stochastic differential equations; Structural damages; Time varying; Differential equations",,,,,"National Institute of Standards and Technology, NIST; Center for Risk-Based Community Resilience Planning: 70NANB15H044","This work was supported by the National Institute of Standards and Technology (NIST) through the Center for Risk-Based Community Resilience Planning under Award No 70NANB15H044. Opinions and findings presented are those of the writers and do not necessarily reflect the views of the sponsor.",,,,,,,,,,"Archuleta, R. J., Steidl, J., Squibb, M., The COSMOS Virtual Data Center: A web portal for strong motion data dissemination (2006) Seismological Research Letters, 77 (6), pp. 651-658; Bocchini, P., Frangopol, D. M., Ummenhofer, T., Zinke, T., Resilience and sustainability of civil infrastructure: Toward a unified approach (2013) Journal of Infrastructure Systems, 20 (2), p. 04014004; Ditlevsen, O., Madsen, H.O., (1996) Structural reliability methods, 178. , New York: Wiley; Euler, L., (1792) Institutiones calculi integralis, 1. , Academia Imperialis Scientiarum; Gardoni, P., Der Kiureghian, A., Mosalam, K. M., Probabilistic capacity models and fragility estimates for reinforced concrete columns based on experimental observations (2002) Journal of Engineering Mechanics, 128 (10), pp. 1024-1038; Gardoni, P., (2017) Risk and Reliability Analysis: Theory and Applications: in Honor of Prof. Armen Der Kiureghian, , Springer; Ghosh, J., Padgett, J. E., Sánchez-Silva, M., Seismic damage accumulation in highway bridges in earthquake-prone regions (2015) Earthquake Spectra, 31 (1), pp. 115-135; Grigoriu, M., (2013) Stochastic calculus: applications in science and engineering, , Springer Science & Business Media; Hu, S., Gardoni, P., Xu, L., Stochastic procedure for the simulation of synthetic main shock-aftershock ground motion sequences (2018) Earthquake Engineering & Structural Dynamics, 47 (11), pp. 2275-2296; Iannacone, L., Gardoni, P., Stochastic Differential Equations for Modeling Deterioration of Engineering Systems and Calibration based on Structural Health Monitoring Data (2018) Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, , Ghent, Belgium; Iannacone, L., Gardoni, P., Stochastic Differential Equations for the Deterioration Processes of Engineering Systems (2019) Proceedings of the 13th International Conference on Applications of Statistics and Probability in Civil Engineering, , Seoul, South Korea; Itô, K., Stochastic integration (1973) Vector and Operator Valued Measures and Applications, pp. 141-148; Jeon, J. S., DesRoches, R., Lowes, L. N., Brilakis, I., Framework of aftershock fragility assessment-case studies: older California reinforced concrete building frames (2015) Earthquake Engineering & Structural Dynamics, 44 (15), pp. 2617-2636; Jia, G., Tabandeh, A., Gardoni, P., Life-cycle analysis of engineering systems: Modeling deterioration, instantaneous reliability, and resilience (2017) Risk and reliability analysis: Theory and applications, , Springer, Cham; Jia, G., Gardoni, P., Simulation-based approach for estimation of stochastic performances of deteriorating engineering systems (2018) Probabilistic Engineering Mechanics, 52, pp. 28-39; Kessler, M., Lindner, A., Sorensen, M., (2012) Statistical methods for stochastic differential equations, , CRC Press; Kumar, R., Gardoni, P., Modeling structural degradation of RC bridge columns subjected to earthquakes and their fragility estimates (2011) Journal of Structural Engineering, 138 (1), pp. 42-51; Kumar, R., Gardoni, P., Effect of seismic degradation on the fragility of reinforced concrete bridges (2014) Engineering Structures, 79, pp. 267-275; Kumar, R., Gardoni, P., Renewal theory-based life-cycle analysis of deteriorating engineering systems (2014) Structural Safety, 50, pp. 94-102; Li, Q., Ellingwood, B. R., Performance evaluation and damage assessment of steel frame buildings under main shock-aftershock earthquake sequences (2007) Earthquake engineering & structural dynamics, 36 (3), pp. 405-427; McKenna, F., Fenves, G. L., Scott, M. H., (2006) OpenSees: Open system for earthquake engineering simulation, , http://opensees.berkeley.edu, Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA; Ozaki, T., Statistical identification of storage models with application to stochastic hydrology (1985) JAWRA Journal of the American Water Resources Association, 21 (4), pp. 663-675; Raghunandan, M., Liel, A. B., Luco, N., Aftershock collapse vulnerability assessment of reinforced concrete frame structures (2015) Earthquake Engineering & Structural Dynamics, 44 (3), pp. 419-439; Rezaeian, S., Der Kiureghian, A., A stochastic ground motion model with separable temporal and spectral nonstationarities (2008) Earthquake Engineering & Structural Dynamics, 37 (13), pp. 1565-1584; Shoji, I., Ozaki, T., A statistical method of estimation and simulation for systems of stochastic differential equations (1998) Biometrika, 85 (1), pp. 240-243; Tibshirani, R., Regression shrinkage and selection via the lasso (1996) Journal of the Royal Statistical Society: Series B (Methodological), 58 (1), pp. 267-288",,"Yokota H.Frangopol D.M.",,"CRC Press/Balkema","10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020","11 April 2021 through 15 April 2021",,172353,,9780429279119; 9780367232788,,,"English","Bridge Maint., Saf., Manag., Life-Cycle Sustain. Innov. - Proc. Int. Conf. Bridge Maint., Saf. Manag., IABMAS",Conference Paper,"Final","",Scopus,2-s2.0-85117560824
"Li X., Ding P., Chen X., Xia L., Tan S.","55839936300;57208024907;57208184202;57285311900;57211264448;","Static mechanical properties evolution analysis of a long-span track cable-stayed bridge",2021,"International Journal of Robotics and Automation","36","10",,"","",,,"10.2316/J.2021.206-0534","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116418167&doi=10.2316%2fJ.2021.206-0534&partnerID=40&md5=d8257a2f840037c1ebeb3991e3cee123","T. Y. Lin International Engineering Consulting (China) Co., Ltd, Chongqing, 401121, China; Traffic Construction Engineering Management Center of Luzhou City, Luzhou, 646000, China","Li, X., T. Y. Lin International Engineering Consulting (China) Co., Ltd, Chongqing, 401121, China; Ding, P., T. Y. Lin International Engineering Consulting (China) Co., Ltd, Chongqing, 401121, China; Chen, X., T. Y. Lin International Engineering Consulting (China) Co., Ltd, Chongqing, 401121, China; Xia, L., Traffic Construction Engineering Management Center of Luzhou City, Luzhou, 646000, China; Tan, S., T. Y. Lin International Engineering Consulting (China) Co., Ltd, Chongqing, 401121, China","To analyse the static mechanical properties evolution law of longspan track cable-stayed bridges, an evaluation mode based on the parameter time-dependent effect change rate was proposed to evaluate a twin-tower and double-cable plane concrete cable-stayed bridge with a main span of 250 m. Finite element method (FEM) analysis was performed, considering the time-dependent change effect (including concrete shrinkage and creep, material strength, and cable elastic modulus) after 2 years of service since bridge completion. The results of the analysis were compared to the measured data detected by the structural health monitoring (SHM) system. The results show that the mechanical parameters of the long-span track cable-stayed bridge, including cable force, internal force of girder, deformation of girder, and displacement of main tower, constantly change with time and are stable with increasing service time. The FEM model can be modified using the measured parameters obtained from the SHM system, so the static mechanical evolution can be effectively predicted. The measured data can objectively describe the static mechanical properties evolution law based on the change rate index of the parameter time-dependent effect. © 2021 Acta Press. All rights reserved.","Cable-stayed bridge; Evolution; Finite element; Static mechanical properties; Structural health monitoring","Cable stayed bridges; Cables; Concretes; Shrinkage; Structural health monitoring; Evaluation modes; Evolution; Evolution analysis; Evolution law; Long span; Mode-based; Parameter-time; Static mechanical properties; Structural health monitoring systems; Time-dependent effects; Finite element method",,,,,"cstc2017rgzn-zdyfX0029; cstc2018jscx-mszdX0084","This work was supported by Major Topic Special Key Research and Development Projects of the Artificial Intelligence Technology Innovation in Chongqing (cstc2017rgzn-zdyfX0029), the Technology Innovation and Application Demonstration Project of Chongqing (cstc2018jscx-mszdX0084).",,,,,,,,,,"Editorial Department of China Journal of Highway and Transport, Review on China's bridge engineering research: 2014 (2014) China Journal of Highway and Transport, 27 (5), pp. 1-96; Zhai, W.M., Zhao, C.F., Frontiers and challenges of sciences and technologies in modern railway engineering (2016) Journal of Southwest Jiaotong University, 51 (2), pp. 209-226; Qin, Q.H., Influence of linear change of urban rail transit bridge on train running behaviour (2018) Railway Engineering, 58 (7), pp. 142-146; Liu, T., Xue, W.C., Wang, W., Calculation on long-term deflections of fully prestressed concrete beams (2016) Engineering Mechanics, 33 (9), pp. 116-122; Chen, L., Shao, C.Y., Influential laws of concrete shrinkage and creep of composite girder cable-stayed bridge (2015) Bridge Construction, 45 (1), pp. 74-78; Xin, J.Z., Zhou, J.T., Zhou, Y.X., Experimental study on bearing capacity evolution of reinforced concrete compressionbending members considering material deterioration (2019) Material Reports, 33 (14), pp. 2362-2369; Wang, Y.B., Liao, P., Jia, Y., Effects of cyclic temperature on time-dependent deformation behaviour of long-span concrete arch bridge (2019) Bridge Construction, 49 (3), pp. 57-62; Liu, M.Y., Li, Q., Huang, Y.B., Ultra-long-time performance of steel-concrete composite continuous beam in Hong Kong-Zhuhai-Macao Bridge with creep and shrinkage of concrete slabs (2016) China Journal of Highway and Transport, 29 (12), pp. 60-69; Zhou, J.T., Tan, S.L., Tan, H., Research on structural improvement test in negative moment section with the change from simple supporting to continuous of the prestressed concrete T-beam (2018) Journal of China & Foreign Highway, 38 (4), pp. 89-95; Li, J.Z., Yu, Z.W., Song, L., Study on fatigue deflection and crack propagation laws of heavy-haul railway bridges (2013) China Civil Engineering Journal, 46 (9), pp. 72-82; Chen, Z.S., Zhang, C., Zhou, J.T., Study of cable force of construction control and alignment control of main girders for long-span railway cable-stayed bridges (2013) Mathematical Models and Methods in Applied Sciences, 7 (9), p. 47; Chen, Z.S., Zhou, X., Wang, X., Deployment of a smart structural health monitoring system for long-span arch bridges: A review and a case study (2017) Sensors, 17 (9), p. 2151; Zhou, J.T., Chen, Y., Li, X.G., A new safety evaluation method for long-span bridges with tele-monitoring systems (2010) Intelligent Automation & Soft Computing, 16 (5), pp. 635-644; Yang, O., Li, H., Ou, J.P., Life-cycle evolution of the ultimate load carrying capacity of RC cable-stayed bridges (2012) China Civil Engineering Journal, 45 (3), pp. 116-126; Chen, S.J., Tang, S.H., Zhang, G.G., Experiment on long-term performance of concrete cable-stayed bridge (2011) China Journal of Highway and Transport, 24 (4), pp. 39-49; Fan, J.S., Nie, J.G., Wang, H., Long-term behavior of composite beams with shrinkage, creep and cracking (I): Experiment and calculation (2009) China Civil Engineering Journal, 42 (3), pp. 8-15; Yu, J.F., Wu, Y.K., Su, X., Study on predication of line optimization of large-span concrete cable-stayed bridges (2018) Journal of Railway Science and Engineering, 15 (1), pp. 133-140; Yang, W.J., Wang, Y., Diachronic change model of compressive strength and elastic modulus of concrete at early age (2007) Journal of China & Foreign Highway, 27 (6), pp. 149-152; Ding, Y.L., Bian, Y., Zhao, H.W., Long-term monitoring and analysis of vertical deflections of a highway-railway cablestayed bridge under operation conditions (2017) Journal of Railway Science and Engineering, 14 (2), pp. 271-277; Wu, H.J., Wei, Y., Huang, Y.B., Null live load point identification method of continuous rigid bridges based on monitoring data (2013) Journal of Chongqing Jiaotong University (Natural Science), 32 (S1), pp. 884-887; Li, X.G., Hui, D., Zhou, J.T., Large span cable-stayed bridge health monitoring and evaluation system building and application (2016) Journal of China & Foreign Highway, 36 (2), pp. 92-97; Zhou, J.T., Li, X.G., Xia, R.C., Health monitoring and evaluation of long-span bridges based on sensing and data analysis: A survey (2017) Sensors, 17 (3), p. 603","Ding, P.; T. Y. Lin International Engineering Consulting (China) Co., China; email: dingpeng@tylin.com.cn",,,"Acta Press",,,,,08268185,,IJAUE,,"English","Int J Rob Autom",Article,"Final","",Scopus,2-s2.0-85116418167
"Losanno D., Caterino N., Chioccarelli E., Rainieri C., Aiello C.","56007578700;24471202600;36170180900;16647433700;57203415359;","Structural Monitoring of a Railway Bridge in Southern Italy for Automatic Warning Strategy",2021,"Lecture Notes in Civil Engineering","156",,,"585","601",,,"10.1007/978-3-030-74258-4_38","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115082916&doi=10.1007%2f978-3-030-74258-4_38&partnerID=40&md5=bda9b9ee7ce5836bbc748b45761fa213","University of Naples Federico II, Napoli, 80125, Italy; Construction Technologies Institute, Secondary Branch of Naples, National Research Council of Italy (CNR), Naples, 80146, Italy; University of Naples Parthenope, Napoli, 80143, Italy; Università Degli Studi Mediterranea Di Reggio Calabria, Reggio Calabria, 89124, Italy","Losanno, D., University of Naples Federico II, Napoli, 80125, Italy, Construction Technologies Institute, Secondary Branch of Naples, National Research Council of Italy (CNR), Naples, 80146, Italy; Caterino, N., Construction Technologies Institute, Secondary Branch of Naples, National Research Council of Italy (CNR), Naples, 80146, Italy, University of Naples Parthenope, Napoli, 80143, Italy; Chioccarelli, E., Università Degli Studi Mediterranea Di Reggio Calabria, Reggio Calabria, 89124, Italy; Rainieri, C., Construction Technologies Institute, Secondary Branch of Naples, National Research Council of Italy (CNR), Naples, 80146, Italy; Aiello, C., Construction Technologies Institute, Secondary Branch of Naples, National Research Council of Italy (CNR), Naples, 80146, Italy","In the last few years, a growing interest towards structural safety of existing infrastructures has been paid. Due to very large number of structures, the scientific community is asked to provide innovative solutions that are both sustainable and reliable from the economical as well as technical point of view. This paper presents the case of Quarto bridge, selected as case study in an Italian research project for Risks and Safety Management of Infrastructures at Regional Scale (GRISIS). The structure is a railway viaduct on an urban train line, located in the Northern metropolitan area of Naples. It consists of 45 simply-supported prestressed girders sustained by reinforced concrete piers, for a total length of approximately one kilometer. According to the project, the viaduct was equipped with an on-site monitoring system for near-real-time mitigation of seismic risk. The system involves some innovative, low-cost, sensors developed and installed to be tested on the field. This paper describes the monitoring system and the implemented strategies for risk mitigation referring to a single beam-column system but its application can be potentially replicated and implemented for large scale mitigation strategies. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Early warning; FEM model; GRISIS project; Innovative sensors; Monitoring system; Seismic alarm","Bridges; Concrete beams and girders; Monitoring; Railroads; Reinforced concrete; Safety engineering; Implemented strategy; Innovative solutions; Mitigation strategy; On-site monitoring system; Prestressed girder; Reinforced concrete pier; Scientific community; Structural monitoring; Structural health monitoring",,,,,,"This work is supported by the GRISIS project (Cup: B63D18000280007, Surf:18033BP000000001, DD prot. 368 24/10/2018), implemented by STRESS scarl in the framework of FESR Campania 2014-2020. Additional acknowledgment is given to Ente Autonomo Volturno (EAV) Srl for the collaboration offered to the realization of the demonstrator site. The authors are grateful to Tecno In Spa and TME of T.R.E. consortium for providing relevant information about the monitoring system.",,,,,,,,,,"Iervolino, I., Performance-based earthquake early warning (2011) Soil Dyn Earthq Eng, 31 (2), pp. 209-222; Ventura, C.E., Kaya, Y., Taale, A., (2019) Seismic Isolation, Structural Health Monitoring, and Performance Based Seismic Design in Earthquake Engineering, , BC earthquake early warning system, a program for seismic structural health monitoring of infrastructure. In Kasimzade A, Şafak E, Ventura C, Naeim F, Mukai Y, Springer, Cham; Rainieri, C., Fabbrocino, G., Cosenza, E., Integrated seismic early warning and structural health monitoring of critical civil infrastructures in seismically prone areas (2011) Struct Health Monitoring—An Int J, 10 (3), pp. 291-308; Wu, S., Beck, J.L., Synergistic combination of systems for structural health monitoring and earthquake early warning for structural health prognosis and diagnosis. In: Proceedings of SPIE 8348 (2012) Health Monitoring of Structural and Biological Systems; Andò, B., Baglio, S., Pistorio, A., A low cost multi-sensor strategy for early warning in structural monitoring exploiting a wavelet multiresolution paradigm (2014) Procedia Eng, 87, pp. 1282-1285; Wang, J., Fu, Y., Yang, X., An integrated system for building structural health monitoring and early warning based on an internet of things approach (2017) Int J Distrib Sens Netw, 13 (1), pp. 1-14; CSI) (2020) SAP2000-Integrated software for structural analysis and design Computers & Structures, Inc, , Computers and Structures; Rainieri, C., Notarangelo, M.A., Fabbrocino, G., Experiences of dynamic identification and monitoring of bridges in serviceability conditions and after hazardous events (2020) Infrastruct, 5 (10). , https://doi.org/10.3390/infrastructures5100086; Porter, K., Mitrani-Reiser, J., Beck, J.L., Near-real-time loss estimation for instrumented buildings (2006) Struct Des Tall Spec Build, 15 (1), pp. 3-20; Magalhães, F., Cunha, A., Caetano, E., Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection (2012) Mech Syst Signal Process, 28, pp. 212-228; Rainieri, C., Magalhaes, F., Gargaro, D., Fabbrocino, G., Cunha, A., Predicting the variability of natural frequencies and its causes by second-order blind Identification (2019) Struct Health Monit, 18 (2), pp. 486-507; Yeo, G.L., Cornell, C.A., A probabilistic framework for quantification of aftershock ground-motion hazard in California: Methodology and parametric study (2009) Earthq Eng Struct Dyn, 38 (1), pp. 45-60; Utsu T (1970) Aftershocks and earthquake statistics (1): Some parameters which characterize an aftershock sequence and their interrelations J Fac Sci Hokkaido Univ, 3 (3), pp. 129-195","Losanno, D.; University of Naples Federico IIItaly; email: daniele.losanno@unina.it","Rainieri C.Fabbrocino G.Caterino N.Ceroni F.Notarangelo M.A.",,"Springer Science and Business Media Deutschland GmbH","8th Civil Structural Health Monitoring Workshop, CSHM-8 2021","31 March 2021 through 2 April 2021",,264479,23662557,9783030742577,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85115082916
"Marcheggiani L., Clementi F., Formisano A.","35148178300;35837136800;54421738200;","Dynamic Identification and Monitoring of a New Highway Bridge",2021,"Lecture Notes in Civil Engineering","156",,,"603","617",,,"10.1007/978-3-030-74258-4_39","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115048519&doi=10.1007%2f978-3-030-74258-4_39&partnerID=40&md5=b13037d307ea63d8f3696f1939c7c3b6","ENEA, Research Centre of Bologna and Research Laboratories of Faenza, Bologna, Italy; Department of Civil and Building Engineering, and Architecture, Polytechnic University of Marche, Ancona, Italy; Department of Structures for Engineering and Architecture, University of Naples Federico II, Naples, Italy","Marcheggiani, L., ENEA, Research Centre of Bologna and Research Laboratories of Faenza, Bologna, Italy; Clementi, F., Department of Civil and Building Engineering, and Architecture, Polytechnic University of Marche, Ancona, Italy; Formisano, A., Department of Structures for Engineering and Architecture, University of Naples Federico II, Naples, Italy","The structural behaviour of viaducts under traffic or seismic excitations can be evaluated using Structural Health Monitoring (SHM) methods, which can also be usefully employed to evaluate their health state under service conditions. These methods allow the calibration of suitable FEM models, based on accurate information on both material properties and structural elements, which are used to both design and evaluate the effectiveness of consolidation interventions, if needed. In the paper, static and dynamic testing procedures applied to a multi-span bridge, called Adda viaduct, along a new highway link inaugurated in 2014 in Northern Italy, are inspected. The structural performances of the investigated viaduct are evaluated based on both experimental static and dynamic loading test results. In particular, Operational and Experimental Modal Analyses are used, and their results are compared to each other. From the comparison it is shown that the dynamic load test can complement the static load one for the structural evaluation of new viaducts and can also be taken as an alternative for the monitoring of operational viaducts. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Bridge; dynamic testing; Experimental modal analysis; Load testing; operational modal analysis; Structural health monitoring","Bridges; Dynamic analysis; Dynamic loads; Highway bridges; Load testing; Modal analysis; Dynamic identification; Experimental modal analysis; Static and dynamic loading; Structural behaviour; Structural elements; Structural evaluation; Structural health monitoring (SHM); Structural performance; Structural health monitoring",,,,,,"The Authors wish to gratefully acknowledge the “Società di Progetto Brebemi S.p.A.” for the permission to use the static and dynamic testing data of the Brebemi Viaducts.",,,,,,,,,,"Gatti, M., Structural health monitoring of an operational bridge: A case study (2019) Eng Struct, 195, pp. 200-209. , https://doi.org/10.1016/j.engstruct.2019.05.102; (2018) DM 17/01/2018, , Upgrading of Technical Codes for Constructions” (in Italian). Rome, Italy; UNI 10985 (2002) Vibrations on Bridges and viaducts—guidelines for the Execution of Dynamic Tests and Surveys, , Italian National Body of Unification (UNI), Rome, Italy; Clementi, F., Pierdicca, A., Formisano, A., Catinari, F., Lenci, S., Numerical model upgrading of a historical masonry building damaged during the 2016 Italian earthquakes: The case study of the Podestà palace in Montelupone (Italy) (2017) J Civ Struct Heal Monit, 7 (5), pp. 703-717. , https://doi.org/10.1007/s13349-017-0253-4; Formisano A, Krstevska L, Di Lorenzo G, Landolfo R, Tashkov L (2018) Experimental ambient vibration tests and numerical investigation on the Sidoni Palace in Castelnuovo of San Pio (L’Aquila, Italy). Int J Masonry Res Innov 3(3):269–294. https://doi.org/10.1504/IJMRI.2018. 093487; Di Lorenzo, G., Formisano, A., Krstevska, L., Landolfo, R., Ambient vibration test and numerical investigation on the St. Giuliano church in Poggio Picenze (L’Aquila, Italy) (2019) J. Civil Struct Health Monit, 9 (4), pp. 477-490. , https://doi.org/10.1007/s13349-019-00346-7; Krstevska, L., Tashkov, L., Naumovski, N., Florio, G., Formisano, A., Fornaro, A., Landolfo R (2010) In-situ experimental testing of four historical buildings damaged during the 2009 L’Aquila earthquake COST ACTION C26: Urban Habitat Constructions under Catastrophic events— Proceedings of the Final Conference, pp. 427-432. , Taylor & Francis, London, pp; Benedettini, F., Dilena, M., Morassi, A., Vibration analysis and structural identification of a curved multi-span viaduct (2015) Mech Syst Signal Process, 54-55, pp. 84-107. , https://doi.org/10.1016/j.ymssp.2014.08.008; Lamonaca, F., Scuro, C., Grimaldi, D., A layered IoT-based architecture for a distributed structural health monitoring system (2019) ACTA IMEKO, 8 (2), pp. 45-52. , https://doi.org/10.21014/acta_imeko.v8i2.640; Lamonaca, F., Sciammarella, P.F., Scuro, C., Internet of things for structural health monitoring. In: 2018 Workshop on metrology for industry 4.0 and IoT. IEEE (2018) Pp 95–100; Lamonaca, F., Sciammarella, P.F., Scuro, C., Synchronization of IoT layers for structural health monitoring. In: 2018 Workshop on metrology for industry 4.0 and IoT. IEEE (2018) Pp 89–94; Formisano, A., Di Lorenzo, G., Krstevska, L., Landolfo, R., Fem model calibration of experimental environmental vibration tests on two churches hit by L’Aquila earthquake (2020) Int J Archit Heritage, , https://doi.org/10.1080/15583058.2020.1719233; Consorzio, B.B.M., Motorway connection between the cities of Brescia and Milano. CUP E3 1 B05000390007. Execution of works (2014) Motorway Body. Major Artworks. Lot 7–VI003. Adda viaduct—Km 43+220,95–44+487,92. Report on the Static Loading Test, , in Italian; Peeters, B., van der Auweraer, H., Guillaume, P., Leuridan, J., The PolyMAX frequency-domain method: A new standard for modal parameter estimation? (2004) Shock Vib, 11, pp. 395-409. , https://doi.org/10.1155/2004/523692; Bedon, C., Morassi, A., Dynamic testing and parameter identification of a base-isolated bridge (2014) Eng Struct, 60, pp. 85-99. , https://doi.org/10.1016/j.engstruct.2013.12.017","Formisano, A.; Department of Structures for Engineering and Architecture, Italy; email: antoform@unina.it","Rainieri C.Fabbrocino G.Caterino N.Ceroni F.Notarangelo M.A.",,"Springer Science and Business Media Deutschland GmbH","8th Civil Structural Health Monitoring Workshop, CSHM-8 2021","31 March 2021 through 2 April 2021",,264479,23662557,9783030742577,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85115048519
"Nguyen V.-M., Phan T.-H., Phan H.-N., Nguyen D.-A., Ha M.-N., Nguyen D.-T.","57225011250;57202899776;56868303700;57226537161;57219487146;57226537160;","Three-Dimensional Study on Aerodynamic Drag Coefficients of Cable-Stayed Bridge Pylons by Finite Element Method",2021,"Lecture Notes in Civil Engineering","148 LNCE",,,"489","498",,,"10.1007/978-981-16-0945-9_40","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111803152&doi=10.1007%2f978-981-16-0945-9_40&partnerID=40&md5=8f5ca8ff50e499a074931a196b5620e5","University of Danang, University of Science and Technology, Da Nang, Viet Nam; School of Mechanical Engineering, Pusan National University, Busan, South Korea","Nguyen, V.-M., University of Danang, University of Science and Technology, Da Nang, Viet Nam; Phan, T.-H., School of Mechanical Engineering, Pusan National University, Busan, South Korea; Phan, H.-N., University of Danang, University of Science and Technology, Da Nang, Viet Nam; Nguyen, D.-A., University of Danang, University of Science and Technology, Da Nang, Viet Nam; Ha, M.-N., University of Danang, University of Science and Technology, Da Nang, Viet Nam; Nguyen, D.-T., University of Danang, University of Science and Technology, Da Nang, Viet Nam","This study aims to determine aerodynamic drag coefficients of cable-stayed bridge pylons with different shapes through a computational fluid dynamics (CFD) approach. The approach is based on a three-dimensional model of the turbulent wind flow around pylon using the finite element ANSYS CFX software. Numerical models of the flow around simple objects are first carried out and the results are validated with the previous work in the literature. To identify the most suitable turbulent modeling approach, numerical analyses of five turbulent models including k-epsilon, k-omega, shear stress transport, BSL Reynolds stress, and SSG Reynolds stress are performed on a single pylon. Next analyses for other examined pylons of cable-stayed bridges in Vietnam are conducted to determine their aerodynamic drag coefficients. Surface pressure acting on each pylon is also investigated. Finally, the discussion on the effects of the pylon shapes on drag coefficients and surface pressures as well as vortex flows is presented in detail. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","Bridge stability; CFD; Drag coefficient; Numerical simulation; Turbulence models","Aerodynamic drag; Aerodynamics; Cable stayed bridges; Cables; Computational fluid dynamics; Drag coefficient; Reynolds number; Shear stress; Structural health monitoring; Vortex flow; Different shapes; Finite element ansys; Reynolds stress; Shear-stress transport; Surface pressures; Three-dimensional model; Turbulent models; Turbulent wind flow; Finite element method",,,,,,,,,,,,,,,,"Larsen, A., Walther, J.H., Aeroelastic analysis of bridge girder sections based on discrete vortex simulations (1997) J. Wind Eng. Ind. Aerodyn., 67-68, pp. 253-265; Vairo, G., A numerical model for wind loads simulation on long-span bridges (2003) Simul. Model. Pract. Theory, 11 (5-6), pp. 315-351; Lin, H., Liao, H., Identification of Flutter Derivatives of Bridge Deck Under Multi-Frequency Vibration (2010) Eng. Appl. Comput. Fluid Mech, 5, pp. 16-25; Ying, X.Y., Xu, F.Y., Zhang, Z., Tan, Y.G., Large Eddy Simulation of Aerodynamic Forces on a Bridge Pylon (2011) Adv. Mater. Res., 243-249, pp. 1578-1582; Wang, D., Zhang, Y., Sun, M., Chen, A., Characteristics of the Wind Environment above Bridge Deck near the Pylon Zone and Wind Barrier Arrangement Criteria (2020) Appl. Sci., 10, p. 1437; Hoerner, S.F., Fluid-dynamic Drag: Theoretical, Experimental and Statistical Information (1965) Hoerner Fluid Dynamics; 2Nd Edition; Tae, Y.K., (2013) Final Report, Detailed Design, , Cao Lanh bridge), Central Mekong delta region connectivity project; Rezaeiha, A., Montazeri, H., Blocken, B., On the accuracy of turbulence models for CFD simulations of vertical axis wind turbines (2019) Energy, 180, pp. 838-857; Jung, H.J., Lee, S.W., The experimental validation of a new energy harvesting system based on the wake galloping phenomenon (2011) Smart Mater. Struct., 20 (5); Buljac, A., Kozmar, H., Pospíšil, S., Macháček, M., Flutter and galloping of cable-supported bridges with porous wind barriers (2017) J. Wind Eng. Ind. Aerodyn., 171, pp. 304-318","Nguyen, V.-M.; University of Danang, Viet Nam; email: nvmy@dut.udn.vn","Bui T.Q.Cuong L.T.Khatir S.",,"Springer Science and Business Media Deutschland GmbH","1st International Conference on Structural Health Monitoring and Engineering Structures, SHM and ES 2020","15 December 2020 through 16 December 2020",,261159,23662557,9789811609442,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85111803152
"Saidin S.S., Kudus S.A., Jamadin A., Amin N.M.","57203661292;57410231400;55433632300;57200341279;","Modal Frequency of Steel and UHPC U-beam Using Finite Element Analysis",2021,"Lecture Notes in Civil Engineering","157 LNCE",,,"21","28",,,"10.1007/978-981-16-2187-1_3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111249011&doi=10.1007%2f978-981-16-2187-1_3&partnerID=40&md5=a0ec05c2a006e0a839377b522f27a97f","Faculty of Civil Engineering, Universiti Teknologi Mara (UiTM), Shah Alam, Selangor, 40450, Malaysia","Saidin, S.S., Faculty of Civil Engineering, Universiti Teknologi Mara (UiTM), Shah Alam, Selangor, 40450, Malaysia; Kudus, S.A., Faculty of Civil Engineering, Universiti Teknologi Mara (UiTM), Shah Alam, Selangor, 40450, Malaysia; Jamadin, A., Faculty of Civil Engineering, Universiti Teknologi Mara (UiTM), Shah Alam, Selangor, 40450, Malaysia; Amin, N.M., Faculty of Civil Engineering, Universiti Teknologi Mara (UiTM), Shah Alam, Selangor, 40450, Malaysia","The high cost of ultra-high performance concrete (UHPC) has discouraged the implementation of the material in Malaysia’s construction industry. Thus, this study is to evaluate the UHPC performance on cost-effective of a first-cost basis which are able to provide superior durability and workability to the structure. This superior durability of UHPC could help to reduce the number of maintenances required, thus could minimize the future maintenance cost. A study was conducted on the modal frequency of the superstructure bridge by modelling the concrete bridge deck with two different types of U-beam; steel and UHPC using Finite Element Analysis (FEA). The natural frequencies for both different model materials obtained from the FEA were compared and used to estimate the deflection at the midspan of the bridge structure. The comparison of these two materials indicated the high natural frequencies of UHPC U-beam resulting in a lesser deflection estimation at the mid-span of the structure compared to the steel U-beam. The least deflection value of UHPC showed the material allowing the structure for higher transmissibility and could reduce the risk of structures from failure. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","Finite element analysis; Natural excitation; Natural frequencies; Structural health monitoring; Ultra-high-performance concrete","Bridge decks; Concrete industry; Construction industry; Cost effectiveness; Costs; Durability; Finite element method; Frequency estimation; Natural frequencies; Ultra-high performance concrete; Bridge structures; Cost effective; High costs; Maintenance cost; Modal frequency; Model materials; Two-materials; Ultra high performance concretes (UHPC); Failure (mechanical)",,,,,,"Acknowledgements The ambient vibration testing exercises were supported by FRGS grant FRG/1/2018/TK01/UITM/02/30 for structural health monitoring of existing bridge structures, reliability and service life of the country’s infrastructures.",,,,,,,,,,"(2005) Highway Subcommittee on Bridges and Structures. Grand Challenges: A Strategic Plan for Bridge Engineering, , Washington (DC; Behloul, M., Ductal, R., Prestressed girders for a traffic bridge in Mayenne, France (2006) In: 7Th International Conference on Short & Medium Span Bridges, , Quebac, Canada; Chang, C., Chang, T., Zhang, Q., Ambient vibration of long-span cable-stayed bridge (2001) J Bridge Eng, 6 (1), pp. 46-53; Chiewanichakorn, M., Aref, A.J., Alampalli, S., Dynamic and fatigue response of a truss bridge with fiber reinforced polymer deck (2007) Int J Fatigue, 29, pp. 1475-1489; Farhey, D.N., Integrated virtual instrumentation and wireless monitoring for infrastructure diagnostics (2006) Struct Health Monit, 5 (1), pp. 29-43; Farrar, C.R., Doebling, S., Cornwell, P., Straser, E., Variability of modal parameters measured on the Alamos Canyon Bridge (1997) Proceedings 15Th International Modal Analysis Conference, pp. 257-263. , Orlando, FL, pp; Farrar, C.R., Jauregui, D., (1996) Damage Detection Algorithms Applied to Experimental and Numerical Modal Data from the I-40 Bridge, , Los Alamos National Library Report, LA-13074 MS; Fehling, E., Bunje, K., Leutbecher, T., Design relevant properties of hardened ultra high performance concrete (2004) Proceedings of the International Symposium on Ultra High Performance Concrete, pp. 327-338. , Kassel University Press, Kassel, Germany, pp; Habel, K., Denari, E., Brühwiler, E., Experimental investigation of composite ultra-high-performance fiber reinforced concrete and conventional concrete members (2007) ACI Struct J, 104, pp. 93-101; Kwasniewskia, L., Lib, H., Wekezerb, J., Malachowskic, J., Finite element analysis of vehicle–bridge interaction (2006) Finite Elem Anal Des, 42 (11), pp. 950-959; Lampo, R., Maher, A., Busel, J.P., Odello, R., Design and development of FRP composite piling systems (1997) Proceedings of the International Composite Expo, , Nashville, TN; Laxmikant, K., (2008) Damage Detection in Structures Using Natural Frequency Measurements, , PhD Thesis, Australia; Li, H., Wekezer, J.F., Kwasniewski, L., Dynamic response of a highway bridge subjected to moving vehicles (2008) J Bridge Eng, 13 (5), pp. 439-448; Memory, T., Brameld, G.H., Thambiratnam, D., A simplified method for estimating the natural frequency of bridge superstructures (1991) Heywood RI, pp. 539-550. , (ed) AUSTROADS conference Brisbane, Bridges—Part of the Transport System, pp; Ozyildirim, C., (2011) Evaluation of Ultra High Performance Fiber Reinforced Concrete. Final Report. Virginia Center for Transportation Innovation & Research, , Charlottesville, VA; Ren, L., Fang, Z., Wang, K., Design and behavior of super-long span cable-stayed bridge with CFRP cables and UHPC members (2018) Compos Part B Eng, 164, pp. 72-81; Schmidt, M., Fehling, V., Ultra-High performance concrete: Research development and application in Europe (2005) The 7Th International Symposium on the Utilization of High-Strength/ High-Performance Concrete, pp. 51-78. , , pp; Wahab, M.A., de Roeck, G., Effect of temperature on dynamic system parameters of a highway bridge (1997) Struct Eng Int, 7 (4), pp. 266-270; Wang, Y., Loh, K.J., Lynch, J.P., Fraser, M., La, K., Elgamal, A., Vibration monitoring of Voigt Bridge using wired and wireless monitoring systems (2007) The Processing of 4Th China– Japan–US Symposium on Structural Control and Monitoring, pp. 16-17. , Hangzhou, pp; Wipf, T.J., Phares, B.M., Sritharan, S., Degen, B.E., Giesmann, M.T., (2009) Design and Evaluation of Single Span Bridge Using Ultra High-Performance Concrete. Final Report; Yen LV, Behzad N, Abu Bakar MS, Balamurugan AG, Tet SY (2012) Application of ultra high-performance fiber reinforced concrete–the Malaysia perspective. Int J Sustain Constr Eng Technol 3(1):26–44. (ISSN: 2180–3242)1; Yun, C.B., Min, J., Smart sensing, monitoring, and damage detection for civil infrastructures (2011) KSCE J Civil Eng, 15 (1), pp. 1-14; Zhang, Z., Zhang, W., Zhai, Z.J., Chen, Q.Y., Evaluation of various turbulence models in predicting airflow and turbulence in enclosed environments by CFD: Part 2—comparison with experimental data from literature (2007) Am Soc Heating Refrigerating Air-Conditioning Eng Inc, 13 (6), pp. 871-886","Saidin, S.S.; Faculty of Civil Engineering, Malaysia; email: shahirahsaidin2011@gmail.com","Mohd Zuki S.S.Mokhatar S.N.Shahidan S.Bin Wan Ibrahim M.H.",,"Springer Science and Business Media Deutschland GmbH","Seminar on Sustainable Concrete Materials and Structures in Concrete Construction, 2020","24 August 2020 through 24 August 2020",,260669,23662557,9789811621864,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85111249011
"Kolla A., Kurapati R.N.S., Meka S.S.V., Vitakula V.S.M.D., Pasupuleti V.D.K.","57222071958;57222078449;57222074748;57222075326;57204056109;","Health Assessment and Modal Analysis of Historical Masonry Arch Bridge",2021,,"127",,,"915","926",,,"10.1007/978-3-030-64594-6_88","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101251855&doi=10.1007%2f978-3-030-64594-6_88&partnerID=40&md5=ea4e31e25f04345f4deb30e2f28ad938","Ecole Centrale College of Engineering, Mahindra University, Hyderabad, India","Kolla, A., Ecole Centrale College of Engineering, Mahindra University, Hyderabad, India; Kurapati, R.N.S., Ecole Centrale College of Engineering, Mahindra University, Hyderabad, India; Meka, S.S.V., Ecole Centrale College of Engineering, Mahindra University, Hyderabad, India; Vitakula, V.S.M.D., Ecole Centrale College of Engineering, Mahindra University, Hyderabad, India; Pasupuleti, V.D.K., Ecole Centrale College of Engineering, Mahindra University, Hyderabad, India","Masonry arch bridges in India indicate the heritage value of the nation. Most of these bridges had been in service for hundreds of years and yet being serviceable even today for transportation purposes indicates the robustness of the design and construction methodology. But, some of these bridges are abandoned due to its deterioration and absence of knowledge to retrofit these structures. Lack of proper maintenance and retrofitting could eventually damage the structural integrity as these structures are old enough to deteriorate and are prone to repeated weathering and unforeseen natural calamities such as earthquakes, floods, etc. In this study, a very old masonry arch bridge ‘Puranapul’ bridge inaugurated in the year 1578 across the river Musi in Hyderabad is considered for investigation of its health through basic visual inspection and non-destructive testing. Furthermore, the same is numerically modeled using the available finite element analysis software ANSYS in three dimensions for assessing the basic mode shapes of the structure and its behavior in different loading conditions. © 2021, Springer Nature Switzerland AG.","Finite element model; Health assessment; Heritage structure; Masonry arch bridge; Nondestructive testing; Visual inspection","Arches; Deterioration; Masonry bridges; Masonry construction; Masonry materials; Modal analysis; Nondestructive examination; Retrofitting; Structural health monitoring; Design and construction; Finite element analysis software; Health assessments; Loading condition; Masonry arch bridges; Non destructive testing; Three dimensions; Visual inspection; Arch bridges",,,,,,,,,,,,,,,,"Sarhosis, V., de Santis, S., de Felice, G., A review of experimental investigations and assessment methods for masonry arch bridges (2016) Struct. Infrastruct. Eng., 12 (11), pp. 1439-1464; Mai, K.Q., Lee, S.M., Lee, K., Assessment of historic stone arch bridge characterisation: Experiments and numerical model (2019) Proc. Inst. Civil Eng.-Struct. Build., 172 (7), pp. 480-489; Sevim, B., Bayraktar, A., Altunişik, A.C., Atamtürktür, S., Birinci, F., Assessment of nonlinear seismic performance of a restored historical arch bridge using ambient vibrations (2011) Nonlinear Dyn, 63 (4), pp. 755-770; MIT Libraries Homepage, , http://dome.mit.edu/handle/1721.3/45288; Appendices: Conservation of Historical Building and Areas in Hyderabad City, 1st edn. Hyderabad Urban Development Authority, Hyderabad (1984); MIT Libraries Homepage, , http://dome.mit.edu/handle/1721.3/20097; Banerji, P., Chikermane, S., Condition assessment of a heritage arch bridge using a novel model updation technique (2012) J. Civil Struct. Health Monit., 2 (1), pp. 1-16; Tóth, A.R., Orbán, Z., Bagi, K., Discrete element analysis of a stone masonry arch (2009) Mech. Res. Commun., 36 (4), pp. 469-480; Ford, T.E., Augarde, C.E., Tuxford, S.S., Modelling masonry arch bridges using commercial finite element software (2003) The 9Th International Conference on Civil and Structural Engineering Computing, pp. 161-203. , Netherlands, pp; Cavicchi, A., Gambarotta, L., Two-dimensional finite element upper bound limit analysis of masonry bridges (2006) Comput. Struct., 84 (31-32), pp. 2316-2328; Gilbert, M., Limit analysis applied to masonry arch bridges: State-of-the-art and recent developments (2007) 5Th International Arch Bridges Conference, pp. 13-28. , , pp; Jiang, K., Esaki, T., Quantitative evaluation of stability changes in historical stone bridges in Kagoshima, Japan, by weathering (2002) Eng. Geol., 63 (1-2), pp. 83-91; Audenaert, A., Fanning, P., Sobczak, L., Peremans, H., 2-D analysis of arch bridges using an elasto-plastic material model (2008) Eng. Struct., 30 (3), pp. 845-855; Crisfield, M.A., Numerical methods for the non-linear analysis of bridges (1988) Comput. Struct., 30 (3), pp. 637-644; Kamiński, T., Three-dimensional modelling of masonry arch bridges based on predetermined planes of weakness (2007) 5Th International Conference on Arch Bridges, pp. 341-348. , Madeira, Portugal, pp; Caddemi, S., et al.: 3D discrete macro-modelling approach for masonry arch bridges. In: IABSE Symposium 2019 Guimarães, Towards a Resilient Built Environment-Risk and Asset Management, 27–29 March, Guimarães, Portugal (2019); Bayraktar, A., Türker, T., Altunişik, A.C., Experimental frequencies and damping ratios for historical masonry arch bridges (2015) Constr. Build. Mater., 75, pp. 234-241; Iyengar, R.N., Kanth, S.R., Strong ground motion estimation during the Kutch, India earthquake (2006) Pure. Appl. Geophys., 163 (1), pp. 153-173","Kolla, A.; Ecole Centrale College of Engineering, India; email: abhinav170113@mechyd.ac.in","Rizzo P.Milazzo A.",,"Springer Science and Business Media Deutschland GmbH","European Workshop on Structural Health Monitoring, EWSHM 2020","6 July 2020 through 9 July 2020",,254359,23662557,9783030645939,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85101251855
"He Y., Jiang Z., Zhao B., Wang S., Wang C., Fan W.","55441375100;57283956600;57283729400;57283508000;57212007977;25724228600;","Structure optimization of four-electrode detector for CFRP damage detection",2020,"Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020",,,,"2205","2209",,,"10.1109/ICISCE50968.2020.00431","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116386616&doi=10.1109%2fICISCE50968.2020.00431&partnerID=40&md5=09cb0a2c3b0fb5c32aa1781e47b599ee","Civil Aviation University of China, College of Electronic Information and Automation, Tianjin, China","He, Y., Civil Aviation University of China, College of Electronic Information and Automation, Tianjin, China; Jiang, Z., Civil Aviation University of China, College of Electronic Information and Automation, Tianjin, China; Zhao, B., Civil Aviation University of China, College of Electronic Information and Automation, Tianjin, China; Wang, S., Civil Aviation University of China, College of Electronic Information and Automation, Tianjin, China; Wang, C., Civil Aviation University of China, College of Electronic Information and Automation, Tianjin, China; Fan, W., Civil Aviation University of China, College of Electronic Information and Automation, Tianjin, China","Due to the requirements for safe operation of aircraft, real-time and accurate structural health monitoring of aircraft composite materials is required. From the perspective of improving the accuracy of composite damage detection, this paper proposes a scanning four-electrode electrical impedance nondestructive test method based on five different electrode radius measurement modes. The finite element analysis software COMSOL is used to construct an anisotropic CFRP laminate model, and the five measurement modes are compared and evaluated from the perspective of measurement accuracy, and three different types of structural damage are simulated to compare and analyze the detection results. In order to verify the validity of the model, by scanning the detection model, the structure damage information is obtained for image reconstruction, and a model suitable for CFRP damage detection is obtained. The research results show that the 0.7mm electrode radius measurement mode is better when detecting scratch damage; the 1.3mm electrode radius measurement mode is better when detecting impact and delamination damage. This research improves the measurement electrode structure, improves the detection accuracy of composite material non-destructive testing, and provides a reliable technical foundation for aircraft maintenance personnel. © 2020 IEEE.","carbon fiber composite; four-electrode; Measurement mode; non-destructive testing","Aircraft; Bridge decks; Carbon fiber reinforced plastics; Carbon fibers; Damage detection; Image reconstruction; Nondestructive examination; Structural health monitoring; Structural optimization; Carbon fibre composites; Composites damages; Electrical impedance; Electrode radii; Four-electrode; Measurement modes; Radius measurements; Real- time; Safe operation; Structure optimization; Electrodes",,,,,,,,,,,,,,,,"Xing, L., Jiang, S., Zhou, Z., Advances in manufacturing technology of advanced resin matrix composites[J] (2013) Journal of Composite Materials, 30 (2), pp. 1-9; Shaojie, C., Composite material technology and large aircraft[J] (2008) Acta Aeronautica Sinica, (3), pp. 605-610; Daijun, L., Yali, C., The application of advanced resin-based composite materials in the aviation industry [J] (2008) Materials Engineering, (Zl), pp. 194-198; Feng, S., BMW i3 pure electric vehicle-full carbon fiber composite body[J] (2012) Fiber Composite Materials, (3), p. 19; Shanyi, D., Advanced composite materials and aerospace[J] (2007) Journal of Composite Materials, (1), pp. 1-12; Ning, Z., Liu, R., Liu, H., Micro-modeling of thermal resistance change of fiber-containing fractured composites[J] (2017) Journalof Composite Materials, 34 (1), pp. 112-120; Minghui, L., Jundong, W., Yiping, Z., Peirui, L., Xuesong, Z., Shanpu, Z., Shuling, J., Ultrasonic characterization and analysis of macroscopic defects in rtm carbon fiber composites[J] (2017) FRP/Composite Materials, (4), pp. 70-74; Songping, L., Feifei, L., Enming, G., Ultrasonic imaging technology of interlayer defects in carbon fiber reinforced composites[J] (2009) Nondestructive Testing, (11), pp. 868-872",,"Li S.Dai Y.Ma J.Cheng Y.","et al.;Hunan University;Hunan University of Humanities, Science and Technology;Swinburne University of Technology;Wayne State University;Xiamen University","Institute of Electrical and Electronics Engineers Inc.","7th International Conference on Information Science and Control Engineering, ICISCE 2020","18 December 2020 through 20 December 2020",,171872,,9781728164069,,,"English","Proc. - Int. Conf. Inf. Sci. Control Eng., ICISCE",Conference Paper,"Final","",Scopus,2-s2.0-85116386616
"Hu S., Wang Y., Qiu L.","57221219912;57193270898;35103240700;","Buckling and Postbuckling Analysis Method of Stretchable and Flexible Sensor Networks Based on ABAQUS",2020,"International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings",,,"9261653","21","26",,,"10.1109/ICSMD50554.2020.9261653","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098581394&doi=10.1109%2fICSMD50554.2020.9261653&partnerID=40&md5=37d70e49646d89a4e5357760302b11ff","Nanjing University of Aeronautics and Astronautics, State Key Laboratory of Mechanical Structures, Nanjing, China","Hu, S., Nanjing University of Aeronautics and Astronautics, State Key Laboratory of Mechanical Structures, Nanjing, China; Wang, Y., Nanjing University of Aeronautics and Astronautics, State Key Laboratory of Mechanical Structures, Nanjing, China; Qiu, L., Nanjing University of Aeronautics and Astronautics, State Key Laboratory of Mechanical Structures, Nanjing, China","Aircraft smart skin technology requires the integration of large-scale and lightweight sensor networks in aircraft structures. To meet these requirements, the design of island-bridge structure is adopted, so that the sensor network can be manufactured in a limited scale and expanded in a large scale. Theoretical analysis, finite element simulation and experimental verification have been carried out on the mechanical properties of island-bridge structures. However, theoretical analysis is difficult to analyze irregular shaped structures, and the existing simulation researches mainly focus on the island-bridge structures with one unit and lack the analysis of networks. In this work, the finite element simulation method of island-bridge structure networks based on ABAQUS has been proposed, which includes buckling mode analysis and postbuckling deformation analysis. The initial buckling deformation state of the structure is extracted by the buckling mode analysis, and the postbuckling deformation state of the structure can be obtained by the postbuckling deformation analysis. The simulation method is applied on the analysis of the mechanical properties and deformation patterns of different island-bridge structure networks, including serpentine, fractal and irregular island-bridge structure networks. This shows that the proposed simulation method can well guide the design of stretchable sensor networks. © 2020 IEEE.","aircraft smart skin; flexible and stretchable; island-bridge structure; sensor network; simulation method; structural health monitoring","ABAQUS; Aircraft; Aircraft manufacture; Airframes; Artificial intelligence; Buckling; Deformation; Mechanical properties; Sensor networks; Serpentine; Aircraft structure; Buckling and post-buckling; Deformation pattern; Finite element simulations; Lightweight sensors; Post buckling deformation; Shaped structures; Simulation research; Finite element method",,,,,"2018ZA52010; National Natural Science Foundation of China, NSFC: 51635007, 51921003, 51975292; Priority Academic Program Development of Jiangsu Higher Education Institutions, PAPD","ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (Grant No. 51635007, 51975292, and 51921003), the Aviation Foundation of China (Grant No. 2018ZA52010) and the Priority Academic Program Development of Jiangsu Higher Education Institutions.",,,,,,,,,,"Foote, P.D., Integration of structural health monitoring sensors with aerospace, composite materials and structures (2015) Mat.-wiss. U. Werkstofftech, 46, pp. 197-203. , February; Qiu, L., Deng, X., Yuan, S., Huang, Y., Ren, Y., Impact monitoring for aircraft smart composite skins based on a lightweight sensor network and characteristic digital sequences (2018) Sensors, 18, p. 2218. , July; Giurgiutiu, V., Tuned lamb wave excitation and detection with piezoelectric wafer active sensors for structural health monitoring (2005) J. Intell. Mater. Syst. Struct., 16, pp. 291-305. , April; Qiu, L., Liu, M., Qing, X., Yuan, S., A quantitative multidamage monitoring method for large-scale complex composite (2013) Struct. Health Monit., 12, pp. 183-196. , March; Qiu, L., Liu, B., Yuan, S., Su, Z., Impact imaging of aircraft composite structure based on a model-independent patial-wavenumber filter (2016) Ultrasonics, 64, pp. 10-24. , January; Lin, M., Chang, F.K., The manufacture of composite structures with a built-in network of piezoceramics (2002) Compos. Sci. Technol., 62, pp. 919-939. , June; Qiu, L., Yuan, F., Shi, X., Huang, T., Design of piezoelectric transducer layer with electromagnetic shielding and high connection reliability (2012) Smart Mater. Struct., 21, p. 075032. , June; Pang, C., Lee, C., Suh, K.Y., Recent advances in flexible sensors for wearable and implantable devices (2013) J. Appl. Polym. Sci., 130, pp. 1429-1441. , August; Forrest, S., The path to ubiquitous and low-cost organic electronic appliances on plastic (2004) Nature, 428, pp. 911-918. , April; Zhang, Y., Xu, S., Fu, H., Lee, J., Su, J., Hwang, K., Buckling in serpentine microstructures and applications in elastomer-supported ultra-stretchable electronics with high areal coverage (2013) Soft Matter, 9, pp. 8062-8070. , June; Zhang, Y., Fu, H., Su, Y., Xu, S., Cheng, H., Fan, J.A., Mechanics of ultra-stretchable self-similar serpentine interconnects (2013) Acta Mater., 61, pp. 7816-7827. , December; Widlund, T., Yang, S., Hsu, Y.Y., Lu, N., Stretchability and compliance of freestanding serpentine-shaped ribbons (2014) Int. J. Solids Struct., 51, pp. 4026-4037. , November; Xu, S., Zhang, Y., Cho, J., Lee, J., Huang, X., Jia, L., Stretchable batteries with self-similar serpentine interconnects and integrated wireless recharging systems (2013) Nat. Commun., 2013, p. 1543. , February; Zhang, Y., Fu, H., Xu, S., Fan, J.A., Hwang, K.C., Jiang, J., A hierarchical computational model for stretchable interconnects with fractal-inspired designs (2014) J. Mech. Phys. Solids, 72, pp. 115-130. , December; Wang, Y., Luo, Y., Qiu, L., Simulation method of an expandable lamb wave sensor network for aircraft smart skin (2020) Ieee Sens. J., 20, pp. 102-112. , January; Wang, Y., Qiu, L., Luo, Y., Ding, R., A stretchable and large-scale guided wave sensor network for aircraft smart skin of structural health monitoring (2019) Struct. Health Monit, , June; Guo, Z., Kim, K., Salowitz, N., Lanzara, G., Wang, Y., Yinan, P., Functionalization of stretchable networks with sensors and switches for composite materials (2018) Struct. Health Monit., 17, pp. 598-623. , May","Qiu, L.; Nanjing University of Aeronautics and Astronautics, China; email: lei.qiu@nuaa.edu.cn",,"National Natural Science Foundation of China","Institute of Electrical and Electronics Engineers Inc.","1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020","15 October 2020 through 17 October 2020",,165335,,9781728192772,,,"English","Int. Conf. Sens., Meas. Data Anal. Era Artif. Intell., ICSMD - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85098581394
"Savard M., Laflamme J.-F.","57525322300;57225373314;","Monitoring and assessment of a prestressed concrete segmental box girder bridge",2020,"American Concrete Institute, ACI Special Publication","SP-342",,,"20","39",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110399749&partnerID=40&md5=fdf4209c62c957b65798f807f83e6e5a","Laval University, Canada","Savard, M., Laval University, Canada; Laflamme, J.-F., Laval University, Canada","Several of the first prestressed concrete segmental bridges in North America were built in Quebec, Canada. The Rivière-aux-Mulets bridge was one of them. Built in the early 1960s, this bridge experienced several disorders due to inadequate design criteria enforced at that time. Despite a structural strengthening in the late 1980s, a bridge behavior follow-up has been required to ensure reliability. The structural health monitoring program implemented to track structural disorders, along with results from modal analysis and diagnostic load tests, is presented with a focus on the instrumentation and the data analysis. A three-dimensional finite element model was developed and calibrated using the frequencies and mode shapes detected under ambient traffic conditions. Data analyses showed that the expansion bearings were frozen, causing bending of the associated piers, which generated axial forces in the deck and decompression of concrete in the area surrounding active cracks. This process enables premature failure of prestressing tendons in the vicinity of these cracks, especially those located in the top flange, which is a corrosion-friendly environment. Development of cracks and associated prestress loss caused a reduction in the bridge load-carrying capacity. Analyses of health monitoring data led to acute assessment of the overall bridge structural performance. © 2020 American Concrete Institute. All rights reserved.","Data analysis; Finite element analysis; Load tests; Modal analysis; Prestressed concrete bridge; Structural damage; Structural health monitoring","Box girder bridges; Concrete bridges; Corrosion; Load testing; Modal analysis; Prestressed concrete; Program diagnostics; Software testing; Steel bridges; Structural analysis; Structural health monitoring; Testing; Diagnostic load tests; Monitoring and assessment; Prestressing tendon; Structural disorders; Structural health monitoring programs; Structural performance; Structural strengthening; Three dimensional finite element model; Concrete beams and girders",,,,,,,,,,,,,,,,"Cremona, C., Qu'est-ce qu'une évaluation dynamique ? Principes et méthodes (2005) Revue européenne de génie civil, 9 (1-2), pp. 11-42; Ouellet, C., Gaumond, Y., Strengthening of Two Prestressed Segmental Box-Girder Bridges (1990) Developments in Short and Medium Span Bridges '90, Third International Conference on Short and Medium Span Bridges, 2. , Toronto, Ontario",,"Dymond B.Z.Massicotte B.","ACI Committee 342, Evaluation of Concrete;ACI Committee 343, Concrete Bridge Design (Joint ACI-ASCE)","American Concrete Institute","Advanced Analysis and Testing Methods for Concrete Bridge Evaluation and Design at the Concrete Convention and Exposition 2019","24 March 2019 through 28 March 2019",,169993,01932527,9781641951043,,,"English","Am. Concr. Inst. ACI Spec. Publ.",Conference Paper,"Final","",Scopus,2-s2.0-85110399749
"Wang Y., Liu Z.","57767885600;57222350001;","Bridge finite element model updating based on improved genetic algorithm",2020,"fib Symposium",,,,"2108","2116",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134849504&partnerID=40&md5=b4dd227db228a4f120a246d3865524a8","School of Highway, Chang’an University, Xi’an, China","Wang, Y., School of Highway, Chang’an University, Xi’an, China; Liu, Z., School of Highway, Chang’an University, Xi’an, China","In order to solve the problems such as difficulty in solving the finite element model updating method and low calculation accuracy in bridge health monitoring, an improved genetic algorithm is proposed to solve the parameters to be updated in the finite element model updating of bridge. Firstly, based on genetic algorithm with better global optimization performance and nonlinear programming with better local optimization performance characteristics, a hybrid optimization algorithm based on genetic algorithm and nonlinear programming is proposed, which can ensure the local optimization as far as possible under the premise of global optimization. Then, in order to verify the feasibility of the algorithm, an improved genetic algorithm was used to update the finite element model of the prestressed concrete continuous rigid frame bridge with a health monitoring system: The real modal parameters of the bridge are obtained from the measured acceleration data and the measured frequency is taken as the objective function. Then the elastic modulus of the continuous rigid frame bridge is selected as the parameter to be updated, and the response surface model is constructed for optimization calculation, and the model updating of the continuous rigid frame bridge is realized. Finally, in order to verify the efficiency of the algorithm, the traditional genetic algorithm is used to update the bridge model and compared with the improved genetic algorithm. The results show that the improved genetic algorithm has significantly improved solution accuracy compared with traditional optimization algorithms, which can better reflect the real stress state of the structure and provide a new idea for concrete bridge finite element model updating and damage identification. © fédération internationale du béton (fib).","Bridge health monitoring; Continuous rigid frame bridge; Damage identification; Finite element model updating; Improved genetic algorithm","Concrete buildings; Concrete construction; Damage detection; Finite element method; Global optimization; Modal analysis; Parameter estimation; Prestressed concrete; Rigidity; Continuous rigid frame bridges; Finite-element model updating; Health monitoring system; Hybrid optimization algorithm; Optimization calculation; Performance characteristics; Response surface modeling; Traditional genetic algorithms; Genetic algorithms",,,,,,,,,,,,,,,,"Berman, A., Mass matrix correction using an incomplete set of measured modes (1979) AIAA Journal, 17, pp. 1147-1148; Huang, Q., Zhang, L.Z., Updating of bridge finite element model based on optimization design theory (2008) Journal of Harbin Institute of Technology, pp. 246-249; Kong, X.R., Qin, Y.L., Luo, W., GA-PSO algorithm model updating (2009) Mechanics in Engineering, 31, pp. 56-60. , (in Chinese); Kwon, K.S., Lin, R.M., Robust finite element model updating using Taguchi method (2005) Journal of Sound & Vibration, 280, pp. 77-99; Li, H.N., Gao, D.W., Yi, T.H., Research status and progress of structural health monitoring system in civil engineering (2008) Advances in Mechanics, pp. 151-166. , (in Chinese); Liu, Y., (2008) High Performance Optimization Algorithms and Model Updating of Structures, p. 152. , Harbin Institute of Technology. (in Chinese); Modak, S.V., Kundra, T.K., Nakra, B.C., Model updating using constrained optimization (2000) Mechanics Research Communications, 27, pp. 543-551; Teughels, A., Roeck, G.D., Suykens, J.A.K., Global optimization by coupled local minimizers and its application to FE model updating (2003) Computers & Structures, 81, pp. 2337-2351; Wan, H.P., Ren, W.X., Parameter selection in finite-element-model updating by global sensitivity analysis using gaussian process metamodel (2015) Journal of Structural Engineering (United States), p. 141; Wan, H.P., Ren, W.X., Stochastic model updating utilizing Bayesian approach and Gaussian process model (2016) Mechanical Systems & Signal Processing, 70-71, pp. 245-268; Zong, Z.H., (2012) Finite Element Model Updating and Model Validation of Bridge Structures, , People’s Communications Publishing House. (in Chinese); Zong, Z.H., Lai, C.L., Lin, Y.Q., Ren, W.X., Analysis of dynamic characteristics of a large-span prestressed concrete continuous rigid frame bridge (2004) Earthquake Engineering and Engineering Dynamics, pp. 98-104. , (in Chinese)","Wang, Y.; School of Highway, China; email: wangyangjet@qq.com","Zhao B.Lu X.",,"fib. The International Federation for Structural Concrete","International fib Symposium on Concrete structures for resilient society, 2020","22 November 2020 through 24 November 2020",,267619,26174820,9782940643042,,,"English","fib. Symp.",Conference Paper,"Final","",Scopus,2-s2.0-85134849504
"Wang Y., Liu Z.","57767885600;57222350001;","Bridge finite element model updating based on improved genetic algorithm",2020,"Proceedings of the fib Symposium 2020: Concrete Structures for Resilient Society",,,,"2108","2116",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102408425&partnerID=40&md5=31af0fd5a69236a82420d9baefeeefe5","School of Highway, Chang'an University, Xi'an, China","Wang, Y., School of Highway, Chang'an University, Xi'an, China; Liu, Z., School of Highway, Chang'an University, Xi'an, China","In order to solve the problems such as difficulty in solving the finite element model updating method and low calculation accuracy in bridge health monitoring, an improved genetic algorithm is proposed to solve the parameters to be updated in the finite element model updating of bridge. Firstly, based on genetic algorithm with better global optimization performance and nonlinear programming with better local optimization performance characteristics, a hybrid optimization algorithm based on genetic algorithm and nonlinear programming is proposed, which can ensure the local optimization as far as possible under the premise of global optimization. Then, in order to verify the feasibility of the algorithm, an improved genetic algorithm was used to update the finite element model of the prestressed concrete continuous rigid frame bridge with a health monitoring system: the real modal parameters of the bridge are obtained from the measured acceleration data and the measured frequency is taken as the objective function. Then the elastic modulus of the continuous rigid frame bridge is selected as the parameter to be updated, and the response surface model is constructed for optimization calculation, and the model updating of the continuous rigid frame bridge is realized. Finally, in order to verify the efficiency of the algorithm, the traditional genetic algorithm is used to update the bridge model and compared with the improved genetic algorithm. The results show that the improved genetic algorithm has significantly improved solution accuracy compared with traditional optimization algorithms, which can better reflect the real stress state of the structure and provide a new idea for concrete bridge finite element model updating and damage identification. © Proceedings of the fib Symposium 2020: Concrete Structures for Resilient Society. All rights reserved.","Bridge health monitoring; Continuous rigid frame bridge; Damage identification; Finite element model updating; Improved genetic algorithm","Concrete buildings; Concrete construction; Damage detection; Finite element method; Global optimization; Modal analysis; Parameter estimation; Prestressed concrete; Rigidity; Continuous rigid frame bridges; Finite-element model updating; Health monitoring system; Hybrid optimization algorithm; Optimization calculation; Performance characteristics; Response surface modeling; Traditional genetic algorithms; Genetic algorithms",,,,,,,,,,,,,,,,"Berman, A., Mass matrix correction using an incomplete set of measured modes (1979) AIAA Journal, 17, pp. 1147-1148; Huang, Q., Zhang, L. Z., Updating of bridge finite element model based on optimization design theory (2008) Journal of Harbin Institute of Technology, pp. 246-249; Kong, X. R., Qin, Y. L., Luo, W., GA-PSO algorithm model updating (2009) Mechanics in Engineering, 31, pp. 56-60. , (in Chinese); Kwon, K. S., Lin, R. M., Robust finite element model updating using Taguchi method (2005) Journal of Sound & Vibration, 280, pp. 77-99; Li, H. N., Gao, D. W., YI, T. H., Research status and progress of structural health monitoring system in civil engineering (2008) Advances in Mechanics, pp. 151-166. , (in Chinese); Liu, Y., (2008) High performance optimization algorithms and model updating of structures, p. 152. , Harbin Institute of Technology. (in Chinese); Modak, S. V., Kundra, T. K., Nakra, B. C., Model updating using constrained optimization (2000) Mechanics Research Communications, 27, pp. 543-551; Teughels, A., Roeck, G. D., Suykens, J. A. K., Global optimization by coupled local minimizers and its application to FE model updating (2003) Computers & Structures, 81, pp. 2337-2351; Wan, H. P., Ren, W. X., Parameter selection in finite-element-model updating by global sensitivity analysis using gaussian process metamodel (2015) Journal of Structural Engineering (United States), p. 141; Wan, H. P., Ren, W. X., Stochastic model updating utilizing Bayesian approach and Gaussian process model (2016) Mechanical Systems & Signal Processing, 70-71, pp. 245-268; Zong, Z. H., (2012) Finite Element Model Updating and Model Validation of Bridge Structures, , People's Communications Publishing House. (in Chinese); Zong, Z. H., Lai, C. L., Lin, Y. Q., Ren, W. X., Analysis of dynamic characteristics of a large -span prestressed concrete continuous rigid frame bridge (2004) Earthquake Engineering and Engineering Dynamics, pp. 98-104. , (in Chinese)","Wang, Y.; School of Highway, China; email: wangyangjet@qq.com","Zhao B.Lu X.","ALLPLAN;Liuzhou OVM Machinery Co., Ltd.","International Federation for Structural Concrete","2020 fib Symposium: Concrete Structures for Resilient Society","22 November 2020 through 24 November 2020",,167100,,9782940643042,,,"English","Proc. fib Symp.: Concrete Struct. Resilient Soc.",Conference Paper,"Final","",Scopus,2-s2.0-85102408425
"Seventekidis P., Giagopoulos D., Dgiagopoulas@uown.gr, Arailopoulos A., MArkogiannaki O.","57079023500;37064557500;56884701500;48662606100;","System identification and damage detection framework using simulated experiments and machine learning techniques",2020,"Proceedings of the International Conference on Structural Dynamic , EURODYN","1",,,"848","856",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099544025&partnerID=40&md5=de80d146d49d518ab573c19ebd0bb3d3","Department of Mechanical Engineering, University of Western Macedonia, Kozani, GR-50100, Greece","Seventekidis, P., Department of Mechanical Engineering, University of Western Macedonia, Kozani, GR-50100, Greece; Giagopoulos, D., Dgiagopoulas@uown.gr; Arailopoulos, A., Department of Mechanical Engineering, University of Western Macedonia, Kozani, GR-50100, Greece; MArkogiannaki, O., Department of Mechanical Engineering, University of Western Macedonia, Kozani, GR-50100, Greece","The present study focuses on the implementation of a methodology to bridge the gap between SHM models with numerically generated data and correspondence with measurements from the real structure to provide reliable damage predictions. In the proposed novel methodology, numerically generated data from simulation models are integrated with measurements from the corresponding real structure to achieve high accuracy in identifying and predicting potential structural damages. A truss structure consisting of composite carbon fiber tubes, aluminum elements and steel bolts for the connections is used for the application of the proposed approach. The process begins with the three-dimensional finite element (FE) models of the examined cylindrical parts, developed in robust finite element analysis software simulating each carbon fiber ply and resin matrix. The real structure and FE models are analyzed in dynamic loading to identify their response. After, the complete assembly FE models are updated based on the data from experimental tests that correspond to the conducted analysis tests on composite cylindrical parts. The potential damage of the structure, set as loose bolts defining a multiclass damage identification problem, is then simulated with the optimal models through a series of stochastic FE load cases for different excitation characteristics. The simulated acceleration time series are then be fed in for the training of a supervised Convolutional Neural Network (CNN) classifier. The trained CNN is finally validated on experimentally measured structural states of the truss. Reliable results prove that optimal FE modeling may be used with machine learning techniques to synthesize a damage identification tool despite the uncertainties, which are tackled by the inherent advantage of numerical generated results to simulate arbitrary number of load cases in small amount of type and minimal effort. © 2020 European Association for Structural Dynamics. All rights reserved.","Damage Identification; Deep Learning; FE updating; Structural Health Monitoring; System Identification","Aluminum coated steel; Bolts; Composite structures; Convolutional neural networks; Damage detection; Dynamic loads; Graphite fibers; Machine learning; Steel fibers; Stochastic models; Stochastic systems; Structural dynamics; Trusses; Uncertainty analysis; Acceleration time series; Damage Identification; Detection framework; Excitation characteristics; Finite element analysis software; Machine learning techniques; Simulated experiments; Three dimensional finite elements; Finite element method",,,,,"Τ6ΥΒΠ-00478","Acknowledgment: This research has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call $TXDFXOWXUH ,QGXVWULDO 0DWHULDOV 2SHQ ,QQRYDWLRQ LQ &XOWXUH (project code: Τ6ΥΒΠ-00478)",,,,,,,,,,"Wickramasinghe, W. R., Thambiratnam, D. P., Chan, T. H. T., Nguyen, T., Vibration characteristics and damage detection in a suspension bridge (2016) Journal of Sound and Vibration, 375, pp. 254-274; Fassois, S. D., Kopsaftopoulos, F. P., Statistical Time Series Methods for Vibration Based Structural Health Monitoring (2013) New Trends in Structural Health Monitoring, 542, pp. 209-264; Tchermak, D., Molgaard, L. L., Active vibration-based structural health monitoring system for wind turbine blade: Demonstration on an operating Vestas V27 wind turbine (2017) Structural Health Monitoring, 16 (5), pp. 536-550; Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M, Inman, D. J., Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks (2017) Journal of Sound and Vibration, 388, pp. 154-170; Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., Gao, R X., Deep learning and its applications to machine health monitoring (2019) Mechanical Systems and Signal Processing, 115, pp. 213-237; Seventekidis, Panagiotis, Giagopoulos, Dimitnos, Arailopoulos, Alexandros, Markogiannaki, Olga, Structural Health Monitoring using deep learning with optimal finite element model generated data (2020) Mechanical Systems and Signal Processing, 145, p. 106972; Ksica, F., Hadas, Z., Hlinka, J., Integration and test of piezocomposite sensors for structure health monitoring in aerospace (2019) Measurement, 147, p. 106861; Dohler, M., Hille, F., Mevel, L., Riicker, W., Structural health monitoring with statistical methods during progressive damage test of S101 Bridge (2014) Engineering Structures, 69, pp. 183-193; Giagopoulos, D., Arailopoulos, A., Dertimanis, V., Papadimitnou, C, Chatzi, E., Grompanopoulos, K., Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating (2019) Structural Health Monitoring, 18 (4), pp. 1189-1206; Zacharakis, Ihas, Arailopoulos, Alexandros, Markogiannaki, Olga, Giagopoulos, Dimitnos, Vibration based Structural Health Monitoring of Composite Carbon Fiber Structural Systems, UNCECOMP 2019 (2019) International Conference on Uncertainty Quantification in Computational Sciences and Engineering, , Crete, Greece, 24-26 June; Giagopoulos, D., Arailopoulos, A., Computational framework for model updating of large scale linear and nonlinear finite element models using state of the art evolution strategy (2017) Computers and Structures, 192, pp. 210-232","Giagopoulos, D.
Giagopoulos, D.","Papadrakakis M.Fragiadakis M.Papadimitriou C.",,"European Association for Structural Dynamics","11th International Conference on Structural Dynamics, EURODYN 2020","23 November 2020 through 26 November 2020",,165382,23119020,9786188507203,,,"English","Proc. Int. Conf. Struct. Dyn., EURODYN",Conference Paper,"Final","",Scopus,2-s2.0-85099544025
"Astroza R., Barrientos N., Li Y., Saavedra Flores E.","55619989200;57209321676;55818794700;36705083200;","Calibration of a large nonlinear finite element model of a highway bridge with many uncertain parameters",2020,"Conference Proceedings of the Society for Experimental Mechanics Series",,,,"177","187",,,"10.1007/978-3-030-12075-7_20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067361132&doi=10.1007%2f978-3-030-12075-7_20&partnerID=40&md5=e288fc8172196204d1467ec7147263ff","Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile; Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada; Departamento de Ingeniería en Obras Civiles, Universidad de Santiago de Chile, Santiago, Chile","Astroza, R., Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile; Barrientos, N., Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile; Li, Y., Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada; Saavedra Flores, E., Departamento de Ingeniería en Obras Civiles, Universidad de Santiago de Chile, Santiago, Chile","Finite element (FE) model updating has emerged as a powerful technique for structural health monitoring (SHM) and damage identification (DID) of civil structures. Updating mechanics-based nonlinear FE models allows for a complete and comprehensive damage diagnosis of large and complex structures. Recursive Bayesian estimation methods, such as the Unscented Kalman filter (UKF), have been used to update nonlinear FE models of civil structures; however, their use have been limited to models with a relatively low number of degrees of freedom and with a limited number of unknown model parameters, because it is otherwise impractical for computationally demanding models with many uncertain parameters. In this paper, a FE model of the Marga-Marga bridge, an eight-span seismically-isolated bridge located in Viña del Mar-Chile, is updated based on numerically simulated response data. Initially, 95 model parameters are considered unknown, and then, based on a simplified sensitivity analysis, a total of 27 model parameters are considered in the estimation. Different measurement sets, including absolute accelerations, relative displacements, strains, and shear deformations of the isolators, are analyzed to investigate the effects of considering heterogeneous responses on the estimation results. In addition, a non-recursive estimation procedure is presented and its effectiveness in reducing the computational cost, while maintaining accuracy and robustness in the estimation, is demonstrated. © Society for Experimental Mechanics, Inc. 2020.","High-dimensional parameter space; Model updating; Nonlinear finite element model; Parameter estimation","Bayesian networks; Damage detection; Degrees of freedom (mechanics); Nonlinear analysis; Parameter estimation; Sensitivity analysis; Shear flow; Structural dynamics; Structural health monitoring; Uncertainty analysis; Finite-element model updating; High-dimensional; Large and complex structures; Model updating; Non-linear finite element model; Number of degrees of freedom; Recursive Bayesian estimation; Structural health monitoring (SHM); Finite element method",,,,,"Comisión Nacional de Investigación Científica y Tecnológica, CONICYT; Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT: 11160009","Acknowledgements R. Astroza acknowledges the financial support from the Chilean National Commission for Scientific and Technological Research (CONICYT), through FONDECYT research grant No. 11160009.",,,,,,,,,,"Friswell, M.I., Mottershead, J.E., (1995) Finite Element Model Updating in Structural Dynamics, , Kluwer Academic Publishers, Dordrecht; Teughels, A., de Roeck, G., Damage detection and parameter identification by FE model updating (2005) Arch. Comput. Methods Eng., 12 (2), pp. 123-164; Wu, A.-L., Yang, J.N., Loh, C.-H., A finite-element based damage detection technique for nonlinear reinforced concrete structures (2015) Struct. Control. Health Monit., 22, pp. 1223-1239; Astroza, R., Nguyen, L.T., Nestorović, T., Finite element model updating using simulated annealing hybridized with unscented Kalman filter (2016) Comput. Struct., 177, pp. 176-191; Olivier, A., Smyth, A.W., A marginalized unscented Kalman filter for efficient parameter estimation with applications to finite element models (2018) Comput. Methods Appl. Mech. Eng., 339, pp. 615-643; Astroza, R., Ebrahimian, H., Conte, J.P., Material parameter identification in distributed plasticity FE models of frame-type structures using nonlinear stochastic filtering (2015) J. Eng. Mech. ASCE., 141 (5); Astroza, R., Ebrahimian, H., Li, Y., Conte, J.P., Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures (2017) Mech. Syst. Signal Process., 93, pp. 661-687; Astroza, R., Ebrahimian, H., Conte, J.P., Performance comparison of Kalman−based filters for nonlinear structural finite element model updating (2019) J. Sound Vib., 438, pp. 520-542; Ebrahimian, H., Astroza, R., Conte, J.P., Extended Kalman filter for material parameter estimation in nonlinear structural finite element models using direct differentiation method (2015) Earthq. Eng. Struct. Dyn., 44 (10), pp. 1495-1522; Sarrazin, M., Moroni, M.O., Neira, C., Venegas, B., Performance of bridges with seismic isolation bearings during the Maule earthquake, Chile (2013) Soil Dyn. Earthq. Eng., 47, pp. 117-131; McKenna, F., Fenves, G.L., Scott, M.H., Open System for Earthquake Engineering Simulation (2000) Pacific Earthquake Engineering Research Center, University of California, , Berkeley, CA; Li, Y., Astroza, R., Conte, J.P., Nonlinear FE model updating and reconstruction of the response of an instrumented seismic isolated bridge to the 2010 Maule Chile earthquake (2017) Earthq. Eng. Struct. Dyn., 46 (15), pp. 2699-2716; Porter, K.A., Beck, J.L., Shaikhutdinov, R.V., Sensitivity of building loss estimates to major uncertain variables (2002) Earthq. Spectra., 18 (4), pp. 719-743","Astroza, R.; Facultad de Ingeniería y Ciencias Aplicadas, Chile; email: rastroza@miuandes.cl","Barthorpe R.",,"Springer New York LLC","37th IMAC, A Conference and Exposition on Structural Dynamics, 2019","28 January 2019 through 31 January 2019",,225789,21915644,9783030120740,,,"English","Conf. Proc. Soc. Exp. Mech. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85067361132
"Fang Y.M., Chou T.Y., Van Hoang T., Lee B.J.","52963402600;8690206800;57210914393;7405439560;","Automatic management and monitoring of bridge lifting: A method of changing engineering in realtime",2019,"Sensors (Switzerland)","19","23","5293","","",,,"10.3390/s19235293","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075943425&doi=10.3390%2fs19235293&partnerID=40&md5=c2feb45dac7afdd696c414a98adbcc6d","Geographic Information Systems Research Center, Feng Chia University, Taichung, 40724, Taiwan; College of Construction, Department of Civil Engineering, Feng Chia University, Taichung, 40724, Taiwan","Fang, Y.M., Geographic Information Systems Research Center, Feng Chia University, Taichung, 40724, Taiwan; Chou, T.Y., Geographic Information Systems Research Center, Feng Chia University, Taichung, 40724, Taiwan; Van Hoang, T., Geographic Information Systems Research Center, Feng Chia University, Taichung, 40724, Taiwan; Lee, B.J., College of Construction, Department of Civil Engineering, Feng Chia University, Taichung, 40724, Taiwan","In recent years, owing to the increase of extreme climate events due to global climate change, the foundational erosion of old bridges has become increasingly serious. When typhoons have approached, bridge foundations have been broken due to the insufficient bearing capacity of the bridge column. The bridge bottoming method involves rebuilding the lower structure while keeping the bridge surface open, and transferring the load of the bridge temporarily to the temporary support frame to remove the bridge base or damaged part with insufficient strength. This is followed by replacing the removed bridge base with a new bridge foundation that meets the requirements of flood and earthquake resistance. Meanwhile, monitoring plans should be coordinated during construction using the bottoming method to ensure the safety of the bridge. In the case of this study, the No. 3 line Wuxi Bridge had a maximum bridge age of 40 years, where the maximum exposed length of the foundation was up to 7.5 m, resulting in insufficient flood and earthquake resistance. Consequently, a reconstruction plan was carried out on this bridge. This study took the reconstruction of Wuxi Bridge as the object and established a finite element model using the SAP 2000 computer software based on the secondary reconstruction design of the Wuxi Bridge. The domestic bridge design specification was used as the basis for the static and dynamic analyses of the Wuxi Bridge model. As a result of the analysis, the management value of the monitoring instrument during construction was determined. The calculated management values were compared with the monitoring data during the construction period to determine the rationality of the management values and to explore changes in the behavior of the old bridges and temporary support bridges. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.","Bridge dynamics; Lifting method; Structural health monitoring (SHM)","Climate change; Earthquake engineering; Earthquakes; Floods; Structural health monitoring; Automatic management; Bridge dynamics; Construction period; Global climate changes; Lifting method; Monitoring instruments; Static and dynamic analysis; Structural health monitoring (SHM); Bridges; adult; article; earthquake; finite element analysis; software",,,,,"Feng Chia University, FCU: MOST20181118","Funding: This article is the result of the state-level project titled “Road No.3 Wuxi Bridge Monitoring System in Taiwan”, and has been financed by Geographic Information Systems Research Center, Feng Chia University, Taiwan. Grant number MOST20181118.",,,,,,,,,,"Xu, Y.L., Xia, Y., (2011) Structural Health Monitoring of Long-Span Suspension Bridges, , CRC Press: Boca Raton, FL, USA; Roberts, G.W., Meng, X., Dodson, A., The use of kinematic GPS and triaxial accelerometers to monitor the deflection of large bridges (2001) Proceedings of the 10Th International Symposium on Deformation Measurement, pp. 19-22. , Orange, CA, USA, March; Tamura, Y., Matui, M., Panini, L.-C., Ishibashi, R., Yoshida, A., Measurement of wind-induced response of buildings using RTK-GPS (2002) J. Wind Eng. Ind. Aerodyn, 90, pp. 1783-1793; Andersen, E., Pederson, L., Structural monitoring of the Great Belt East Bridge (1994) Symp. Strait Crossings, 94, pp. 189-195; Sumitoro, S., Matsui, Y., Kono, M., Okamoto, T., Fujii, K., Long span bridge health monitoring system in Japan (2011) Proceedings of the 6Th Annual International Symposium on NDE for Health Monitoring and Diagnostics, pp. 4-8. , Newport Beach, CA, USA, March; Chan, T.H., Yu, L., Tam, H.Y., Ni, Y.Q., Liu, S., Chung, W., Cheng, L., Fiber bragg grating sensor for structural health monitoring of Tsing Ma Bridge: Background and experimental observation (2006) Eng. Struct., 28, pp. 648-659; Wang, H., Tao, T., Li, A., Zhang, Y., Structural health monitoring system for Sutong cable-stayed bridge (2016) Smart Struct. Syst., 18, pp. 317-334; Zhou, G.-D., Yi, T.-H., Recent development on wireless sensor network technology for bridge health monitoring (2013) Math. Probl. Eng., 2013, pp. 1-3; Li, H.-N., Li, D.-S., Ren, L., Yi, T.-H., Jia, Z.-G., Li, K.-P., Structural health monitoring of innovative civil engineering structures in mainland China (2016) Struct. Monit. Maint., 3, pp. 1-32; Meng, X., Roberts, G.W., Dodson, A., Ince, S., Waugh, S., GNSS for structural deformation and deflection monitoring: Implementation and data analysis (2006) Proceedings of the 3Rd Iag/12Th FIG Symposium, pp. 22-24. , Baden, Germany, May; Roberts, G.W., Brown, C.J., Meng, X., Ogundipe, O., Atkins, C., Colford, B., Deflection and frequency monitoring of the Forth Road Bridge, Scotland, by GPS (2012) Proc. Inst. Civ. Eng. Bridge Eng., 165, pp. 105-123; Meng, X., Xie, Y., Bhatia, P., Sowter, A., Psimoulis, P., Colford, B., Ye, J., Ge, M., Research and development of a pilot project using GNSS and Earth Observation (GeoSHM) for structural health monitoring of the Forth Road Bridge in Scotland (2016) Proceedings of the Joint International Symposium on Deformation Monitoring, , Vienna, Austria, 30 March–1 April; Meng, X., Nguyen, D.T., Xie, Y., Owen, J.S., Psimoulis, P., Ince, S., Chen, Q., Bhatia, P., Design and Implementation of a New System for Large Bridge Monitoring—GeoSHM (2018) Sensors, 18, p. 775; Jenkins, C.H., Kjerengtroen, L., Oestensen, H., Sensitivity of parameter changes in structural damage detection (1997) Shock Vib, 4, pp. 27-37; Jang, P.A., (2011) Videogrammetric Technique-Based Monitoring of Structural Vibration, , Master’s Thesis, Zhejiang University, Hangzhou, China; Chang, P.C., Flatau, A., Liu, S.C., Review paper: Health monitoring of civil infrastructure (2003) Struct. Health Monit., 2, pp. 257-267; Zhao, X., Liu, H., Yu, Y., Xu, X., Hu, W., Li, M., Ou, J., Bridge Displacement Monitoring Method Based on Laser Projection-Sensing Technology (2015) Sensors, 15, pp. 8444-8463; Lovse, J.W., Teskey, W.F., Lachapelle, G., Cannon, M.E., 7-Dynamic Deformation Monitoring of Tall Structure Using GPS Technology (1995) J. Surv. Eng., 121, pp. 35-40; Psimoulis, P.A., Stiros, S.C., Measurement of deflections and of oscillation frequencies of engineering structures using robotic theodolites (RTS) (2007) Eng. Struct., 29, pp. 3312-3324; Zhou, J.T., Li, X.G., Xia, R.C., Yang, J., Zhang, H., Health monitoring and evaluation of long-span bridges based on sensing and data analysis: A survey (2017) Sensors, 17, p. 603; Schumacher, T., Shariati, A., Monitoring of structures and mechanical systems using virtual visual sensors for video analysis: Fundamental concept and proof of feasibility (2013) Sensors, 13, pp. 16551-16564; Palazzo, D., Friedmann, R., Nadal, C., Santos, F.M., Veiga, L., Faggion, P., Dynamic monitoring of structures using a robotic total station (2006) Proceedings of the Shaping the Change XXIII FIG Congress, pp. 8-13. , Munich, Germany, October; Park, H.S., Lee, H.M., Adeli, H., Lee, I., A New Approach for Health Monitoring of Structures: Terrestrial Laser Scanning (2007) Comput. Aided Civ. Infrastruct. Eng., 22, pp. 19-30; Zhang, B., Wang, H., Mao, C., Study on Displacement Sensor Based on Difference Operation Spot Center Location Algorithm (2011) Chin. J. Sens. Actuators, 24, pp. 215-219; Andersen, E.Y., (1994) Structural Monitoring of the Great Belt East Bridge., , Ålesund, Norway, 12–15 May 1994; A.A., Balkema: Rotterdam, The Netherlands; Myroll, F., Dibiagio, E., Instrumentation for monitoring the Skarnsunder Cable-stayed Bridge (1994) Proceedings of the 3Rd Symposium on Strait Crossing, pp. 207-215. , Ålesund, Norway, 12–15 June; Fang, Y.M., Pu, J.P., Field tests and simulation of Lion-Head River Bridge (2007) J. Shock Vib. Sci., 48, pp. 181-228; Xu, Y.L., Zhu, L.D., Buffeting response of long-span cable-supported bridges under skew winds. Part 2 case study (2004) J. Sound Vib., 23, pp. 675-697; Lahdensivu, J., Köliö, A., Husaini, D., Alkali-silica reaction in Southern-Finland’s bridges (2018) J. Case Stud. Constr. Mater., 7, pp. 469-475; Chen, Z., Zhou, X., Wang, X., Dong, L., Qian, Y., Deployment of a smart structural health monitoring system for long-span Arch Bridges: A review and a case study (2017) Sensors, 17, p. 2151; Xin, J., Zhou, J., Yang, S.X., Li, X., Wang, Y., Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model (2018) Sensors, 18, p. 298; Bedon, C., Bergamo, E., Izzi, M., Noè, S., Prototyping and Validation of MEMS Accelerometers for Structural Health Monitoring—The Case Study of the Pietratagliata Cable-Stayed Bridge (2018) J. Sens. Actuator Netw., 7, p. 30; Reilly, J., Glisic, B., Identifying Time Periods of Minimal Thermal Gradient for Temperature-Driven Structural Health Monitoring (2018) Sensors, 18, p. 734; Thalla, O., Stiros, S.C., Wind-Induced Fatigue and Asymmetric Damage in a Timber Bridge (2018) Sensors, 18, p. 3867; (1990) Highway Bridge Design Code; the Ministry of Transportation and Communications, , Taipei, Taiwan, In Chinese","Van Hoang, T.; Geographic Information Systems Research Center, Taiwan; email: van@gis.tw",,,"MDPI AG",,,,,14248220,,,"31805645","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85075943425
"Zhou C., Liu Y., Teng W., Ma Z., Wu J.","57214831370;23094877400;36679299100;55479111700;57215328064;","Optimal Placement of Health Monitoring Sensor for Bridge Structure of Air-Cooled Island",2019,"2019 Prognostics and System Health Management Conference, PHM-Qingdao 2019",,,"8943038","","",,,"10.1109/PHM-Qingdao46334.2019.8943038","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078019951&doi=10.1109%2fPHM-Qingdao46334.2019.8943038&partnerID=40&md5=fba96ec0cff4153977898e42b60f8ba9","North China Electric Power University, Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, Beijing, 102206, China","Zhou, C., North China Electric Power University, Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, Beijing, 102206, China; Liu, Y., North China Electric Power University, Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, Beijing, 102206, China; Teng, W., North China Electric Power University, Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, Beijing, 102206, China; Ma, Z., North China Electric Power University, Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, Beijing, 102206, China; Wu, J., North China Electric Power University, Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, Beijing, 102206, China","The air-cooled island of air-cooled thermal power unit consists of dozens of cooling units with fans and their bridges. Its structure and excitation load are complex. The health status of the air-cooled island has an impact on the safe operation of thermal power unit. In this paper, an optimal placement of health monitoring sensor for bridge structure of air-cooled island is proposed based on effective independence method. Through the finite element analysis, the main mode shapes of the bridge structure were obtained. Based on this, a given number of sensors were optimally arranged according to the Fisher information matrix. Then the optimal placement of the health monitoring sensor for the bridge structure of air-cooled island was obtained. The modal guarantee criterion was used to evaluate the placement scheme. The result shows that the sensor placement scheme can ensure the accuracy of modal identification and effectively reflect the structural characteristics of the bridge structure of the air-cooled island. The result can provide a reference for the health monitoring of the bridge structure of air-cooled island. © 2019 IEEE.","air-cooled island bridge; effective independence method; Fisher information matrix; health monitoring; sensor placement","Cooling systems; Fisher information matrix; Fossil fuel power plants; Health; Structures (built objects); Systems engineering; Bridge structures; Effective independence methods; Health monitoring; Modal identification; Optimal placements; Sensor placement; Structural characteristics; Thermal power units; Structural health monitoring",,,,,"2017YFC0805905","ACKNOWLEDGMENT The research was supported by the China National Key Research and Development Project(2017YFC0805905).",,,,,,,,,,"Hongxing, L., ChunLian, Z., XiaoHu, S., Air cooling fan trays harmonic response calculation and analysis (2008) Journal of Wuhan University (Engineering Science ), pp. 104-107; Xue, F., Ren, Z., Analysis of harmonic response of fan bridge truss and structural desigen (2010) Journal of Wuhan University (Engineering Science), 43, pp. 77-80; Qi, Z., Vibration characteristics analysis and contrast of bridge structures between two ACC Fans (2011) Electric Power Construction, (9), pp. 6-10; Zhao, F., Qu, T., Research on vibrational characteristics of fan bridge truss under multiple air cooling fans working condition (2012) North China University of Technology, 1 (24), pp. 68-71; Dou, R., (2010) Experimental Study on Vibration Characteristics of Air Cooling Fan System under Working Conditions, , North China University of Technology; Shao, Y., Vibration analysis of the bridge structures for the direct air cooling system (2016) Applied Energy Technology, (1), pp. 4-7; Zhou, C., Ma, Z., Song, Y., Teng, W., Hu, L., Modal analysis and test study of air-cooled island bridge (2016) Equipment Management and Maintenance, pp. 22-23; Kammer, D.C., Sensor placement for on-orbit modal identification and correlation of large space structures (1991) Journal of Guidance Control & Dynamics, 14 (2), pp. 251-259; Heo, G., Wang, M.L., Satpathi, D., Optimal transducer placement for health monitoring of long span bridge (1997) Soil Dynamics & Earthquake Engineering, 16 (7-8), pp. 495-502; Fu, Y.M., Yu, L., Optimal sensor placement based on MAC and SPGA algorithms (2012) Advanced Materials Research, 594-597, pp. 1118-1122; Poston, W.L., Tolson, R.H., Maximizing the determinant of the information matrix with the effective independence method (1992) Journal of Guidance, Control, and Dynamics, 15 (6), pp. 1513-1514; Chen, Y., Zixing, L.U., An interval effective independence method for optimal sensor placement based on non-probabilistic approach (2017) Science China(Technological Sciences), (2), pp. 16-28; Kim, T., Youn, B.D., Oh, H., Development of a stochastic effective independence (SEFI) method for optimal sensor placement under uncertainty (2018) Mechanical Systems & Signal Processing, 111, pp. 615-627",,"Guo W.Li S.Miao Q.","et al.;Key Laboratory of Aviation Technology for Fault Diagnosis and Health Management Research, AVIC SAMRI;Key Laboratory of Space Utilization, CAS;Qinda Technology Co., Ltd;Qingdao West Coast New Area Association for Science and Technology;Reliability Division of Operations and Research Society of China","Institute of Electrical and Electronics Engineers Inc.","10th Prognostics and System Health Management Conference, PHM-Qingdao 2019","25 October 2019 through 27 October 2019",,156405,,9781728108612,,,"English","Progn. Syst. Heal. Manag. Conf., PHM-Qingdao",Conference Paper,"Final","",Scopus,2-s2.0-85078019951
"Mohammed M.I., Sulaeman E., Mustapha F.","57194004730;6602169596;9038392000;","Adopting dynamic transient response analysis for sensors positioning to monitor cable stayed bridge",2019,"International Journal of Recent Technology and Engineering","7","6",,"54","60",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065181915&partnerID=40&md5=3e2fbc549c99d296203bdb1239535617","Efficomm Global Resources, Sdn Bhd, Kuala Lumpur, Malaysia; Department of Mechanical Engineering, International Islamic University Malaysia, Kuala Lumpur, 53100, Malaysia; Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400, Malaysia","Mohammed, M.I., Efficomm Global Resources, Sdn Bhd, Kuala Lumpur, Malaysia; Sulaeman, E., Department of Mechanical Engineering, International Islamic University Malaysia, Kuala Lumpur, 53100, Malaysia; Mustapha, F., Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400, Malaysia","Periodically, long span bridges require constant structural assessment and continuous monitoring. Recently, existed bridges and vehicles loading mechanism have influenced many studies to predicate the dynamic bridge response and monitor damage occurrence. The objective of present study is set to monitor the Penang (I) Bridge using finite element model to verify the positioning of sensors. 3D model was developed to evaluate the modal parameter’s momentous attitude alteration of the bridge selected grid points and elements. Discussion is focused upon the output parameters such as displacements and stresses generated by vehicles weights. Three types of vehicles were chosen for the purpose of crossing the bridge. In conclusion, from the six lanes of the bridge, high displacements were obtained at the lane 6 (the most left or right side lane) due to vehicles loads at the grid points while maximal stresses were enhanced at lane 6 and 4 (either of the two middle lanes) of the chosen girder beam at bridge spans and cable elements of the infrastructure. Subsequently, sensors were positioned at the grid points in lane 6 and elements located at both lanes due to the mixed loading events. © BEIESP.","Cable stayed bridge; Dynamic traffic load; Finite element method; Structural health monitoring; Weight in motion",,,,,,"International Islamic University Malaysia, IIUM: RIGS17-033-0608","The support of International Islamic University Malaysia under the research grant RIGS17-033-0608 is gratefully acknowledged.",,,,,,,,,,"Roy, K., Ogai, H., Bhattacharya, B., Ray-Chaudhuri, S., Qin, J., Damage Detection of Bridge using Wireless Sensors (2012) In IFAC Workshop on Automation in the Mineral and Metal Industries, 45 (23), pp. 107-111; Shigeishi, M., Colombo, S., Broughton, K.J., Rutledge, H., Batchelor, A.J., Forde, M.C., Acoustic emission to assess and monitor the integrity of bridges (2001) Journal Construction and Building Materials, 15 (1), pp. 35-49; Estes, A.C., Dan, M., Frangopol, and Stuart D. Foltz. Updating reliability of steel miter gates on locks and dams using visual inspection results (2004) Journal of Engineering Structures, 26 (3), pp. 319-333; Wang, Y.-M., Elhag, T.M., Evidential reasoning approach for bridge condition assessment (2008) Expert Systems with Applications, 34 (1), pp. 689-699; Chupanit, P., Phromsorn, C., The importance of bridge health monitoring (2012) International Science Index, 6, pp. 135-138; Emin Aktan, A., Necati Catbas, F., Grimmelsman, K.A., Pervizpour, M., Development of a model health monitoring guide for major bridges (2002) Rep. Dev. FHWA Res. Dev, , , September; Ruiz-Sandoval, B.F.M., Kurata, N., Smart sensing technology for structural health monitoring (2004) In Proceedings of the 13Th World Conference on Earthquake Engineering, pp. 1-6. , http://www.iitk.ac.in/nicee/wcee/article/13_1791; Farrar, C.R., Worden, K., An introduction to structural health monitoring (2007) Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 365 (1851), pp. 303-315; Zou, Y., (2011) The Role of Structural Health Monitoring in Bridge Assessment and Management, pp. 1-168; Collins, J., Mullins, G., Lewis, C., Winters, D., State of the practice and art for structural health monitoring of bridge substructures. Foundation and Geotechnical Engineering, No (2014) FHWA-HRT-09-040, pp. 1-100. , www.fhwa.dot.gov; Hemphill, D., Structural Health Monitoring System for the East 12th Bridge (2004) 2004 Transportation Scholars Conference Iowa State University, Ames; Feng, M.Q., Fukuda, Y., Chen, Y., Soyoz, S., Lee, S., Long-term structural performance monitoring of bridges (2006) Phase II: Development of Baseline Model and Methodology—Report to the California Department of Transportation, pp. 1-248; Balageas, D., Fritzen, C.-P., Güemes, A., (2010) Structural Health Monitoring, 90, pp. 3-370. , John Wiley & Sons; Worden, K., Cross, E.J., On switching response surface models, with applications to the structural health monitoring of bridges (2018) Journal Mechanical Systems and Signal Processing, 98, pp. 139-156; James, M.W., Brownjohn, Structural health monitoring of civil infrastructure (2007) Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 365 (1851), pp. 589-622; Lin, H., Xiang, Y., Jia, Y., . Study on Health Monitoring System Design of Cable-Stayed Bridge (2017) . in International Congress and Exhibition Sustainable Civil Infrastructures: Innovative Infrastructure Geotechnology, pp. 216-228. , Springer, Cham; Nowak, A., Eom, J., Sanli, A., Control of live load on bridges (2000) Transportation Research Record: Journal of the Transportation Research Board, 1696 (55), pp. 136-143; Yail, J.K., Tanovic, R., Gordon Wight, R., Recent ad-vances in performance evaluation and flexural response of existing bridges (2009) Journal of Performance of Constructed Facilities, 23 (3), pp. 190-200; Del Grosso, A.E., (2012) On the Static Monitoring of Bridges and Bridge-Like Structures, pp. 362-367. , CRC Press/Balkema, Leiden; Casadei, P., McCombie, P., Nanni, A., Galati, N., NDT monitoring of bridges using innovative high precision surveying system. In IABSE Symposium Report (2006) International Association for Bridge and Structural Engineering, 92 (2), pp. 50-57; Mohammed, M.I., Mustapha, F., Hrairi, M., Sulaeman, E., Khairol, A.M., Dyang, L., Hojazi, F., (2013) Penang Bridge 1 Loading Analysis Using British Standard and Finite Element Method for Structural Health Monitoring, pp. 1-8; Maeck, J., Peeters, B., De Roeck, G., Damage identification on the Z24 bridge using vibration monitoring (2001) Journal of Smart Materials and Structures, 10 (3), pp. 512-523; Brownjohn, J.M.W., Moyo, P., Omenzetter, P., Yong, L., Assessment of highway bridge upgrading by dynamic testing and finite-element model updating (2003) Journal of Bridge Engineering, 8 (3), pp. 162-172; Ren, W.-X., Peng, X.-L., Lin, Y.-Q., Experimental and analytical studies on dynamic characteristics of a large span cable-stayed bridge (2005) Journal of Engineering Structures, 27 (4), pp. 535-548; Watson, C., Watson, T., Coleman, R., Structural monitoring of cable-stayed bridge: Analysis of GPS versus modeled deflections (2007) Journal of Surveying Engineering, 133 (1), pp. 23-28; Darjani, S., Saadeghvaziri, M.A., Aboobaker, N., Serviceability considerations of high performance steel bridges (2010) In Structures Congress 2010, 369 (69), pp. 752-761; Koo, K.-Y., Brownjohn, J.M.W., List, D.I., Cole, R., Structural health monitoring of the Tamar suspension bridge (2013) Structural Control and Health Monitoring, 20 (4), pp. 609-625; Mohammed, M.I., Mustapha, F., Sulaeman, E., Majid, D.L., (2017) Sensor Placement Based on FE Modal Analysis: Dynamic Characteristic of Cable Stayed Penang (I) Bridge, 4 (9), pp. 145-151. , www.irjet.net/archives/V4/i9/IRJET-V4I929; Mohammed, M.I., Sulaeman, E., Mustapha, F., Dynamic response for structural health monitoring of the Penang (I) cable-stayed bridge (2017) In IOP Conference Series: Materials Science and Engineering, IOP Publishing, 184 (1), pp. 1-10; Gregory, A.J., A. Critical Analysis of the Queen Elizabeth II Bridge (2007) Proceedings of Bridge Engineering 2 Conference 2007, , www.bath.ac.uk, University of Bath, Bath, UK; Hernandez, S., Baldomir, A., Nieto, F., Jurado, J.A., Conceptual design of the cable stayed Miradoiros Bridge in La Coruna (Spain) (2010) In Structures Congress 2010, 369 (196), pp. 2164-2175; Peck, T., A critical Analysis of The Franjo Tudman Bridge in Dubrovnik, Croatia (2011) Proceedings of Bridge Engineering 2 Conference, , www.bath.ac.uk, University of Bath, Bath, UK, April; Chin, F.K., The Penang bridge: Planning, design and construction (1988) Lembaga Lebuhraya Malaysia; Cho, J.-W., Jeon, S., Sang-Hwa, Y., Chang, S.-H., Optimum spacing of TBM disc cutters: A numerical simulation using the three-dimensional dynamic fracturing method (2010) Tunnelling and Underground Space Technology, 25 (3), pp. 230-244; Mohammed, M.I., Sulaeman, E., Mustapha, F., Mohd Khairolariffin, A.M., Sensor Placement Based on Static Finite Element Data of Cable Stayed Bridge (2017) International Journal of Emerging Technology and Advanced Engineering, 7 (7), pp. 427-432. , www.ijetae.com/Volume7Issue7.html; Yi, T.-H., Li, H.-N., Methodology developments in sensor placement for health monitoring of civil infrastructures (2012) International Journal of Distributed Sensor Networks, 8 (8); Caprani, C.C., Obrien, E.J., McLachlan, G.J., Characteristic traffic load effects from a mixture of loading events on short to medium span bridges (2008) Structural Safety, 30 (5), pp. 394-404","Mohammed, M.I.; Efficomm Global Resources, Sdn Bhd, Malaysia; email: esulaeman@iium.edu.my",,,"Blue Eyes Intelligence Engineering and Sciences Publication",,,,,22773878,,,,"English","Int. J. Recent Technol. Eng.",Article,"Final","",Scopus,2-s2.0-85065181915
"Feng K., Casero M., González A.","57208625402;55848371300;12782485200;","The use of accelerometers in UAVs for bridge health monitoring",2019,"13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019",,,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126508692&partnerID=40&md5=98f06000689930e3785e3022aabe6fe0","School of Civil Engineering, University College Dublin, Dublin, Ireland","Feng, K., School of Civil Engineering, University College Dublin, Dublin, Ireland; Casero, M., School of Civil Engineering, University College Dublin, Dublin, Ireland; González, A., School of Civil Engineering, University College Dublin, Dublin, Ireland","Unmanned Aerial Vehicles (UAVs) technology has gained considerable popularity in bridge structural health monitoring for its strengths, such as low cost, safety and high energy efficiency. This paper envisions a scenario in which accelerometers are mounted onto UAVs, which then are able to gather acceleration signals by self-attaching to the bridge. However, battery life is an issue in UAVs with the subsequent limitation in the duration of the measurements. Therefore, this paper carries out a simulation on mode shape extraction from a short data burst by utilising an output only technique, the so-called frequency domain decomposition (FDD). Modal assurance criterion (MAC) is used as a statistical indicator to check differences between the estimated mode shapes and the eigenvectors from finite element analysis. The short acceleration response is generated using a planar vehicle-bridge interaction system where the moving load is modelled as two quarter-cars and the bridge is modelled as a simply supported beam. The impact of signal noise, vehicle speed and signal duration on the accuracy of the estimated mode shapes is investigated. FDD is shown to achieve high values of MAC even for short data bursts. Damping ratio is identified as a significant source of MAC discrepancy in the extraction of mode shapes. The stiffness loss due to a crack is introduced in the beam to evaluate how damage affects the mode shape compared to operational effects. How the MAC values vary with crack location and damage severity is discussed for the first three mode shapes. © 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019. All rights reserved.",,"Accelerometers; Antennas; Bridges; Domain decomposition methods; Energy efficiency; Extraction; Frequency domain analysis; Vehicles; Acceleration response; Bridge health monitoring; Bridge structural health monitoring; Frequency domain decomposition; High energy efficiency; Modal assurance criterion; Simply supported beams; Statistical indicators; Structural health monitoring",,,,,"Santa Fe Institute, SFI; Science Foundation Ireland, SFI: 16/US/I3277","This research has received funding from Science Foundation Ireland (SFI)'s US-Ireland R&D partnership programme under the proposal id. 16/US/I3277 titled MARS-Fly.","7. ACKNOWLEDGEMENTS This research has received funding from Science Foundation Ireland (SFI)’s US-Ireland R&D partnership programme under the proposal id. 16/US/I3277 titled MARS-Fly.",,,,,,,,,"Brincker, R., Zhang, L., Andersen, P., Modal identification of output-only systems using frequency domain decomposition (2001) Smart Materials and Structures, 10 (3), pp. 441-445; Cantero, D., González, A., Location and evaluation of maximum dynamic effects on a simply supported beam due to a quarter-car model (2008) Bridge and Infrastructure Research in Ireland (BRI 2008), , Galway, Ireland, December, 2008; Chen, S., Laefer, D.F., Mangina, E., State of technology review of civilian UAVs (2016) Recent Patents on Engineering, 10 (3), pp. 160-174; González, A., OBbrien, E.J., Li, Y.-Y., Cashell, K., The use of vehicle acceleration measurements to estimate road roughness (2008) Vehicle System Dynamics, 46 (6), pp. 483-499; Li, J., Hao, H., A review of recent research advances on structural health monitoring in Western Australia (2016) Structural Monitoring and Maintenance, 3 (1), pp. 33-49; Malekjafarian, A., O'Brien, E.J., Identification of bridge mode shapes using short time frequency domain decomposition of the responses measured in a passing vehicle (2014) Engineering Structures, 81, pp. 386-397; Pastor, M., Binda, M., Harčarik, T., Modal assurance criterion (2012) Procedia Engineering, 48, pp. 543-548; Sinha, J.K., Friswell, M., Edwards, S., Simplified models for the location of cracks in beam structures using measured vibration data (2002) Journal of Sound and Vibration, 251 (1), pp. 13-38; Weng, J.-H., Loh, C.-H., Lynch, J.P., Lu, K.-C., Lin, P.-Y., Wang, Y., Output-only modal identification of a cable-stayed bridge using wireless monitoring systems (2008) Engineering Structures, 30 (7), pp. 1820-1830; Yang, Y., Li, Y., Chang, K., Constructing the mode shapes of a bridge from a passing vehicle: A theoretical study (2014) Smart Structures and Systems, 13 (5), pp. 797-819",,,,"Seoul National University","13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019","26 May 2019 through 30 May 2019",,149815,,,,,"English","Int. Conf. Appl. Stat. Probab. Civ. Engi., ICASP",Conference Paper,"Final","",Scopus,2-s2.0-85126508692
"Daneshvar M.H., Gharighoran A.R., Karamodin A., Zareei S.R.","57217529921;25936131000;57216260318;57191225558;","Damage detection of Multi span beam with column supports by Rayleigh-Ritz method",2019,"9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings","2",,,"1556","1561",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091659899&partnerID=40&md5=f334dee8fc7b7c630cdaea47b5448a0e","Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran; Department of Civil Engineering and Transportation, University of Isfahan, Iran; Department of Civil Engineering, Ferdowsi University of Mashhad, Iran","Daneshvar, M.H., Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran; Gharighoran, A.R., Department of Civil Engineering and Transportation, University of Isfahan, Iran; Karamodin, A., Department of Civil Engineering, Ferdowsi University of Mashhad, Iran; Zareei, S.R., Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran","In this paper, a new method for determining the location and the severity of damage in multi-spans beams and ""beam and column"" is proposed, which has an ability to reveal the structural damage with any type of support conditions. The proposed method is an expanded version of the finite element method (FE) by the assistance of the Ritz method which is called RDDM (Ritz Damage Detection Method). The innovation of the proposed method in this paper is the omission of some limitations of the RDDM method, including unique support conditions, and applying the effects of ""beam and column"". In this paper, by using the shape function, the definition of support conditions as a spring, and entering the interaction effect of ""beam and column"", the equations of RDDM method are developed to provide possible damage detection of the bridges with all support conditions. Also, SVD (singular value decomposition) method is used to determine the quantity and the severity of the damage, which is sensitive to dynamic characteristics changes, originated from the damage. The efficiency and capability of the proposed method for damage detection are evaluated by a numerical sample of ""a multi-span beam"" and ""beam to column connections. The investigation results show that the proposed method has the ability to identify the location and the severity of the damages. Copyright © SHMII 2019. All rights reserved.",,"Numerical methods; Singular value decomposition; Structural analysis; Structural health monitoring; Beam-to-column connections; Dynamic characteristics; Interaction effect; Multi-span beams; Rayleigh-Ritz methods; Structural damages; Support conditions; SVD(singular value decomposition); Damage detection",,,,,,,,,,,,,,,,"Chopra, A. K., (2001) Dynamics of structures: Theory and applications to earthquake engineering, , Prentice-Hall; Craig, R. R., Kurdila, A. J., (2006) Fundamentals of structural dynamics, , John Wiley & Sons; Eraky, A., Anwar, A. M., Saad, A., Abdo, A., Damage detection of flexural structural systems using damage index method ? Experimental approach (2015) Alexandria Engineering Journal, 54 (3), pp. 497-507. , http://dx.doi.org/10.1016/j.aej.2015.05.015; Farrar, C. R., Baker, W. E., Bell, T. M., Cone, K. M., Darling, T. W., Duffey, T. A., Migliori, A., (1994) Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande, , Retrieved from; García, P. M., Araújo dos Santos, J. V., Lopes, H., A new technique to optimize the use of mode shape derivatives to localize damage in laminated composite plates (2014) Composite Structures, 108, pp. 548-554. , http://dx.doi.org/10.1016/j.compstruct.2013.09.050; Gharighoran, A., Daneshjoo, F., Khaji, N., Use of Ritz method for damage detection of reinforced and post-Tensioned concrete beams (2009) Construction and Building Materials, 23 (6), pp. 2167-2176. , http://dx.doi.org/10.1016/j.conbuildmat.2008.12.017; Ilanko, S., Monterrubio, L., Mochida, Y., (2015) The Rayleigh-Ritz method for structural analysis, , John Wiley & Sons; Kokot, S., Zembaty, Z., Vibration based stiffness reconstruction of beams and frames by observing their rotations under harmonic excitations?numerical analysis (2009) Engineering structures, 31 (7), pp. 1581-1588; Lee, H., Ng, T., Natural frequencies and modes for the flexural vibration of a cracked beam (1994) Applied Acoustics, 42 (2), pp. 151-163; Li, Y. Y., Cheng, L., Yam, L. H., Wong, W. O., Identification of damage locations for plate-like structures using damage sensitive indices: strain modal approach (2002) Computers & Structures, 80 (25), pp. 1881-1894. , http://dx.doi.org/10.1016/S0045-7949(02)00209-2; Limongelli, M. P., Siegert, D., Merliot, E., Waeytens, J., Bourquin, F., Vidal, R., Cottineau, L. M., Damage detection in a post tensioned concrete beam ? Experimental investigation (2016) Engineering structures, 128, pp. 15-25. , http://dx.doi.org/10.1016/j.engstruct.2016.09.017; Maghsoodi, A., Ghadami, A., Mirdamadi, H. R., Multiple-crack damage detection in multi-step beams by a novel local flexibility-based damage index (2013) Journal of Sound and Vibration, 332 (2), pp. 294-305. , http://dx.doi.org/10.1016/j.jsv.2012.09.002; Sarker, L., Xiang, Y., Uy, B., Zhu, X. Q., Damage detection of circular cylindrical shells by Ritz method (2011) the 9th International Conference on Damage Assessment of Structures, , Paper presented at","Gharighoran, A.R.; Department of Civil Engineering and Transportation, Iran; email: a.ghari@trn.ui.ac.ir","Chen G.Alampalli S.",,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019","4 August 2019 through 7 August 2019",,161240,,9780000000002,,,"English","Int. Conf. Struct. Health Monit. Intell. Infrastruct.: Transf. Res. Pract., SHMII - Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85091659899
"Casero M., Feng K., González A.","55848371300;57208625402;12782485200;","Modal analysis of a bridge using short-duration accelerations",2019,"9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings","1",,,"174","179",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091468830&partnerID=40&md5=6801567d0f598db735fc8f49199974c1","School of Civil Engineering, University College Dublin, Dublin, Ireland","Casero, M., School of Civil Engineering, University College Dublin, Dublin, Ireland; Feng, K., School of Civil Engineering, University College Dublin, Dublin, Ireland; González, A., School of Civil Engineering, University College Dublin, Dublin, Ireland","The application of unmanned aerial vehicle technology to bridge structural health monitoring has become a hot research topic due to its low cost, safety and high energy efficiency. However, flight duration and battery life are substantial technical limitations. Is a short data burst sufficient for damage detection? This paper intends to answer this question by developing a novel approach based on frequency domain decomposition to obtain the mode shapes from a short data burst. Then, the modal assurance criterion is used as an indicator of the differences between the estimated mode shapes from the short data burst and the exact eigenvectors from finite element analysis. Here, the short data burst is obtained from the simulated acceleration response of a bridge beam model due to the crossing of two quarter-cars. A new damage indicator based on the modal assurance criterion profile along the beam is proposed to locate and quantify damage. © 2019 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings. All rights reserved.",,"Antennas; Damage detection; Domain decomposition methods; Energy efficiency; Frequency domain analysis; Modal analysis; Acceleration response; Bridge structural health monitoring; Frequency domain decomposition; High energy efficiency; Hot research topics; Modal assurance criterion; Short durations; Technical limitations; Structural health monitoring",,,,,"Science Foundation Ireland, SFI: 16/US/I3277","This research has received funding from Science Foundation Ireland (SFI)’s US-Ireland R&D partnership programme under the proposal id. 16/US/I3277 titled MARS-Fly.",,,,,,,,,,"Brincker, R., Zhang, L., Andersen, P., Modal identification of output-only systems using frequency domain decomposition (2001) Smart Mater. Struct, 10 (3), pp. 441-445; Cantero, D., González, A., Location and evaluation of maximum dynamic effects on a simply supported beam due to a quarter-car model (2008) Bridge and Infrastructure Research In Ireland (BRI 2008), , Galway, Ireland, December, 2008; Cao, M. S., Sha, G. G., Gao, Y. F., Ostachowicz, W., Structural damage identification using damping: a compendium of uses and features (2017) Smart Mater. Struct, 26 (4), p. 043001; Chen, S., Laefer, D. F., Mangina, E., State of technology review of civilian UAVs (2016) Recent Pat. Eng, 10 (3), pp. 160-174; Dahak, M., Touat, N., Kharoubi, M., Damage detection in beam through change in measured frequency and undamaged curvature mode shape (2019) Inverse Probl. Sci. Eng, 27 (1), pp. 89-114; O'Brien, E. J., Malekjafarian, A., A mode shape‐based damage detection approach using laser measurement from a vehicle crossing a simply supported bridge (2016) Struct. Control. Health Monit, 23 (10), pp. 1273-1286; Pastor, M., Binda, M., Harčarik, T., Modal assurance criterion (2012) Procedia Eng, 48, pp. 543-548; Sinha, J. K., Friswell, M., Edwards, S., Simplified models for the location of cracks in beam structures using measured vibration data (2002) J. Sound Vibr, 251 (1), pp. 13-38","Casero, M.; School of Civil Engineering, Ireland; email: miguel.caseroflorez@ucd.ie","Chen G.Alampalli S.",,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019","4 August 2019 through 7 August 2019",,161240,,9780000000002,,,"English","Int. Conf. Struct. Health Monit. Intell. Infrastruct.: Transf. Res. Pract., SHMII - Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85091468830
"Lee J., Lee K.-C., Sim S.-H., Lee J., Lee S., Lee Y.-J.","56389236700;55653115800;55440211700;57119017900;57191575782;36548206500;","Probabilistic prediction of vertical deflection of bridges using Gaussian process regression with FE analysis",2019,"9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings","1",,,"133","138",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091440605&partnerID=40&md5=30b36a1aff53e732473f2fb24b72db34","School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea; Advanced Railroad Civil Engineering Division, Korea Railroad Research Institute, Uiwang, 16105, South Korea","Lee, J., School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea; Lee, K.-C., Advanced Railroad Civil Engineering Division, Korea Railroad Research Institute, Uiwang, 16105, South Korea; Sim, S.-H., School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea; Lee, J., School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea; Lee, S., School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea; Lee, Y.-J., School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea","The prediction of the long-term deflection of concrete bridges is not easy because it is induced by several complex physical phenomena such as creep and shrinkage. Several physics-based equations have been suggested in various standards. However, the predictions based on these equations can be different from actual measurements owing to various uncertainty sources including material properties, traffic loads, and temperature. In this study, a probabilistic method is proposed to provide a reliable probabilistic prediction on the long-term vertical deflection of bridges. The proposed method adopts Finite Element (FE) analysis model based on a conventional physics-based equation as a basis function and introduces a Gaussian process to construct a probabilistic prediction model. Based on the actual measurements of bridge vertical deflection, the parameters of the Gaussian process model are determined through optimization to maximize the probability of observing the given measurement data. The constructed Gaussian process model can provide 95% and 99% prediction intervals as well as the predictive mean on bridge vertical deflection. The proposed method is applied to an actual bridge in the Republic of Korea, and the prediction results show good agreement with the actual measurements. © 2019 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings. All rights reserved.",,"Deflection (structures); Electric measuring bridges; Finite element method; Forecasting; Gaussian distribution; Gaussian noise (electronic); Shrinkage; Structural health monitoring; Uncertainty analysis; Creep and shrinkages; Gaussian process models; Gaussian process regression; Long-term deflections; Probabilistic methods; Probabilistic prediction; Uncertainty sources; Vertical deflections; Predictive analytics",,,,,"Ministry of Land, Infrastructure and Transport, MOLIT; Korea Railroad Research Institute, KRRI; Korea Agency for Infrastructure Technology Advancement, KAIA","This research was supported by a grant from Smart Civil Infrastructure Research Program (19SCIP-B138406-04) funded by Ministry of Land, Infrastructure and Transport (MOLIT) of the Korean government and Korea Agency for Infrastructure Technology Advancement (KAIA). This research was also supported by a grant from R&D Program of the Korean Railroad Research Institute, Republic of Korea.",,,,,,,,,,"(2008) Guide for Modeling and Calculating Shrinkage and Creep in Hardened Concrete (ACI 209.2R-09), , ACI Committee 209 American Concrete Institute, Farmington Hills, MI; Barr, P. J., Angomas, F., Differences between calculated and measured long-term deflections in a prestressed concrete girder bridge (2010) Journal of Performance of Constructed Facilities, 24 (6), pp. 603-609; Bažant, Z. P., Baweja, S., Creep and shrinkage prediction model for analysis and design of concrete structures: Model B3 (2000) ACI Special Publications, 194, pp. 1-84; Bažant, Z. P., Yu, Q., Li, G. H., Klein, G. J., Kristek, V., Excessive deflections of record-span prestressed box girder (2010) Concrete International, 32, pp. 44-52. , (06); Structural Concrete: Textbook on Behaviour, Design and performance, Updated Knowledge of the CEB/FIP Model Code 190 (1999), 1, pp. 35-52. , CEB-FIP Bulleti 2, fib, Lausanne, Switzerland; Kamatchi, P., Rao, K. B., Dhayalini, B., Saibabu, S., Parivallal, S., Ravisankar, K., Iyer, N. R., Long-term prestress loss and camber of box-girder bridge (2014) ACI Structural Journal, 111 (6), p. 1297; (2012) Concrete Design Code and Commentary, , KCI Committee. Korea Concrete Institute; Lee, J., Lee, K.-C., Cho, S., Sim, S.-H., Computer vision-based structural displacement measurement robust to light-induced image degradation for in-service bridges (2017) Sensors, 17 (10), p. 2317; Lee, J., Lee, K.-C., Lee, Y.-J., Long-Term Deflection Prediction from Computer Vision-Measured Data History for High-Speed Railway Bridges (2018) Sensors, 18 (5), p. 1488; Lee, Y.-J., Sim, S.-H., Lee, J.H., Jeong, S., Lee, S.H., Lee, S.M., Lee, J.B., Jeong, D.J., (2018) Demonstrative Study on Smart Sensing and Forecasting of Long-term Deformation of Railroad Bridges, , Korea Railroad Research Institute Report PK1801C-6 (in Korean); Lee, J., Lee, K.-C., Lee, S., Lee, Y.-J., Sim, S.-H., Long-term displacement measurement of bridges using LiDAR system Structural Control and Health Monitoring, Under Review; Muirhead, R. J., (2009) Aspects of multivariate statistical theory, 197. , John Wiley & Sons; Murphy, K.P., (2014) Machine Learning, A Probabilistic Perspective, , The MIT Press: Cambridge, MA, USA; Rasmussen, C. E., (2003) Prediction Interval Estimation Techniques for Empirical Modeling Strategies and Their Applications to Signal Validation Tasks, , Ph.D. Thesis, University of Tennessee Knoxville, TN, USA; Rasmussen, C. E., Williams, C. K., (2006) Gaussian process for machine learning, , MIT press","Lee, J.; School of Urban and Environmental Engineering, South Korea; email: jblee@unist.ac.kr","Chen G.Alampalli S.",,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019","4 August 2019 through 7 August 2019",,161240,,9780000000002,,,"English","Int. Conf. Struct. Health Monit. Intell. Infrastruct.: Transf. Res. Pract., SHMII - Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85091440605
"Zolghadri N., Grimmelsman K.A.","55879677500;6602332002;","Evaluation of finite element model calibration for a multi-beam highway bridge by static and dynamic test measurements",2019,"9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings","1",,,"320","325",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091430599&partnerID=40&md5=d5141d0204cd2ed63edd73ac23dc8ef0","Pennoni Associates Inc., United States","Zolghadri, N., Pennoni Associates Inc., United States; Grimmelsman, K.A., Pennoni Associates Inc., United States","Field-measurement-calibrated finite element models are often an essential tool for the condition evaluation and performance assessments of existing bridges. Such calibrated finite element models are able to better capture and represent the actual, in-situ behavior characteristics of the structure than do the highly idealized conceptualizations and analytical models typically employed for their design. Field-measurement calibrated finite element models are generally updated by the structural identification framework using static measurements such as strains and displacements from controlled truck load tests or using dynamic characteristics such as natural frequencies and mode shapes identified from a number of possible variations of vibration testing of the structure. The selection of data types employed for the model updating will impact the required computations to minimize error functions between the experiment and finite element analysis. This study presents a comparison of the properties from a calibrated finite-element model by using different sets of static and dynamic measurements. A multi-beam highway bridge instrumented with strain transducers and accelerometers was subjected to a controlled truck load test and ambient vibration testing. The static and dynamic bridge characteristics extracted from these field tests were used to calibrate the same a-priori finite-element of the bridge. Different sets of model parameters were selected to be updated, including material properties and boundary condition. The updated model characteristics and prediction results using static and dynamic measurements are compared and evaluated. Recommendations are provided relative to the differences in the calibrated finite element models observed from each calibration approaches. © 2019 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings. All rights reserved.",,"Electric measuring bridges; Highway bridges; Load testing; Strain; Strain measurement; Structural design; Structural health monitoring; Trucks; Vibration analysis; Ambient Vibration Testing; Behavior characteristic; Dynamic characteristics; Finite element model calibrations; Natural frequencies and modes; Performance assessment; Static and dynamic tests; Structural identification; Finite element method",,,,,,,,,,,,,,,,"Aktan, A. E., Farhey, D. N., Helmicki, A. J., Brown, D. L., Hunt, V. J., Lee, K. L., Levi, A., Structural Identification for Condition Assessment: Experimental Arts (1997) J. Struct. Eng, 123 (12), pp. 1674-1684; Farrar, C. R., Worden, K., An Introduction to Structural Health Monitoring (2007) Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365 (1851), pp. 303-315; Friswell, M., Mottershead, J. E., (2013) Finite Element Model Updating in Structural Dynamics, 38. , Springer Science & Business Media; Grimmelsman, K. A., Dynamic Characterization of a Prestressed Concrete Bridge by Strain and Acceleration Measurements (2019) Dynamics of Civil Structures, Volume 2, Conference Proceedings of the Society for Experimental Mechanics Series, , Springer, Cham; Ren, W. X., Chen, H. B., Finite Element Model Updating in Structural Dynamics by Using the Response Surface Method (2010) Eng. Struct, 32 (8), pp. 2455-2465; Sanayei, M., Khaloo, A., Gul, M., Catbas, F.N., Automated Finite Element Model Updating of a Scale Bridge Model using Measured Static and Modal Test Data (2015) Eng. Struct., Elsevier Ltd, 102, pp. 66-79; Schlune, H., Plos, M., Gylltoft, K., Improved Bridge Evaluation through Finite Element Model Updating using Static and Dynamic Measurements (2009) Eng. Struct, 31 (7), pp. 1477-1485; Torres, V., Zolghadri, N., Maguire, M., Barr, P., Halling, M., Experimental and Analytical Investigation of Live-Load Distribution Factors for Double Tee Bridges (2018) J. Perf. Constr. Facil, 33 (1), p. 04018107; Zolghadri, N., (2017) Short and Long-Term Structural Health Monitoring of Highway Bridges, p. 5626. , Ph.D. Disseration, Utah State University. All Graduate Theses and Dissertations",,"Chen G.Alampalli S.",,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019","4 August 2019 through 7 August 2019",,161240,,9780000000002,,,"English","Int. Conf. Struct. Health Monit. Intell. Infrastruct.: Transf. Res. Pract., SHMII - Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85091430599
"Cervantes D.A., Guerrero H., Cecilio V., Escobar J.A., Gómez R.","57214472356;57189238909;57219147082;7101961394;7402250472;","Structural behavior of the support of a railway bridge that the footing was rebuilt",2019,"9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings","1",,,"620","625",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091414829&partnerID=40&md5=4b642ffac6a566feac93db1094cfdc14","Institute of Engineering, UNAM, Mexico","Cervantes, D.A., Institute of Engineering, UNAM, Mexico; Guerrero, H., Institute of Engineering, UNAM, Mexico; Cecilio, V., Institute of Engineering, UNAM, Mexico; Escobar, J.A., Institute of Engineering, UNAM, Mexico; Gómez, R., Institute of Engineering, UNAM, Mexico","A study on the behavior of a bridge support (column, footing and foundation piles) with re-foundation is presented. Re-foundation was carried out because two out-of-six piles, that make up its foundation, presented doubtful integrity. Therefore, the construction of three new piles and expansion of the shoe to improve the load capacity was proposed. For this study three numerical models were developed: two elastic (based on finite elements) and one non-linear (based on multi-springs). In one of the finite element models, ""solid"" elements were used; while in the other used frame elements. The models were calibrated with the results of ambient vibration. The multi-springs had the ability to represent the inelastic behavior of the structural components. Likewise, each model was analyzed for five case studies emulating scenarios the foundation pile with doubtful integrity. To determine the support's behavior, the numerical models were studied applying dynamic response analysis using a series of grond motions (i.e. synthetic earthquakes obtained from the project design spectrum). For the multi-springs model, additional non-linear static analysis (pushover) was performed to obtain the yielding displacement. According to the obtained results, it was seen that, from a structural point of view, the improved support has adequate load capacity to resist the expected acting loads. © 2019 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings. All rights reserved.",,"Finite element method; Numerical models; Pile foundations; Piles; Springs (components); Ambient vibrations; Dynamic response analysis; Foundation piles; Inelastic behavior; Non-linear static analysis; Structural behaviors; Structural component; Structural point; Structural health monitoring",,,,,,,,,,,,,,,,"Abrahamson, N., (1993) Non-Stationary spectral matching program RSPMATCH; (2014) Building code requirements for structural concrete (ACI 318-14), , American Concrete Institute, Farmington Hills, Michigan, EUA; Bendat, J.S., Piersol, A.G., (1989) Random data: analysis and measurements procedures, , 2ª edition, Wiley Interscience, Nueva York, EUA; (2017) SAP2000 Version 19.2.1, Integrated Finite Element Analysis and Design of Structures, , CSI, Computers and Structures Inc., Berkeley, California, EUA; Idriss, I.M., Sun, J.I., (1992) User's manual for SHAKE91, , Center for Geotechnical Modeling. Department of Civil and Environmental Engineering. University of California. Davis, EUA; Kang Ning, L., Three-dimensional nonlinear static/dynamic structural analysis computer program (2010) Data-Input Manual, , Vancouver, Canada; (2004) Complementary technical standards for design and construction of concrete structures, , NTCC-04, Gaceta Oficial del Distrito Federal, México; (2004) Building Regulations for the Distrito Federal, , RCDF, Gaceta Oficial del Distrito Federal, México",,"Chen G.Alampalli S.",,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019","4 August 2019 through 7 August 2019",,161240,,9780000000002,,,"English","Int. Conf. Struct. Health Monit. Intell. Infrastruct.: Transf. Res. Pract., SHMII - Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85091414829
"Au F.T.K., Si X.T.","7005204072;36676577700;","Effects of long-term time-dependent behaviour on dynamic properties of cable-stayed bridges",2019,"EG-ICE 2010 - 17th International Workshop on Intelligent Computing in Engineering",,,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083946657&partnerID=40&md5=ddd665a06827a686430fb9ff9e12e7f5","University of Hong Kong, Hong Kong, Hong Kong","Au, F.T.K., University of Hong Kong, Hong Kong, Hong Kong; Si, X.T., University of Hong Kong, Hong Kong, Hong Kong","A structural health monitoring system installed on a bridge provides the necessary data for engineers to evaluate its integrity, durability and reliability through the observation of changes in bridge properties caused by any damage or deterioration. However, the time-dependent behaviour of construction materials such as concrete and steel cables also causes changes in structural characteristics. If these are not taken into account properly, false alarms may result. This paper presents a systematic and efficient method to study the effects of long-term time-dependent behaviour due to concrete creep, concrete shrinkage and cable relaxation on the dynamic properties of cable-stayed bridges. The finite element model of the cable-stayed bridge is built up with beam elements and proper cable elements considering their geometric nonlinearity and time-dependent effects. The long-term time-dependent analysis is carried out using an efficient single-step finite element method using the age-adjusted elasticity modulus and shrinkage-adjusted elasticity modulus for concrete, and the relaxation-adjusted elasticity modulus for steel cables. Then the dynamic properties of the bridge can be obtained by the subspace iteration method. The effects of long-term time-dependent behaviour including concrete creep, concrete shrinkage and cable relaxation on the dynamic properties of typical cable-stayed bridges are examined in detail. © Nottingham University Press","Cable-stayed bridges; FEM; Free vibration analysis; Single-step method; Time-dependent","Cable stayed bridges; Concretes; Creep; Deterioration; Elastic moduli; Elasticity; Finite element method; Intelligent computing; Iterative methods; Shrinkage; Structural health monitoring; Vibration analysis; Free-vibration analysis; Single-step method; Structural characteristics; Structural health monitoring systems; Subspace iteration method; Time dependent; Time-dependent analysis; Time-dependent behaviour; Cables",,,,,"HKU 7102/08E","The work described in this paper has been supported by the Research Grants Council (RGC) of the Hong Kong Special Administrative Region, China (RGC Project No. HKU 7102/08E).",,,,,,,,,,"Au, F.T.K., Cheng, Y.S., Cheung, Y.K., Effects of random road surface roughness and long-term deflection of prestressed concrete girder and cable-stayed bridges on impact due to moving vehicles (2001) Computers and Structures, 79, pp. 853-872; Au, F.T.K., Cheng, Y.S., Cheung, Y.K., Zheng, D.Y., On the determination of natural frequencies and mode shapes of cable-stayed bridges (2001) Applied Mathematical Modelling, 25, pp. 1099-1115; Au, F.T.K., Liu, C.H., Lee, P.K.K., Shrinkage analysis of reinforced concrete floors using shrinkage-adjusted elasticity modulus (2007) Computer and Concrete, 4, pp. 477-497; Au, F.T.K., Liu, C.H., Lee, P.K.K., Creep and shrinkage analysis of reinforced concrete frames by history-adjusted and shrinkage-adjusted elasticity moduli (2009) The Structural Design of Tall and Special Buildings, 18, pp. 13-35; Au, F.T.K., Si, X.T., Time-dependent analysis of frames taking into account creep, shrinkage and cable relaxation (2009) 7th International Conference on Tall Buildings 2009, , Hong Kong; (1993) CEB-FIP Model Code 1990, , COMITÉ EURO-INTERNATIONAL DU BÉTON, London, Thomas Telford; Cook, R.D., Malkus, D.S., Plesha, M.E., Witt, R.J., (2001) Concepts and Application of Finite Element Analysis, , New York, NY, Wiley; Curley, N.C., Shepherd, R., Analysis of concrete cable-stayed bridges for creep, shrinkage and relaxation effects (1996) Computer and Structures, 58, pp. 337-350; Ghali, A., Favre, R., Elbadry, M., (2002) Concrete Structures: Stresses and Deformations, , London, Spon Press; Magura, D.D., Sozen, M.A., Siess, C.P., A study of stress relaxation in prestressing reinforcement (1964) PCI Journal; McGuire, W., Gallagher, R.H., Ziemian, R.D., (2002) Matrix Structural Analysis, , New York, John Wiley; Sapountzakis, E.J., Katsikadelis, J.T., Creep and shrinkage effect on the dynamics of reinforced concrete slab-and-beam structures (2003) Journal of Sound and Vibration, 260, pp. 403-416; Si, X.T., Au, F.T.K., Su, R.K.L., Tsang, N.C.M., Time-dependent analysis of concrete bridges with creep, shrinkage and cable relaxation (2009) The Twelfth International Conference on Civil, Structural and Environmental Engineering Computing, , 2009, Funchal, Madeira, Portugal; Zienkiewicz, O.C., Taylor, R.L., (1989) The Finite Element Method, , London, McGraw-Hill",,"Tizani W.","AceCad Software;Acumen;Autodesk, Inc.;SOFiSTiK AG;Tekla International","Nottingham","17th International Workshop on Intelligent Computing in Engineering, EG-ICE 2010","30 June 2010 through 2 July 2010",,149385,,9781907284601,,,"English","EG-ICE - Int. Workshop Intell. Comput. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85083946657
"Schnellenbach-Held M., Karczewski B.","56051120400;56052041200;","Physical nonlinear model identification in model-based long-term structural health monitoring",2019,"EG-ICE 2010 - 17th International Workshop on Intelligent Computing in Engineering",,,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083945259&partnerID=40&md5=1144d32cc22418dc2e5d4955afe329bc","Institute of Structural Concrete Essen, University of Duisburg-Essen, Germany","Schnellenbach-Held, M., Institute of Structural Concrete Essen, University of Duisburg-Essen, Germany; Karczewski, B., Institute of Structural Concrete Essen, University of Duisburg-Essen, Germany","In this paper a model identification approach for concrete bridge superstructures is presented. The approach aims at identifying structural characteristics during long-term structural health monitoring, i.e. when the bridge is open for traffic and weights and locations of currently passing vehicles are unknown. Thus, system and load properties have to be determined at the same time. The approach is based on the analysis of measured static responses, such as strains and deformations. To obtain further information for the identification of the loading, reaction forces need to be recorded additionally. The actual model-updating problem is solved by an optimization technique taken from the field of evolutionary algorithms. To evaluate the deformation behavior of concrete structures in detail, physical nonlinear finite element analyses are carried out. The approach is verified by conducting numerical simulations on two damaged reinforced concrete girders, which are loaded with a single force. © Nottingham University Press","Model adaptation; Model-updating; Structural health monitoring; System identification","Deformation; Identification (control systems); Intelligent computing; Reinforced concrete; Bridge superstructure; Model Adaptation; Model identification; Model updating; Non-linear finite-element analysis; Nonlinear model identification; Optimization techniques; Structural characteristics; Structural health monitoring",,,,,,,,,,,,,,,,"Cairns, J., Du, Y., Law, D., Structural performance of corrosion-damaged concrete beams (2008) Magazine of Concrete Research, 60 (5), pp. 359-370. , June; (1993) CEB-FIP Model Code 1990, , CEB-FIP, Comitée Euro-International du Béton; Cornelissen, H.A.W., Hordijk, D.A., Reinhardt, H.W., Experimental determination of crack softening characteristics of normalweight and lightweight concrete (1986) Heron, 31, p. 2; (2008) DIN 1045-1: Tragwerke Aus Beton, Stahlbeton und Spannbeton, Teil 1: Bemessung und Konstruktion, , DIN 1045-1, Deutsches Institut für Normung; He, R.S., Hwang, S.F., Damage detection by an adaptive real-parameter simulated annealing genetic algorithm (2006) Computers and Structures, 84, pp. 2231-2243; Hjelmstad, K.D., Shin, S., Damage detection and assessment of structures from static responses (1997) Journal of Engineering Mechanics, 123 (6), pp. 568-576; Hordijk, D.A., (1991) Local Approach to Fatigue of Concrete, , PhD thesis, Delft University of Technology; Huth, O., Czaderski, C., Hejll, A., Feltrin, G., Motavalli, M., Tendon Breakages Effect on Static and Modal Parameters of a Post-tensioned Concrete Girder (2005) SHMII-2'2005, pp. 847-853. , Shenzhen, China; Karczewski, B., Schnellenbach-Held, M., Model-updating in structural health monitoring: A novel genetic programming and neural networks approach (2009) 16th International EG-ICE Workshop, , 2009, Berlin, Germany; Koza, J.R., (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection, , Cambridge, MA: MIT Press; Pullmann, T., Schnellenbach-Held, M., Lubasch, P., GPcore - A generic framework for genetic programming (2007) 14th International EG-ICE Workshop, , Maribor, Slovenia; Thorenfeldt, E., Tomaszewicz, A., Jensen, J.J., Mechanical properties of high-strength concrete and applications in design (1987) Symposium on Utilization of High-Strength Concrete, , Stavanger, Norway; Terlaje, A.S., Truman, K.Z., Parameter identification and damage detection using structural optimization and static response data (2007) Advances in Structural Engineering, 10 (6), pp. 607-621; Unger, J.F., Teughels, A., De Roeck, G., System identification and damage detection of a prestressed concrete beam (2006) Journal of Structural Engineering, ASCE, 132 (11), pp. 1691-1698",,"Tizani W.","AceCad Software;Acumen;Autodesk, Inc.;SOFiSTiK AG;Tekla International","Nottingham","17th International Workshop on Intelligent Computing in Engineering, EG-ICE 2010","30 June 2010 through 2 July 2010",,149385,,9781907284601,,,"English","EG-ICE - Int. Workshop Intell. Comput. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85083945259
"Schmidova N., Dvorak M., Ruzicka M.","56712982800;26430842800;7007090680;","Comparison of the published contact configurations for the determination of electrical resistivity of CFRP composite",2019,"Experimental Stress Analysis - 57th International Scientific Conference, EAN 2019 - Conference Proceedings",,,,"462","468",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071247836&partnerID=40&md5=c68a943c5788068f642deb2c6c8468b1","Department of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University in Prague, TechnickA 4, Praha, Czech Republic","Schmidova, N., Department of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University in Prague, TechnickA 4, Praha, Czech Republic; Dvorak, M., Department of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University in Prague, TechnickA 4, Praha, Czech Republic; Ruzicka, M., Department of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University in Prague, TechnickA 4, Praha, Czech Republic","Damage detection methods for electrically conductive composite materials based on the electrical potential or electrical resistance measurement have been widely investigated in the literature. Damage growth inside the material has been also studied using finite element simulation. For the numerical simulations, it is necessary to know nominal resistivity of the material. Several contact configurations have been published in literature for determination of the nominal electrical resistivity in the in-plane and through-Thickness directions. For in-plane and through-Thickness directions electrical resistivity was experimentally investigated using electrical contact configurations published in literature. Measured electrical resistivity was used for finite element analysis of delamination growth in Carbon Fibre-Reinforced Polymer composites (CFRP). © Experimental Stress Analysis - 57th International Scientific Conference, EAN 2019 - Conference Proceedings. All rights reserved.","Carbon Fibre-Reinforced Polymer (CFRP) composites; Delamination; Electrical resistance; fourprobe method; Structural Health Monitoring (SHM)","Bridge decks; Carbon fiber reinforced plastics; Carbon fibers; Conductive materials; Damage detection; Delamination; Electric conductivity; Electric resistance; Finite element method; Glass ceramics; Reinforcement; Stress analysis; Structural health monitoring; Carbon fibre reinforced polymer; Electrical potential; Electrical resistance measurement; Electrical resistances; Electrically conductive composites; Finite element simulations; Four-probe methods; Structural health monitoring (SHM); Electric variables measurement",,,,,,"The authors would like to thank the Grant Agency of the Czech Technical University in Prague for supporting this research with grant No. SGS18/175/OHK2/3T/12.",,,,,,,,,,"Abry, J.C., Bochard, S., Chateauminois, A., Salvia, M., Giraud situ detection of damage in CFRP laminates by electrical resistance measurement (1999) Composites Science and Technology, (59), pp. 925-935; Yamane, T., Todoroki, A., Analysis of electric current densityin carbon fiber reinforced plastic laminated plates with angeled plies Composites Structures, 2017 (166), pp. 268-276; Zappalorto, M., Panozzo, F., Carraro, P.A., Quaresimin, M., Electrical response of laminate with a delamination: Modelling and experiments Composite Science and Technology, 143 (2017), pp. 31-45; Schmidova, N., Dvorak, M., Kadlec, M., Ruzicka, M., Monitoring of Delamination Growth on the MMB Specimens Using FBG Sensors and Electrical Resistance Measurement Method, 2018, pp. 367-373. , EAN 2018 56th conference on experimental stress analysis, Conference Proceedings. Praha: Ceska spolecnost pro mechaniku ISBN 978-80-270-4062-9",,"Petruska J.Navrat T.Houfek L.Sebek F.",,"Czech Society for Mechanics","57th International Scientific Conference on Experimental Stress Analysis, EAN 2019","3 June 2019 through 6 June 2019",,149710,,9788021457669,,,"English","Exp. Stress Anal. - Int. Sci. Conf., EAN - Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85071247836
"Jiao P., Lu K., Hasni H., Alavi A.H., Al-Ansari A.M., Lajnef N.","55604705500;57209642172;56964369900;33867483600;57209644305;14047090600;","A multistable mechanism to detect thermal limits for structural health monitoring (SHM)",2019,"Proceedings of SPIE - The International Society for Optical Engineering","10970",,"109700Y","","",,,"10.1117/12.2513389","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068348250&doi=10.1117%2f12.2513389&partnerID=40&md5=ceaac992110235fa2ea554a7505163ba","Ocean College, Zhejiang University, Zhoushan, Zhejiang, 316021, China; Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48823, United States; Department of Civil and Environmental Engineering, University of Missouri, Columbia, MO  65211, United States","Jiao, P., Ocean College, Zhejiang University, Zhoushan, Zhejiang, 316021, China; Lu, K., Ocean College, Zhejiang University, Zhoushan, Zhejiang, 316021, China; Hasni, H., Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48823, United States; Alavi, A.H., Department of Civil and Environmental Engineering, University of Missouri, Columbia, MO  65211, United States; Al-Ansari, A.M., Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48823, United States; Lajnef, N., Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI  48823, United States","This study proposes a novel multistable mechanism to detect thermal limits though harvesting energy from thermally induced deformation. A detecting device is developed consisting of a bilaterally constrained beam equipped with a piezoelectric polyvinylidene fluoride (PVDF) transducer. Under thermally induced displacement, the bilaterally confined beam is buckled. The post-buckling response is deployed to convert low-rate and low-frequency excitations into high-rate motions. The attached PVDF transducer harvests the induced energy and converts it to electrical signals, which are later used to measure the thermal limits. The efficiency of the proposed method was verified through a numerical study on a prestressed concrete bridge. To this aim, finite element simulations were conducted to obtain the thermally induced deformation of the bridge members between the deck and girder. In addition, an experimental study was carried out on a 3D printed measuring device to simulate the thermal loading of bridge. In this phase, the correlation between the electrical signals generated by the PVDF film and the corresponding deck-girder displacement was investigated. Based on the results, the proposed method effectively measures the mechanical response of concrete bridges under thermal loading. © 2019 SPIE.","experiment; finite element (FE) modelling; piezoelectricity; Prestressed concrete bridge girder; structural health monitoring (SHM); thermal response","3D printers; Concrete beams and girders; Concrete bridges; Crystallography; Deformation; Experiments; Finite element method; Fluorine compounds; Numerical methods; Piezoelectricity; Prestressed concrete; Thermal load; Transducers; Finite element simulations; Harvesting energies; Mechanical response; Polyvinylidene fluorides; Postbuckling response; Structural health monitoring (SHM); Thermal response; Thermally induced deformations; Structural health monitoring",,,,,"Zhejiang University, ZJU","This study is supported by the Startup Foundation of the Hundred Talents Program at the Zhejiang University.",,,,,,,,,,"Roy, M., Ray, I., Davalos, J.F., High-performance fiber-reinforced concrete: Development and evaluation as a repairing material (2013) J. Mater. Civ. Eng., 26 (10), p. 1; Bogas, J.A., Brito, J.D., Cabaco, J., Long-term behavior of concrete produced with recycled lightweight expanded clay aggregate concrete (2014) Construct. Building Mater., 65, pp. 470-479; Gamage, J.C.P.H., Al-Mahaidi, R., Wong, M.B., Integrity of CFRP-concrete bond subjected to long-term cyclic temperature and mechanical stress (2016) Compos. Struct., 149, pp. 423-433; Kodur, V.K.R., Agrawal, A., An approach for evaluating residual capacity of reinforced concrete beams exposed to fire (2016) Eng. Struct., 110, pp. 293-306; Shakya, A.M., Kodur, V.K.R., Effect of temperature on the mechanical properties of low relaxation sevenwire prestressing strand (2016) Construct. Building Mater., 124, pp. 47-84; Roberts-Wollman, C.L., Breen, J.E., Cawrse, J., Measurements of thermal gradients and their effects on segmental concrete bridge (2002) J. Bridge Eng., 7 (3), pp. 166-174; Washer, G., Fenwick, R., Nelson, S., Rumbayan, R., Guidelines for thermographic inspection of concrete bridge components in shaded conditions (2013) Transp. Res. Record: J. Transp. Res. Board, p. 2360; Sousa, H., Bento, J., Figueiras, J., Construction assessment and long-term prediction of prestressed concrete bridges based on monitoring data (2013) Eng. Struct., 52, pp. 26-37; Xia, Y., Chen, B., Zhou, X.Q., Xu, Y.L., Field monitoring and numerical analysis of Tsing Ma suspension Bridge temperature behavior (2013) Struct. Control Health Monitor, 20, pp. 560-575; Kulprapha, N., Warnitchai, P., Structural health monitoring of continuous prestressed concrete bridges using ambient thermal responses (2012) Eng. Struct., 40, pp. 20-38; Battista, N., Brownjohn, J.M.W., Tan, H.P., Koo, K.Y., Measuring and modelling the thermal performance of the Tamar Suspension Bridge using a wireless sensor network (2015) Struct. Infrastruct. Eng., 11 (2), pp. 176-193; Barroca, N., Borges, L.M., Velez, F.J., Monteiro, F., Gorski, M., Castro-Gomes, J., Wireless sensor networks for temperature and humidity monitoring within concrete structures (2013) Construct. Building Mater., 40, pp. 1156-1166; Alavi, A.H., Hasni, H., Lajnef, N., Chatti, K., An intelligent structural damage detection approach based on self-powered wireless sensor data (2015) Auto. Construct, 62, pp. 24-44; Alavi, A.H., Hasni, H., Lajnef, N., Chatti, K., Continuous health monitoring of pavement systems using smart sensing technology (2016) Construct. Build. Mater, 114, pp. 719-736; Jiao, P., Borchani, W., Soleimani, S., McGraw, B., Lateral-torsional buckling analysis of wood composite Ibeams with sinusoidal corrugated web (2017) Thin-Walled Struct., 119, pp. 72-82; Hasni, H., Alavi, A.H., Jiao, P., Lajnef, N., Detection of fatigue cracking in steel bridge girders: A support vector machine approach (2017) Archi. Civil Mech. Eng., 17 (3), pp. 609-622; Jiao, P., Borchani, W., Hasni, H., Lajnef, N., Enhancement of quasi-static strain energy harvesters using nonuniform cross-section post-buckled beams (2017) Smart Mater. Struct., 26, p. 085045; Mostafavi, E.S., Mousavi, S.M., Jiao, P., Next generation prediction model for daily solar radiation on horizontal surface using a hybrid neural network and simulated annealing method (2017) Energy Convers. Manag., 153, pp. 671-682; Borchani, W., Jiao, P., Burgueno, R., Lajnef, N., Control of postbuckling mode transitions using assemblies of axially loaded bilaterally constrained beams (2017) J Eng Mech., 143 (10), p. 04017116; Yang, D., Mosadegh, B., Ainla, A., Lee, B., Khashai, F., Suo, Z., Bertoldi, K., Whitesides, G.M., Buckling of elastomeric beams enables actuation of soft machines (2015) Adv. Mater., 27, pp. 6323-6327; Cleary, J., Su, H.J., Modeling and experimental validation of actuating a bistable buckled beam via moment input (2015) J Appl. Mech., 82, pp. 051005-051011; Green, P.L., Papatheou, E., Sims, N.D., Energy harvesting from human motion and bridge vibrations: An evaluation of current nonlinear energy harvesting solutions (2013) J. Intell. Mater. Syst. Struct., 24, pp. 1494-1505; Jiao, P., Borchani, W., Hasni, H., Lajnef, N., Static and dynamic post-buckling analyses of irregularly constrained beams under the small and large deformation assumptions (2017) Int. J. Mech. Eng., 124, pp. 203-215; (2003) PCI Bridge Design Manual, , Precast/Prestressed Concrete Institute. PCI. Chicago, IL; (2012) Record of the Climatological Observations for Atlanta, , https://www.ncdc.noaa.gov/, Georgia; Lee, J.H., Behavior of precast prestressed concrete bridge girders involving thermal effects and initial imperfections during construction (2012) Eng. Struct., 42, pp. 1-8; Bosi, F., Misseroni, D., Corso, D., Bigoni, D., Development of configurational forces during the injection of an elastic rod (2015) Extre. Mech. Letter, 4, pp. 83-88","Jiao, P.; Ocean College, China; email: pjiao@zju.edu.cn","Lynch J.P.Huang H.Sohn H.Wang K.-W.","OZ Optics, Ltd.;Polytec, Inc.;The Society of Photo-Optical Instrumentation Engineers (SPIE)","SPIE","Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019","4 March 2019 through 7 March 2019",,148997,0277786X,9781510625952,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85068348250
"Biliszczuk J., Hawryszków P., Teichgraeber M.","6505849416;36101176300;57202970467;","SHM system vs. FEM model – comparison between measured and calculated data of a cable-stayed bridge",2019,"IABSE Symposium, Guimaraes 2019: Towards a Resilient Built Environment Risk and Asset Management - Report",,,,"1512","1519",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065306519&partnerID=40&md5=6030cef2e4f2575f7eeb9df3d9430cfa","Wrocław University of Science and Technology, Faculty of Civil Engineering, Wrocław, Poland","Biliszczuk, J., Wrocław University of Science and Technology, Faculty of Civil Engineering, Wrocław, Poland; Hawryszków, P., Wrocław University of Science and Technology, Faculty of Civil Engineering, Wrocław, Poland; Teichgraeber, M., Wrocław University of Science and Technology, Faculty of Civil Engineering, Wrocław, Poland","The Rędziński Bridge in Wrocław is the biggest Polish concrete cable-stayed bridge. It is equipped in a large Structural Health Monitoring system which has been collecting the measured data since the bridge opening – from the year 2011. After 7 years [2] a comparison between the measured data and the FEM calculations is presented in this paper. © 2019 IABSE. All rights reserved.","Bridge; Cable-stayed bridge; Concrete bridge; FEM analysis; SHM; Structural Health Monitoring","Asset management; Bridges; Cable stayed bridges; Concrete bridges; Concretes; Environmental management; Finite element method; Structural health monitoring; Concrete cable-stayed bridges; FEM analysis; FEM calculation; FEM modeling; Structural health monitoring systems; Cables",,,,,,,,,,,,,,,,"Biliszczuk, J., Onysyk, J., Barcik, W., Prabucki, P., Sułkowski, M., Szczepański, J., Toczkiewicz, R., Ast, A., Rędziński Bridge along the Wrocław ringroad (2011) Proceedings of the Conference Wrocław Bridge Days, , 24th-25th November 2011. Poland, Wrocław, DWE; Polish; Biliszczuk, J., Hawryszków, P., Onysyk, J., Teichgraeber, M., (2017) Data Analysis of the SHM System of the Rędziński Bridge. 2nd Report, , from the year 2017. Wrocław; Polish; Biliszczuk, J., Hawryszków, P., Teichgraeber, M., Structural Health Monitoring system of a concrete cable-stayed bridge (2017) Proceedings of the Central European Congress on Concrete Engineering CCC 2017, , Hungary, Tokaj; SOFiSTiK Manual Instruction; Hawryszków, P., Hildebrand, M., Installation of the largest stay cable system in Poland – the Rędziński bridge in Wrocław (2012) Proceedings of the 18th IABSE Congress “Innovative Infrastructures – Toward Human Urbanism, , Korea, Seul; Bień, J., Kużawa, M., Kamiński, T., Validation of numerical models of concrete box bridges based on load test results (2015) Archives of Civil and Mechanical Engineering, 15 (4), pp. 1046-1060","Biliszczuk, J.; Wrocław University of Science and Technology, Poland; email: jan.biliszczuk@pwr.edu.pl",,"Allplan;Brisa;Maurer;S and P","International Association for Bridge and Structural Engineering (IABSE)","IABSE Symposium 2019 Guimaraes: Towards a Resilient Built Environment - Risk and Asset Management","27 March 2019 through 29 March 2019",,147396,,9783857481635,,,"English","IABSE Symp., Guimaraes: Towards Resilient Built Environ. Risk Asset Manag. - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85065306519
"Teichgraeber M., Biliszczuk J., Hawryszkow P.","57202970467;6505849416;36101176300;","Shm system of a cable styed bridge as a data source for probabilistic durability assessment",2018,"fib Symposium",,,,"1055","1062",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134853865&partnerID=40&md5=c855e0dc06c4755f13b23ce0f79c70df","Faculty of Civil Engineering, Department of Bridges and Railways, Wroclaw university and Science and Technology Wyb, Wyspianskiego 27, Wroclaw, 50-370, Poland","Teichgraeber, M., Faculty of Civil Engineering, Department of Bridges and Railways, Wroclaw university and Science and Technology Wyb, Wyspianskiego 27, Wroclaw, 50-370, Poland; Biliszczuk, J., Faculty of Civil Engineering, Department of Bridges and Railways, Wroclaw university and Science and Technology Wyb, Wyspianskiego 27, Wroclaw, 50-370, Poland; Hawryszkow, P., Faculty of Civil Engineering, Department of Bridges and Railways, Wroclaw university and Science and Technology Wyb, Wyspianskiego 27, Wroclaw, 50-370, Poland","Over the last 20 years big bridges in Poland have been built and equipped in Structural Health Monitoring systems (SHM). It is a new method that provides a constant overview on a bridge. One of those objects is the Redzinski Bridge in Wroclaw. It is a cable-stayed concrete bridge built along the motorway A8 in 2011. A long term behaviour observation of the bridge gave same interesting conclusions which were compared with a FEM model of the Redzinski Bridge. After five years of being in usage the forces in cables have been changing. The diagrams of force change generated by the SHM application are a convenient basis for durability calculation. First probability density diagrams for amplitudes of the cable force will be shown and interpreted. © fédération internationale du béton (fib).",,"Cables; Durability; Structural health monitoring; Cable forces; Data-source; Durability assessment; FEM modeling; Long term behaviours; Probability densities; Structural health monitoring systems; Cable stayed bridges",,,,,,,,,,,,,,,,"Marzahn, G., Langenberg, P., Groten, G., Paschen, M., (2015) Sicherung Der Rheinbrucke Leverkusen - Von Der Schadensaufnahme Zum Instandsetzungsmanagement Unter Berucksich- Tigung Der Altstahlproblematik, , Paper presented at the 25. Dresdner Bruckenbausymposium, Dresden, Germany, March 9-10; Sheer, J., (2010) Failed Bridges. Case Studies. Causes and Consequences, , Ernst&Sohn; Wenzel, H., (2009) Health Monitoring of Bridges, , John Wiley & Sons; Onysyk, H., (2014) Safety Assessment of Bridge in Operation Based on Structural Health Moni-Toring System Data, , PhD diss., Politechnika Wroclawska; Hawryszkow, P., Hildebrand, M., (2012) Installation of the Largest Stay Cable System in Poland - the Redzinski Bridge in Wroclaw, pp. 19-21. , Paper presented at the 18th IABSE Congress Innovative Infrastructures - Toward Human Urbanism, Seoul, Korea. September; (2011) Instruction of the SHM Application for the Bridge along the Wroclaw Ring Road; Biliszczuk, J., Onysyk, J., Eichgraeber, M.T., (2016) Data Analysis of the SHM System of the Redzinski Bridge, , 1st Rapport from the period August 2011 - January 2016; Hui, L., Shunlong, L., Jinping, O., Hongwei, L., Reliability assessment of cable- stayed bridges based on structural health monitoring techniques (2012) Structure and Infrastructure Engineering, 8, pp. 829-845; Winkler, J., Georgakis, C., Fisher, G., Wood, S., Ghannoum, W., Structural Response of a Multi-Strand Stay Cable to Cyclic Bending Load (2015) Structural Engineering International, 25, pp. 141-150; Kocanda, S., Szala, J., (1997) Basics of Fatigue Calculations, , Warsaw: PWN",,"Kohoutkova A.Bily P.Vitek J.L.Frantova M.",,"fib. The International Federation for Structural Concrete","12th fib International PhD Symposium in Civil Engineering, 2018","29 August 2018 through 31 August 2018",,267659,26174820,9788001064016,,,"English","fib. Symp.",Conference Paper,"Final","",Scopus,2-s2.0-85134853865
"Lutton R.E.M., McFarland B.J., McKenna E., O’Higgins C., Magee B., Gyftaki E., Kearney J.","56217716200;7005950139;55203698300;57210557208;57210552348;57210552935;57200545134;","How theoretical modelling compares to real-life through SHM using FBG sensors – A case study",2018,"9th European Workshop on Structural Health Monitoring, EWSHM 2018",,,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070864369&partnerID=40&md5=0cc4d8e37712ff04f88dd341198b9d90","McFarland Associates, United Kingdom","Lutton, R.E.M., McFarland Associates, United Kingdom; McFarland, B.J., McFarland Associates, United Kingdom; McKenna, E., McFarland Associates, United Kingdom; O’Higgins, C., McFarland Associates, United Kingdom; Magee, B., McFarland Associates, United Kingdom; Gyftaki, E., McFarland Associates, United Kingdom; Kearney, J., McFarland Associates, United Kingdom","The construction of Castle Cornet is estimated to have commenced in 1206, shortly after King John lost Normandy to the French Crown. Since this time Castle Cornet has defended St Peter Port and the island of Guernsey against invasion. No longer serving a military role, it became part of one of the breakwaters of St Peter Port's harbour in 1859. Castle Cornet is linked to the mainland by Castle Cornet Bridge, which provides pedestrian and vehicular access to both Castle Cornet and the Castle breakwater beyond. The bridge is a nine-span, reinforced concrete structure, built in 1951 replacing a timber and iron bridge, reusing the granite piers. A previous evaluation determined that the bridge was safe for pedestrian and some vehicular access; however, restricted access to the bridge for vehicles resulted in operational difficulties for both the castle and the breakwater. Therefore, it was concluded that the bridge needed to be repaired or replaced so that access for vehicles, such as mobile cranes and emergency vehicles, could be reinstated. After 67 years, the structure has deteriorated due to its extremely harsh, marine environment. A visual inspection of the underside of the structure revealed large areas of reinforcement corrosion and concrete spalling to both the main beams and the deck slab spanning between. Concrete Non-Destructive Testing (NDT) works were undertaken to determine the nature, source, extent and significance of the observed deterioration. An underbridge was used as opposed to scaffolding offering time and cost savings. Finite Element Analysis (FEA) modelling of the structure was undertaken to provide an indication of what the expected strains were on the structure prior to a load test being undertaken. Structural health monitoring (SHM) was also implemented using Fibre Bragg Grating (FBG) sensors to measure the structural response of the bridge during the load test. The results of the SHM data using FBG sensors were compared with the FEA model to experimentally validate the theoretical model. © 2018 NDT.net. All rights reserved.",,"Breakwaters; Bridge decks; Cranes; Deterioration; Electric sensing devices; Electrochemical corrosion; Fiber Bragg gratings; Load testing; Pedestrian safety; Ports and harbors; Reinforced concrete; Structural health monitoring; Vehicles; Fibre Bragg grating sensors; Non destructive testing; Pedestrian and vehicular; Reinforcement corrosion; Structural health monitoring (SHM); Structural response; Theoretical modeling; Theoretical modelling; Nondestructive examination",,,,,,,,,,,,,,,,"Taylor, S.E., Rankin, G.I.B., Cleland, D.J., Arching action in high-strength concrete slabs (2001) Proc Inst Civ Eng - Struct Build, 146 (4), pp. 353-362; Grace, N.F., Jensen, E.A., Noamesi, D.K., Flexural performance of carbon fiber-reinforced polymer prestressed concrete side-by-side box beam bridge (2011) J Compos Constr, 15 (5), pp. 663-671; Taylor, S.E., Meng, Y.Z., Robinson, D., Analysis of compressive membrane action in concrete slabs analysis of compressive membrane action in concrete slabs (2008) Proc ICE - Bridg Eng, 161 (1), pp. 21-31","Lutton, R.E.M.; McFarland AssociatesUnited Kingdom; email: rebecca.lutton@mcfassoc.com",,,"NDT.net","9th European Workshop on Structural Health Monitoring, EWSHM 2018","10 July 2018 through 13 July 2018",,149614,,,,,"English","Eur. Workshop Struct. Heal. Monit., EWSHM",Conference Paper,"Final","",Scopus,2-s2.0-85070864369
"Park J.S., Park K.T., Seo D.W., Kim B.C., Jung K.S.","57202854302;57202316530;57202858949;56070836500;56924254400;","Investigation of Structural Performance of Bridge Steel Cable According to Strand Damage Location using FEA Analysis",2018,"Proceedings of the 7th Asia-Pacific Workshop on Structural Health Monitoring, APWSHM 2018",,,,"1219","1224",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064684173&partnerID=40&md5=d4267ee4fbf220761c2d93c6c38c1e74","Sustainable Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, Goyang, South Korea","Park, J.S., Sustainable Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, Goyang, South Korea; Park, K.T., Sustainable Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, Goyang, South Korea; Seo, D.W., Sustainable Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, Goyang, South Korea; Kim, B.C., Sustainable Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, Goyang, South Korea; Jung, K.S., Sustainable Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, Goyang, South Korea","In this study, the tendency of structural performance deterioration according to damage and damage location due to corrosion of steel cables of cable-stayed bridges was analytically analyzed. The analytical analysis was carried out using the MIDAS FEA, a general - purpose finite element analysis program, and the structural performance of the cable was investigated by nonlinear analysis considering the number of damaged wires of 7-wire strand and the position (symmetric, asymmetric in the crosssection area) as variables. The analytical model uses a three - dimensional solid element. The boundary condition assumes that one end of the cable model is fixed and the other end is allowed only. The assumption of frictional condition between the center wire and its surrounded wires is taken as the contact plane. As a result, the structural performance difference was not significant according to the number of damaged wires, and the structural performance and behavior were different due to damage patterns either symmetry or asymmetry of the arrangement of the wires. Also, it can be seen that the section stress is increased due to the asymmetric arrangement. © APWSHM 2018. All rights reserved.","Cable-stayed bridge; Deterioration; Finite element analysis; Midas fea","Bridge cables; Cable stayed bridges; Deterioration; Microalloyed steel; Nonlinear analysis; Steel corrosion; Structural analysis; Structural health monitoring; Analytical analysis; Bridge steels; Corrosion of steel; Cross-section area; Damage location; Finite element analysis program; Structural performance; Three dimensional solid element; Finite element method",,,,,"Korea Research Institute of Standards and Science, KRISS","This research was supported by a grant from “Infrastructure safety evaluation technology development using local-global measurement-based data (2018)” funded by Korea Research Institute of Standards and Science (KRISS).",,,,,,,,,,"Chiang, Y.J., Characterizing simple stranded wire cables under axial loading (1996) Journal of Finite Elements in Analysis and Design, 24, pp. 49-66; Gerdemeli, I., Kurt, S., Anil, A.S., Analysis with finite element method of wire rope (2012) Faculty of Mechanical Engineering, Istanbul Technical University; Jiang, W.G., Henshall, J.L., The analysis of termination effects in wire strand using the finite element method (1999) Journal of Strain Analysis, 34 (1), pp. 31-38; (2017) MIDAS FEA User's Guide, , MIDAS, Inc; Shibu, G., Mohankumar, K.V., Devendiran, S., Analysis of a three layered straight wire rope strand using finite element method (2011) Proceedings of the World Congress on Engineering","Park, J.S.; Sustainable Infrastructure Research Center, South Korea; email: joonseokpark@kict.re.kr","Su Z.Yuan S.Sohn H.",,"NDT.net","7th Asia-Pacific Workshop on Structural Health Monitoring, APWSHM 2018","12 November 2018 through 15 November 2018",,147194,,9783000603594,,,"English","Proc. Asia-Pacific Workshop Struct. Heal. Monit., APWSHM",Conference Paper,"Final","",Scopus,2-s2.0-85064684173
"Azarbayejani M.","25651677300;","Fuzzy pattern recognition in vibration-based structural health monitoring",2018,"Lecture Notes in Civil Engineering","5",,,"283","292",,,"10.1007/978-3-319-67443-8_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060209473&doi=10.1007%2f978-3-319-67443-8_24&partnerID=40&md5=67af28e0d3f4581ff872a89b5fe35e7b","Civil and Environmental Engineering Department, New Mexico Institute of Mining and Technology, Socorro, NM, United States","Azarbayejani, M., Civil and Environmental Engineering Department, New Mexico Institute of Mining and Technology, Socorro, NM, United States","In this paper, fuzzy sets are established for different states of a reinforced concrete bridge based on finite element model of the bridge. This bridge has been monitored continuously using vibrational signals obtained from accelerometer and strain gauge sensors installed on girders of the bridge. A Finite element model of the bridge is calibrated based on real data gathered from the bridge to be a close representation of the real structure. The calibrated finite element models of the bridge are then constructed for healthy, medium damaged and severe damaged states of the bridge. Using fuzzy set principles, the healthy, medium and severe damage states of the bridge are constructed with fuzzy bell functions. Fuzzy pattern recognition using similarity between fuzzy sets is then utilized to identify any unknown states of the bridge that can give authorities an unbiased tool for efficient maintenance of the bridge. © Springer International Publishing AG 2018.","Bridges; Finite element model; Fuzzy pattern recognition; Fuzzy sets; Structural health monitoring","Bridges; Fuzzy sets; Pattern recognition; Reinforced concrete; Strain gages; Structural health monitoring; Damage state; Fuzzy pattern recognition; Real structure; Strain gauge sensors; Unknown state; Vibration-based structural health monitoring; Vibrational signals; Finite element method",,,,,,,,,,,,,,,,"Lemaitre, J., Desmorat, R., (2002) Engineering Damage Mechanics: Ductile, Creep, Fatigue and Brittle Failures, , Springer, New York; Farrar, C.R., Lieven, N.A.J., Bement, M.T., An Introduction to Damage Prognosis (2005) Damage Prognosis for Aerospace, Civil and Mechanical System. Wiley, , Inman, D.J., Farrar, C.R., Lopes, V., Steffen, V. (eds.), West Sussex, England; Broek, D., (1986) Elementary Engineering Fracture Mechanics, , Martinus Nijhoff Publishers, Dordrecht; Worden, K., Dulieu-Barton, J.M., An overview of intelligent fault detection in systems and structures (2004) Struct. Health Monit., 3 (1), pp. 85-98; Lemaitre, J., Desmorat, R., (2002) Engineering Damage Mechanics: Ductile, Fatigue and Brittle Failures, , Springer, New York; Neild, S.A., McFadden, P.D., Williams, M.S., A review of time-frequency methods for structural vibration analysis (2003) Eng. Struct., 25, pp. 713-728; Chang, C.-C., Chen, L.-W., Damage detection of cracked thick rotating blades by a spatial wavelet based approach (2004) Appl. Acoust., 65, pp. 1095-1111; Reda Taha, M., Noureldin, A., Osman, A., El-Sheimy, N., Introduction to the use of wavelet multi-resolution analysis for intelligent structural health monitoring (2004) Can. J. Civ. Eng., 31 (5), pp. 719-731; Thein, A., (2006) Pipeline Structural Health Monitoring Using Macro-Fiber Composite Active Sensors, , Master Thesis, Department of Mechanical, Industrial, and Nuclear Engineering, University of Cincinnati; Hu, X., Wang, B., Ji, H., A wireless sensor network-based structural health monitoring system for highway bridges (2013) Comput. Civ. Infrastruct. Eng., 28 (3), pp. 193-209; Catbas, F.N., Susoy, M., Frangopol, D.M., Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data (2008) Eng. Struct., 30 (9), pp. 2347-2359; Yang, C., Wu, Z., Zhang, Y., Structural health monitoring of an existing PC box girder bridge with distributed HCFRP sensors in a destructive test (2008) Smart Mater. Struct., 17 (3); Torres, B., Payá-Zaforteza, I., Calderón, P.A., Adam, J.M., Analysis of the strain transfer in a new FBG sensor for structural health monitoring (2011) Eng. Struct., 33 (2), pp. 539-548; Bao, Y., Beck, J.L., Li, H., Compressive sampling for accelerometer signals in structural health monitoring (2010) Struct. Health Monit., 10, pp. 235-246; Ooijevaar, T.H., Loendersloot, R., Warnet, L.L., de Boer, A., Akkerman, R., Vibration based structural health monitoring of a composite T-beam (2010) Compos. Struct., 92 (9), pp. 2007-2015; Feeney, A., Heit, E., (2007) Inductive Reasoning: Experimental, Developmental and Computational Approach, , Cambridge University Press, UK; Tenenbaum, J.B., Griffiths, T.L., Kemp, C., Theory-based Bayesian models of inductive learning and reasoning (2006) Trends Cogn. Sci., 10 (7), pp. 309-318; Shannon, C.E., A mathematical theory of communication (1948) Bell Syst. Tech. J., 27, pp. 379-423; Kim, C.J., Russell, B.D., A learning method for use in intelligent computer relays for high impedance faults (1991) IEEE Trans. Power Delivery, 6 (1), pp. 109-115; Ross, T.J., (2004) Fuzzy Logic with Engineering Applications, , Wiley, West Sussex; Applebaum, D., (2003) Probability and Information: An Integrated Approach, , Cambridge University Press, NY; Klir, G.J., Yuan, B., (1995) Fuzzy Sets and Fuzzy Logic, Theory and Application, , Prentice Hall, Upper Saddle River; Zhang, J., Sato, T., Iai, S., Hutchinson, T., A pattern recognition technique for structural identification using observed vibration signals: Nonlinear case studies (2008) Eng. Struct., 30 (5), pp. 1417-1423; Sheyka, M., (2008) Analytical and Experimental Investigations of Photonic Crystals for Sub-Micron Damage Detection., , M.Sc. Thesis, Department of Civil Engineering, University of New Mexico, NM, USA","Azarbayejani, M.; Civil and Environmental Engineering Department, United States; email: mohammad.azarbayejani@nmt.edu",,,"Springer",,,,,23662557,,,,"English","Lect. Notes Civ. Eng.",Book Chapter,"Final","",Scopus,2-s2.0-85060209473
"O’Neill K., Bishop E., Swanson D., Aveyard D., Skolnik D., Fraser M.","57205482588;54580571800;35176650600;57205488369;14630833300;56374903200;","Strong motion structural monitoring in practice",2018,"Lecture Notes in Civil Engineering","5",,,"445","454",,,"10.1007/978-3-319-67443-8_38","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060189055&doi=10.1007%2f978-3-319-67443-8_38&partnerID=40&md5=58c1f9f8a3197c0328616e898d6a11db","Reid Middleton, Inc., Everett, WA  98204, United States; Structural Engineering Group, Reid Middleton, Inc., Everett, WA  98204, United States; Kinemetrics, Inc., Pasadena, CA  91107, United States; Naval Facilities Engineering Command Southwest, San Diego, CA  92132, United States","O’Neill, K., Reid Middleton, Inc., Everett, WA  98204, United States; Bishop, E., Reid Middleton, Inc., Everett, WA  98204, United States; Swanson, D., Structural Engineering Group, Reid Middleton, Inc., Everett, WA  98204, United States; Aveyard, D., Reid Middleton, Inc., Everett, WA  98204, United States; Skolnik, D., Kinemetrics, Inc., Pasadena, CA  91107, United States; Fraser, M., Naval Facilities Engineering Command Southwest, San Diego, CA  92132, United States","Thousands of buildings, bridges, and structures worldwide have been instrumented with systems (including vibration accelerometers, strong-motion sensors, strain gauges, etc.) for the sole purpose of recording structural response data. Researchers, structural engineers, and seismologists use these data to further our understanding of actual building dynamic behavior, validate theories, and ultimately lead to building code improvements. However, these structural health monitoring and instrumentation systems are rarely implemented directly on real-world building projects. The objective of this paper is to present the following case studies on how structural instrumentation has been implemented and how these technologies have been executed. The case studies are intended to show the potential usefulness of leveraging measured data and stimulate further use of structural instrumentation technology in practice: 1. California Hospital with Seismic Movements: Each seismically separated hospital wing of the Naval Medical Center San Diego (NMCSD) is a pre-Northridge steel moment frame. The entire medical facility encompasses over 1 million square feet of space. One seismically separated wing of this building, the nursing tower, has some structural irregularities and some unique building properties. Additionally, recent structural analysis has been conducted on this building for the purposes of conducting seismic upgrades in addition to strong-motion accelerometers being installed in the building in 2015. The M5.2 Borrego Springs earthquake that occurred on June 10, 2016 was measured by the building’s strong motion accelerometers and some of the measurement data is compared with other structural analysis calculations.2. Washington Hospital with Seismic Movements: The hospital and clinic at Naval Hospital Bremerton (NHB) are pre-Northridge steel moment frame structures that represent 249,000 square feet of space used for essential medical services. In 1980, the building was instrumented with 12 channels of strong-motion accelerometers with an analog data recorder. After the 2001 M6.8 Nisqually Earthquake event, the data from this recording was digitized and allowed the nonlinear dynamic time-history analysis required for a seismic evaluation and upgrades design to be tuned to the actual dynamic characteristics of the building rather than the generalized assumptions provided by building standards. © Springer International Publishing AG 2018.","Finite element analysis; Modal analysis; Monitoring system; Seismic response; Strong motion","Accelerometers; Building codes; Earthquakes; Finite element method; Hospitals; Modal analysis; Monitoring; Motion sensors; Seismic design; Seismic response; Structural health monitoring; Dynamic characteristics; Instrumentation systems; Instrumentation technologies; Monitoring system; Nonlinear dynamic time-history analysis; Strong motion; Structural monitoring; Structural response; Structural analysis",,,,,,,,,,,,,,,,"http://earthquake.usgs.gov/earthquakes/eventpage/ci37374687#shakemap, CA ShakeMap: (2016). Accessed 30 Jan 2017","O’Neill, K.; Reid Middleton, United States; email: koneill@reidmiddleton.com",,,"Springer",,,,,23662557,,,,"English","Lect. Notes Civ. Eng.",Book Chapter,"Final","",Scopus,2-s2.0-85060189055
"Teichgraeber M., Biliszczuk J., Hawryszków P.","57202970467;6505849416;36101176300;","SHM system of a cable styed bridge as a data source for probabilistic durability assessment",2018,"Proceedings of the 12th fib International PhD Symposium in Civil Engineering",,,,"1055","1062",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053841936&partnerID=40&md5=9ae97a24d471d1da6651d22da9ea27c8","Faculty of Civil Engineering, Department of Bridges and Railways Wrocław university and Science and Technology, Wyb. Wyspiańskiego 27, Wrocław, 50-370, Poland","Teichgraeber, M., Faculty of Civil Engineering, Department of Bridges and Railways Wrocław university and Science and Technology, Wyb. Wyspiańskiego 27, Wrocław, 50-370, Poland; Biliszczuk, J., Faculty of Civil Engineering, Department of Bridges and Railways Wrocław university and Science and Technology, Wyb. Wyspiańskiego 27, Wrocław, 50-370, Poland; Hawryszków, P., Faculty of Civil Engineering, Department of Bridges and Railways Wrocław university and Science and Technology, Wyb. Wyspiańskiego 27, Wrocław, 50-370, Poland","Over the last 20 years big bridges in Poland have been built and equipped in Structural Health Monitoring systems (SHM). It is a new method that provides a constant overview on a bridge. One of those objects is the Rędziński Bridge in Wrocław. It is a cable-stayed concrete bridge built along the motorway A8 in 2011. A long term behaviour observation of the bridge gave same interesting conclusions which were compared with a FEM model of the Rędziński Bridge. After five years of being in usage the forces in cables have been changing. The diagrams of force change generated by the SHM application are a convenient basis for durability calculation. First probability density diagrams for amplitudes of the cable force will be shown and interpreted. © 2018 Czech Technical University in Prague. All rights reserved.",,"Cables; Durability; Probability density function; Structural health monitoring; Cable forces; Data-source; Durability assessment; FEM modeling; Long term behaviours; Probability densities; Structural health monitoring systems; Bridges",,,,,,,,,,,,,,,,"Marzahn, G., Langenberg, P., Groten, G., Paschen, M., Sicherung der Rheinbrücke Leverkusen - Von der Schadensaufnahme zum Instandsetzungsmanagement unter Berücksich-tigung der Altstahlproblematik (2015) Dresdner Brückenbausymposium, , Paper 25. Dresden, Germany, March 9-10; Sheer, J., Failed bridges. Case studies. Causes and consequences (2010) Health Monitoring of Bridges, , Ernst&Sohn. 3 Wenzel, H. 2009. John Wiley & Sons; Onysyk, H., (2014) Safety Assessment of Bridge in Operation Based on Structural Health Monitoring System Data, , PhD diss., Politechnika Wrocławska; Hawryszków, P., Hildebrand, M., Installation of the largest stay cable system in Poland - The Rędziński bridge in Wrocław (2012) The 18th IABSE Congress Innovative Infrastructures - Toward Human Urbanism, , Paper Seoul, Korea. September 19-21; Neostrain, (2011) Instruction of The SHM Application for The Bridge Along The Wrocław Ring Road; Biliszczuk, J., Onysyk, J., Teichgraeber, M., Data analysis of the SHM System of the Rędziński Bridge (2016) 1st Rapport from The Period August 2011 - January 2016; Hui, L., Shunlong, L., Jinping, O., Hongwei, L., Reliability assessment of cable-stayed bridges based on structural health monitoring techniques (2012) Structure and Infrastructure Engineering, 8, pp. 829-845; Winkler, J., Georgakis, C., Fisher, G., Wood, S., Ghannoum, W., Structural response of a multi-strand stay cable to cyclic bending load (2015) Structural Engineering International, 25, pp. 141-150; Kocańda, S., Szala, J., (1997) Basics of Fatigue Calculations, , Warsaw: PWN",,"Bily P.Kohoutkova A.Vitek J.L.Frantova M.","BASF Construction Chemicals;BetoTech, s.r.o.;Cervenka Consulting s.r.o.;Metrostav a.s.;Pontex Consulting Engineers, Ltd.;VALBEK-EU, a.s.","Czech Technical University","12th fib International PhD Symposium in Civil Engineering","29 August 2018 through 31 August 2018",,138883,,9788001064016,,,"English","Proc. fib Int. PhD Symp. Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85053841936
"Li A.Q., Deng Y.","57204331975;55218285200;","Fatigue and dynamic overload of metallic components and structures",2017,"ICF 2017 - 14th International Conference on Fracture","1",,,"463","465",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065973926&partnerID=40&md5=dabf9a91901a76d6c64e9895f4f87aba","Beijing University of Civil Engineering and Architecture, Beijing, 100044, China","Li, A.Q., Beijing University of Civil Engineering and Architecture, Beijing, 100044, China; Deng, Y., Beijing University of Civil Engineering and Architecture, Beijing, 100044, China","The safety of engineering structures subjected to fatigue and occasional overload involves many factors. Special investigations have been given to the fatigue evaluation of steel bridges with consideration of many aspects including new fatigue life evaluation method, deterministic and reliability fatigue evaluation and inspection and mitigation of fatigue cracks. Joint using of new fatigue evaluation model, reliability theory and advanced finite element analysis technique with structural health monitoring resulted in many meaningful findings. Fatigue design and in-service assessment for steel bridges can greatly benefit from the findings proposed in this paper. © 2017 ICF 2017 - 14th International Conference on Fracture. All rights reserved.",,"Reliability analysis; Reliability theory; Steel bridges; Structural health monitoring; Element analysis; Engineering structures; Fatigue cracks; Fatigue design; Fatigue evaluation; Fatigue life evaluation; Metallic component; Fatigue of materials",,,,,"National Natural Science Foundation of China, NSFC: 51278104, 51308073, 51308073,51278104, 51438002","The authors would like to acknowledge the financial support of National Natural Science Foundation of China (51438002, 51308073,51278104).",,,,,,,,,,"Li, A.Q., Ding, Y.L., Wang, H., Guo, T., Analysis and assessment of bridge health monitoring mass data-progress in research/development of “Structural Health Monitoring (2012) Sci. China Technol. Sc., 55, pp. 2212-2224; Ding, Y.L., Song, Y.S., Cao, B.Y., Wang, G.X., Li, A.Q., Full-range S-N fatigue-life evaluation method for welded bridge structures considering hot-spot and welding residual stress (2016) J. Bridge Eng., 21, p. 04016096; Deng, Y., Liu, Y., Feng, D.M., Li, A.Q., Investigation of fatigue performance of welded details in long-span steel bridges using long-term monitoring strain data (2015) Struct. Control Health Monit., 22, pp. 1343-1358; Guo, T., Li, A.Q., Wang, H., Influence of ambient temperature on the fatigue damage of welded bridge decks (2008) Int. J. Fatigue, 30, pp. 1092-1102; Guo, T., Li, A.Q., Li, J.H., Fatigue life prediction of welded joints in orthotropic steel decks considering temperature effect and increasing traffic flow (2008) Struct. Health Monit., 7, pp. 189-202; Guo, T., Liu, Z.X., Zhu, J.S., Fatigue reliability assessment of orthotropic steel bridge decks based on probabilistic multi-scale finite element analysis (2015) Adv. Steel Constr., 11, pp. 334-346; Liu, Z.X., Guo, T., Chai, S., Probabilistic fatigue life prediction of bridge cables based on mul-tiscaling and mesoscopic fracture mechanics (2016) Appl. Sci., 6, pp. 99-112; Deng, Y., Ding, Y.L., Li, A.Q., Zhou, G.D., Fatigue reliability assessment for bridge welded details using long-term monitoring data (2011) Sci. China Technol. Sc, 54, pp. 3371-3381; Song, Y.S., Ding, Y.L., Wang, G.X., Li, A.Q., Fatigue-life evaluation of a high-speed railway bridge with an orthotropic steel deck integrating multiple factors (2016) J. Perform. Constr. Fac., 30, p. 04016036; Guo, T., Liu, Z.X., Pan, S.J., Pan, Z.H., Cracking of longitudinal diaphragms in long-span cable-stayed bridges (2015) J. Bridge Eng., 20, p. 04015011; Guo, T., Liu, Z.X., Liu, J., Han, D.Z., Diagnosis and mitigation of fatigue damage in longitudinal diaphragms of cable-stayed bridges (2016) J. Bridge Eng., 21, p. 05016007; Xing, C., Wang, H., Li, A., Xu, Y., Study on Wind-induced vibration control of a long-span cable-stayed bridge using TMD-type counterweight (2014) J. Bridge Eng., 19 (1), pp. 141-148","Li, A.Q.; Beijing University of Civil Engineering and ArchitectureChina; email: aiqunli@sina.com","Gdoutos E.E.",,"International Conference on Fracture","14th International Conference on Fracture, ICF 2017","18 June 2017 through 20 June 2017",,115095,,9780000000002,,,"English","ICF - Int. Conf. Fract.",Conference Paper,"Final","",Scopus,2-s2.0-85065973926
"Faraonis P., Wuttke F., Zabel V.","53866432700;6507529343;6603096282;","Numerical and experimental identification of soil-foundation-bridge system dynamic characteristics",2017,"Lecture Notes in Civil Engineering","2",,,"259","267",,,"10.1007/978-3-319-56136-3_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061061524&doi=10.1007%2f978-3-319-56136-3_14&partnerID=40&md5=d151e4b4b3e20fb3d20feb7003e8ae60","Department of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Kiel University, Ludewig-Meyn St. 10, Kiel, 24118, Germany; Institute of Applied Geo-Sciences, Christian-Albrecht’s University, Kiel, 24118, Germany; Faculty Civil Engineering, Formerly, Bauhaus-University Weimar, Coudraystrasse 11C, Weimar, 99423, Germany; Research Associate and Lecturer, Bauhaus-University Weimar, Marienstrasse 15, Weimar, 99421, Germany","Faraonis, P., Department of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Wuttke, F., Kiel University, Ludewig-Meyn St. 10, Kiel, 24118, Germany, Institute of Applied Geo-Sciences, Christian-Albrecht’s University, Kiel, 24118, Germany, Faculty Civil Engineering, Formerly, Bauhaus-University Weimar, Coudraystrasse 11C, Weimar, 99423, Germany; Zabel, V., Research Associate and Lecturer, Bauhaus-University Weimar, Marienstrasse 15, Weimar, 99421, Germany","The natural frequencies of the Metsovo bridge during construction are identified both in actual scale and in 1:100 scale. Finite element models of increasing modeling complexity are developed in order to investigate their efficiency in representing the measured dynamic stiffness of the bridge-foundation-soil system. The results highlight the importance of accurately simulating boundary conditions in Structural Health Monitoring applications. © Springer International Publishing AG 2017.","Finite element modeling; Soil-structure interaction; System identification","Bridges; Identification (control systems); Soil structure interactions; Soils; Structural health monitoring; Bridge foundation; Bridge systems; Dynamic stiffness; Experimental identification; Model complexity; Soil foundation; Structural health; Finite element method",,,,,,,,,,,,,,,,"Basseville, M., Benveniste, A., Goursat, M., Hermans, L., Mevel, L., van der Auweraer, H., Output-only subspace-based structural identification: From theory to industrial testing practice (2001) J Dyn Sys Meas Control, 123 (4), pp. 668-676; Bridgman, P.W., (1931) Dimensional Analysis, , 2nd edn. Yale University Press, New Haven; Chaudhary, M.T.A., Abe, M., Fujino, Y., System identification of two base-isolated buildings using seismic records (2000) J Struct Eng, 126 (10), pp. 1187-1195; Chaudhary, M., Abe, M., Fujino, Y., Identification of soil-structure interaction effects in base isolated bridges from earthquake records (2001) Soil Dyn Earthq Eng, 21, pp. 713-725; Crouse, C., Hushmand, B., Martin, G., Dynamic soil-structure interaction of single-span bridge (1987) Earthq Eng Struct Dyn, 15, pp. 711-729; DIN EN 459-1:2010–12, Building lime—Part 1: Definitions, specifications and conformity criteria; Elsabee, F., Morray, J.P., (1977) Dynamic Behaviour of Embedded Foundations, , Research Report R77–33 MIT; Kausel, E., (1974) Soil-Forced Vibrations of Circular Foundations on Layered Media, , Research Report R74–11 MIT; Morassi, A., Tonon, S., Dynamic testing for structural identification of a bridge (2008) J Bridge Eng, 13 (6), pp. 573-585; Panetsos, P., Ntotsios, E., Liokos, N., Papadimitriou, C., (2009) Identification of Dynamic Models of Metsovo (Greece) Bridge Using Ambient Vibration Measurements, , ECCOMAS thematic conference on computational methods in structural dynamics and earthquake engineering (COMPDYN’09), Rhodes; Peeters, B., de Roeck, G., Stochastic system identification for operational modal analysis: A review (2001) J Dyn Sys Meas Control, 123 (4), pp. 659-667; Varun, A.D., Gazetas, G., A simplified model for lateral response of large diameter caisson foundations—Linear elastic formulation (2009) Soil Dyn Earthq Eng, 29 (2), pp. 268-291; Werner, S.D., Beck, J.L., Levine, M.B., Seismic response evaluations of Meloland road overpass using 1979 imperial valley earthquake records (1987) Earthq Eng Struct Dyn, 15, pp. 49-274; Wolf, J.P., Soil-structure interaction analysis in time domain (1989) Nucl Eng Des, 11 (3), pp. 381-393","Faraonis, P.; Department of Civil Engineering, Greece; email: pfaraoni@civil.auth.gr",,,"Springer",,,,,23662557,,,,"English","Lect. Notes Civ. Eng.",Book Chapter,"Final","",Scopus,2-s2.0-85061061524
"Azim M.R., Gul M.","57203927510;22940711700;","Sensor clustering based damage detection framework for railway bridges using acceleration response",2017,"Proceedings, Annual Conference - Canadian Society for Civil Engineering","2017-May",,,"54","62",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053609070&partnerID=40&md5=93a908beae39e5c46ae06708bf619337","University of Alberta, Canada","Azim, M.R., University of Alberta, Canada; Gul, M., University of Alberta, Canada","Bridges are critical components of the railway infrastructure system and the majority of these bridges are approaching their estimated design life. Moreover, the demands on the bridges have been burgeoning both in terms of increased axle loads and operational frequency. It is paramount that; these systems are maintained effectively. Therefore, crafting powerful Structural Health Monitoring (SHM) systems for railroad infrastructure is relied upon to help the proprietors with their decision-making policies. Hence, developing damage investigation strategies specifically tailored for railroad bridges is the principle goal of this on-going study. In this paper, we present our preliminary findings to build up a damage identification framework based on acceleration measurements for railroad bridges. Initially, a Finite Element Model (FEM) of an open-deck railway bridge is developed. The model is then utilized to conduct numerical studies and gather acceleration response under moving train for both baseline and damaged conditions. This info is then further scrutinized by a sensor clustering based damage identification technique using time-series modeling. The damage in the bridge is investigated by observing the damage features of the damaged and undamaged bridge. The investigation demonstrates the damage features by comparing the fit ratios of locations of interest so that damage could be identified and located. The relative severity of the damage can also be assessed by comparing the magnitude of the damage features. Assessing the condition of our railway bridges continuously in this manner and early detection of potential structural changes are deemed very valuable for the infrastructure owners for developing more economical and effective maintenance strategies. © 2017Canadian Society for Civil Engineering. All rights reserved.","Acceleration measurement; Damage identification and localization; Damage quantification; Railway bridges; Sensor clustering; Structural health monitoring","Acceleration measurement; Accelerometers; Decision making; Railroad bridges; Railroad transportation; Railroads; Structural health monitoring; Damage Identification; Damage quantification; Maintenance strategies; Railroad infrastructure; Railway bridges; Railway infrastructure; Sensor clustering; Structural health monitoring (SHM); Damage detection",,,,,,,,,,,,,,,,"Banerji, P., Chikermane, S., Structural health monitoring of a steel railway bridge for increased axle loads (2011) Structural Engineering International, 21 (2), pp. 1-7; Banerji, P., Chikermane, S., Condition assessment of a heritage arch bridge using a novel model updation technique (2012) Journal of Civil Structural Health Monitoring, 2 (1), pp. 1-16; Choi, J.-Y., Park, Y.-G., Choi, E.-S., Choi, J.-H., Applying precast slab panel track to replace timber track in an existing steel plate girder railway bridge (2010) Journal of Rail and Rapid Transit, 224 (3), pp. 1-9; (2014) CSiBridge: ReadMe, , Computers and Structures, Inc; Gaudreault, V., Lemire, P., (2006) The Age of Public Infrastructure in Canada, , Statistics Canada, Ottawa, Ontario, Canada; Giil, M., Catbas, F.N., Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering (2011) Journal of Sound and Vibration, 330, pp. 1196-1210; Scott, R.H., Banerji, P., Chikermane, S., Srinivasan, S., Basheer, P.A.M., Surre, F., Sun, T., Grattan, K.T.V., Commissioning and evaluation of a fiber-optic sensor system for bridge monitoring (2013) IEEE Sensors Journal, 13 (7), pp. 2555-2562; Wiberg, J., Bridge monitoring to allow for reliable dynamic fe modelling (2006) A Case Study of the New Arsta Railway Bridge, , KTH Royal Institute of Technology, Stockholm, Sweden",,,"","Canadian Society for Civil Engineering","Canadian Society for Civil Engineering Annual Conference and General Meeting 2017: Leadership in Sustainable Infrastructure","31 May 2017 through 3 June 2017",,139003,,9781510865358,PCSEE,,"English","Proc Annu Conf Can Soc Civ Eng",Conference Paper,"Final","",Scopus,2-s2.0-85053609070
"Cao W.-J., Vernay D., Koh C.G., Smith I.F.C.","57220984892;55904305600;7201749853;7404426235;","Improving prediction capability of finite element models of bridges using static and dynamic data",2017,"SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings",,,,"789","796",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050160649&partnerID=40&md5=86aad74e33cda9233376a08b48b4ec7c","Department of Civil and Environmental Engineering, National University of Singapore, Singapore; ETH Zurich, Future Cities Laboratory, Singapore; ETH Centre, CREATE, Singapore; Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland","Cao, W.-J., Department of Civil and Environmental Engineering, National University of Singapore, Singapore, ETH Zurich, Future Cities Laboratory, Singapore, ETH Centre, CREATE, Singapore; Vernay, D., ETH Zurich, Future Cities Laboratory, Singapore, ETH Centre, CREATE, Singapore; Koh, C.G., Department of Civil and Environmental Engineering, National University of Singapore, Singapore; Smith, I.F.C., ETH Zurich, Future Cities Laboratory, Singapore, ETH Centre, CREATE, Singapore, Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland","Although finite-element models are commonly used in designing civil infrastructure, they may fail to represent the true operational behaviour. Measurements from monitoring and inspection can provide valuable information to improve the prediction capability of models. A significant amount of research has focused on system identification using either dynamic or static measurements separately. However, few research includes the systematic nature of many sources of uncertainties. In this paper, the methodology of error-domain model falsification recently proposed by Goulet and Smith is adopted with emphasis on the combination of both dynamic and static measurements. This methodology is most useful when uncertainties are systematic such as those originating from epistemic sources. Two case studies are presented to demonstrate this methodology. The first involves a simply supported beam in which the static and dynamic responses can be derived analytically. In the second case, a field test on an in-service prestressed concrete highway bridge in Singapore was conducted. The values of deflection and inclination as well as natural frequencies were measured. The results of model falsification using both static and dynamic measurements show higher falsification capacity compared with using only dynamic measurements. It is also shown that updating models using a model-falsification approach is more robust than using a traditional implementation of Bayesian inference. © 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved.",,"Bayesian networks; Crime; Inference engines; Prestressed concrete; Structural health monitoring; Civil infrastructures; Concrete highway bridges; Dynamic measurement; Prediction capability; Simply supported beams; Sources of uncertainty; Static and dynamic response; Static measurements; Finite element method",,,,,"Land Transport Authority - Singapore, LTA","The research was conducted at the Future Cities Laboratory at the Singapore-ETH Center, which was established collaboratively between ETH Zurich and Singapore’s National Research Foundation (FI 370074016) under its Campus for Research Excellence and Technological Enterprise programme. The authors would like to gratefully acknowledge the support of the Land Transport Authority of Singapore (LTA) to perform the case study.",,,,,,,,,,"Aktan, A.E., Farhey, D.N., Structural identification for condition assessment: Experimental arts (1997) Journal of Structural Engineering, 123 (12), pp. 1674-1684; Brownjohn, J.M., Structural health monitoring of civil infrastructure (2007) Philosophical Transactions of The Royal Society of London A: Mathematical, Physical and Engineering Sciences, 365 (1851), pp. 589-622; Araujo, I.G., Maldonado, E., Cho, G.C., Ambient vibration testing and updating of the finite element model of a simply supported beam bridge (2011) Frontiers of Architecture and Civil Engineering in China, 5 (3), pp. 344-354; Beck, J.L., Katafygiotis, L.S., Updating models and their uncertainties. I: Bayesian statistical framework (1998) Journal of Engineering Mechanics, 124 (4), pp. 455-461; Behmanesh, I., Moaveni, B., Lombaert, G., Papadimitriou, C., Hierarchical Bayesian model updating for structural identification (2015) Mechanical Systems and Signal Processing, 64, pp. 360-376; Goulet, J.A., Smith, I.F., Structural identification with systematic errors and unknown uncertainty dependencies (2013) Computers & Structures, 128, pp. 251-258; Rasmussen, C.E., Williams, C.K., (2006) Gaussian Processes for Machine Learning, 1. , Cambridge: MIT press",,"Mahini S.Mahini S.Chan T.","Geomotion;Mainmark;Queensland Government;Worldsensing","International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2017","5 December 2017 through 8 December 2017",,137293,,9781925553055,,,"English","SHMII - Int. Conf. Struct. Health Monit. Intell. Infrastruct., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85050160649
"Zhang Y., Miyamori Y., Oshima T., Shirakawa Y., Mikami S., Saito T.","57193324587;16402891800;7401663190;57192828720;7004347736;56303308300;","Effect of ballast state on dynamic parameters of a multispan ballasted prestressed concrete railway bridge",2017,"SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings",,,,"625","635",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050131217&partnerID=40&md5=a8a6ee6044661a4d5e59dc005c28f7d8","Kitami Institute of Technology, Japan","Zhang, Y., Kitami Institute of Technology, Japan; Miyamori, Y., Kitami Institute of Technology, Japan; Oshima, T., Kitami Institute of Technology, Japan; Shirakawa, Y., Kitami Institute of Technology, Japan; Mikami, S., Kitami Institute of Technology, Japan; Saito, T., Kitami Institute of Technology, Japan","In this paper a phenomenon that the structural dynamic parameters of a multispan ballasted prestressed concrete railway bridge vary significantly according to the different ballast states is presented. In high latitude regions, the ballasts on the railway bridges would be frozen in winter annually owning to the repeated melting and freezing of snow when the temperature fluctuates near 0 °C. Based on the bridge dynamic experiment results and visual checks of ballast states in different seasons, a hypothesis was proposed that the state of ballasts could affect the structural stiffness of the bridge directly, and it was also assumed that a reasonable explanation of the seasonal variation of structural dynamic parameters exists. In order to prove the rationality of the above assumption, a high resolution finite element model was performed which was strictly associated with the real structure. The ballasts in frozen state were simulated by increasing the stiffness of the solid elements of the ballasts and the stiffness was assumed empirically considering to the high difficulty of field stiffness measurement of the frozen ballasts. The finite element model analysis result of the structural natural frequencies was in good agreement with the filed dynamic experiment result. Thus, the rationality of the hypothesis that the frozen ballasts could increase the structural stiffness and vary the dynamic parameters of the bridge was well evaluated. Meanwhile, the irregularity of damping ratio variations from the experimental results was also discussed. The nonuniform distribution of the ice and snow may be considered an acceptable explanation of this phenomenon. © 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved.",,"Concrete beams and girders; Finite element method; Prestressed concrete; Railroad bridges; Railroads; Snow; Stiffness; Structural dynamics; Dynamic experiment; Dynamic parameters; Finite element model analysis; High-latitude regions; Non-uniform distribution; Seasonal variation; Stiffness measurements; Structural stiffness; Structural health monitoring",,,,,"Japan Society for the Promotion of Science, JSPS: 15K06176","This research was supported by Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research(C) Grant Number 15K06176. The contributions of Mr. Takanori Kadota, Dr. Tomoyuki Yamazaki and the Kitami City Government were greatly acknowledged.",,,,,,,,,,"Boller, C., Chang, F., Fujino, Y., (2009) Encyclopedia of Structural Health Monitoring, , Wiley, West Sussex; Farahani, R.V., Penumadu, D., Damage identification of a full-scale five-girder bridge using time-series analysis of vibration data (2016) Engineering Structures, 115, pp. 129-139; Dilena, M., Morassi, A., Dynamic identification of a reinforced concrete damaged bridge (2011) Mechanical Systems and Signal Processing, 25 (8), pp. 2990-3009; Chang, K.C., Kim, C.W., Modal-parameter identification and vibration-based damage detection of a damaged steel truss bridge (2016) Engineering Structures, 122, pp. 156-173; Gonzales, I., Ülker-Kaustell, M., Seasonal effects on the stiffness properties of a ballasted railway bridge (2013) Engineering Structures, 57, pp. 63-72; Zabel, V., Brehm, M., The influence of temperature varying material parameters on the dynamic behavior of short span railway bridges (2010) Proc. International Conference on Noise and Vibration Engineering (ISMA), 74. , Leuven, Belgium, 20-22 September; Rice, J.A., Mechitov, K., Flexible smart sensor framework for autonomous structural health monitoring (2010) Smart Structures and Systems, 6 (5-6), pp. 423-438; Yang, Z.J., Dutta, U., Seasonal frost effects on the soil-foundation-structure interaction system (2007) Journal of Cold Regions Engineering, 21 (4), pp. 108-120; Xia, Y., Chen, B., Temperature effect on vibration properties of civil structures: A literature review and case studies (2012) Journal of Civil Structural Health Monitoring, 2 (1), pp. 29-46; Alampalli, S., Influence of in-service environment on modal parameters (1998) Proceedings-SPIE The International Society for Optical Engineering, 1, pp. 111-116. , Colorado, American, 10-13 May",,"Mahini S.Mahini S.Chan T.","Geomotion;Mainmark;Queensland Government;Worldsensing","International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2017","5 December 2017 through 8 December 2017",,137293,,9781925553055,,,"English","SHMII - Int. Conf. Struct. Health Monit. Intell. Infrastruct., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85050131217
"Jayasundara N., Thambiratnam D.P., Chan T.H.T., Nguyen A.","57205601977;35583914600;7402687570;57310688400;","Damage detection in arch bridges using vibration based damage detection techniques",2017,"SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings",,,,"1633","1642",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050113768&partnerID=40&md5=6bdf916fb141edd6393bb62f9e48e0ef","School of Civil Engineering and Built Environment, Queensland University of Technology, Australia","Jayasundara, N., School of Civil Engineering and Built Environment, Queensland University of Technology, Australia; Thambiratnam, D.P., School of Civil Engineering and Built Environment, Queensland University of Technology, Australia; Chan, T.H.T., School of Civil Engineering and Built Environment, Queensland University of Technology, Australia; Nguyen, A., School of Civil Engineering and Built Environment, Queensland University of Technology, Australia","Most structures are built to have a long life. However, during this long life, they can be incurred damage due to structural deterioration, environmental effects and random actions such as impacts. Early damage assessment and appropriate retrofitting will enable the continued safe and efficient functions of the structures. In this context, vibration based techniques have emerged with the potential for reliable damage assessment. This study develops a dual criteria approach based on the vibration characteristics for detecting and locating damage in arch bridges. Steel arch bridges are one of the aesthetically pleasing bridge types in which damage has been rarely studied. In particular, the arch rib and struts (or columns in deck type bridges) which are important structural components, have received much less attention in damage detection. This study will therefore focus on damage detection in arch bridge structural components using indices based on modified Modal Flexibility (MF) and Modal Strain Energy (MSE) methods. The study is carried out through numerical simulations supported by limited experimental testing. The modelling techniques are first validated through experimental testing of a laboratory scale arch bridge model and this is followed by damage detection studies in this bridge structure. Data obtained from finite element analyses of the healthy and damaged arch bridge models are applied into the modified MF and MSE algorithms for detecting and locating the damage. Results demonstrate that the proposed method is capable of detecting damage in the arch rib and vertical columns of deck type arch bridges. © 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved.",,"Arch bridges; Arches; Steel bridges; Strain energy; Structural health monitoring; Damage assessments; Experimental testing; Modal strain energy; Modelling techniques; Structural component; Structural deterioration; Vibration characteristics; Vibration-based damage detection; Damage detection",,,,,,,,,,,,,,,,"Release 18, Help System, Coupled Field Analysis Guide, , ANSYS® Academic Research, ANSYS, Inc; Chan, T.H.T., Wong, K.Y., A Li, Z.X., Ni, Y.-Q., (2011) Structural Health Monitoring for Long Span Bridges: Hong Kong Experience & Continuing onto Australia Structural Health Monitoring in Australia, pp. 1-32. , Nova Publishers; Doebling, S.W., Farrar, C.R., Prime, M.B., Shevitz, D.W., (1996) Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review (No. LA-13070MS), , Los Alamos National Lab., NM United States; Law, S., Shi, Z., Zhang, L., Structural damage detection from incomplete and noisy modal test data (1998) Journal of Engineering Mechanics, 124 (11), pp. 1280-1288; Moragaspitiya, H.P., Thambiratnam, D.P., Perera, N.J., Chan, T.H., Development of a vibration based method to update axial shortening of vertical load bearing elements in reinforced concrete buildings (2013) Engineering Structures, 46, pp. 49-61; Nguyen, T., Chan, T.H., Thambiratnam, D.P., Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests (2014) Advances in Structural Engineering, 17 (3), pp. 319-330; Pandey, A.K., Biswas, M., Damage detection in structures using changes in flexibility (1994) Journal of Sound and Vibration, 169 (1), pp. 3-17; Pandey, A., Biswas, M., Experimental verification of flexibility difference method for locating damage in structures (1995) Journal of Sound and Vibration, 184 (2), pp. 311-328; Shih, H.W., Thambiratnam, D.P., Chan, T.H., Vibration based structural damage detection in flexural members using multi-criteria approach (2009) Journal of Sound and Vibration, 323 (3), pp. 645-661; Shi, Z.Y., Law, S.S., Zhang, L.M., Damage localization by directly using incomplete mode shapes (2000) Journal of Engineering Mechanics, 126 (6), pp. 656-660; Shih, H.W., Thambiratnam, D.P., Chan, T.H., Damage detection in truss bridges using vibration based multi-criteria approach (2011) Structural Engineering and Mechanics, 39 (2), pp. 187-206; Stubbs, N., Kim, J., Topole, K., An efficient and robust algorithm for damage localization in offshore platforms (1992) Proc. ASCE Tenth Structures Congress., , Paper; Stubbs, N., Kim, J.T., Farrar, C.R., Field verification of a nondestructive damage localization and severity estimation algorithm (1995) Proceedings-SPIE The International Society for Optical Engineering, p. 210. , February. SPIE INTERNATIONAL SOCIETY FOR OPTICAL; Wickramasinghe, W.R., Thambiratnam, D.P., Chan, T.H., Nguyen, T., Vibration characteristics and damage detection in a suspension bridge (2016) Journal of Sound and Vibration, 375, pp. 254-274",,"Mahini S.Mahini S.Chan T.","Geomotion;Mainmark;Queensland Government;Worldsensing","International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2017","5 December 2017 through 8 December 2017",,137293,,9781925553055,,,"English","SHMII - Int. Conf. Struct. Health Monit. Intell. Infrastruct., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85050113768
"Yamaguchi K., Matsuda H., Kawamura T., Saigyo T., Kimoto K., Nishikawa T.","57202980614;35085252800;57202986723;56018977100;55316083300;56828692300;","Structural vibration identification of bridges by 3D measurement FE analysis and the actual vibration measurement",2017,"SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings",,,,"1683","1688",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050093549&partnerID=40&md5=3f0ded5c390d732538d5082379610d7c","Graduate School of Engineering Nagasaki University, Nagasaki, Japan; PAL Corporation, Nagasaki, Japan; Keisoku Research Consultant Corporation, Hiroshima, Japan","Yamaguchi, K., Graduate School of Engineering Nagasaki University, Nagasaki, Japan; Matsuda, H., Graduate School of Engineering Nagasaki University, Nagasaki, Japan; Kawamura, T., Graduate School of Engineering Nagasaki University, Nagasaki, Japan; Saigyo, T., PAL Corporation, Nagasaki, Japan; Kimoto, K., Keisoku Research Consultant Corporation, Hiroshima, Japan; Nishikawa, T., Graduate School of Engineering Nagasaki University, Nagasaki, Japan","In this study, short span bridges with a span length less than 15m, which are numerous in local government were no design documents, and the year of construction is often unknown. It was proposed a method to evaluate its safety and risk for those bridges and examined its effectiveness and usefulness by field test. Specifically, a 3D model was created by a relatively inexpensive 3D laser scanner and structural analysis was carried out. After that, it was compared the result with actual vibration measurement data and presented the method of structural vibration identification of bridges by the simplest possible method. © 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved.",,"Bridges; Electric measuring bridges; Finite element method; Scanning; Structural dynamics; Structural health monitoring; Vibration measurement; 3-D measurement; 3-d modeling; 3D laser scanners; Design documents; FE analysis; Local government; Short-span bridges; Structural vibrations; Vibration analysis",,,,,,,,,,,,,,,,"(2014) Transport and Tourism, , http://www.mlit.go.jp/common/001034659.pdf, Ministry of Land, Infrastructure, Government of Japan: Press Release, April; (2017) Government of Japan: SIP: Cross-Ministerial Strategic Innovation Promotion Program, , http://www8.cao.go.jp/cstp/gaiyo/sip/keikaku/7_infura.pdf, Cabinet Office, April; Matsuda, H., (2016) Development of Efficient and Low Cost New Inspection Method Using Optical Measurement Method for Bridges, , http://www.mlit.go.jp/tec/gijutu/kaihatu/josei/162seika.pdf, May; Yoneda, M., Some considerations on damping characteristics of bridge structures due to coulomb friction force at movable supports (1994), pp. 137-145. , 492/Vi-23, Journal Of Jsce, June",,"Mahini S.Mahini S.Chan T.","Geomotion;Mainmark;Queensland Government;Worldsensing","International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2017","5 December 2017 through 8 December 2017",,137293,,9781925553055,,,"English","SHMII - Int. Conf. Struct. Health Monit. Intell. Infrastruct., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85050093549
"Gao X., Luo Y.-F., Wang L., Feng J.","57200174926;7404333100;57201266555;57202994465;","A finite element model updating method considering changes of boundary conditions during construction",2017,"SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings",,,,"835","844",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050078090&partnerID=40&md5=d8a84a5231e8768a721ee7a231279f8a","Department of Structural Engineering, Tongji University, Shanghai, 20092, China; Tongen Civil Engineering Technology CO., Ltd., Shanghai, China","Gao, X., Department of Structural Engineering, Tongji University, Shanghai, 20092, China; Luo, Y.-F., Department of Structural Engineering, Tongji University, Shanghai, 20092, China; Wang, L., Tongen Civil Engineering Technology CO., Ltd., Shanghai, China; Feng, J., Department of Structural Engineering, Tongji University, Shanghai, 20092, China","To ensure the safety of a large-span and complicated structures during construction, modern construction process monitoring techniques adopting the finite element (FE) model updating method are widely used in the servo-control of construction processes. The model updating technique is indispensable to the control of construction processes. During construction, boundary conditions of a structure may change substantially for several times, which can significantly influence the distribution of the member stresses and deformations of the structure. Therefore, boundary condition will be one of critical parameters for FE model updating during construction. In this paper, based on the step-by-step modeling (SSM) method for analyzing the mechanical behavior of a structure during construction, an updating method of structural FE model using static measurements considering changes of boundary conditions is proposed. The proposed method is used to update the FE model of Zhejiang Lu Bridge, in Shanghai, during the moving process. The results show that the proposed method is effective and practical. © 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved.",,"Boundary conditions; Construction; Process control; Process monitoring; Structural health monitoring; Complicated structures; Construction process; FE model updating; Finite-element model updating; Mechanical behavior; Model updating techniques; Monitoring techniques; Static measurements; Finite element method",,,,,,,,,,,,,,,,"Ahmadian, H., Gladwell, G.M.L., Ismail, F., Parameter selection strategies in finite element model updating (1997) Journal of Vibration and Acoustics-Transactions of The Asme, 119 (1), pp. 37-45; (2004) Release 10.0 Documentation for ANSYS, , ANSYS Inc. Canonsburg: ANSYS Inc; Belytschko, L.T., Moran, W.K., (2000) Nonlinear Finite Element Analysis for Continua and Structures; Chacon, R., Zorrilla, R., Structural health monitoring in incrementally launched steel bridges: Patch loading phenomena modeling (2015) Automation in Construction, 58, pp. 60-73; Esfandiari, A., Bakhtiari-Nejad, F., Rahai, A., Structural model updating using frequency response function and quasi-linear sensitivity equation (2009) Journal of Sound and Vibration, 326 (3-5), pp. 557-573; Liu, X.W., Guo, Y.L., Construction mechanics analytical procedures for steel structures in view of the geometric nonlinearity (2008) Journal of Xi'An University of Architecture &Technology (Natural Science Edition), 40 (2), pp. 161-169; Luo, Y.F., Ye, Z.W., Chen, X.M., Research on key parameter selection and monitoring point arrangement in construction monitoring of spatial steel structures (2014) Journal of Building Structures, 35, pp. 108-115; Mottershead, J.E., Friswell, M.I., Ng, G.H.T., Geometric parameters for finite element model updating of joints and constraints (1996) Mechanical Systems and Signal Processing, 10 (2), pp. 171-182; Ren, W.X., Chen, H.B., Finite element model updating in structural dynamics by using the response surface method (2010) Engineering Structures, 32 (8), pp. 2455-2465; Shadan, F., Khoshnoudian, F., Esfandiari, A., A frequency response-based structural damage identification using model updating method (2016) Structural Control & Health Monitoring, 23 (2), pp. 286-302; Sanayei, M., Imbaro, G.R., McClain, J.A.S., Structural model updating using experimental static measurements (1997) Journal of Structural Engineering-Asce, 123 (6), pp. 792-798. , ，; Sanayei, M., McClain, J.A.S., Wadia-Fascetti, S., Parameter estimation incorporating modal data and boundary conditions (1999) Journal of Structural Engineering-Asce, 125 (9), pp. 1048-1055; Sanayei, M., Phelps, J.E., Sipple, J.D., Instrumentation, nondestructive testing, and finite-element model updating for bridge evaluation using strain measurements (2012) Journal of Bridge Engineering, 17 (1), pp. 130-138",,"Mahini S.Mahini S.Chan T.","Geomotion;Mainmark;Queensland Government;Worldsensing","International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2017","5 December 2017 through 8 December 2017",,137293,,9781925553055,,,"English","SHMII - Int. Conf. Struct. Health Monit. Intell. Infrastruct., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85050078090
"Golub M.V., Moll J., Shpak A.N., Malinowski P., Wandowski T., Ostachowicz W.M.","36608070200;35620714600;56034862900;55521551200;16647930600;24756515200;","Theoretical and experimental studies of guided waves propagation in dry and one-side immersed waveguides with surface inhomogeneities",2017,"14th International Conference of the Slovenian Society for Non-Destructive Testing: &amp;quot;Application of Contemporary Non-Destructive Testing in Engineering&amp;quot;, Conference Proceedings","2017-September",,,"15","22",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049234804&partnerID=40&md5=3dfc31027d9f939bedbc876f934fcb5b","Institute for Mathematics, Mechanics and Informatics, Kuban State University, Krasnodar, Russian Federation; Department of Physics, Terahertz Photonics, Goethe University of Frankfurt am Main, Frankfurt am Main, Germany; Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Gdansk, Poland; Warsaw University of Technology, Warsaw, Poland","Golub, M.V., Institute for Mathematics, Mechanics and Informatics, Kuban State University, Krasnodar, Russian Federation; Moll, J., Department of Physics, Terahertz Photonics, Goethe University of Frankfurt am Main, Frankfurt am Main, Germany; Shpak, A.N., Institute for Mathematics, Mechanics and Informatics, Kuban State University, Krasnodar, Russian Federation; Malinowski, P., Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Gdansk, Poland; Wandowski, T., Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Gdansk, Poland; Ostachowicz, W.M., Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Gdansk, Poland, Warsaw University of Technology, Warsaw, Poland","Guided waves can be efficiently used for non-destructive testing and structural health monitoring of elastic waveguides. In layered structures submerged into fluid guided waves have greater attenuation and leaky waves can be observed. Guided leaky waves still have great potential in non-destructive ultrasonic inspection of immersed thin-walled structures like ships or underwater tanks. Compared to bulk waves guided waves provide a longer inspection range and allow to identify internal and surface damages. Strong resonance effects have been predicted and experimentally observed in layered plates with a single strip-like cracks or notches. The present study focuses on numerical and experimental analysis of guided waves interaction with inhomogeneities at the surface of an elastic plate. An experimental setup includes Laser Doppler vibrometry, a water bath and isotropic plates with surface inhomogeneities. Mathematical models are based on the integral approach as well as the higher order finite element method also called the spectral finite element method. The semi-analytical nature of the mathematical models provides information on propagation of guided waves in structure and their interaction with inhomogeneities. Reflection and resonance effects observed numerically and experimentally are carefully analyzed. © 2017 Slovenian Society for Non-Destructive Testing. All rights reserved.","Guided wave; Resonance; Scattering; Surface inhomogeneity","Bridge decks; Finite element method; Guided electromagnetic wave propagation; Laser Doppler velocimeters; Nondestructive examination; Plates (structural components); Resonance; Scattering; Structural health monitoring; Thin walled structures; Waveguides; Higher order finite element method; Laser Doppler Vibrometry; Non destructive testing; Numerical and experimental analysis; Spectral finite element method; Surface inhomogeneities; Surface inhomogeneity; Ultrasonic inspections; Ultrasonic testing",,,,,"Bundesministerium für Wirtschaft und Energie, BMWi: 03SX422B; Russian Science Foundation, RSF: 17-11-01191","The theoretical part of the research was supported by the Russian Science Foundation (Project 17-11-01191).","The experimental part of the research was done within the framework of the project “Reliable and Autonomous Monitoring System for Maritime Structures” (MARTECII/RAMMS/1/2016) granted by National Centre for Research and Development in Poland. The authors gratefully acknowledge the support of this research by the Federal Ministry for Economic Affairs and Energy (grant number: 03SX422B).",,,,,,,,,"Mitra, M., Gopalakrishnan, S., Guided wave based structural health monitoring: A review (2016) Smart Materials and Structures, 25, p. 053001; Eybpoosh, M., Berges, M., Noh, H., An energy-based sparse representation of ultrasonic guided-waves for online damage detection of pipelines under varying environmental and operational conditions (2017) Mechanical Systems and Signal Processing, 82 (1), pp. 260-278; Roy, S., Ladpli, P., Chang, F.-K., Load monitoring and compensation strategies for guided-waves based structural health monitoring using piezoelectric transducers (2015) Journal of Sound and Vibration, 351 (1), pp. 206-220; Leinov, E., Lowe, M., Cawley, P., Investigation of guided wave propagation and attenuation in pipe buried in sand (2015) Journal of Sound and Vibration, 347, p. 96114; Nedospasova, I.A., Mozhaev, V.G., Kuznetsova, I.E., Unusual energy properties of leaky backward Lamb waves in a submerged plate (2017) Ultrasonics, 77, pp. 95-99; Yu, L., Tian, Z., Case study of guided wave propagation in a one-side water immersed steel plate (2015) Case Studies in Nondestructive Testing and Evaluation, 3, pp. 1-8; Glushkov, E.V., Glushkova, N.V., On the efficient implementation of the integral equation method in elastodynamics (2001) Journal of Computational Acoustics, 9 (3), pp. 889-898; Glushkov, Y., Glushkova, N., Krivonos, A., The excitation and propagation of elastic waves in multilayered anisotropic composites (2010) Journal of Applied Mathematics and Mechanics, 74 (3), pp. 297-305; Ostachowicz, W., Kudela, P., Krawczuk, M., Zak, A., Guided waves in structures for SHM (2012) The Time-Domain Spectral Element Method, , John Wiley & Sons Ltd. Publication; Komatitsch, D., Tromp, J., Introduction to the spectral element method for three-dimensional seismic wave propagation (1999) Geophysical Journal International, 139, pp. 806-822; Rose, J.L., (1999) Ultrasonic Waves in Solid Media, , Cambridge University Press; Glushkov, E., Glushkova, N., Golub, M., Moll, J., Fritzen, C.-P., Wave energy trapping and localization in a plate with a delamination (2012) Smart Materials and Structures, 21, p. 125001",,"Kek T.Grum J.","European Federation for Non-Destructive Testing (EFNDT);Slovenian Society for Non-Destructive testing (SSNDT);TEAM TRADE d.o.o.","Slovenian Society for Non-Destructive Testing","14th International Conference of the Slovenian Society for Non-Destructive Testing: Application of Contemporary Non-Destructive Testing in Engineering","4 September 2017 through 6 September 2017",,136172,,9789619353738,,,"English","Int. Conf. Slov. Soc. Non-Destr. Test.: ""Appl. Contemp. Non-Destr. Test. Eng."", Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85049234804
"Yau J.D., Chen W.F., Urushadze S.","7102167551;55899353500;6507471737;","Indirect frequency measurement of cable-stayed bridges in cross winds",2017,"WSEAS Transactions on Applied and Theoretical Mechanics","12",,,"99","104",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032939460&partnerID=40&md5=6135cfe7acf60dede798ea885ae68494","Department of Architecture, Tamkang University, No.151, Yingzhuan Rd., Tamsui Dist, New Taipei City, 25137, Taiwan; Department of Civil Engineering, National Taiwan University, Taipei City, 106, Taiwan; Institute of Theoretical and Applied Mechanics, Academy of Sciences of the Czech Republic, v.v.i, Prague, Czech Republic","Yau, J.D., Department of Architecture, Tamkang University, No.151, Yingzhuan Rd., Tamsui Dist, New Taipei City, 25137, Taiwan; Chen, W.F., Department of Civil Engineering, National Taiwan University, Taipei City, 106, Taiwan; Urushadze, S., Institute of Theoretical and Applied Mechanics, Academy of Sciences of the Czech Republic, v.v.i, Prague, Czech Republic","In conventional bridge health monitoring, a number of sensors are deployed on a bridge directly for detecting its various dynamic properties. This is so called “direct method”. But the drawbacks of the direct method are: laborious deployment of sensors, time-consuming, and not portable. Following the previous Yang’s works (2004) in indirect method by using a passing test vehicle as a message receiver of bridge response, this study regards cross winds as lateral excitational sources to detect the lateral bridge frequencies from the lateral response of the moving test vehicle. To account for the wind-vehicle-bridge interactions in performing dynamic analysis, an iteration-based 3D vehicle-bridge interaction (VBI) finite element method is developed. The whole wind/VBI system is decomposed into two subsystems: the wind-bridge subsystem and the wind-vehicle subsystem. Then the iterative scheme is carried out to compute the interaction response between the two subsystems independently and iterate for removing unbalance forces. The numerical results indicated that the present indirect bridge monitoring is a simple and feasible method to measure the lateral frequency of a long-span bridge in cross winds. © 2017, World Scientific and Engineering Academy and Society. All rights reserved.","Aerodynamics; Frequency; Indirect measurement; Vehicle-bridge system; Wind engineering",,,,,,"103-2923-E-032-002-MY3, 105-2221-E-032-005, 106-2923-E-002-007-MY3, GA CR 17-26353J; Ministry of Science and Technology, Taiwan","This research was partially supported by the Ministry of Science and Technology in Taiwan through the Taiwan-Czech joint project with the Grants: MOST 103-2923-E-032-002-MY3, 105-2221-E-032-005, 106-2923-E-002-007-MY3, and GA CR 17-26353J.",,,,,,,,,,"Baker, C.J., A simplified analysis of various types of wind-induced road vehicle accidents (1986) J. Wind Eng. Ind. Aerodyn, 22, pp. 69-85; Cai, C.S., Chen, S.R., Framework of vehicle– bridge–wind dynamic analysis (2004) J. Wind Eng. and Ind. Aerodyn, 92, pp. 579-607; Ernst, J.H., Der E-modul von seilen unter Berucksichtigung des durchhanges (1965) Bauingenieur, 40 (2), pp. 52-55; Simiu, E., Scanlan, R.H., (1996) Wind Effects on Structures-Fundamentals and Applications to Design, , 3rd Ed., John Wiely & Sons, Inc., New York; Xu, Y.L., Xia, H., Yan, Q.S., Dynamic response of long suspension bridge to high wind and running train (2003) ASCE J. Bridge Eng, 8, pp. 46-55; Xu, Y.L., Zhang, N., Xia, H., Vibration of coupled train and cable-stayed bridge systems in cross winds (2004) Eng. Struct, 26, pp. 1389-1406; Yang, Y.B., Lin, C.W., Yau, J.D., Extracting bridge frequencies from the dynamic response of a passing vehicle (2004) J. Sound and Vib, 272 (3-5), pp. 471-493; Yang, Y.B., Lin, C.W., Vehicle-bridge interaction dynamics and potential applications (2005) J. Sound and Vib, 284 (1-2), pp. 205-226; Yang, Y.B., Chang, K.C., Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique (2009) J. Sound and Vib, 322 (4-5), pp. 718-739; Yang, Y.B., Li, Y.C., Chang, K.C., Using two connected vehicles to measure the frequencies of bridges with rough surface: A theoretical study (2012) Acta Mech, 223 (8), pp. 1851-1861; Yang, Y.B., Chang, K.C., Li, Y.C., Filtering techniques for extracting bridge frequencies from a test vehicle moving over the bridge (2013) Eng. Struct, 48, pp. 353-362; Yang, Y.B., Chen, W.F., Yu, H.W., Chan, C.S., Experimental study of a hand-drawn cart for measuring the bridge frequencies (2013) Eng. Struct, 57, pp. 222-231; Yang, Y.B., Cheng, M.C., Chang, K.C., Frequency variation in vehicle-bridge interaction systems (2013) Intl. J. Struct. Stab. Dyna, 13 (2); Yang, Y.B., Li, Y.C., Chang, K.C., Constructing the mode shapes of a bridge from a passing vehicle: A theoretical study (2014) Smart Struct. Syst, 13 (5), pp. 797-819; Yau, J.D., Kuo, S.R., Study on interaction aerodynamics of vehicle-bridge system under wind actions (2014) 12Th International Conference on Fluid Mechanics & Aerodynamics (FMA '14), Dec.29-31, Geneva, Switzerland",,,,"World Scientific and Engineering Academy and Society",,,,,19918747,,,,"English","WSEAS Trans. Appl. Theor. Mech.",Article,"Final","",Scopus,2-s2.0-85032939460
"Verma A.K., Su D.","57194509404;15120080700;","Load Rating Based Maintenance Approach for Corrosive Twin I-Girder Railway Bridges in India",2017,"Procedia Engineering","188",,,"56","63",,,"10.1016/j.proeng.2017.04.457","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020456235&doi=10.1016%2fj.proeng.2017.04.457&partnerID=40&md5=65315df855bb8e4346a05ab78a3b95b7","University of Tokyo, Department of Civil Engineering, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1138656, Japan","Verma, A.K., University of Tokyo, Department of Civil Engineering, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1138656, Japan; Su, D., University of Tokyo, Department of Civil Engineering, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1138656, Japan","In Indian Railways, about 75% of the bridges are exceeding 60 years' life period and 80% of steel bridges population adopt simply supported twin I-girder structure. Corrosion has become one of the biggest challenge for maintenance of these old steel bridges. Currently the index called Condition Rating Number (CRN), utilized to assess the structural conditions and determine the maintenance strategy. CRN is a qualitative approach with individual bias in decision, therefore it is difficult to identify the prioritization of corrosion damages in same CRN category. In this study, a new approach based on load rating factor (LRF) for corrosion maintenance on typical twin I-girder bridges is proposed. Firstly, by considering frequently occurring corrosive location and the absolute maximum load effect, three categories of critical corrosive location and pattern are summarized. Secondly, LRF based on physical inspection is calculated using simplified theoretical approach and FEM approach. Various degree of corrosion is utilized to investigate the influence on the load capacity by incorporating the nonlinear phenomena such as buckling. It is found that the simplified approach is conservative enough to evaluate the structure practically within safety side. Finally, the practical decision flow regarding to the maintenance action is proposed, summarizing the relationship between the corrosion and the remaining load capacity by means of LRF. This approach is expected to provide not only an objective evaluation to load capacity of corrosive railway bridges, but also instructive information for the retrofit of structures with limited fund availability. © 2017 The Authors.","Corrosion; Load rating; Maintenance","Corrosion; Maintenance; Railroad bridges; Railroads; Steel corrosion; Structural health monitoring; Rating; Transportation; Load ratings; Maintenance approaches; Maintenance strategies; Non-linear phenomena; Objective evaluation; Qualitative approach; Structural condition; Theoretical approach; Steel bridges",,,,,,,,,,,,,,,,"(2010) Broad Guidelines for Instrumentation of Bridges for Running Higher Axle Loads, , R.D.S.O. Bridge & Structure Directorate's Report No. BS-106 April; (1998) Indian Railways Bridge Manual, , First edition; Kayser, J.R., Nowak, A.S., Capacity loss due to corrosion in steel girder bridges (1989) Journal of Structural Engineering, 115 (6); Fukuda, M., Fujii, K., Nakayama, T., Matsui, S., An Evaluation Method for the Remaining Strength of a Plate Girder with Local Corrosion under Sleepers (2011) 12th East Asia-Pacific Conference on Structural Engineering and Construction, , January 26-28 Hong Kong SAR, China; Khurram, N., Sasaki, E., Katsuchi, H., Yamada, H., Finite element investigation of shear capacity of locally corroded end panel of steel plate girder (2013) International Journal of Steel Structure, 13 (4); (2013) 2013 Interim Revision to Manual for Bridge Evaluation, , AASHTO Second edition 2010 American Association of State Highway and Transportation Officials; Indian Railways Standard Code of Practice for Designing of Steel Bridges Carrying Rail, Road or Pedestrian Traffic (Adopted 1941), , (Incorporating A&C slip no. 19, 2014); Indian Railway Standard Rules Specifying the Loads for Design of Super-Structure and Sub-Structure of Bridges (Adopted 1941), , (Incorporating A&C slip no. 39, 2008)","Verma, A.K.; University of Tokyo, 7-3-1 Hongo, Japan; email: verma@bridge.t.u-tokyo.ac.jp","Chiu W.K.Galea S.Mita A.Takeda N.","Australian Government Department of Defence;EMBRAER;GLOBAL Office of Naval Research science and technology;MONASH University;OLYMPUS","Elsevier Ltd","6th Asia Pacific Workshop on Structural Health Monitoring, APWSHM 2016","7 December 2016 through 9 December 2016",,135958,18777058,,,,"English","Procedia Eng.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85020456235