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,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
"Kang J.-S., Chung K., Hong E.J.","57211502119;25927027500;57212106477;","Multimedia knowledge‐based bridge health monitoring using digital twin",2021,"Multimedia Tools and Applications","80","26-27",,"34609","34624",,18,"10.1007/s11042-021-10649-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101092359&doi=10.1007%2fs11042-021-10649-x&partnerID=40&md5=3d26586c1ef82a8cbc8f6684db6673d3","Department of Computer Science, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do  16227, South Korea; Division of AI Computer Science and Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do  16227, South Korea; Department of Software, Yonsei University, Wonju-si, 26493, South Korea","Kang, J.-S., Department of Computer Science, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do  16227, South Korea; Chung, K., Division of AI Computer Science and Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do  16227, South Korea; Hong, E.J., Department of Software, Yonsei University, Wonju-si, 26493, South Korea","Digital twins are virtual replicas of real physical entities in computers. They can be considered as abstract digital models of data and behavior for objects of interest. Nevertheless, they are not perfectly consistent with conventional data or simulation models because they achieve prediction and optimization by simulating the abstract digital model of a particular system. To maintain the characteristics of digital twins in the virtual space, digital simulation models that continue to update, change, and evolve according to continuous changes of corresponding physical factors must be used. Owing to the various advantages of digital twin technology, digital twins have gained more attention. However, the method to create digital twins is still unclear. Additionally, the availability and sufficiency of information on physical entities to which digital twins will be applied must be considered, and a model suitable for their application must be designed. Therefore, multimedia knowledge-based bridge health monitoring using digital twins is proposed herein. It synchronizes real and virtual spaces to reflect the reality based on various data collected using sensors of real systems. In this study, various situations of virtual bridge twins in a facility management area are simulated to provide digital services to ensure bridge health. This digital bridge health service analyzes situations based on a small amount of data collected from a bridge, predicts the optimal time point for maintenance, and then applies it to the real world. Hence, maintenance costs can be reduced and the bridge’s lifespan extended. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.","Bridge health monitoring; Data model; Digital twin; Knowledge; Modeling and simulation","Bridges; Health; Information services; Knowledge based systems; Office buildings; Bridge health monitoring; Digital services; Digital simulation models; Facility management; Knowledge based; Maintenance cost; Physical factors; Virtual bridges; Digital twin",,,,,"Ministry of Land, Infrastructure and Transport, MOLIT: 21CTAP-C157011-02; Korea Agency for Infrastructure Technology Advancement, KAIA","This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21CTAP-C157011-02).",,,"Bondarenko, O., Fukuda, T., Development of a diesel engine’s digital twin for predicting propulsion system dynamics (2020) Energy, 196. , (,),., https://doi.org/10.1016/j.energy.2020.117126; City Brain Alibaba Cloud, , https://www.alibabacloud.com/solutions/intelligence-brain/city; Fan, C., Zhang, C., Yahja, A., Mostafavi, A., Disaster city digital twin: a vision for integrating artificial and human intelligence for disaster management (2019) Int J Inf Manag; Kabak, K., Hinkeldeyn, J., Dekkers, R., Analyses of outcomes that used simulation modelling towards building theory (2019) Procedia Manuf, 39, pp. 794-803; Kang, J., Shin, D., Baek, J., Chung, K., Activity recommendation model using rank correlation for chronic stress management (2019) Appl Sci, 9 (20), pp. 4284-4296; Kim, J.C., Chung, K., Multi-modal stacked denoising autoencoder for handling missing data in health big data (2020) IEEE Access, 8 (1), pp. 104933-104943; Kim, B.S., Kim, T.G., Modeling and simulation using artificial neural network-embedded cellular automata (2020) IEEE Access, 8 (1), pp. 24056-24061; Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W., Digital twin in manufacturing: a categorical literature review and classification (2018) IFAC-PapersOnLine, 51 (11), pp. 1016-1022; Lee, J., Bagheri, B., Kao, H., A cyber-physical systems architecture for industry 4.0-based manufacturing systems (2015) Manuf Lett, 3, pp. 18-23; Lee, K.H., Hong, J.H., Kim, T.G., System of systems approach to formal modeling of cps for simulation-based analysis (2015) ETRI J, 37 (1), pp. 175-185; Madni, A., Madni, C., Lucero, S., Leveraging digital twin technology in model-based systems engineering (2019) Systems, 7 (1), p. 7; Maria, A., Introduction to modeling and simulation (1997) Proceedings of the 29Th Conf. Winter Simulation, pp. 7-13; Predix, G.E., Digital, , https://www.ge.com/digital/iiot-platform; Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Nee, A., Enabling technologies and tools for digital twin (2019) J Manuf Syst; Schroeder, G., Steinmetz, C., Pereira, C., Muller, I., Garcia, N., Espindola, D., Rodrigues, R., Visualising the digital twin using web services and augmented reality (2016) 2016 IEEE 14Th Int. Conf. Ind Inform (INDIN), pp. 522-527; Schroeder, G.N., Steinmetz, C., Pereira, C.E., Espindola, D.B., Digital twin data modeling with automationML and a communication methodology for data exchange (2016) IFAC-PapersOnLine, 49 (30), pp. 12-17; Schwab, K., (2017) The Fourth Industrial Revolution, , Currency, New York; Siemens, M., https://www.siemens.mindsphere.io/en; https://smartcity.go.kr/; Söderberg, R., Wärmefjord, K., Carlson, J., Lindkvist, L., Toward a digital twin for real-time geometry assurance in individualized production (2017) CIRP Ann, 66 (1), pp. 137-140; Stark, R., Fresemann, C., Lindow, K., Development and operation of digital twins for technical systems and services (2019) CIRP Ann, 68 (1), pp. 129-132; Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F., Digital twin-driven product design, manufacturing and service with big data (2018) Int J Adv Manuf Technol, 94, pp. 3563-3576; Vachálek, J., Bartalský, L., Rovný, O., Šišmišová, D., Morháč, M., Lokšík, M., The digital twin of an industrial production line within the industry 4.0 concept (2017) 21St Int. Conf. Process Control (PC), pp. 258-262; Velosa, A., Natis, Y., Lheureux, B., (2016) Use the Iot Platform Reference Model to Pan Your Iot Business Solutions, , Gartner Research, Stamford; Ye, C., Butler, L., Bartek, C., Iangurazov, M., Lu, Q., Gregory, A., Girolami, M., Middleton, C., A digital twin of bridges for structural health monitoring (2019) Proceedings of the 12Th International Workshop on Structural Health Monitoring. Stanford University, , https://doi.org/10.12783/shm2019/32287; https://www.nrf.gov.sg/programmes/virtual-singapore","Hong, E.J.; Department of Software, South Korea; email: ellenhong@yonsei.ac.kr",,,"Springer",,,,,13807501,,MTAPF,,"English","Multimedia Tools Appl",Article,"Final","",Scopus,2-s2.0-85101092359
"Ye S., Lai X., Bartoli I., Aktan A.E.","56783517100;57196260436;8856150200;7006947953;","Technology for condition and performance evaluation of highway bridges",2020,"Journal of Civil Structural Health Monitoring","10","4",,"573","594",,18,"10.1007/s13349-020-00403-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084367794&doi=10.1007%2fs13349-020-00403-6&partnerID=40&md5=13a3541cb9b282f4c1a6a608f9ec9875","Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA, United States","Ye, S., Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA, United States; Lai, X., Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA, United States; Bartoli, I., Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA, United States; Aktan, A.E., Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA, United States","Today, bridge owners must consider increasing traffic demands (in both volume and weight) and also face concerns related to sustainability, resilience and liveability which were virtually unknown in the 1950s. Furthermore, legislators demand data-driven asset management decisions based on objective, quantitative and reliable bridge condition and performance evaluation. To explore the current state-of-the-art in objective performance and condition evaluation of constructed systems by leveraging technology, a 30-year old freeway bridge in New Jersey, exhibiting multiple complex performance deficiencies, was transformed into a field laboratory. To identify the root causes of performance concerns, Visual Inspection, Operational Monitoring, Forced Excitation Testing, Controlled Load Testing, Non-destructive Probes, Long-term Monitoring, Finite Element Modelling and Parameter Identification were conducted within a Structural Identification framework. The results showed that root causes of some performance deficiencies of the test bridge were identified definitively only through the application of field measurements and analyses integrated by following a scientific approach—i.e. Structural Identification. Controlled Proof-Load Testing was especially useful in demonstrating the location and impacts of damage and the remaining capacity although such an approach can only be considered for the most critical cases due to its high cost and disruption to operations. Operational monitoring was shown as a sufficient and much cheaper alternative for structural identification permitting the development of a 3D digital twin of the bridge, which proved critical in identifying the root causes of its deficiencies and formulating meaningful interventions. Without an a-priori model used for designing the experiments as well as a model (i.e. a digital twin) calibrated by parameter identification and used for simulations, it was not possible to offer options for corrective measures confidently. The study demonstrated the challenges in relying only on visual inspection when a multitude of interdependent mechanisms lead to damage and deterioration, and the information value of different experimental methods such as vibration testing, proof load testing, wide-area NDE scans and multi-year SHM in being able to understand the root causes of various damages. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.","Condition and performance evaluation; Controlled load testing; Finite element model updating; Non-destructive evaluation; Structural health monitoring; Structural identification; Vibration testing","Bridges; Deterioration; Digital twin; Load testing; Nondestructive examination; Structural analysis; Condition evaluation; Corrective measures; Experimental methods; Finite element modelling; Long term monitoring; Management decisions; Operational monitoring; Structural identification; Parameter estimation",,,,,"New Jersey Department of Transportation, NJDOT; Federal Highway Administration, FHWA; Princeton University","The structural system of the test bridge and its design is a common one throughout the US highway system. Various field experiments were conducted on the same span during 2012–2013 with the participation of experienced bridge engineers and bridge research experts from Japan, Korea, the United Kingdom, Austria, Switzerland and the US. The study was funded by the USDOT–FHWA and NJDOT as a part of the FHWA’s Long-Term Bridge Performance Study, and it was coordinated by Drexel University with participation from Rutgers and Princeton University.","This study was funded by the USDOT-FHWA and NJDOT as part of the Pilot Phase of FHWA’s Long-Term Bridge Performance Program. The authors are deeply grateful to the contributions of their US-based academic collaborators Professors Franklin Moon, Jeff Weidner [], Nenad Gucunski, Branko Glisic, Haluk Aktan, Marvin Halling as well as Yun Zhou, Jian Zhang and John Prader. FHWA researchers and officials Dr. Steven Chase and Dr. Hamid Ghasemi initiated the Long-Term Bridge Performance Program. Authors are especially grateful to International participants from Japan, Korea and Europe (Yozo Fujino, Tomonori Nagayama, Hyun-Moo Koh, Helmut Wenzel, James Brownjohn and Ian Smith as well as their teams) who generously supported and played very critical roles in this study. Finally, the senior authors deeply appreciate the current support and guidance by their FHWA colleagues Drs. Hoda Azari and David Kuehn. Additional information about this and other studies can be found on the “NDE Virtual Laboratory website” at: http://vlab.asklab.net/VirtualLab/index.html .","This study was funded by the USDOT-FHWA and NJDOT as part of the Pilot Phase of FHWA?s Long-Term Bridge Performance Program. The authors are deeply grateful to the contributions of their US-based academic collaborators Professors Franklin Moon, Jeff Weidner?[56], Nenad Gucunski, Branko Glisic, Haluk Aktan, Marvin Halling as well as Yun Zhou, Jian Zhang and John Prader. FHWA researchers and officials Dr. Steven Chase and Dr. Hamid Ghasemi initiated the Long-Term Bridge Performance Program. Authors are especially grateful to International participants from Japan, Korea and Europe (Yozo Fujino, Tomonori Nagayama, Hyun-Moo Koh, Helmut Wenzel, James Brownjohn and Ian Smith as well as their teams) who generously supported and played very critical roles in this study. Finally, the senior authors deeply appreciate the current support and guidance by their FHWA colleagues Drs. Hoda Azari and David Kuehn.?Additional information about this and other studies can be found on the ?NDE Virtual Laboratory website? at:?http://vlab.asklab.net/VirtualLab/index.html.","(2019) 2019 Bridge Report, , https://artbabridgereport.org/reports/2019-ARTBA-Bridge-Report.pdf, . 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Rutgers, The State University of New Jersey; Weidner, J., (2012) Structural Identification of a Complex Structure Using Both Conventional and Multiple Model Approaches, , http://hdl.handle.net/1860/3818, Doctoral Thesis. Drexel University","Ye, S.; Department of Civil, United States; email: shi.ye@drexel.edu",,,"Springer",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85084367794
"Shao S., Zhou Z., Deng G., Du P., Jian C., Yu Z.","57215084079;15830628600;57201408119;57215083266;57215089050;57215088172;","Experiment of structural geometric morphology monitoring for bridges using holographic visual sensor",2020,"Sensors (Switzerland)","20","4","1187","","",,13,"10.3390/s20041187","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079884461&doi=10.3390%2fs20041187&partnerID=40&md5=b5ade0d6eee3b69933e4f456ab6faf21","School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China; College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518061, China","Shao, S., School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518061, China; Zhou, Z., College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518061, China; Deng, G., School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China; Du, P., School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China; Jian, C., School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China; Yu, Z., School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China","To further improve the precision and efficiency of structural health monitoring technology and the theory of large‐scale structures, full‐field non‐contact structural geometry morphology monitoring is expected to be a breakthrough technology in structural safety state monitoring and digital twins, owing to its economic, credible, high frequency, and holographic advantages. This study validates a proposed holographic visual sensor and algorithms in a computer‐vision‐based full‐field non‐contact displacement and vibration measurement. Using an automatic camera patrol experimental device, original segmental dynamic and static video monitoring data of a model bridge under various damage/activities were collected. According to the temporal and spatial characteristics of the series data, the holographic geometric morphology tracking algorithm was introduced. Additionally, the feature points set of the structural holography geometry and the holography feature contours were established. Experimental results show that the holographic visual sensor and the proposed algorithms can extract an accurate holographic full‐field displacement signal, and factually and sensitively accomplish vibration measurement, while accurately reflecting the real change in structural properties under various damage/action conditions. The proposed method can serve as a foundation for further research on digital twins for large‐scale structures, structural condition assessment, and intelligent damage identification. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Bridge safety; Computer‐vision‐based measurement technology; Dense full‐field measurement; Digital twins; Holographic visual sensor; Structural geometry monitoring","Damage detection; Digital twin; Displacement measurement; Electric measuring bridges; Geometry; Holography; Monitoring; Morphology; Vibration measurement; Bridge safety; Field measurement; Measurement technologies; Structural geometry; Visual sensor; Structural health monitoring",,,,,"National Natural Science Foundation of China, NSFC: 51778094; China National Funds for Distinguished Young Scientists: 51608080, 51708068; Chongqing Jiaotong University, CQJTU: 2019S0141","Funding: This research was funded by the National Natural Science Foundation of China (Grant No. 51778094), the National Science Foundation for Distinguished Young Scholars of China (Grant No. 51608080), and the National Science Foundation for Distinguished Young Scholars of China (Grant No. 51708068), and the Science and Technology Innovation Project of Chongqing Jiaotong University (Grant No. 2019S0141).",,,"Feng, D.M., Feng, M.Q., Computer Vision for SHM of Civil Infrastructure: From Dynamic Response Measurement to Damage Detection‐A review (2018) Eng. Sturct., 156, pp. 105-117; China, J., (2014) Highway Transp., (27), pp. 1-96; Sun, L.M., Shang, Z.Q., Xia, Y., Development and Prospect of Bridge Structural Health Monitoring in the Context of Big Data (2019) China J. Highway Transp., 32, pp. 1-20; Ye, X.W., Dong, C.Z., Review of Computer Vision‐based Structural Displacement Monitoring (2019) China J. Highway Transp., 32, pp. 20-39; Shao, S., Zhou, Z.X., Deng, G.J., Wang, S.R., Experiment of Structural Morphology Monitoring for Bridges Based on Non‐contact Remote Intelligent Perception Method (2019) China J. Highway Transp., 32, pp. 91-102; Bao, Y.Q., Li, H., Ou, J.P., Emerging Data Technology in Structural Health Monitoring: Compressive Sensing Technology (2012) J. Civ. Struct. Health Monit., 4, pp. 77-90; Bao, Y.Q., Yu, Y., Li, H., Mao, X.Q., Jiao, W.F., Zou, Z.L., Ou, J.P., Compressive Sensing Based Lost Data Recovery of Fast‐moving Wireless Sensing for Structural Health Monitoring (2015) Struct. 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Graph., 34, pp. 1-7","Zhou, Z.; College of Civil and Transportation Engineering, China; email: zhixiangzhou@szu.edu.cn",,,"MDPI AG",,,,,14248220,,,"32098079","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85079884461
"Meixedo A., Santos J., Ribeiro D., Calçada R., Todd M.D.","56940709200;36810314200;24476782300;7801603531;7202805915;","Online unsupervised detection of structural changes using train–induced dynamic responses",2022,"Mechanical Systems and Signal Processing","165",,"108268","","",,10,"10.1016/j.ymssp.2021.108268","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112019043&doi=10.1016%2fj.ymssp.2021.108268&partnerID=40&md5=9563683e0a15f5f35befc0cd28a1d8e1","CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Portugal; LNEC, Laboratório Nacional de Engenharia Civil, Portugal; CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Portugal; Department of Structural Engineering, University California San Diego, United States","Meixedo, A., CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Portugal; Santos, J., LNEC, Laboratório Nacional de Engenharia Civil, Portugal; Ribeiro, D., CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Portugal; Calçada, R., CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Portugal; Todd, M.D., Department of Structural Engineering, University California San Diego, United States","This paper exploits unsupervised data-driven structural health monitoring (SHM) in order to propose a continuous online procedure for damage detection based on train-induced dynamic bridge responses, taking advantage of the large-magnitude loading for enhancing sensitivity to small-scale structural changes. While such large responses induced by trains might create more damage-sensitive information in the measured response, it also amplifies the effects on those measurements from the environment. Thus, one of the biggest contributions herein is a methodology that exploits the large bridge responses induced by train passage while rejecting the confounding influences of the environment in such a way that false positive detections are mitigated. Furthermore, this research work introduces an adaptable confidence decision threshold that further improves damage detection over time. To ensure an online continuous assessment, a hybrid combination of autoregressive exogenous input (ARX) models, principal components analysis (PCA), and clustering algorithms was sequentially applied to the monitoring data, in a moving window process. A comparison between the performance obtained from autoregressive (AR) and ARX models as feature extractors was conducted, and it was concluded that ARX models lead to increased sensitivity to damage due to their ability to capture cross information between the sensors. The PCA proved its importance and effectiveness in removing observable changes induced by variations in train speed or temperature without the need to measure them, and the clustering methods allowed for an automatic classification of the damage-sensitive features. Since it was not possible to introduce damage to the bridge, several structural conditions were simulated with a highly reliable digital twin of the Sado Bridge, tuned with experimental data acquired from a SHM system installed on site, in order to test and validate the efficiency of the proposed procedure. The strategy proved to be robust when detecting a comprehensive set of damage scenarios with a false detection incidence of 2%. Moreover, it showed sensitivity to smaller damage levels (earlier in life), even when it consists of small stiffness reductions that do not impair structural safety and are imperceptible in the original signals. © 2021 Elsevier Ltd","ARX model; Cluster analysis; Damage detection; Online assessment; PCA; Structural health monitoring; Traffic-induced dynamic responses; Unsupervised learning","Bridges; Cluster analysis; Clustering algorithms; Damage detection; E-learning; Principal component analysis; Structural health monitoring; Unsupervised learning; ARX model; Bridge response; Data driven; Health monitoring; Online assessments; Principal-component analysis; Structural health; Traffic-induced dynamic response; Unsupervised data; Unsupervised detection; Dynamic response",,,,,"POCI-01-0145-FEDER-031054; Fundação para a Ciência e a Tecnologia, FCT: SFRH/BD/93201/2013; Ministério da Ciência, Tecnologia e Ensino Superior, MCTES; Institute of Research and Development in Structures and Construction","This work was financially supported by the Portuguese Foundation for Science and Technology (FCT) through the PhD scholarship SFRH/BD/93201/2013. The authors would like to acknowledge the support of the Portuguese Road and Railway Infrastructure Manager (Infraestruturas de Portugal, I.P), the Portuguese National Laboratory for Civil Engineering (LNEC), the SAFESUSPENSE project - POCI-01-0145-FEDER-031054 (funded by COMPETE2020, POR Lisboa and FCT) and the Base Funding - UIDB/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções - financed by national funds through the FCT/MCTES (PIDDAC).",,,"Huynh, C.P., Mustapha, S., Runcie, P., Porikli, F., Multi-class support vector machines for paint condition assessment on the Sydney Harbour Bridge using hyperspectral imaging (2015) Struct. Monitor. Maintenance, 2 (3), pp. 181-197; C.R. Farrar K. Worden Structural Health Monitoring: a machine learning perspective. Wiley 2013 1 45; Mustapha, S., Braytee, A., Ye, L., Multisource data fusion for classification of surface cracks in steel pipes (2018) J. Nondestructive Eval. Diagnost. Prognost. Eng. 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"Dan D., Ying Y., Ge L.","7004963131;57375558900;57209056473;","Digital Twin System of Bridges Group Based on Machine Vision Fusion Monitoring of Bridge Traffic Load",2022,"IEEE Transactions on Intelligent Transportation Systems","23","11",,"22190","22205",,6,"10.1109/TITS.2021.3130025","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121394671&doi=10.1109%2fTITS.2021.3130025&partnerID=40&md5=f664ff8e4903815c772725c427b790a8","Tongji University, Key Lab. of Performance Evolution and Control for Engineering Structures of Ministry of Education, The School of Civil Engineering, Shanghai, 200092, China; Tongji University, School of Civil Engineering, Shanghai, 200092, China","Dan, D., Tongji University, Key Lab. of Performance Evolution and Control for Engineering Structures of Ministry of Education, The School of Civil Engineering, Shanghai, 200092, China; Ying, Y., Tongji University, School of Civil Engineering, Shanghai, 200092, China; Ge, L., Tongji University, School of Civil Engineering, Shanghai, 200092, China","Bridges play an important role in transportation infrastructure systems. Intelligent and digital management of bridges group is an essential part of the future intelligent transportation infrastructure system. This paper proposes a digital twin system for bridges group in the regional transportation infrastructure network, which is interconnected by measured traffic loads. In physical space, a full-bridge traffic load monitoring system based on information fusion of weigh-in-motion (WIM) and multi-source heterogeneous machine vision is set up on the target bridge to measure traffic loads, also lightweight sensors are employed on the bridges group for structural response information. Furthermore, by establishing mechanical analysis models in the corresponding digital space and using the measured traffic loads as links, the working condition perception and safety warning of all bridges in the regional transportation network is achieved, forming an important support for further intelligent transportation infrastructure system. The proposed digital twin system has been preliminarily implemented in a bridges group around Shanghai, China, demonstrating the feasibility of the technical framework proposed in this paper and the bright prospects. © 2000-2011 IEEE.","AI-driven machine vision; bridge digital twin system; multi-source information fusion; structural health monitoring; traffic load monitoring","Computer vision; Information fusion; Weigh-in-motion (WIM); AI-driven machine vision; Bridge digital twin system; Load modeling; Load monitoring; Machine-vision; Multi-source information fusion; Structural health monitoring.; Traffic load monitoring; Traffic loads; Transportation infrastructures; Structural health monitoring",,,,,,,,,"Lu, Y., Liu, C., Wang, K.I.-K., Huang, H., Xu, X., Digital twindriven smart manufacturing: Connotation, reference model, applications and research issues (2020) Robot. Comput.-Integr. 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TJ20200826; Dan, D., Xu, Z., Zhang, K., Yan, X., Monitoring index of transverse collaborative working performance of assembled beam bridges based on transverse modal shape (2019) Int. J. Struct. Stability Dyn., 19 (8). , Aug","Dan, D.; Tongji University, China; email: dandanhui@tongji.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15249050,,,,"English","IEEE Trans. Intell. Transp. Syst.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85121394671
"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
"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. 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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
"Mohammadi M., Rashidi M., Mousavi V., Karami A., Yu Y., Samali B.","57210426046;36350170200;24462551500;57225367591;56430081600;7003397589;","Case study on accuracy comparison of digital twins developed for a heritage bridge via UAV photogrammetry and terrestrial laser scanning",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1713","1720",,5,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125655909&partnerID=40&md5=1974fb7b12d50de23388c740e9db4036","Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia; Department of Information Engineering and Computer Science, University of Trento, Italy","Mohammadi, M., Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia; Rashidi, M., Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia; Mousavi, V., Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia; Karami, A., Department of Information Engineering and Computer Science, University of Trento, Italy; Yu, Y., Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia; Samali, B., Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia","Over the last decade, advanced remote technologies have been considerably utilised for digitisation of civil infrastructure assets, particularly bridges, and providing accurate data for indirect inspection and assessment through digital twins of their physical counterparts. Although advanced emerging technologies such as Unmanned Aerial Vehicles (UAVs) photogrammetry and Terrestrial Laser Scanning (TLS) established a suitable alternative against labour-intensive and expensive methods of direct inspections, the research is still lacking a comparative analysis of accuracy and reliability of the associated digital twins. This paper serves to investigate and evaluate the geometric accuracy of two 3D reality models generated from an existing heritage bridge in Australia extracted from both UAV-based photogrammetry and TLS based point clouds. The comparative results show the capability of both UAV photogrammetry and TLS based point clouds for bridge inspection; however, TLS expresses a higher level of points' density with suitable accuracy subject to implementation of precise as-built 3D reconstruction method. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Accuracy; Bridge; Digital twin; Terrestrial Laser Scanning (TLS); UAV-based photogrammetry","Antennas; Bridges; Laser applications; Photogrammetry; Reliability analysis; Scanning; Steel beams and girders; Surveying instruments; Unmanned aerial vehicles (UAV); Accuracy; Accuracy comparisons; Case-studies; Civil infrastructures; Digitisation; Infrastructure assets; Point-clouds; Terrestrial laser scanning; Unmanned aerial vehicle-based photogrammetry; Structural health monitoring",,,,,,"The authors would like to acknowledge the technical support from Transport for New South Wales (TfNSW), Australia, and greatly appreciate the valuable advice and support of Houman Hatamian, Syed F. Nowmani, and Bradley Edwards during this research project.",,,"Rashidi, M., Mohammadi, M., Sadeghlou Kivi, S., Abdolvand, M. M., Truong-Hong, L., Samali, B., A Decade of Modern Bridge Monitoring Using Terrestrial Laser Scanning: Review and Future Directions (2020) Remote Sensing, 12 (22), p. 3796; Rashidi, M., Ghodrat, M., Samali, B., Kendall, B., Zhang, C., Remedial Modelling of Steel Bridges through Application of Analytical Hierarchy Process (AHP) (2017) Applied Sciences, 7, p. 168; Chen, S., Laefer, D. F., Mangina, E., Zolanvari, S. M. I., Byrne, J., UAV Bridge Inspection through Evaluated 3D Reconstructions (2019) Journal of Bridge Engineering, 24 (4). , Articl; Jahanshahi, M. R., Masri, S. F., Sukhatme, G. 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P., Troisi, S., (2012) Low-Cost and Open-Source Solutions for Automated Image Orientation - A Critical Overview, pp. 40-54. , Berlin, Heidelberg, Springer Berlin Heidelberg; Seo, J., Duque, L., Wacker, J., Drone-enabled bridge inspection methodology and application (2018) Automation in Construction, 94, pp. 112-126; Dorafshan, S., Maguire, M., Bridge inspection: human performance, unmanned aerial systems and automation (2018) Journal of Civil Structural Health Monitoring, 8 (3), pp. 443-476; Yu, Y., Rashidi, M., Samali, B., Yousefi, A. M., Wang, W., Multi-Image-Feature-Based Hierarchical Concrete Crack Identification Framework Using Optimized SVM Multi-Classifiers and D-S Fusion Algorithm for Bridge Structures (2021) Remote Sensing, 13 (2), p. 240; Mohammadi, M., Kafi, M. A., Kheyroddin, A., Ronagh, H., Performance of innovative composite buckling-restrained fuse for concentrically braced frames under cyclic loading (2020) Steel and Composite Structures, An International Journal; Rashidi, M., Ghodrat, M., Samali, B., Mohammadi, M., Decision Support Systems (2018) Management of information systems: IntechOpen; Mohammadi, M., Kafi, M. A., Kheyroddin, A., Ronagh, H. R., Rashidi, M., Experimental and Numerical Investigation of Innovative Composite Buckling-Restrained Fuse (2020) ACMSM25, Lecture Notes in Civil Engineering, Queensland, Australia, 37, pp. 113-121. , Springer Singapore; Mohammadi, M., Kafi, M. A., Kheyroddin, A., Ronagh, H. R., Experimental and numerical investigation of an innovative buckling-restrained fuse under cyclic loading (2019) Structures, 22, pp. 186-199; Tang, P., Akinci, B., Garrett, J., Laser Scanning for Bridge Inspection and Management (2007) IABSE Symposium Report, 93, pp. 17-24; Tang, P., Akinci, B., Automatic execution of workflows on laser-scanned data for extracting bridge surveying goals (2012) Advanced Engineering Informatics, Conference Paper, 26 (4), pp. 889-903; Gyetvai, N., Truong-Hong, L., Laefer, D. F., Laser scan-based structural assessment of wrought iron bridges: Guinness Bridge, Ireland (2018) Proceedings of the Institution of Civil Engineers - Engineering History and Heritage, 171 (2), pp. 76-89. , Articl; Gawronek, P., Makuch, M., TLS Measurement during Static Load Testing of a Railway Bridge (2019) ISPRS International Journal of Geo-Information, 8 (44); Lu, R., Rausch, C., Bolpagni, M., Brilakis, I., Haas, C. 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Available",,,,"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-s2.0-85125655909
"Meixedo A., Ribeiro D., Santos J., Calçada R., Todd M.D.","56940709200;24476782300;36810314200;7801603531;7202805915;","Real-Time Unsupervised Detection of Early Damage in Railway Bridges Using Traffic-Induced Responses",2022,"Structural Integrity","21",,,"117","142",,3,"10.1007/978-3-030-81716-9_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117968122&doi=10.1007%2f978-3-030-81716-9_6&partnerID=40&md5=a29b09b8a94ae4fa9bc32865915f4edb","CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Porto, Portugal; CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Porto, Portugal; LNEC, Laboratório Nacional de Engenharia Civil, Lisbon, Portugal; Department of Structural Engineering, University California San Diego, San Diego, CA, United States","Meixedo, A., CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Porto, Portugal; Ribeiro, D., CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Porto, Portugal; Santos, J., LNEC, Laboratório Nacional de Engenharia Civil, Lisbon, Portugal; Calçada, R., CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Porto, Portugal; Todd, M.D., Department of Structural Engineering, University California San Diego, San Diego, CA, United States","This chapter addresses unsupervised damage detection in railway bridges by presenting a novel AI-based SHM strategy using traffic-induced dynamic responses. To achieve this goal a hybrid combination of wavelets, PCA, and cluster analysis is implemented. Damage-sensitive features from train-induced dynamic responses are extracted and allow taking advantage not only of the repeatability of the loading, but also, of its large magnitude, thus enhancing sensitivity to small-magnitude structural changes. The effectiveness of the proposed methodology is validated in a long-span bowstring-arch railway bridge with a permanent structural monitoring system installed. A digital twin of the bridge was used, along with experimental values of temperature, noise, trains loadings, and speeds, to realistically simulate baseline and damage scenarios. The methodology proved highly sensitive in detecting early damage, even in case of small stiffness reductions that do not impair structural safety, as well as highly robust to false detections. The ability to identify early damage, imperceptible in the original signals, while avoiding observable changes induced by environmental and operational variations, is achieved by carefully defining the modelling and fusion sequence of the information. A damage detection strategy capable of characterizing multi-sensor data while being sensitive to identify local changes is proposed as a tool for real-time structural assessment of bridges without interfering with the normal service condition. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Artificial intelligence; Damage detection; Data-driven; Railway bridges; Structural health monitoring; Traffic-induced dynamic responses; Unsupervised learning",,,,,,"Horizon 2020 Framework Programme, H2020; Fundação para a Ciência e a Tecnologia, FCT: SFRH/BD/93201/2013; Ministério da Ciência, Tecnologia e Ensino Superior, MCTES; Institute of Research and Development in Structures and Construction","Acknowledgements This work was financially supported by the Portuguese Foundation for Science and Technology (FCT) through the PhD scholarship SFRH/BD/93201/2013. The authors would like to acknowledge the support of the R&D project RISEN through the H2020|ES|MSC— H2020|Excellence Science|Marie Curie programme, the Portuguese Road and Railway Infrastructure Manager (I.P), the Portuguese National Laboratory for Civil Engineering (LNEC), and the Base Funding—UIDB/04708/2020 of the CONSTRUCT—Instituto de I&D em Estruturas e Construções—financed by national funds through the FCT/MCTES (PIDDAC).","This work was financially supported by the Portuguese Foundation for Science and Technology (FCT) through the PhD scholarship SFRH/BD/93201/2013. The authors would like to acknowledge the support of the R&D project RISEN through the H2020|ES|MSC? H2020|Excellence Science|Marie Curie programme, the Portuguese Road and Railway Infrastructure Manager (I.P), the Portuguese National Laboratory for Civil Engineering (LNEC), and the Base Funding?UIDB/04708/2020 of the CONSTRUCT?Instituto de I&D em Estruturas e Constru??es?financed by national funds through the FCT/MCTES (PIDDAC).",,"Melo, L.R.T., Ribeiro, D., Calçada, R., Bittencourt, T.N., Validation of a vertical train–track– bridge dynamic interaction model based on limited experimental data (2020) Struct Infrastruct Eng, 16 (1), pp. 181-201. , https://doi.org/10.1080/15732479.2019.1605394; Meixedo, A., Ribeiro, D., Calçada, R., Delgado, R., Global and local dynamic effects on a railway viaduct with precast deck. In: Proceedings of the second international conference on railway technology: Research (2014) Development and Maintenance, , https://doi.org/10.4203/ccp.104.77, Civil-Comp Press, Stirlingshire; Rytter, A., Vibrational based inspection of civil engineering structures. Dept (1993) Of Building Technology and Structural Engineering, , Aalborg University, Aalborg; Meixedo A, Alves V, Ribeiro D, Cury A, Calçada R (2016) Damage identification of a railway bridge based on genetic algorithms. In: Maintenance, monitoring, safety, risk and resilience of bridges and bridge networks—proceedings of the 8th international conference on bridge maintenance, safety and management, IABMAS 2016, Foz Do Iguaçu; Brazil; Cury A, Cremona C (2012) Assignment of structural behaviours in long-term monitoring: application to a strengthened railway bridge. Struct Health Monit 11(4):422–441. https://doi. org/10.1177/1475921711434858; Posenato, D., Kripakaran, P., Smith, I.F.C., Methodologies for model-free data interpretation of civil engineering structures (2010) Comput Struct, 88 (7-8), pp. 467-482. , https://doi.org/10.1016/j.com pstruc.2010.01.001; Meixedo, A., Santos, J., Ribeiro, D., Calçada, R., Todd, M., Damage detection in railway bridges using traffic-induced dynamic responses (2021) Eng Struct, 238. , https://doi.org/10. 1016/j.engstruct.2021.112189; Mujica, L.E., Gharibnezhad, F., Rodellar, J., Todd, M., Considering temperature effect on robust principal component analysis orthogonal distance as a damage detector (2020) Struct Health Monit, 19 (3), pp. 781-795. , https://doi.org/10.1177/1475921719861908; Cavadas, F., Smith, I.F.C., Figueiras, J., Damage detection using data-driven methods applied to moving-load responses (2013) Mech Syst Signal Process, 39 (1-2), pp. 409-425. , https://doi.org/10. 1016/j.ymssp.2013.02.019; Santos, J.P., Crémona, C., Orcesi, A.D., Silveira, P., Multivariate statistical analysis for early damage detection (2013) Eng Struct, 56, pp. 273-285. , https://doi.org/10.1016/j.engstruct.2013.05.022; Hu, W.H., Moutinho, C., Caetano, E., Magalhães, F., Cunha, Á., Continuous dynamic monitoring of a lively footbridge for serviceability assessment and damage detection (2012) Mech Syst Signal Process, 33 (November), pp. 38-55. , https://doi.org/10.1016/j.ymssp.2012.05.012; Farrar, C.R., Worden, K., (2013) Structural Health Monitoring: A Machine Learning Perspective, pp. 1-45. , Wiley, New York, pp; De, L.O.R., Omenzetter, P., Damage classification and estimation in experimental structures using time series analysis and pattern recognition (2010) Mech Syst Signal Process, 24 (5), pp. 1556-1569. , https://doi.org/10.1016/j.ymssp.2009.12.008; Gonzalez, I., Karoumi, R., BWIM aided damage detection in bridges using machine learning (2015) J Civ Struct Heal Monit, 5 (5), pp. 715-725. , https://doi.org/10.1007/s13349-015-0137-4; Cardoso, R., Cury, A., Barbosa, F., Automated real-time damage detection strategy using raw dynamic measurements (2019) Eng Struct, 196. , https://doi.org/10.1016/j.engstruct.2019. 109364; Azim R, Gül M (2019) Damage detection of steel girder railway bridges utilizing operational vibration response. Struct Control Health Monit 26(e2447):1–15. https://doi.org/10.1002/stc. 2447; Nie, Z., Lin, J., Li, J., Hao, H., Ma, H., Bridge condition monitoring under moving loads using two sensor measurements (2019) Struct Health Monit, 19 (3), pp. 917-937. , https://doi.org/10.1177/1475921719868930; Farrar, C.R., Doebling, S.W., Nix, D.A., Vibration–based structural damage identification (2001) Philos Trans R Soc London A: Math Phys Eng Sci, 359 (1778), pp. 131-149. , https://doi.org/10. 1098/rsta.2000.0717; Academic Research, A.N.S.Y.S., (2016) Release, 17, p. 1; Meixedo, A., Ribeiro, D., Santos, J., Calçada, R., Todd, M., Progressive numerical model validation of a bowstring-arch railway bridge based on a structural health monitoring system (2021) J Civ Struct Heal Monit, 11 (2), pp. 421-449. , https://doi.org/10.1007/s13349-020-00461-w; Min, X., Santos, L., (2011) Ensaios dinâmicos Da Ponte ferroviária Sobre O Rio Sado Na Variante De alcácer, , Lisboa [Portuguese; Meixedo, A., Gonçalves, A., Calçada, R., Gabriel, J., Fonseca, H., Martins, R., On-line monitoring system for tracks (2016) Exp.At 2015—3rd Experiment International Conference, , https://doi.org/10.1109/EXPAT.2015.7463240, Sao Miguel Island, Azores; Pimentel, R., Ribeiro, D., Matos, L., Mosleh, A., Calçada, R., Bridge weigh-in-motion system for the identification of train loads using fiber-optic technology (2020) Structures, 2021 (30), pp. 1056-1070. , https://doi.org/10.1016/j.istruc.2021.01.070; Ren, W.X., Sun, Z.S., Structural damage identification by using wavelet entropy (2008) Eng Struct, 30, pp. 2840-2849. , https://doi.org/10.1016/j.engstruct.2008.03.013; Cohen, A., Ryan, R.D., Wavelets and multiscale signal processing (1995) Chapman & Hall, , Boundary Row, London; Cantero, D., Ülker-Kaustell, M., Karoumi, R., Time–frequency analysis of railway bridge response in forced vibration (2016) Mech Syst Signal Process, 76-77, pp. 518-530; Ülker-Kaustell, M., Karoumi, R., Influence of non-linear stiffness and damping on the train-bridge resonance of a simply supported railway bridge (2012) Eng Struct, 41, pp. 350-355. , https://doi.org/10.1016/j.engstruct.2012.03.060; Teolis, A., (1998) Computational Signal Processing with Wavelets, , Birkhauser; Ribeiro, D., Leite, J., Meixedo, A., Pinto, N., Calçada, R., Todd, M., Statistical methodologies for removing the operational effects from the dynamic responses of a high-rise telecommunications tower (2021) Struct Control Health Monit, 28 (4), p. e2700. , https://doi.org/10.1002/stc.2700; Yan, A., Kerschen, G., De, B.P., Golinval, J., Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis (2005) Mech Syst Signal Process, 19 (4), pp. 847-864. , https://doi.org/10.1016/j.ymssp.2004.12.002; Jolliffe, I.T., (2002) Principal Component Analysis, pp. 112-147. , 2nd edn. Springer, New York, pp; Hastie, T., Tibshirani, R., Friedman, J., (2011) The Elements of Statistical Learning, Data Mining Inference, and Prediction, pp. 460-462. , 2nd edn. Springer, Stanford, pp; Santos, J., Crémona, C., Calado, L., Real-time damage detection based on pattern recognition (2016) Struct Concrete, 17 (3), pp. 338-354. , https://doi.org/10.1002/suco.201500092","Meixedo, A.; CONSTRUCT-LESE, Portugal; email: ameixedo@fe.up.pt",,,"Springer Science and Business Media Deutschland GmbH",,,,,2522560X,,,,"English","Structur. Integr.",Book Chapter,"Final","All Open Access, Green",Scopus,2-s2.0-85117968122
"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). 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Eng, 13, pp. 58-66. , https://doi.org/10.1109/MCSE.2011.66; (2013), https://dot.ca.gov/-/media/dot-media/programs/engi-neering/documents/seismicdesigncriteria-sdc/f0007585seismicdesigncriteriasdc17fullversionoeereleasea11y.pdf, (accessed on 20 December 2021); Aviram, A., Mackie, K., Stojadinovic, B., (2008) Guidelines for Nonlinear Analysis of Bridge Structures in California, , https://ntrl.ntis.gov/NTRL/dashboard/searchRe-sults/titleDetail/PB2012106297.xhtml, Pacific Earthquake Engineering Research Center: Berkeley, CA, USA, (accessed on 20 December 2021); Kaviani, P., Zareian, F., Taciroglu, E., Seismic behavior of reinforced concrete bridges with skew-angled seat-type abutments (2012) Eng. Struct, 45, pp. 137-150. , https://doi.org/10.1016/j.engstruct.2012.06.013; Mander, J.B., Priestley, M.J.N., Park, R., Theoretical stress-strain model for confined concrete (2008) J. Struct. Eng, 114. , https://doi.org/10.1061/(asce)0733-9445(1988)114:8(1804); Timoshenko, S., (1940) Theory of Plates and Shells, , McGraw–Hill Book Co., Inc.: New York, NY, USA, ISBN 0-07-085820-9; Silva, P.F., Megally, S., Seible, F., Seismic performance of sacrificial exterior shear keys in bridge abutments (2009) Earthq. Spectra, 25, pp. 643-664. , https://doi.org/10.1193/1.3155405; Kim, S., Stewart, J.P., Kinematic soil-structure interaction from strong motion recordings (2003) J. Geotech. Geoenviron. Eng, 129, pp. 323-335. , https://doi.org/10.1061/(ASCE)1090-0241(2003)129:4(323); Khalili-Tehrani, P., Shamsabadi, A., Stewart, J.P., Taciroglu, E., Backbone curves with physical parameters for passive lateral response of homogeneous abutment backfills (2016) Bull. Earthq. Eng, 14, pp. 3003-3023. , https://doi.org/10.1007/s10518-016-9934-3; (2005) Department of Transportation. Engineering Service Center, S.S.R. Project, Field Investigation Report for Abutment Backfill Characterization, Citeseer, , https://searchworks.stanford.edu/view/8447399, (accessed on 20 December 2021); Stewart, J.P., Taciroglu, E., Wallace, J.W., Ahlberg, E.R., Lemnitzer, A., Rha, C., Tehrani, P., Salamanca, A., (2007) Full Scale Cyclic Testing of Foundation Support Systems for Highway Bridges. Part II: Abutment Backwalls, , Structural and Geotech-nical Engineering Laboratory, University of California: Los Angeles, CA, USA; Gazetas, G., Formulas and charts for impedances of surface and embedded foundations (2008) J. Geotech. Eng, 117, pp. 1363-1381. , https://doi.org/10.1061/(asce)0733-9410(1991)117:9(1363); Pais, A., Kausel, E., Approximate formulas for dynamic stiffnesses of rigid foundations (1988) Soil Dyn. Earthq. 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
"Törmä S., Toivola P., Kiviniemi M., Puntila P., Lampi M., Mätäsniemi T.","6602149776;12782596400;6602426056;57215330840;55416083100;6504155486;","Ontology-based sharing of structural health monitoring data",2019,"20th Congress of IABSE, New York City 2019: The Evolving Metropolis - Report",,,,"2214","2221",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074453225&partnerID=40&md5=f55afd0ddd1b66419b9819998cf10957","VisuaLynk, Espoo, Finland; Savcor Helsinki, Finland; VTT, Espoo, Finland; Trimble Solutions, Espoo, Finland; VTT, Tampere, Finland","Törmä, S., VisuaLynk, Espoo, Finland; Toivola, P., Savcor Helsinki, Finland; Kiviniemi, M., VTT, Espoo, Finland; Puntila, P., Trimble Solutions, Espoo, Finland; Lampi, M., Savcor Helsinki, Finland; Mätäsniemi, T., VTT, Tampere, Finland","A structural health monitoring system installed in a bridge produces a vast amount of sensor data that is analyzed and periodically reported to a bridge owner at an aggregate level. The data itself typically remains in the monitoring service of a service provider; it may be accessible to clients and third parties through a dedicated user interface and API. This paper presents an ontology to defining the monitoring model based on the Semantic Sensor Network Ontology by W3C. The goal is to enable an asset owner to utilize preferred tools to view and access monitoring data from different service providers, and in longer term, increase the utilization of monitoring data in facility management. The ultimate aim is to use BrIM as a digital twin of a bridge and to link external datasets to improve information management and maintenance over its lifecycle. © 20th Congress of IABSE, New York City 2019: The Evolving Metropolis - Report. All rights reserved.","Bridge information model; Facility management; Linked data; Monitoring; Ontology","Application programming interfaces (API); Information management; Life cycle; Linked data; Office buildings; Ontology; Semantics; Sensor networks; Structural health monitoring; User interfaces; Access monitoring; Different services; Facility management; Information Modeling; Monitoring models; Monitoring services; Service provider; Structural health monitoring systems; Monitoring",,,,,"City, University of London, City","This research belongs to a collaborative research project SmartBridgeFM (2017-2019), partially funded by Business Finland. Special thanks for City of Helsinki for providing the BrIMs of Crusell bridge.",,,"Endsley, M.R., Toward a theory of situation awareness in dynamic systems (1995) Human Factors, 37 (1), pp. 85-104; W3C Data Activity, , https://www.w3.org/2013/data/, W3C; Dijkstra, E., On the role of scientific thought (1982) Selected Writings on Computing: A Personal Perspective, pp. 60-66. , NY, Springer; (2017) OPC Unified Architecture, , https://opcfoundation.org/ua/, OPC Foundation; Cyganiak, R., Wood, D., Lanthaler, M., (2014) RDF 1.1 Concepts and Abstract Syntax, W3C Recommendation, , https://www.w3.org/TR/rdf11-concepts/; (2012) OWL 2 Web Ontology LanguageDocument Overview, , https://www.w3.org/TR/owl-overview/, W3C Recommendation; Berners-Lee, T., (2006) Linked Data -Design Issues, W3C Note, , http://www.w3.org/DesignIssues/LinkedData.html; Linked Open Vocabularies, , https://lov.linkeddata.es/dataset/lov/; Törmä, S., Semantic linking of building information models (2013) IEEE Seventh International Conference on Semantic Computing, , Irvine, CA; Pauwels, P., Supporting decision-making in the building life-cycle using linked building data (2014) Buildings, 4 (3), pp. 549-579; Beetz, J., Van Leeuwen, J., De Vries, B., IFCOwl: A case of transforming EXPRESS schemas into ontologies (2009) AI EDAM, 23 (1), pp. 89-101; Pauwels, P., Terkaj, W., EXPRESS to OWL for construction industry: Towards a recommendable and usable ifcOWL ontology (2016) Automation in Construction, 63, pp. 100-133; Hoang, N.V., Törmä, S., Implementation and experiments with an IFC-to-linked data converter (2015) 32nd International Conference of CIB W78; Bonduel, M., Oraskari, J., Pauwels, P., Vergauwen, M., Klein, R., The IFC to linked building data converter - Current status (2018) 6th Linked Data in Architecture and Construction Workshop, , London; Haller, A., Janowicz, K., Cox, S., Le Phuoc, D., Taylor, K., Lefrançois, M., Semantic Sensor Network Ontology, W3C Recommendation, , https://www.w3.org/TR/vocab-ssn/, W3C; Cox, S., (2018) Extensions to the Semantic Sensor Network Ontology, , https://www.w3.org/TR/vocab-ssn-ext/, W3C; Turunen, M., Pulkkinen, P., Toivola, P., Structural health monitoring of Crusell bridge (2016) 19th IABSE Congress, , Stockholm; Crusell Bridge Trimble, , https://www.tekla.com/references/crusellbridge","Kiviniemi, M.; VTTFinland; email: markku.kiviniemi@vtt.fi",,"Allplan (Gala);et al.;Hardesty and Hanover;Silman;Wiss, Janney, Elstner Associates, Inc.;WSP","International Association for Bridge and Structural Engineering (IABSE)","20th IABSE Congress, New York City 2019: The Evolving Metropolis","4 September 2019 through 6 September 2019",,152767,,9783857481659,,,"English","Congr. IABSE, New York City: Evol. Metropolis - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85074453225
"Andersen J.E., Rex S.","7403571045;57211567139;","Structural health monitoring of henry Hudson I89",2019,"20th Congress of IABSE, New York City 2019: The Evolving Metropolis - Report",,,,"2121","2131",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074449290&partnerID=40&md5=c7cb2a2736050aa2c3864840846e6b29","COWI, Lyngby, Denmark","Andersen, J.E., COWI, Lyngby, Denmark; Rex, S., COWI, Lyngby, Denmark","To increase construction safety during and after establishing an alternative load path for the arch supports of the Henry Hudson I89 Bridge in New York a Structural Health Monitoring System (SHMS) was employed in combination with a digital twin calibrated by the SHMS data. This method is one of the first in the United States used on a large-scale bridge installation. The calibration was done by moving a Live Load across the bridge and use the strain gauges to detect weight, speed, and spacing of the passing trucks. The measured configuration of the Live Load has subsequently been applied to a Digital Twin creating the digital responses. The measured forces analysed by applying linear stress theory and least square method on the strain gauge measurements, and the calculated Digital Twin forces show the same behaviour and the absolute values do not deviate more than 5%. This despite of a very small utilisation. After the Live load validation of the measurements a data-driven approach has been applied to identify critical behavior of the bridge while the digital twin has been held as backup for analyzing consequences of any extreme load combination, should it occur. © 20th Congress of IABSE, New York City 2019: The Evolving Metropolis - Report. All rights reserved.","Calibrated FE-Model; Digital twin; Predicative maintenance tool; Safe operation; Structural Health Monitoring; Warning System","Alarm systems; Arch bridges; Least squares approximations; Loads (forces); Monitoring; Strain gages; Alternative load paths; Data-driven approach; Digital twin; FE model; Least square methods; Predicative maintenance; Safe operation; Structural health monitoring systems; Structural health monitoring",,,,,,,,,,"Andersen, J.E.; COWIDenmark; email: JCA@COWI.com",,"Allplan (Gala);et al.;Hardesty and Hanover;Silman;Wiss, Janney, Elstner Associates, Inc.;WSP","International Association for Bridge and Structural Engineering (IABSE)","20th IABSE Congress, New York City 2019: The Evolving Metropolis","4 September 2019 through 6 September 2019",,152767,,9783857481659,,,"English","Congr. IABSE, New York City: Evol. Metropolis - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85074449290
"Rageh A., Azam S.E., Alomari Q., Linzell D., Wood R.","55635603300;57196712513;57954270900;6602678682;55377863400;","Model Updating and Parameter Identification for Developing Digital Twins for Riveted Steel Railway Bridges",2022,"Recent Developments In Structural Health Monitoring And Assessment - Opportunities And Challenges: Bridges, Buildings And Other Infrastructures",,,,"285","318",,1,"10.1142/9789811243011 0010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135915264&doi=10.1142%2f9789811243011+0010&partnerID=40&md5=1e6e8b832ed0fb090faf9f5fedf5d321","SDR Engineering Consultants, Inc., 2260 Wednesday St #500, Tallahassee, FL  32308, United States; Department of Civil and Environmental Engineering, University of New Hampshire, 33 Academic Way, Durham, NH  03824, United States; Department of Civil and Environmental Engineering, University of Nebraska - Lincoln, 114L Othmer Hall, Lincoln, NE  68588, United States","Rageh, A., SDR Engineering Consultants, Inc., 2260 Wednesday St #500, Tallahassee, FL  32308, United States; Azam, S.E., Department of Civil and Environmental Engineering, University of New Hampshire, 33 Academic Way, Durham, NH  03824, United States; Alomari, Q., Department of Civil and Environmental Engineering, University of Nebraska - Lincoln, 114L Othmer Hall, Lincoln, NE  68588, United States; Linzell, D., Department of Civil and Environmental Engineering, University of Nebraska - Lincoln, 114L Othmer Hall, Lincoln, NE  68588, United States; Wood, R., Department of Civil and Environmental Engineering, University of Nebraska - Lincoln, 114L Othmer Hall, Lincoln, NE  68588, United States","One of the significant concerns for maintaining railway network reliability is the structural health of aged steel riveted railway bridges, some of which in the United States were built at the beginning of the twentieth century. A reliable structural health monitoring (SHM) network that would anticipate riveted steel bridge deficiencies, optimize maintenance, and, ultimately, extend bridge service life to reduce railway network interruption would be a key component to assuring that the network remains viable. A class of SHM methods that can also potentially predict the remaining useful life of a structure to take advantage of a physics based model would provide a cost-effective option to current methods that commonly rely on extensive instrumentation. In this regard, the validity of the physical model, its parameterization, and accuracy in emulating actual response is of utmost importance for SHM prognosis of remaining useful life. In the context of Industry 4.0, those physics-based numerical replicas of the physical asset are called digital twins of the structure. This chapter presents and examines the eficacy of a frame-work for automated model calibration using measured operational and ambient structural response for the development of a precise digital twin of a physical structure. The guidelines provided in this chapter will assist in choosing the right model class for accurate response prediction. The study used an in-service, double-track, riveted steel plate girder railway bridge as a testbed for the proposed framework. © 2022 World Scientific Publishing Company. All rights reserved.",,,,,,,,,,,"Arisoy, B., Erol, O., Finite element model calibration of a steel railway bridge via ambient vibration test (2018) Steel Compos. Struct., 27 (3), pp. 327-335; 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, pp. 328-345; 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 (3), pp. 162-172; Caglayan, O., Ozakgul, K., Tezer, O., Uzgider, E., Evaluation of a steel railway bridge for dynamic and seismic loads (2011) J. Cons. Steel Res., 67 (8), pp. 1198-1211; Caglayan, O., Ozakgul, K., Tezer, O., Assessment of existing steel railway bridges (2012) J. Cons. Steel Res., 69 (1), pp. 54-63; Chang, C.C., Chang, T., Xu, Y.G., Adaptive neural networks for model updating of structures (2000) Smart Mater. Struct., 9 (1), p. 59; Chen, X., Omenzetter, P., Beskhyroun, S., Calibration of the finite element model of a twelve-span prestressed concrete bridge using ambient vibration data Conference: 7th European Workshop on Structural Health Monitoring, pp. 1388-1395. , (Nantes, France, 2014); Costa, B.J., Figueiras, J.A., Rehabilitation and condition assessment of a centenary steel truss bridge (2013) J. Cons. Steel Res., 89, pp. 185-197; Costa, B.J.A., Magalhaes, F., Cunha, A., Figueiras, J., Modal analysis for the rehabilitation assessment of the Luiz I Bridge (2014) J. Bridge Eng., 19 (12). , 05014006-1-05014006-11; Chotickai, P., Kanchanalai, T., Field testing and performance evaluation of a through-plate girder railway bridge (2010) Transp. Res. Rec. :J. Transp. Res. Board, 2172 (1), pp. 132-141; Cunha, A., Caetano, E., Ribeiro, P., Modal analysis of the Jalon Viaduct using FE updating In Proceedings of the 9th International Conference on Structural Dynamics (EURODYN 2014), pp. 2311-2317. , (Porto, Portugal, July 2014); Feng, D., Feng, M.Q., Model updating of railway bridge using in situ dynamic displacement measurement under trainloads (2015) J. Bridge. Eng., 20 (12); He, X., Yu, Z., Chen, Z., Finite element model updating of existing steel bridge based on structural health monitoring (2008) J. Cent. South Univ. T, 15 (3), pp. 399-403; Jaishi, B., Ren, W., Structural finite element model updating using ambient vibration test results (2005) J. Struct. Eng., 131 (4), pp. 617-628; Lee, J.W., Kim, J.D., Yun, C.B., Yi, J.H., Shim, J.M., Healthmonitoring method for bridges under ordinary trafic loadings (2002) J. Sound Vib., 257 (2), pp. 247-264; Lee, Y., Cho, S., SHM-based probabilistic fatigue life prediction for bridges based on FE model updating (2016) Sensors, 16 (3), p. 317; Marques, F., Moutinho, C., Magalhaes, F., Caetano, E., Cunha, A., Analysis of dynamic and fatigue effects in an old metallic riveted bridge (2014) J. Cons. Steel Res., 99, pp. 85-101; Nagaraja, R., Material properties of structural carbon and high strength steels, , Lehigh University, Report No. 249.20, (1963); Parkinson, A.R., Balling, R., Hedengren, J.D., (2013) Optimization Methods for Engineering Design; Rageh, A., (2018) Optimized health monitoring plans for a steel, Double-Track Railway Bridge, , [Thesis], (University of Nebraska-Lincoln, NE, USA; Rageh, A., Linzell, D.G., Eftekhar Azam, S., Automated, strain-based, output-only bridge damage detection (2018) J. Civ. Struct. Health Monit., 8 (5), pp. 833-846; Ribeiro, D., Cal cada, 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; 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; Schueller, W., Building Support Structures, Analysis and Design with SAP2000 Software (2008), (Computers and Structures Inc; Shabbir, F., Omenzetter, P., Model updating using genetic algorithms with sequential niche technique (2016) Eng. Struct., 120, pp. 166-182; Teughels, A., Maeck, J., De Roeck, G., Damage assessment by FE model updating using damage functions (2002) Comput. Struct., 80 (25), pp. 1869-1879; Tobias, D.H., Foutch, D.H., Choros, J., Investigation of an open deck through-truss railway bridge: Work train tests (1993), American Association of Railroads Research and Test Department, Report No. R-830; Xia, C., De Roeck, G., Modal analysis of the Jalon Viaduct using FE updating In Proceedings of the 9th International Conference on Structural Dynamics (EURODYN 2014), , (European Assoc Structural Dynamics, Porto, Portugal, July 2014); Zhong, R., Zong, Z., Niu, J., Yuan, S., A damage prognosis method of girder structures based on wavelet neural networks (2014) Math. Prob. Eng., p. 2014","Rageh, A.; SDR Engineering Consultants, 2260 Wednesday St #500, United States; email: arageh@sdrengineering.com",,,"World Scientific Publishing Co. Pte. Ltd.",,,,,,9789811243011,,,"English","Recent Developments In Structural Health Monitoring And assess. - Opportunities And Challenges: Bridges, Buildings And Other Infrastructures",Book Chapter,"Final","",Scopus,2-s2.0-85135915264
"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
"Kleiser D., Woock P.","57223051163;36626808600;","Towards Automated Structural Health Monitoring for Offshore Wind Piles",2020,"2020 Global Oceans 2020: Singapore - U.S. Gulf Coast",,,"9389437","","",,1,"10.1109/IEEECONF38699.2020.9389437","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104661932&doi=10.1109%2fIEEECONF38699.2020.9389437&partnerID=40&md5=b0605ab2f93a0dc47daae3683c818a7b","Systems of Measurement, Control and Diagnosis (MRD), Fraunhofer Iosb, Karlsruhe, Germany","Kleiser, D., Systems of Measurement, Control and Diagnosis (MRD), Fraunhofer Iosb, Karlsruhe, Germany; Woock, P., Systems of Measurement, Control and Diagnosis (MRD), Fraunhofer Iosb, Karlsruhe, Germany","Simulation plays an important role in the development, testing and evaluation of new robotic applications, reducing implementation time, cost and risk. In this paper we show a digital twin simulation model of an inspection ROV which is capable of performing structural health monitoring by automated creation of a map of an offshore wind monopile. The data is compared to a known reference model. The digital twin simulation model is extended by a physical sensor data input device to bridge the gap between simulation and testing in water. © 2020 IEEE.","digital twin; monopile; offshore wind; ROV; simulation; Structural health monitoring","Digital twin; Offshore oil well production; Monopile; Offshore winds; Physical sensors; Reference modeling; Robotic applications; Simulation and testing; Simulation model; Testing and evaluation; Structural health monitoring",,,,,,"This work is funded by the German Federal Ministry for Economic Affairs and Energy",,,"Ziegler, L., Muskulus, M., Lifetime extension of offshore wind monopiles: Assessment process and relevance of fatigue crack inspection (2016) 12th EAWE PhD Seminar, , DTU Lyngby, Denmark; Mathiesen, T., Black, A., Grønvold, F., Alle, P., Monitoring and inspection options for evaluating corrosion in offshore wind foundations (2016) NACE Corrosion-2016, 7702. , paper no C-2016; Cook, D., Vardy, A., Lewis, R., A survey of auv and robot simulators for multi-vehicle operations (2014) 2014 IEEE/OES Autonomous Underwater Vehicles (AUV), pp. 1-8; Robotic Operating System, , https://www.ros.org, Stanford Artificial Intelligence Laboratory et al; Koenig, N., Howard, A., Design and use paradigms for gazebo, an open-source multi-robot simulator (2004) 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), 3, pp. 2149-2154; Manhães, M.M.M., Scherer, S.A., Voss, M., Douat, L.R., Rauschenbach, T., UUV simulator: A gazebo-based package for underwater intervention and multi-robot simulation (2016) OCEANS 2016 MTS/IEEE Monterey. IEEE, , https://doi.org/10.1109%2Foceans.2016.7761080, sep; UUV Simulator, , https://github.com/uuvsimulator/uuvsimulator; Vio, R.P., Cristi, R., Smith, K.B., Uuv localization using acoustic communications, networking, and a priori knowledge of the ocean current (2017) OCEANS 2017-Aberdeen, pp. 1-7; Paull, L., Saeedi, S., Seto, M., Li, H., Auv navigation and localization: A review (2013) IEEE Journal of Oceanic Engineering, 39 (1), pp. 131-149; Foresti, G.L., Visual inspection of sea bottom structures by an autonomous underwater vehicle (2001) IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 31 (5), pp. 691-705; Choi, J., Lee, Y., Kim, T., Jung, J., Choi, H.-T., Development of a rov for visual inspection of harbor structures (2017) 2017 IEEE Underwater Technology (UT). IEEE, pp. 1-4; McLeod, D., Jacobson, J., Autonomous uuv inspection-revolutionizing undersea inspection (2011) OCEANS'11 MTS/IEEE KONA, pp. 1-4; Vissiere, D., Martin, A., Petit, N., Using distributed magnetometers to increase imu-based velocity estimation into perturbed area (2007) 2007 46th IEEE Conference on Decision and Control, pp. 4924-4931; Moore, T., Stouch, D., A generalized extended kalman filter implementation for the robot operating system (2014) Proceedings of the 13th International Conference on Intelligent Autonomous Systems (IAS-13), , Springer, July; Torr, P.H., Zisserman, A., MLESAC: A new robust estimator with application to estimating image geometry (2000) Computer Vision and Image Understanding, 78 (1), pp. 138-156; Fitzgibbon, A., Robust registration of 2d and 3d point sets (2002) Image and Vision Computing, 21, pp. 1145-1153. , 04",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020","5 October 2020 through 30 October 2020",,168366,,9781728154466,,,"English","Glob. Oceans: Singapore - U.S. Gulf Coast",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85104661932
"Yoon S., Lee S., Kye S., Kim I.-H., Jung H.-J., Spencer B.F., Jr","57202858776;57202134166;57202363434;56555571800;57304560200;57456552900;","Seismic fragility analysis of deteriorated bridge structures employing a UAV inspection-based updated digital twin",2022,"Structural and Multidisciplinary Optimization","65","12","346","","",,,"10.1007/s00158-022-03445-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142392879&doi=10.1007%2fs00158-022-03445-0&partnerID=40&md5=eb99b58a1c0492508d033072a27f5287","Department of Artificial Intelligence, Hannam University, 70 Hannam-ro, Daedeok-gu, Daejeon, 34430, South Korea; Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, 44919, South Korea; Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea; Department of Civil Engineering, Kunsan National University, 558 Daehak-ro, Kunsan, 54150, South Korea; Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL  61801, United States","Yoon, S., Department of Artificial Intelligence, Hannam University, 70 Hannam-ro, Daedeok-gu, Daejeon, 34430, South Korea; Lee, S., Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, 44919, South Korea; Kye, S., Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea; Kim, I.-H., Department of Civil Engineering, Kunsan National University, 558 Daehak-ro, Kunsan, 54150, South Korea; Jung, H.-J., Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea; Spencer, B.F., Jr, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL  61801, United States","Aging bridges require regular inspection due to performance deterioration. For this purpose, numerous researchers have considered the use of unmanned aerial vehicle (UAV) systems for structural health monitoring and inspection. However, present UAV-based inspection methods only represent the type and extent of external damage, but does not assess the seismic performance. In this study, a seismic fragility analysis of deteriorated bridges employing a UAV inspection-based updated digital twin is proposed. The proposed method consists of two phases: (1) bridge condition assessment using UAV inspection for updating the digital twin and (2) seismic fragility analysis based on the updated digital twin. To update the digital twin, the bridge damage grade is assigned based on the UAV inspection, and subsequently, the corresponding damage index is calculated. The damage index is utilized as a percentage reduction in the stiffness of finite element (FE) model, based on a previously proposed research. Using the updated digital twin, the seismic fragility analysis is conducted with different earthquake motions and magnitudes. To demonstrate the proposed method, an inservice pre-stressed concrete box bridge is examined. In particular, the seismic fragility curves of deteriorated bridges are compared with those of intact bridges. The numerical results show that the maximum failure probability of the deteriorated bridges is 3.6% higher than that of intact bridges. Therefore, the proposed method has the potential to updated the digital twin effectively using UAV inspection, allowing for seismic fragility analysis of deteriorated bridges to be conducted. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.","Bridge condition assessment; Deteriorated bridge structure; Digital twin; Seismic fragility analysis; Unmanned aerial vehicle (UAV); Visual inspection","Concretes; Damage detection; Deterioration; Earthquakes; Structural health monitoring; Unmanned aerial vehicles (UAV); Aerial vehicle; Bridge condition assessment; Bridge structures; Condition assessments; Damage index; Deteriorated bridge structure; Seismic fragility analysis; Unmanned aerial vehicle; Vehicle inspections; Visual inspection; Antennas",,,,,"Ministry of Education, MOE: 2022R1I1A1A01056139; National Research Foundation of Korea, NRF","This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 2022R1I1A1A01056139)",,,"Barroso, L.R., Rodriguez, R., Damage detection utilizing the damage index method to a benchmark structure (2004) J Eng. 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Yoon, S., Kim, J., Kim, M., Tak, H.-Y., Lee, Y.-J., Accelerated system-level seismic risk assessment of bridge transportation networks through artificial neural network-based surrogate model (2020) Appl Sci, 10 (18), p. 6476; Yoon, S., Gwon, G.-H., Lee, J.-H., Jung, H.-J., Three-dimensional image coordinate-based missing region of interest area detection and damage localization for bridge visual inspection using unmanned aerial vehicles (2021) Struct Health Monit, 20 (4), pp. 1462-1475; Yoon, S., Spencer, B.F., Jr., Lee, S., Jung, H.-J., Kim, I.-H., A novel approach to assess the seismic performance of deteriorated bridge structures by employing UAV-based damage detection (2022) Struct Control Health Monit, 29 (7); Zimmerman, D.C., Kaouk, M., Structural damage detection using a minimum rank update theory (1994) J Vibration Acoustic, 116 (2), pp. 222-231","Jung, H.-J.; Department of Civil and Environmental Engineering, 291 Daehak-ro, Yuseong-gu, South Korea; email: hjung@kaist.ac.kr",,,"Springer Science and Business Media Deutschland GmbH",,,,,1615147X,,SMOTB,,"English","Struct. Mutltidiscip. Opt.",Article,"Final","",Scopus,2-s2.0-85142392879
"Hughes A.J., Bull L.A., Gardner P., Dervilis N., Worden K.","57211513237;57194613339;57193994973;55210881700;7005669331;","On robust risk-based active-learning algorithms for enhanced decision support",2022,"Mechanical Systems and Signal Processing","181",,"109502","","",,,"10.1016/j.ymssp.2022.109502","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133905643&doi=10.1016%2fj.ymssp.2022.109502&partnerID=40&md5=43198425928b4a6952ff81485ee8a000","Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom; The Alan Turing Institute, The British Library, 96 Euston RoadLondon  NW1 2DB, United Kingdom","Hughes, A.J., Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom; Bull, L.A., The Alan Turing Institute, The British Library, 96 Euston RoadLondon  NW1 2DB, United Kingdom; Gardner, P., Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom; Dervilis, N., Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom; Worden, K., Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom","Classification models are a fundamental component of physical-asset management technologies such as structural health monitoring (SHM) systems and digital twins. Previous work introduced risk-based active learning, an online approach for the development of statistical classifiers that takes into account the decision-support context in which they are applied. Decision-making is considered by preferentially querying data labels according to expected value of perfect information (EVPI). Although several benefits are gained by adopting a risk-based active learning approach, including improved decision-making performance, the algorithms suffer from issues relating to sampling bias as a result of the guided querying process. This sampling bias ultimately manifests as a decline in decision-making performance during the later stages of active learning, which in turn corresponds to lost resource/utility. The current paper proposes two novel approaches to counteract the effects of sampling bias: semi-supervised learning, and discriminative classification models. These approaches are first visualised using a synthetic dataset, then subsequently applied to an experimental case study, specifically, the Z24 Bridge dataset. The semi-supervised learning approach is shown to have variable performance; with robustness to sampling bias dependent on the suitability of the generative distributions selected for the model with respect to each dataset. In contrast, the discriminative classifiers are shown to have excellent robustness to the effects of sampling bias. Moreover, it was found that the number of inspections made during a monitoring campaign, and therefore resource expenditure, could be reduced with the careful selection of the statistical classifiers used within a decision-supporting monitoring system. © 2022 The Author(s)","Active learning; Decision-making; Digital twins; Risk; Sampling bias; Structural health monitoring; Value of information","Classification (of information); Decision support systems; E-learning; Health; Health risks; Learning algorithms; Learning systems; Sampling; Structural health monitoring; Supervised learning; Active Learning; Classification models; Decision supports; Decisions makings; Performance; Risk-based; Sampling bias; Semi-supervised learning; Statistical classifier; Value of information; Decision making",,,,,"Alan Turing Institute, ATI; UK Research and Innovation, UKRI: EP/W006022/1; Engineering and Physical Sciences Research Council, EPSRC: EP/R003625/1, EP/R004900/1, EP/R006768/1","The authors would like to acknowledge the support of the UK EPSRC via the Programme Grants EP/R006768/1 and EP/R004900/1 . KW would also like to acknowledge support via the EPSRC Established Career Fellowship, UK EP/R003625/1 . LAB was supported by Wave 1 of The UKRI Strategic Priorities Fund, UK under the EPSRC Grant EP/W006022/1 , particularly the Ecosystems of Digital Twins, UK theme within that grant and The Alan Turing Institute, UK .",,,"Farrar, C.R., Worden, K., Structural Health Monitoring: A Machine Learning Perspective (2013), John Wiley & Sons, Ltd; Grieves, M., Vickers, J., Digital twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems (2017), pp. 85-113. , Transdisciplinary Perspectives on Complex Systems, Berlin, Germany; Niederer, S.A., Sacks, M.S., Girolami, M., Willcox, K., Scaling digital twins from the artisanal to the industrial (2021) Nat. Comput. 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Dyn., 30 (2), pp. 149-171; Bull, L.A., Towards Probabilistic and Partially-Supervised Structural Health Monitoring (2020), (Ph.D. thesis) University of Sheffield; Worden, K., Cross, E.J., Barthorpe, R.J., Wagg, D.J., Gardner, P., On digital twins, mirrors, and virtualizations: Frameworks for model verification and validation (2020) ASCE-ASME J. Risk Uncert. Engrg. Sys. Part B Mech. Engrg., 6 (3); Gardner, P., Dal Borgo, M., Ruffini, V., Hughes, A.J., Zhu, Y., Wagg, D.J., Towards the development of an operational digital twin (2020) Vibration, 3 (3), pp. 235-265; Tsialiamanis, G., Wagg, D.J., Dervilis, N., Worden, K., On generative models as the basis for digital twins (2021) Data-Centric Eng., 2; Gardner, P., Bull, L.A., Gosliga, J., Dervilis, N., Worden, K., Foundations of population-based SHM, Part III: Heterogeneous populations – mapping and transfer (2021) Mech. Syst. Signal Process., 148; Murphy, K.P., Machine Learning: A Probabilistic Perspective (2012), MIT Press; Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D., Bayesian Data Analysis (2013), Chapman and Hall/CRC; Barber, D., Bayesian Reasoning and Machine Learning (2012), Cambridge University Press; Tipping, M.E., Faul, A.C., Fast marginal likelihood maximisation for sparse Bayesian models (2003) Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, R4, pp. 276-283. , Bishop C.M. Frey B.J. PMLR","Hughes, A.J.; Dynamics Research Group, United Kingdom; email: ajhughes2@sheffield.ac.uk",,,"Academic Press",,,,,08883270,,MSSPE,,"English","Mech Syst Signal Process",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85133905643
"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
"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. U1709207, 51921006, and 51778104.",,"Pfeil, M.S., Battista, R.C., Mergulhão, A.J., Stress concentration in steel bridge orthotropic decks (2005) J Constr Steel Res, 61 (8), pp. 1172-1184; Fricke, W., Paetzoldt, H., Fatigue strength assessment of scallops—an example for the application of nominal and local stress approaches (1995) Mar Struct, 8 (4), pp. 423-447; Fasl, J.D., (2013) Estimating the remaining fatigue life of steel bridges using field measurements, , Ph.D. dissertation., Austin, TX, Univ of Texas at Austin; Maljaars, J., Steenbergen, H.M.G.M., Vrouwenvelder, A.C.W.M., Probabilistic model for fatigue crack growth and fracture of welded joints in civil engineering structures (2012) Int J Fatigue, 38, pp. 108-117; Cao, J., Shao, X., Zhang, Z., Zhao, H., Retrofit of an orthotropic steel deck with compact reinforced reactive powder concrete (2016) Civ Eng Infrastruct, 12 (3), pp. 411-429; Battista, R.C., Pfeil, M.S., Carvalho, E.M., Fatigue life estimates for a slender orthotropic steel deck (2008) J Constr Steel Res, 64 (1), pp. 134-143; 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Part 1.9: Fatigue, , London, British Standards Institution; (2015) Specifications for Design of Highway Steel Bridge (JTG D64-2015), , Beijing, China Communication Press; (2012) AASHTO LRFD Bridge Design Specifications, , Washington, American Association of State Highway and Transportation Officials, AASHTO; (2014) Guide to Fatigue Design and Assessment of Steel Products, BS 7608:2014, , London, The British Standards Institution","Ou, J.; School of Civil and Environmental Engineering, China; email: oujinping@hit.edu.cn",,,"John Wiley and Sons Ltd",,,,,15452255,,,,"English","J. Struct. Control Health Monit.",Article,"Final","",Scopus,2-s2.0-85128285952
"Rojas-Mercedes N., Erazo K., Di Sarno L.","56685658700;55805452100;6506371363;","Seismic fragility curves for a concrete bridge using structural health monitoring and digital twins",2022,"Earthquake and Structures","22","5",,"503","515",,,"10.12989/eas.2022.22.5.503","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131428696&doi=10.12989%2feas.2022.22.5.503&partnerID=40&md5=6d064f837047f5a01cfc74486cfe2cf1","School of Engineering, Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic; Department of Civil and Environmental Engineering, Rice University, Houston, TX, United States; Department of Civil Engineering and Industrial Design, School of Engineering, University of Liverpool, Liverpool, United Kingdom; Department of Engineering, University of Sannio, Benevento, Italy","Rojas-Mercedes, N., School of Engineering, Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic; Erazo, K., School of Engineering, Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic, Department of Civil and Environmental Engineering, Rice University, Houston, TX, United States; Di Sarno, L., Department of Civil Engineering and Industrial Design, School of Engineering, University of Liverpool, Liverpool, United Kingdom, Department of Engineering, University of Sannio, Benevento, Italy","This paper presents the development of seismic fragility curves for a precast reinforced concrete bridge instrumented with a structural health monitoring (SHM) system. The bridge is located near an active seismic fault in the Dominican Republic (DR) and provides the only access to several local communities in the aftermath of a potential damaging earthquake; moreover, the sample bridge was designed with outdated building codes and uses structural detailing not adequate for structures in seismic regions. The bridge was instrumented with an SHM system to extract information about its state of structural integrity and estimate its seismic performance. The data obtained from the SHM system is integrated with structural models to develop a set of fragility curves to be used as a quantitative measure of the expected damage; the fragility curves provide an estimate of the probability that the structure will exceed different damage limit states as a function of an earthquake intensity measure. To obtain the fragility curves a digital twin of the bridge is developed combining a computational finite element model and the information extracted from the SHM system. The digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that are consistent with the seismic fault and site characteristics. The fragility curves show that for the maximum expected acceleration (with a 2% probability of exceedance in 50 years) the structure has a 62% probability of undergoing extensive damage. This is the first study presenting fragility curves for civil infrastructure in the DR and the proposed methodology can be extended to other structures to support disaster mitigation and post-disaster decision-making strategies © 2022. Techno-Press, Ltd","Civil infrastructure; Digital twins; Earthquake engineering; Fragility curves; Structural health monitoring","Concrete bridges; Decision making; Disasters; Earthquake engineering; Earthquakes; Faulting; Precast concrete; Reinforced concrete; Seismic response; Uncertainty analysis; Active seismic; Civil infrastructures; Fragility curves; Local community; Precast reinforced concrete; Reinforced concrete bridge; Seismic faults; Seismic fragility curves; Seismic regions; Structural health monitoring systems; Structural health monitoring",,,,,"Ministerio de Educación Superior, Ciencia y Tecnología, República Dominicana, MESCYT","This work was partially supported by the Ministry of Higher Education, Sciences and Technology of the Dominican Republic (MESCYT). 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"Zhao H.-W., Ding Y.-L., Li A.-Q., Chen B., Wang K.-P.","57191694306;55768944900;7403291516;57467103600;55648912700;","Digital modeling approach of distributional mapping from structural temperature field to temperature-induced strain field for bridges",2022,"Journal of Civil Structural Health Monitoring",,,,"","",,,"10.1007/s13349-022-00635-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140052808&doi=10.1007%2fs13349-022-00635-8&partnerID=40&md5=cfa45ece3e937a6c15a58997c2ce4560","Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing, 210096, China; School of Civil Engineering, Southeast University, Nanjing, 210096, China; Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China; China Railway Bridge and Tunnel Technologies Co., Ltd., Nanjing, 210061, China; CCCC Highway Bridges National Engineering Research Centre Co., Ltd., Beijing, 100088, China","Zhao, H.-W., Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing, 210096, China, School of Civil Engineering, Southeast University, Nanjing, 210096, China; Ding, Y.-L., Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing, 210096, China, School of Civil Engineering, Southeast University, Nanjing, 210096, China; Li, A.-Q., School of Civil Engineering, Southeast University, Nanjing, 210096, China, Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China; Chen, B., China Railway Bridge and Tunnel Technologies Co., Ltd., Nanjing, 210061, China; Wang, K.-P., CCCC Highway Bridges National Engineering Research Centre Co., Ltd., Beijing, 100088, China","Zero-point of strain data representing the structural state without stress is hard to determine, but change in strain can be accurately measured. It is a good choice to quantify the complex strain behavior under non-uniform temperature field by deep learning the variation of distributional features from different sensing points. Taking a long-span steel cable-stayed bridge as the case study, features of long-term time series data of temperature and temperature-induced strain are analyzed. A digital approach of distributional mapping from the structural temperature field to the temperature-induced strain field is presented. Based on the coordinates clustering of sensing points and the correlation knowledge between structural temperature and temperature-induced strain, clusters of sensing points of temperature and strain can be determined. Distributional feature parameters (difference sequence and adjacency matrix of difference) about the per-minute mean of each cluster’s structural temperature and temperature-induced strain data are calculated. The model of mapping relation from structural temperature field to temperature-induced strain field is established based on the learning of the big data of distributional feature parameters by the bidirectional long short-term memory regression network. The results demonstrated that redistribution of temperature-induced strain field can be perceived according to the residual between regression results of network models and real-time monitoring results, which means extreme changes of temperature field or potential deterioration in structure of bridge. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.","Cluster; Deep learning; Digital twin; Structural health monitoring; Temperature-induced strain","Cable stayed bridges; Deep learning; Deterioration; E-learning; Mapping; Structural health monitoring; Cluster; Deep learning; Digital modeling; Distributional features; Feature parameters; Induced strain; Strain data; Strain fields; Temperature-induced; Temperature-induced strain; Temperature",,,,,"National Natural Science Foundation of China, NSFC: 51978154, 52008099; Natural Science Foundation of Jiangsu Province: BK20190013, BK20200369; National Key Research and Development Program of China, NKRDPC: 2021YFF0500900; Fundamental Research Funds for the Central Universities: 2242022k3003, 2242022k30031","This research work was jointly supported by the National Key R&D Program of China (Grant. 2021YFF0500900), National Natural Science Foundation of China (Grants. 52008099 and 51978154), Natural Science Foundation of Jiangsu Province (Grants. BK20200369 and BK20190013), and Fundamental Research Funds for the Central Universities (Grants. 2242022k30031 and 2242022k3003).",,,"Bao, Y.Q., Chen, Z.C., Wei, S.Y., Xu, Y., Tang, Z.Y., Li, H., The state of the art of data science and engineering in structural health monitoring (2019) Engineering, 5 (2), pp. 234-242; Sun, L., Shang, Z., Xia, Y., Bhowmick, S., Nagarajaiah, S., Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection (2020) J Struct Eng, 146 (5), p. 04020073; 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; Huang, H.B., Yi, T.H., Li, H.N., Liu, H., Strain-based performance warning method for bridge main girders under variable operating conditions (2020) J Bridg Eng, 25 (4), p. 04020013; Zhang, J., Xia, Q., Cheng, Y.Y., Wu, Z.S., Strain flexibility identification of bridges from long-gauge strain measurements (2015) Mech Syst Signal Process, 62-63, pp. 272-283; 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Computer Science arXiv:1412.6980v9","Ding, Y.-L.; Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, China; email: civilchina@hotmail.com",,,"Springer Science and Business Media Deutschland GmbH",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Article in Press","",Scopus,2-s2.0-85140052808
"Catbas N., Avci O.","57204279590;6701761980;","A Review of Latest Trends in Bridge Health Monitoring",2022,"Proceedings of the Institution of Civil Engineers: Bridge Engineering",,,,"","",,,"10.1680/jbren.21.00093","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139495576&doi=10.1680%2fjbren.21.00093&partnerID=40&md5=c77293c0f2b75edf60d4c9fac47e895c","Department of Civil, Environmental and Construction Engineering, Civil Infrastructure Technologies for Resilience and Safety (CITRS), University of Central Florida, Orlando, FL, United States; Department of Civil and Environmental Engineering, West Virginia University, Morgantown, WV, United States","Catbas, N., Department of Civil, Environmental and Construction Engineering, Civil Infrastructure Technologies for Resilience and Safety (CITRS), University of Central Florida, Orlando, FL, United States; Avci, O., Department of Civil and Environmental Engineering, West Virginia University, Morgantown, WV, United States","Structural damage is inherent in civil engineering structures and bridges are no exception. It is vital to monitor and keep track of damage on bridge structures due to multiple mechanical, environmental, and traffic-induced factors. Monitoring the formation and propagation of structural damage is also pertinent for enhancing the service life of bridges. Bridge Health Monitoring (BHM) has always been an active research area for engineers and stakeholders. While all monitoring techniques intend to provide accurate and decisive information on the remaining useful life, safety, integrity, and serviceability of bridges; maintaining the uninterrupted operation of a bridge highly relies on understanding the development and propagation of damage.¯BHM methods have been extensively researched on bridges over the decades, and new methodologies have started to be used by domain experts, especially within the last decade.¯ Emerging methods, as the products of the technology advancements, resulted in handy tools that have been quickly adopted by bridge engineers. State-of-The-Art techniques such as LiDAR, Photogrammetry, Virtual Reality (VR) and Augmented Reality (AR), Digital Twins, Computer Vision, Machine Learning, and Deep Learning are now integrated part of the new-generation BHM operations.¯This paper presents a brief overview of these latest BHM technologies. © 2022 ICE Publishing: All rights reserved.","Augmented Reality (AR); Bridge Health Monitoring; Bridges; Computer Vision; Deep Learning; Digital Twins; LiDAR; Machine Learning; Photogrammetry; Structural Health Monitoring; UN SDG 11: Sustainable cities and communities; UN SDG 9: Industry innovation and infrastructure; United Nations Sustainable Development Goals; Virtual Reality (VR)","Augmented reality; Bridges; Computer vision; Deep learning; E-learning; Learning systems; Lithium compounds; Optical radar; Structural health monitoring; Sustainable development; Virtual reality; Augmented reality; Bridge health monitoring; Deep learning; Industry infrastructure; Industry innovations; LiDAR; Machine-learning; Sustainable cities; Sustainable communities; UN SDG 11: sustainable city and community; UN SDG 9: industry innovation and infrastructure; United nation sustainable development goal; United Nations; Virtual reality; Photogrammetry",,,,,,,,,"Abdeljaber, O., Avci, O., Do, N.T., Gul, M., Celik, O., Necati Catbas, F., Quantification of structural damage with self-organizing maps (2016) Conference Proceedings of the Society for Experimental Mechanics Series; 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 (2017) Neurocomputing; Abdeljaber, O., Sassi, S., Avci, O., Kiranyaz, S., Ibrahim, A.A., Gabbouj, M., Fault detection and severity identification of ball bearings by online condition monitoring (2019) IEEE Transactions on Industrial Electronics; Abu Dabous, S., Feroz, S., Condition monitoring of bridges with non-contact testing technologies (2020) Automation in Construction; Adhikari, R.S., Moselhi, O., Bagchi, A., Image-based retrieval of concrete crack properties for bridge inspection (2014) Automation in Construction, 39, pp. 180-194. , Elsevier B.V; Almutairi, M., Avci, O., Nikitas, N., Efficiency of 1d cnns in finite element model parameter estimation using synthetic dynamic responses (2020) Proceedings of the International Conference on Structural Dynamic, EURODYN; Almutairi, M., Nikitas, N., Abdeljaber, O., Avci, O., Bocian, M., A methodological approach towards evaluating structural damage severity using 1D CNNs (2021) Structures, 34; A comprehensive assessment of America?s Infrastructure (2021) ASCE, , American Society of Civil Engineers; Failure to Act: Economic Impacts of Status Quo Investment Across Infrastructure Systems (2021) ASCE, , American Society of Civil Engineers; Apicella, A., Donnarumma, F., Isgrò, F., Prevete, R., A survey on modern trainable activation functions (2021) Neural Networks; Arias, P., Armesto, J., Di-Capua, D., González-Drigo, R., Lorenzo, H., Pérez-Gracia, V., Digital photogrammetry, GPR and computational analysis of structural damages in a mediaeval bridge (2007) Engineering Failure Analysis; Athanasiou, A., Salamone, S., Acquisition and management of field inspection data using augmented reality (2020); Avci, O., Abdeljaber, O., Self-organizing maps for structural damage detection: A novel unsupervised vibration-based algorithm (2016) Journal of Performance of Constructed Facilities, 30, p. 3; Avci, O., Abdeljaber, O., Kiranyaz, S., Structural Damage Detection in Civil Engineering with Machine-Learning: Current State of the Art (2021) Conference Proceedings of the Society for Experimental Mechanics Series; Avci, O., Abdeljaber, O., Kiranyaz, S., Boashash, B., Sodano, H., Inman, D.J., Efficiency Validation of One Dimensional Convolutional Neural Networks for Structural Damage Detection Using a SHM Benchmark Data (2018) 25th International Congress on Sound and Vibration; 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) Mechanical Systems and Signal Processing; 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; Avci, O., Abdeljaber, O., Kiranyaz, S., Inman, D., Structural damage detection in real time: Implementation of 1d convolutional neural networks for shm applications (2017) Structural Health Monitoring & Damage Detection Volume 7: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics 2017, C. 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Xu, Y., Brownjohn, J., Kong, D., A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge (2018) Structural Control and Health Monitoring, 25 (5), pp. 1-23; Ye, C., Butler, L., Calka, B., Iangurazov, M., Lu, Q., Gregory, A., Girolami, M., Middleton, C., A digital twin of bridges for structural health monitoring (2019) Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT)-Proceedings of the 12th International Workshop on Structural Health Monitoring; Ye, X.W., Jin, T., Yun, C.B., A review on deep learning-based structural health monitoring of civil infrastructures (2019) Smart Structures and Systems; Zhang, L., Shen, J., Zhu, B., A research on an improved Unet-based concrete crack detection algorithm (2020) Structural Health Monitoring, 29; Zollini, S., Alicandro, M., Dominici, D., Quaresima, R., Giallonardo, M., UAV photogrammetry for concrete bridge inspection using object-based image analysis (OBIA) (2020) Remote Sensing","Catbas, N.; Department of Civil, United States; email: catbas@ucf.edu",,,"ICE Publishing",,,,,14784637,,,,"English","Proc. Inst. Civ. Eng. Bridge Eng.",Review,"Article in Press","",Scopus,2-s2.0-85139495576
"Karaaslan E., Zakaria M., Catbas F.N.","57193868803;57880106700;6603396768;","Mixed reality-assisted smart bridge inspection for future smart cities",2022,"The Rise of Smart Cities: Advanced Structural Sensing and Monitoring Systems",,,,"261","280",,,"10.1016/B978-0-12-817784-6.00002-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137578880&doi=10.1016%2fB978-0-12-817784-6.00002-3&partnerID=40&md5=b3ad80f5ab16e5579a1214546941c050","Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, United States","Karaaslan, E., Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, United States; Zakaria, M., Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, United States; Catbas, F.N., Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, United States","Smart infrastructures aim more efficient and accurate methods of routine inspection for long-term monitoring of the infrastructure to make smarter decision on maintenance and rehabilitation. Although some recent technologies (i.e., robotic techniques) that are currently in practice can collect objective, quantified data, the inspector’s own expertise is still critical in many instances. Yet, these technologies are designed to replace human expertise, or are ineffective in terms of saving time and labor. This chapter investigates a new methodology for structural inspections with the help of mixed reality technology and real-time machine learning to accelerate certain tasks of the inspector such as detection, measurement, and assessment of defects, and easy accessibility to defect locations. A functional, real-time machine learning system that can be ideally deployed in mixed reality devices and headsets which can be used by inspectors during their routine concrete infrastructure inspection is introduced. The deep learning models to be employed in the AI system can localize a concrete defect in real time and further analyze it by performing pixel wise segmentation while running on a mobile device architecture. First, a sufficiently large database of concrete defect images is gathered from various sources including publicly available crack and spalling datasets, real-world images taken during bridge inspections, and the public images from the internet search results. For defect localization, various state-of-the-art deep learning model architectures are investigated based on their memory allocation, inference speed, and flexibility to deploy different deep learning platforms. YoloV5s model was found to be the optimal model architecture for concrete defect localization to be deployed in the mixed reality system. For defect quantification, several segmentation architectures with three different classification backbones are trained on the collected image dataset with segmentation labels. Based on the model evaluation results, the PSPNet with EfficientNet-b0 backbone is found to be the best performing model in terms of inference speed and accuracy. The selected models for defect localization and quantification are deployed to the mixed reality platform and image tracking libraries are configured in the platform environment, and accurate distance estimation is accomplished using a calibration process. Lastly, a methodology for condition assessment of concrete defects using the mixed reality system is discussed. The proposed methodology can locate and track the defects using the mixed reality platform, which can eventually be transferred to cloud data and potentially used for remote assessments or updating a digital twins or BIMs. © 2022 Elsevier Inc. All rights reserved.","Human-computer interaction; Mixed reality; Real-time machine learning; Smart cities; Smart infrastructure inspection; Structural health monitoring",,,,,,,,,,"Adhikari, R.S., Moselhi, O., Bagchi, A., Image-based retrieval of concrete crack properties for bridge inspection (2014) Automation in Construction, 39, pp. 180-194; Alavi, A.H., Hasni, H., Jiao, P., Aono, K., Lajnef, N., Chakrabartty, S., Self-charging and self-monitoring smart civil infrastructure systems: Current practice and future trends (2019) Sensors and smart structures technologies for civil, mechanical, and aerospace systems 2019 International Society for Optics and Photonics, 10970, p. 109700W. , March; Azuma, R., Behringer, R., Feiner, S., Julier, S., Macintyre, B., Recent advances in augmented reality (2001), 2011, pp. 1-27. , IEEE computer graphics and applications, December; Badrinarayanan, V., Kendall, A., Cipolla, R., SegNet: A deep convolutional encoder-decoder architecture for image segmentation (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (12), pp. 2481-2495; Bae, H., Golparvar-Fard, M., White, J., High-precision vision-based mobile augmented reality system for context-aware architectural, engineering, construction and facility management (AEC/FM) applications (2013) Visualization in Engineering, 1 (1), p. 3; Behzadan, A.H., Dong, S., Kamat, V.R., Augmented reality visualization: A review of civil infrastructure system applications (2015) Advanced Engineering Informatics, 29 (2), pp. 252-267; Behzadan, A.H., Kamat, V.R., Georeferenced registration of construction graphics in mobile outdoor augmented reality (2007) Journal of Computing in Civil Engineering, 21 (4), pp. 247-258; Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M., YOLOv4: Optimal speed and accuracy of object detection (2020), arXiv preprint arXiv:2004.10934; Catbas, F.N., Gul, M., Zaurin, R., Gokce, H.B., Terrell, T., Dumlupinar, T., Long term bridge maintenance monitoring demonstration on a movable bridge (2010), p. 186. , June,, Final Report for Research Project, No; Catbas, F.N., Hiasa, S., Dong, C., Pan, Y., Celik, O., Karaaslan, E., Comprehensive structural health monitoring at local and global level with vision-based technologies 26th ASNT research symposium.; https://doi.org/10.1061/(ASCE)ST.1943-541X.0000682, Catbas, F. N., T. Kijewski-Correa, T. Kijewski-Correa, and A. M. Asce. Structural identification of constructed systems: Collective effort toward an integrated approach that reduces barriers to adoption. doi:; Coutrix, C., Nigay, L., Mixed reality: A model of mixed interaction (2006), pp. 43-50. , Proceedings of the working conference on advanced visual interfaces—AVI’06; Dong, C.-Z., Bas, S., Catbas, F.N., A portable monitoring approach using cameras and computer vision for bridge load rating in smart cities (2020) Journal of Civil Structural Health Monitoring, 10 (5), pp. 1001-1021; German, S., Brilakis, I., Desroches, R., Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments (2012) Advanced Engineering Informatics, 26 (4), pp. 846-858; Hiasa, S., Investigation of infrared thermography for subsurface damage detection of concrete structures (2016), UCF Libraries, UCF Electronic Theses and Dissertations (2004-2019); Hiasa, S., Catbas, F.N., Matsumoto, M., Mitani, K., Monitoring concrete bridge decks using infrared thermography with high speed vehicles (2016) Structural Monitoring and Maintenance, 3 (3), pp. 277-296; 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Kamat, V.R., El-Tawil, S., Evaluation of augmented reality for rapid assessment of earthquake-induced building damage (2007) Journal of Computing in Civil Engineering, 21 (5), pp. 303-310; Karaaslan, E., Bagci, U., Catbas, F.N., Artificial intelligence assisted infrastructure assessment using mixed reality systems (2019) Transportation Research Record 2673, no. 12, pp. 413-424; Karaaslan, E., Bagci, U., Catbas, F.N., Attention-guided analysis of infrastructure damage with semi-supervised deep learning (2021) Automation in Construction, 125. , 103634; Karaaslan, E., Catbas, F.N., Bagci, U., A novel decision support system for long term management of bridge networks (2021) Applied Sciences, 11, p. 5928; Knott, P., Carroll, M., Devlin, S., Ciosek, K., Hofmann, K., Dragan, A.D., Evaluating the robustness of collaborative agents (2021), arXiv preprint arXiv:2101.05507; Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., Fieguth, P., A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure (2015) Advanced Engineering Informatics, 29 (2), pp. 196-210; 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Milgram, P., Kishino, F., A taxonomy of mixed reality visual displays (1994) IEICE Transactions on Information and Systems, 77 (12), pp. 1321-1329; Moreu, F., Bleck, B., Vemuganti, S., Rogers, D., Mascarenas, D., Augmented reality tools for enhanced structural inspection (2017) Structural Health Monitoring 2017, pp. 3124-3130; Rashidi, M., Samali, B., Sharafi, P., A new model for bridge management: Part B: Decision support system for remediation planning (2016) Australian Journal of Civil Engineering, 14 (1), pp. 46-53; Ronneberger, O., Fischer, P., Brox, T., U-net: Convolutional networks for biomedical image segmentation (2015) International conference on medical image computing and computer-assisted intervention, pp. 234-241. , Cham: Springer; Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition (2014), arXiv preprint arXiv:1409.1556; Tan, M., Pang, R., Le, Q.V., EfficientDet: Scalable and efficient object detection (2020) Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10781-10790; Game engine, tools and multiplatform Unity Technologies; Wada, K., Labelme: Image Polygonal Annotation with Python ; Xiong, Y., Liu, H., Gupta, S., Akin, B., Bender, G., Wang, Y., MobileDets: Searching for object detection architectures for mobile accelerators (2021) Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3825-3834; Yokoyama, S., Matsumoto, T., Development of an automatic detector of cracks in concrete using machine learning (2017) Procedia Engineering, 171, pp. 1250-1255; Zaurin, R., Khuc, T., Catbas, F.N., Asce, F., Hybrid sensor-camera monitoring for damage detection: Case study of a real bridge (2015) Journal of Bridge Engineering, 21 (6), pp. 1-27; Zhang, Q., Barri, K., Babanajad, S.K., Alavi, A.H., Real-time detection of cracks on concrete bridge decks using deep learning in the frequency domain (2020) Engineering; Zhu, X., Goldberg, A.B., Introduction to semi-supervised learning (2009) Synthesis Lectures on Artificial Intelligence and Machine Learning, 3 (1), pp. 1-130",,,,"Elsevier",,,,,,9780128177846; 9780128177853,,,"English","The Rise of Smart Cities: Advanced Structural Sens. and Monitoring Systems",Book Chapter,"Final","",Scopus,2-s2.0-85137578880
"Joye S., De Witt M.","57783644500;57783481000;","Structural Health Monitoring of the Çanakkale Bridge in Turkey, the largest monitoring system for the longer bridge in the world",2022,"IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report",,,,"314","317",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133531808&partnerID=40&md5=d50939d40114ea74530c7b001dbe5c14","Sixense Monitoring, Nanterre, France; Sixense Monitoring, Brussels, Belgium","Joye, S., Sixense Monitoring, Nanterre, France; De Witt, M., Sixense Monitoring, Brussels, Belgium","This bridge is outstanding and so will be its Structural health Monitoring. Designed and installed by Sixense upon the specification of the Client, the system will manage more than 1000 sensors. This monitoring architecture will allow to measure any external event happening on the bridge such as strong, wind, earthquake, lightning, heavy traffic. It will also record all the reaction behavior of the structure such as temperature, displacement, vibration, fatigue stresses. Amongst all the recorded data, dynamic record of such a slender structure is key. The acquisition system provided by Sixense will allow to record, compute, sample, analyses, store and analyse a tremendous quantity of data from all kind of mechanical beaviour of the structure. It will for instance allow rainflow treatment of the stress of the orthotropic slab in order to survey and prevent fatigue failure. But more than simply recording data for visualization and immediate maintenance, the system provided by Sixense will also allow to perform predictive computation of the future behavior of the infrastructure: regression analysis from the available data combined with tuneable environment parameters (temperature, traffic), will allow the owner of the bride to anticipate the behavior of the bridge and plan the maintenance operation far in advance out of critical service activity phases. This will increase the safety of the bridge, save money for the owner and allow this signature bridge to last for a very long time. © 2022 IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report. All rights reserved.","Bridge; Digital twin; Longest; record; Sensors; SHM; Strain Gages; Structural Health Monitoring; Suspension; Turkey; World","Bridges; Fatigue of materials; Regression analysis; Structural health monitoring; Long; Long bridges; Monitoring architecture; Monitoring system; Record; SHM; Strain-gages; Strong winds; Turkey; World; Data visualization",,,,,,,,,,"Joye, S.; Sixense MonitoringFrance; email: stephane.joye@sixense-group.com",,"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-s2.0-85133531808
"Futai M.M., Bittencourt T.N., Carvalho H., Ribeiro D.M.","12142761800;6603036318;56656114300;25930078000;","Challenges in the application of digital transformation to inspection and maintenance of bridges",2022,"Structure and Infrastructure Engineering","18","10-11",,"1581","1600",,,"10.1080/15732479.2022.2063908","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129636635&doi=10.1080%2f15732479.2022.2063908&partnerID=40&md5=b016f92daa1c3b1fd9ef68ce9768efb2","Polytechnic School, University of São Paulo, São Paulo, Brazil; Department of Structural Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil; PhDsoft, Rio de Janeiro, Brazil","Futai, M.M., Polytechnic School, University of São Paulo, São Paulo, Brazil; Bittencourt, T.N., Polytechnic School, University of São Paulo, São Paulo, Brazil; Carvalho, H., Department of Structural Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil; Ribeiro, D.M., PhDsoft, Rio de Janeiro, Brazil","Bridges constitute an important part of the infrastructure and are subjected to damage and deterioration of materials and support conditions, as well as exposure to adverse environmental conditions. Continuous or repeated monitoring of structural responses may add important information for decision-making regarding their maintenance, repair and reinforcement. The use of these data, in conjunction with techniques of structural reliability for the treatment of the uncertainties, allows a better understanding of the structural behaviour and integrity. Modern Information and Communication Technologies can greatly contribute to the improvement of the maintenance capacity and, consequently, to the reliability of the assets and to their operational availability. New wireless communication technologies, such as 5 G networks, are considered as the enabling technologies of the digital transformation, integrated with the concept of the Internet of Things. High connectivity capacity and intensive automation enable, for example, changes in inspection paradigms and asset maintenance, by transferring the product focus to service platforms, bringing gains to productivity, comfort, operational safety and costs. New predictive maintenance approaches, which make use of a large amount of data available, can improve the efficiency of maintenance processes, producing more accurate and reliable anticipated diagnostics. The Digital Twins incorporate all these tools and allow a real-time view of the evolution of the asset behaviour. This concept applied to a railway bridge is presented and discussed in detail in this paper. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","bridge inspection; bridge maintenance; Digital transformation; digital twin; information and communication technologies; Internet of Things; structural health monitoring","5G mobile communication systems; Decision making; Deterioration; Inspection; Internet of things; Life cycle; Repair; Bridge inspection; Bridges maintenance; Decisions makings; Digital transformation; Environmental conditions; Information and Communication Technologies; Inspection and maintenance; Material conditions; Structural response; Support conditions; Structural health monitoring",,,,,"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq","Authors would like to acknowledge CNPq (Brazilian Ministry of Science and Technology Agency), CAPES (Higher Education Improvement Agency) and VALE Catedra Under Rail for providing an important part of the financial support needed to develop this paper. The work described in this paper has been partially supported by VLI and VALE Railway Companies. The opinions and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organizations. We also acknowledge the members of lab-Infra, GeoInfraUSP and GMEC research group: A.P. da Conceição Neto, A. Colombo, A.L.D. Pereira Filho, F.Y. Toriume, F.K. Toome, G.V. Menezes, J.J. Arrieta Baldovino, L.B. Machado and R. R. Santos. 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Infrastructure Eng.",Article,"Final","",Scopus,2-s2.0-85129636635
"Ye C., Kuok S.-C., Butler L.J., Middleton C.R.","57216481422;36015370900;55795448200;7005340597;","Implementing bridge model updating for operation and maintenance purposes: examination based on UK practitioners’ views",2022,"Structure and Infrastructure Engineering","18","12",,"1638","1657",,,"10.1080/15732479.2021.1914115","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107481456&doi=10.1080%2f15732479.2021.1914115&partnerID=40&md5=ea0c2448c705890bb6cb03482005b338","Civil Engineering Division, Department of Engineering, University of Cambridge, Cambridge, United Kingdom; State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau; Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Canada","Ye, C., Civil Engineering Division, Department of Engineering, University of Cambridge, Cambridge, United Kingdom; Kuok, S.-C., Civil Engineering Division, Department of Engineering, University of Cambridge, Cambridge, United Kingdom, State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau; Butler, L.J., Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Canada; Middleton, C.R., Civil Engineering Division, Department of Engineering, University of Cambridge, Cambridge, United Kingdom","There has been a vision of creating bridge digital twins as virtual simulation models of bridge assets to facilitate remote management. Bridge model updating is one digital twin technology which can enable the continuous updating of the structural model as new monitoring data is collected. This paper examines why there is currently little industry uptake of monitoring, modelling and model updating for the operation and maintenance of bridges despite over two decades of research in these fields. The study analyses the findings from a series of semi-structured industry interviews with expert bridge professionals in the U.K. and from an extensive literature survey of bridge model updating studies to examine the disconnects between research and practice and the practical issues of implementing bridge model updating. In particular, the study found that localised damage resulting in local reduction in structural stiffness, a key assumption made in the majority of research, is subject to question by practitioners as many common types of bridge damage may not induce noticeable change in structural stiffness that existing model updating techniques would identify. Key recommendations for future research are proposed to drive adoption of bridge monitoring, modelling and model updating and thus realise their industrial value. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","Bridge operation and maintenance; digital twin technology; industry practice; structural health monitoring; structural model updating","Bridges; Digital storage; Industrial research; Maintenance; Stiffness; Bridge model; Bridge operation and maintenance; Digital twin technology; Industry practices; Model updating; Monitoring models; Operations and maintenance; Structural model updating; Structural stiffness; Virtual simulation models; Structural health monitoring",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/L016095/1","The authors would like to thank the 19 bridge professionals for participating in the industry facing interviews. 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I: Modeling and influence line analysis (2015) Journal of Bridge Engineering, 20 (10), p. 04014112; Živanović, S., Pavic, A., Reynolds, P., Finite element modelling and updating of a lively footbridge: The complete process (2007) Journal of Sound and Vibration, 301 (1-2), pp. 126-145","Ye, C.; Civil Engineering Division, United Kingdom; email: cy273@cam.ac.uk",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85107481456
"Moradi S., Eftekhar Azam S., Mofid M.","57222984604;57195073631;56458005600;","A Physics Informed Neural Network Integrated Digital Twin for Monitoring of the 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",,,,"771","778",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139236074&partnerID=40&md5=f29765682c5e02a7b3f38c5633296c4d","Department of Civil Engineering, Sharif University of Technology, Tehran, Iran; College of Engineering and Physical Sciences, University of New Hampshire, Durham, NH, United States","Moradi, S., Department of Civil Engineering, Sharif University of Technology, Tehran, Iran; Eftekhar Azam, S., College of Engineering and Physical Sciences, University of New Hampshire, Durham, NH, United States; Mofid, M., Department of Civil Engineering, Sharif University of Technology, Tehran, Iran","In recent years the Digital Twin (DT) paradigm has been studied as a futuristic tool for the next generation of infrastructures. Due to the interdisciplinary nature of the design, construction, monitoring, and maintenance of the infrastructures and the cooperation of several stakeholders throughout their lifetime, it is indispensable to introduce a comprehensive platform for the digital representation of infrastructures. Although the DT emphasizes the role of digital modeling and data analysis, there is a gap between physical modeling and data-driven tools. The newly introduced Physics Informed Neural Networks (PINNs) are capable of not only filling this gap but also representing a unified real-time platform for different users from various fields. These algorithms suggest an agile environment for users to introduce different criteria from the design stage to the health monitoring period. The PINN integrates both physical modeling and data analysis in a unique algorithm, helping them interact simultaneously and providing real-time, reliable responses. By means of the PINN, the DT can learn and update the model from various data sources with a unique platform, which plays an essential role in the rapid flow of information and transparency of data-based calculations. The dynamic ambiance of the PINN enables the users to interact with the modeling procedure and track the analysis. In this study, the details of the proposed platform for the integration of the PINNs in the DT are addressed for monitoring the bridges. Extensive numerical studies are provided for various scenarios of sensor equipment, including sensor type, data accuracy, and installation pattern. The performance of the proposed platform is evaluated for predicting subsequent responses to ensure the reliability of the responses in future decision makings. © 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.","Bridges; Digital Twin; Health Monitoring; Physics Informed Neural Networks","Cyber Physical System; Data handling; Embedded systems; Information analysis; Structural health monitoring; Construction monitoring; Design construction; Digital datas; Digital modeling; Digital representations; Health monitoring; Neural-networks; Physic informed neural network; Physical data; Physical modelling; Decision making",,,,,,,,,"Sepasgozar, S. M. E., Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment (2021) Buildings, 11 (4); Mertala-lindsay, T., Strålman, J., From Early Design to Project Delivery Master’ s thesis in Design and Construction Project Management DIVISION OF CONSTRUCTION MANAGEMENT (2021) CHALMERS UNIVERSITY OF TECHNOLOGY; Chen, Z., Huang, L., Digital Twin in Circular Economy: Remanufacturing in Construction (2020) IOP Conf. Ser. Earth Environ. Sci, 588 (3); Zhu, Y.-C., Wagg, D., Cross, E., Barthorpe, R., (2020) Real-Time Digital Twin Updating Strategy Based on Structural Health Monitoring Systems, 3, pp. 55-64; Jiang, F., Ding, Y., Song, Y., Geng, F., Wang, Z., Digital Twin-driven framework for fatigue life prediction of steel bridges using a probabilistic multiscale model: Application to segmental orthotropic steel deck specimen (2020) Eng. Struct, 241, p. 2021. , December; Jiang, F., Ma, L., Broyd, T., Chen, K., Digital twin and its implementations in the civil engineering sector (2021) Autom. Constr, 130, p. 103838. , July; Shim, C. S., Kang, H. R., Dang, N. S., Digital twin models for maintenance of cable-supported bridges (2019) Int. Conf. Smart Infrastruct. Constr. 2019, ICSIC 2019 Driv. Data-Informed Decis, 2019 (2017), pp. 737-742; Raissi, M., Perdikaris, P., Karniadakis, G. E., (2017) Physics informed deep learning (Part II): Data-driven discovery of nonlinear partial differential equations, pp. 1-19. , arXiv Part II; Raissi, M., Perdikaris, P., Karniadakis, G. E., (2017) Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations, , arXiv, Nov; Mao, Z., Jagtap, A. D., Karniadakis, G. E., Physics-informed neural networks for high-speed flows (2020) Comput. Methods Appl. Mech. Eng, 360, p. 112789. , Mar; Chen, Y., Lu, L., Karniadakis, G. E., Dal Negro, L., Physics-informed neural networks for inverse problems in nano-optics and metamaterials (2020) Opt. Express, 28 (8), p. 11618. , Apr; Misyris, G. S., Venzke, A., Chatzivasileiadis, S., Physics-Informed Neural Networks for Power Systems (2020) 2020 IEEE Power & Energy Society General Meeting (PESGM), pp. 1-5",,"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-s2.0-85139236074
"Kazemian M., Nikdel S., Mohammadesmaeili M., Nik V., Zandi K.","57914651800;57914651900;57915708400;54978401900;57433878000;","Kalix Bridge Digital Twin—Structural Loads from Future Extreme Climate Events",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",,,,"746","755",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139223801&partnerID=40&md5=5cfa4d988ed1e4de20a57e79a21790ac",,"Kazemian, M.; Nikdel, S.; Mohammadesmaeili, M.; Nik, V.; Zandi, K.","Environmental loads, such as wind and river flow, play an essential role in the structural design and structural assessment of long-span bridges. Climate change and extreme climatic events are threats to the reliability and safety of the transport network. This has led to a growing demand for digital twin models to investigate the resilience of bridges under extreme climate conditions. Kalix bridge, constructed over the Kalix river in Sweden in 1956, is used as a testbed in this context. The bridge structure, made of post-tensioned concrete, consists of five spans, with the longest one being 94 m. In this study, aerodynamic characteristics and extreme values of numerical wind simulation such as surface pressure are obtained by using Spalart-Allmaras Delayed Detached Eddy Simulation (DDES) as a hybrid RANS-LES turbulence approach which is both practical and computationally efficient for near-wall mesh density imposed by the LES method. Surface wind pressure is obtained for three extreme climate scenarios, including extreme windy weather, extremely cold weather, and design value for a 3000-year return period. The result indicates significant differences in surface wind pressure due to time layers coming from transient wind flow simulation. In order to assess the structural performance under the critical wind scenario, the highest value of surface pressure for each scenario is considered. Also, a hydrodynamic study is conducted on the bridge pillars, in which the river flow is simulated using the VOF method, and the water movement process around the pillars is examined transiently and at different times. The surface pressure applied by the river flow with the highest recorded volumetric flow is calculated on each of the pier surfaces. In simulating the river flow, information and weather conditions recorded in the past periods have been used. The results show that the surface pressure at the time when the river flow hit the pillars is much higher than in subsequent times. This amount of pressure can be used as a critical load in fluid-structure interaction (FSI) calculations. Finally, for both sections, the wind surface pressure, the velocity field with respect to auxiliary probe lines, the water circumferential motion contours around the pillars, and the pressure diagram on them are reported in different timesteps. © 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.",,"Climate change; Climate models; Cyber Physical System; Embedded systems; Fluid structure interaction; Rivers; Structural health monitoring; Velocity; Climate event; Environmental loads; Extreme climates; River flow; Structural assessments; Structural design assessments; Surface pressures; Surface winds; Wind flow; Wind pressures; Structural dynamics",,,,,,"The authors greatly appreciate the support of Dlubal Software for providing RWIND Simulation license, as well as Flow Sciences Inc. for providing FLOW-3D license.",,,"Jančula, M., Jošt, J., Gocál, J., Influence of aggressive environmental actions on bridge structures (2021) Transportation Research Procedia, 55, pp. 1229-1235; Wang, X., (2010) Analysis of climate change impacts on the deterioration of concrete infrastructure–synthesis Report, 643 (10364), p. 1. , Published by CSIRO, Canberra. ISBN978 0; Kemayou, B.T.M., (2016) Analysis of Bridge Deck Sections by Pseudo-compressibility method based on FDM and LES: Improving Performance through Implementation of Parallel Computing, , University of Arkansas; Larsen, A., Walther, J.H., Aeroelastic analysis of bridge girder sections based on discrete vortex simulations (1997) Journal of Wind Engineering and Industrial Aerodynamics, 67, pp. 253-265; Standard, B., (2006) Eurocode 1: Actions on structures; (2013) Minimum design loads for buildings and other structures, , ASCE. American Society of Civil Engineers; Nik, V.M., Making energy simulation easier for future climate–Synthesizing typical and extreme weather data sets out of regional climate models (RCMs) (2016) Applied Energy, 177, pp. 204-226; Perera, A., (2020) Quantifying the impacts of future climate variations and extreme climate events on energy systems; Nik, V.M., Application of typical and extreme weather data sets in the hygrothermal simulation of building components for future climate–A case study for a wooden frame wall (2017) Energy and Buildings, 154, pp. 30-45; Hosseini, M., Javanroodi, K., Nik, V.M., High-resolution impact assessment of climate change on building energy performance considering extreme weather events and microclimate–Investigating variations in indoor thermal comfort and degree-days (2021) Sustainable Cities and Society, p. 103634; Spalart, P.R., Detached-eddy simulation (2009) Annual review of fluid mechanics, 41, pp. 181-202; Spalart, P.R., A new version of detached-eddy simulation, resistant to ambiguous grid densities (2006) Theoretical and computational fluid dynamics, 20 (3), pp. 181-195; Spalart, P.R., Comments on the feasibility of LES for wings, and on a hybrid RANS/LES approach (1997) Proceedings of first AFOSR international conference on DNS/LES, , Greyden Press; Boudreau, M., Dumas, G., Veilleux, J.-C., Assessing the ability of the ddes turbulence modeling approach to simulate the wake of a bluff body (2017) Aerospace, 4 (3), p. 41; Wang, Y.-h., Analysis of water flow pressure on bridge piers considering the impact effect (2015) Mathematical Problems in Engineering, 2015; Qi, H., Zheng, J., Zhang, C., Numerical simulation of velocity field around two columns of tandem piers of the longitudinal bridge (2020) Fluids, 5 (1), p. 32; Herzog, S.D., Spring flood induced shifts in Fe speciation and fate at increased salinity (2019) Applied Geochemistry, 109, p. 104385",,"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-s2.0-85139223801
"Ai L., Bayat M., Comert G., Ziehl P.","57208900408;36561127900;24476244900;6602561216;","An Autonomous Bridge Load Rating Framework Using Digital Twin",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",,,,"796","804",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139210530&partnerID=40&md5=4c78a49808288385a0464e05be8b3f81","Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC  29201, United States; Department of Engineering, Benedict College, Columbia, SC, United States","Ai, L., Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC  29201, United States; Bayat, M., Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC  29201, United States; Comert, G., Department of Engineering, Benedict College, Columbia, SC, United States; Ziehl, P., Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC  29201, United States","Load rating of bridges is used to understand the working status and carrying capacity of bridge structures and components and is necessary to the safety of transportation. The current manual load rating procedure is, however, time-consuming. An intelligent and automatic load rating approach can be beneficial to supplement or eventually perhaps replace the current manual procedures. The innovation of this paper lies in developing an autonomous load rating framework by leveraging Digital Twin (DT) techniques. Full-scale laboratory testing of a bridge slab was conducted to verify the efficiency of the proposed framework. The ultimate moment capacity of the slab was obtained by carrying out four-point bending test. The testing procedure was monitored in real-time with multiple strain gauges. A real-scale finite element model of the slab was developed and calibrated with the testing results. The proposed DT framework of the bridge slabs was developed by integrating the numerical modeling and the strain monitoring. The proposed DT framework is intended for field application, and field results will be discussed. © 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.",,"Embedded systems; Structural health monitoring; 'current; Bridge load ratings; Bridge slabs; Bridge structures; Four-point bending test; Laboratory testing; Load ratings; Rating procedures; Twin techniques; Ultimate moment capacity; Cyber Physical System",,,,,,,,,"(2013) Report card on America’s infrastructure, , ASCE. Reston, VA; Islam, A. A., Jaroo, A.S., Li, F., F. Bridge load rating using dynamic response (2015) Journal of Performance of Constructed Facilities, 29 (4), p. 04014120; (2015) Manual for Bridge Evaluation, 2nd Ed. with 2016 Interim Revisions, , American Association of State and Highway Transportation Officials (AASHTO). American Association of State Highway and Transportation Officials,.""Washington, D.C; Alampalli, S., Frangopol, D.M., Grimson, J., Halling, M.W., Kosnik, D.E., Lantsoght, E.O., Yang, D., Zhou, Y.E., Bridge load testing: state-of-the-practice (2021) Journal of Bridge Engineering, 26 (3), p. 03120002; Zulifqar, A., Cabieses, M., Mikhail, A., Khan, N., (2014) Design of a bridge inspection system (BIS) to reduce time and cost, , George Mason University: Farifax, VA, USA; American Road & Transportation Builders Association (ARTBA); (2011) Manual for bridge evaluation, , AASHTO 2nd Ed., Washington, D.C., 2019; Lantsoght, E. O. L., van der Veen, C., Hordijk, D. A., de Boer, A., State-of-the-art on load testing of concrete bridges (2017) Eng. Struct, 150, pp. 231-241. , https://doi.org/10.1016/j.engstruct.2017.07.050; Fu, G., Pezze, F., Alampalli, S., Diagnostic load testing for bridge load rating (1997) Transp. Res. Rec, 1594, pp. 125-133. , https://doi.org/10.3141/1594-13; Hernandez, E. S., Myers, J. J., Diagnostic test for load rating of a prestressed SCC bridge (2018) ACI Spec. Publ, 323. , 11.1–11.16; Kim, Y. J., Tanovic, R., Wight, R. G., Recent advances in performance evaluation and flexural response of existing bridges (2009) J. Perform. Constr. Facil, 23 (3), pp. 190-200. , https://doi.org/10.1061/(ASCE)CF.1943-5509.0000007; Aguilar, C. V., Jáuregui, D. V., Newtson, C. M., Weldon, B. D., Cortez, T. M., Load rating a prestressed concrete double-tee beam bridge without plans by proof testing (2015) Transp. Res. Rec, (2522), pp. 90-99; Anay, R., Cortez, T. M., Jáuregui, D. V., ElBatanouny, M. K., Ziehl, P., On-site acoustic-emission monitoring for assessment of a prestressed concrete double-tee-beam bridge without plans (2016) J. Perform. Constr. Facil, 30 (4), p. 04015062. , https://doi.org/10.1061/(ASCE)CF.1943-5509.0000810; Casas, J. R., Gómez, J. D., Load rating of highway bridges by proof-loading (2013) KSCE J. Civ. Eng, 17 (3), pp. 556-567. , https://doi.org/10.1007/s12205-013-0007-8",,"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-s2.0-85139210530
"Meixedo A., Santos J., Ribeiro D., Calçada R., Todd M.","56940709200;36810314200;24476782300;7801603531;7202805915;","Data-driven approach for detection of structural changes using train-induced dynamic responses",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"441","448",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130716567&partnerID=40&md5=4fe01c82b6c65f2c94aad2466b276a29","CONSTRUCT-LESE, Department of Civil Eng., Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, Porto, 4200-465, Portugal; Laboratório Nacional de Engenharia Civil, LNEC, Avenidade do Brasil 101, Lisbon, 1700-075, Portugal; CONSTRUCT-LESE, Department of Civil Eng., School of Engineering, Polytechnic of Porto, R. Dr. António Bernardino de Almeida 431, Porto, 4200-072, Portugal; Department of Structural Engineering, University of California, La Jolla, San Diego, CA  92093-0085, United States","Meixedo, A., CONSTRUCT-LESE, Department of Civil Eng., Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, Porto, 4200-465, Portugal; Santos, J., Laboratório Nacional de Engenharia Civil, LNEC, Avenidade do Brasil 101, Lisbon, 1700-075, Portugal; Ribeiro, D., CONSTRUCT-LESE, Department of Civil Eng., School of Engineering, Polytechnic of Porto, R. Dr. António Bernardino de Almeida 431, Porto, 4200-072, Portugal; Calçada, R., CONSTRUCT-LESE, Department of Civil Eng., Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, Porto, 4200-465, Portugal; Todd, M., Department of Structural Engineering, University of California, La Jolla, San Diego, CA  92093-0085, United States","This work considers the detection of structural changes in railway bridge vibration response induced by train traffic using structural health monitoring systems. To achieve this goal, an innovative data-driven unsupervised methodology is proposed, consisting of a combination of time series analysis and advanced multivariate statistical techniques such as autoregressive models, multiple linear regression, and outlier analysis. The efficiency of the proposed methodology is verified on a complex bowstring-arch railway bridge. A digital twin of the bridge is used to simulate baseline and damage conditions by performing finite element time-history analysis using as input measurements of real temperatures, noise effects, and train speeds, and loads. The methodology proven to be highly robust to false detections and sensitive to early damage by automatically detecting small stiffness reductions in the concrete slab, diaphragms, and arches, as well as friction increase in the bearing devices. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Damage detection; Data-driven approach; Structural health monitoring; Unsupervised learning","Arch bridges; Arches; Damage detection; Linear regression; Multivariant analysis; Railroads; Time series analysis; Unsupervised learning; Autoregressive modelling; Bridge vibration; Data driven; Data-driven approach; Multivariate statistical techniques; Railway bridges; Structural health monitoring systems; Time-series analysis; Train traffic; Vibration response; Structural health monitoring",,,,,"Horizon 2020 Framework Programme, H2020; European Commission, EC; Fundação para a Ciência e a Tecnologia, FCT: SFRH/BD/93201/2013; Ministério da Ciência, Tecnologia e Ensino Superior, MCTES; Institute of Research and Development in Structures and Construction; Laboratório Nacional de Engenharia Civil, LNEC","This work was financially supported by the Portuguese Foundation for Science and Technology (FCT) through the PhD scholarship SFRH/BD/93201/2013. The authors would like to acknowledge the support of the R&D project RISEN - Rail Infrastructure Systems Engineering Network - financed by European Commission through the H2020|ES|MSC - H2020|Excellence Science|Marie Curie programme, the Portuguese Road and Railway Infrastructure Manager (Infraestruturas de Portugal, I.P), the Portuguese National Laboratory for Civil Engineering (LNEC), and the Base Funding - UIDB/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções - financed by national funds through the FCT/MCTES (PIDDAC).",,,"Malveiro, J, Sousa, C, Ribeiro, D, Calçada, R., Impact of track irregularities and damping on the fatigue damage of a railway bridge deck slab (2018) Structure and Infrastructure Engineering, 14 (9), pp. 1257-1268; Meixedo, A, Ribeiro, D, Calçada, R, Delgado, R., Global and Local Dynamic Effects on a Railway Viaduct with Precast Deck (2014) Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance, , Civil-Comp Press, Stirlingshire, UK; Carey, CH, O'Brien, EJ, Keenahan, J., Investigating the Use of Moving Force Identification Theory in Bridge Damage Detection (2013) Key Engineering Materials, 569-570, pp. 215-222. , (January 2016); Cavadas, F, Smith, IFC, Figueiras, J., Damage detection using data-driven methods applied to moving-load responses (2013) Mechanical Systems and Signal Processing, 39 (1-2), pp. 409-425; Nie, Z, Lin, J, Li, J, Hao, H, Ma, H., Bridge condition monitoring under moving loads using two sensor measurements (2019) Structural Health Monitoring, pp. 1-21; Gonzalez, I, Karoumi, R., BWIM aided damage detection in bridges using machine learning (2015) Journal of Civil Structural Health Monitoring, 5 (5), pp. 715-725; Neves, AC, González, I, Leander, J, Karoumi, R., Structural health monitoring of bridges: a model-free ANN-based approach to damage detection (2017) Journal of Civil Structural Health Monitoring, (7), pp. 689-702; Azim, R, Gül, M., Damage detection of steel girder railway bridges utilizing operational vibration response (2019) Structural Control and Health Monitoring, pp. 1-15. , (August); Ribeiro, D, Leite, J, Meixedo, A, Pinto, N, Calçada, R, Todd, M., Statistical methodologies for removing the operational effects from the dynamic responses of a high-rise telecommunications tower (2021) Structural Control and Health Monitoring; Meixedo, A, Ribeiro, D, Santos, J, Calçada, R, Todd, M., Progressive numerical model validation of a bowstring-arch railway bridge based on a structural health monitoring system (2021) Journal of Civil Structural Health Monitoring; 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) Engineering Structures, 40, pp. 413-435; Santos, J., (2014) Smart Structural Health Monitoring Techniques for Novelty Identification in Civil Engineering Structures, , PhD Thesis. Instituto Superior Técnico University of Lisbon; Farrar, CR, Worden, K., (2013) Structural Health Monitoring: a machine learning perspective, , Wiley; Bisgaard, S, Kulahci, M., (2011) Time series analysis and forecasting by example, , Hoboken, NJ: John Wiley & Sons; Johnson, RA, Wichern, DW., (2013) Applied Multivariate Statistical Analysis, , 6th ed. Harlow: Pearson; Santos, JP, Crémona, C, Calado, L, Silveira, P, Orcesi, AD., On-line unsupervised detection of early damage (2015) Structural Control and Health Monitoring",,,,"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-s2.0-85130716567
"Westcott B.J., Hag-Elsafi O., Mosaferchi G., Alampalli S.","56928077300;57204982897;57709792600;7003686588;","Lifting load restrictions on the NYS Fort Plain Bridge: A case study in SHM and the internet of things",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1135","1139",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130715606&partnerID=40&md5=26eaa86e741146ec7ebaa5804d66b85c","Intelligent Structures, Inc., Menlo Park, CA, United States; Structures Evaluation Bureau, New York State Department of Transportation, United States","Westcott, B.J., Intelligent Structures, Inc., Menlo Park, CA, United States; Hag-Elsafi, O., Structures Evaluation Bureau, New York State Department of Transportation, United States; Mosaferchi, G., Intelligent Structures, Inc., Menlo Park, CA, United States; Alampalli, S., Structures Evaluation Bureau, New York State Department of Transportation, United States","The global bridge asset inventory is in a state of deterioration and requires new methods of measuring the bridge condition performance state. Budget decisions often require more quantitative information than is provided by visual inspection alone. A Smart Bridge uses innovative digital monitoring, Digital Twin modeling, and analysis using measured performance on a cloud-based Internet of Things Platform (IoT). The Smart Bridge will obtain better information to supplement visual inspections for bridge decision analysis at a lower cost. Despite rehabilitation of the NYS Fort Plain bridge it remains load posted. NYS DOT used a Smart Bridge approach with load testing followed by IoT based performance monitoring and analysis to understand the structural capacity and live load demands on the structure. The results showed that the Ft. Plain bridge performance at or near its original design and lifting of the load restrictions. The structure is now monitored continuously to gain insights on its behavior and to determine the feasibility of the IoT based monitoring system for future structural monitoring. This paper summarizes this case study and the IoT structural monitoring system that was used for improved decision making for bridge management. The economics of Smart bridges and the impact on a fleet of bridges is presented showing a high ROI from performance monitoring. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.",,"Bridge approaches; Budget control; Decision making; Deterioration; Life cycle; Load testing; Structural health monitoring; Budget decisions; Case-studies; Condition; Digital monitoring; Load restrictions; Modelling and analysis; Performance States; Quantitative information; Smart bridge; Visual inspection; Internet of things",,,,,,,,,"Alampalli, S., Frangopol, D.M., Grimson, J., Kosnik, D., Halling, M., Lantsoght, E. O. L., Weidner, J. S., Zhou, Y.E., (2019) Primer on bridge load testing. Transportation Research Circular E-C257, , Washington, DC: Transportation Research Board; Alampalli, S., Frangopol, D.M., Grimson, J., Halling, M.W., Kosnik, D., Lantsoght, E.O.L., Yang, D.Y., Zhou, Y.E., Bridge Load Testing: State-of-the-Practice (2021) Journal of Bridge Engineering, ASCE, 26 (3); New York State Transportation Asset Management Plan June 2019 Marie Therese Dominguez, , https://www.dot.ny.gov/programs/capitalplan/repository/Final%20TAMP%20June%2028%202019.pdf, Commissioner; Heath, D. R., Richard, C., Benefits of Live Load Testing and Finite Element Modeling in Rating Bridges (2015) MassDOT Innovation and Tech Transfer Exchange, , Presented at Worcester, MA March 12; Mufti, A, Bakht, B, Horosko, A, Eden, R, A Case for Adding An Inspection level Related to SHM for Bridge Evaluation by CHBDC (2018) 10th International Conference on Short and Medium Span Bridges, , Quebec City, Quebec, Canada July 31; Hag-Elsafi, Osman, Kunin, Jonathan, (2006) Load testing for Bridge Rating dean's Mill Over Hannacrois Creek, , Special report 147 TRANSPORTATION RESEARCH AND DEVELOPMENT BUREAU New York State Department of Transportation February; Westcott, P., Azhari, F., THE ECONOMICS OF INTEGRATING INNOVATIVE MONITORING TECHNOLOGIES INTO BRIDGE MANAGEMENT POLICY TRB committee AHD35 Standing Committee on Bridge Management TRB 96th Annual Meeting Compendium of Papers Transportation Research Board 96th Annual Meeting Location, , Washington DC, United States Date: 2017-1-8 to 2017-1-12 Report/Paper Numbers: 17-04030",,,,"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-s2.0-85130715606
"Bhouri M.A.","57190835913;","Model-order-reduction approach for structural health monitoring of large deployed structures with localized operational excitations",2021,"Proceedings of the ASME Design Engineering Technical Conference","10",,"V010T10A022","","",,,"10.1115/DETC2021-70375","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119984148&doi=10.1115%2fDETC2021-70375&partnerID=40&md5=8c461fe932b7a2b2cfce4b5690affd54","Massachusetts Institute of Technology, Cambridge, MA, United States","Bhouri, M.A., Massachusetts Institute of Technology, Cambridge, MA, United States","We present a simulation-based classification approach for large deployed structures with localized operational excitations. The method extends the two-level Port-Reduced Reduced-Basis Component (PR-RBC) technique to provide faster solution estimation to the hyperbolic partial differential equation of time-domain elastodynamics with a moving load. Time-domain correlation function-based features are built in order to train classifiers such as Artificial Neural Networks and Support-Vector Machines and perform damage detection. The method is tested on a bridge-shaped structure with a moving vehicle (playing the role of a digital twin) in order to detect cracks' existence. Such problem has 45 parameters and shows the merits of the two-level PR-RBC approach and of the correlation function-based features in the context of operational excitations, other nuisance parameters and added noise. The quality of the classification task is enhanced by the sufficiently large synthetic training dataset and the accuracy of the numerical solutions, reaching test classification errors below 0.1% for disjoint training set of size 7x103 and test set of size 3x103. Effects of the numerical solutions accuracy and of the sensors locations on the classification errors are also studied, showing the robustness of the proposed approach and the importance of constructing a rich and accurate representation of possible healthy and unhealthy states of interest. Copyright © 2021 by ASME","Domain decomposition; Model order reduction; Neural networks; Parametrized partial differential equations; Simulation-based classification; Structural health monitoring","Classification (of information); Damage detection; Domain decomposition methods; Large dataset; Statistical tests; Structural health monitoring; Support vector machines; Time domain analysis; Base components; Correlation function; Domain decompositions; Localised; Model order reduction; Neural-networks; Numerical solution; Parametrized partial differential equations; Reduced basis; Simulation-based classification; Neural networks",,,,,"Office of Naval Research, ONR: N00014-17-1-2077; Army Research Office, ARO: W911NF1910098","This work was supported by the ONR Grant [N00014-17-1-2077] and by the ARO Grant [W911NF1910098]. We would like to thank Professor Anthony T. Patera, Dr. Tommaso Taddei and Professor Masayuki Yano for the helpful comments and software they provided us with.",,,"Khatir, S., Wahab, M., Fast simulations for solving fracture mechanics inverse problems using pod-rbf xiga and jaya algorithm (2019) Engineering Fracture Mechanics, 205, pp. 285-300; Farrar, C. R., Worden, K., (2013) Structural Health Monitoring: a Machine Learning Perspective, , 1st Edition, John Wiley & Sons, Ltd., Chichester, West Sussex, UK; Peeters, B., Maeck, J., DeRoeck, G., Vibration-based damage detection in civil engineering: excitation sources and temperature effects (2001) Smart Materials and Structures, 10 (3), pp. 518-527; 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, 7 (5), p. 510; Deraemaeker, A., Reynders, E., DeRoeck, G., Kullaa, J., Vibration-based structural health monitoring using output-only measurements under changing environment (2008) Mechanical Systems and Signal Processing, 22 (1), pp. 34-56; Au, S., Zhang, F.-L., Ni, Y.-C., Bayesian operational modal analysis: Theory, computation, practice (2013) Computers & Structures, 126, pp. 3-14; Zhang, L., Brincker, R., Andersen, P., An overview of operational modal analysis: Major development and issues (2005) Proceedings of the International Operational Modal Analysis Conference, pp. 26-27. , Copenhagen, Denmark; Gillich, G.-R., Furdui, H., Wahab, M., Korka, Z.-I., A robust damage detection method based on multi-modal analysis in variable temperature conditions (2019) Mechanical Systems and Signal Processing, 115, pp. 361-379; Taddei, T., Penn, J. D., Yano, M., Patera, A. T., Simulation-Based Classification; a Model-Order-Reduction Approach for Structural Health Monitoring (2018) Archives of Computational Methods in Engineering, 25 (1), pp. 23-45; Cruz, P., Salgado, R., Performance of Vibration-Based Damage Detection Methods in Bridges (2008) Computer-Aided Civil and Infrastructure Engineering, 24, pp. 62-79; Talebinejad, I., Fischer, C., Ansari, F., Numerical Evaluation of Vibration-Based Methods for Damage Assessment of Cable-Stayed Bridges (2011) Computer-Aided Civil and Infrastructure Engineering, 26, pp. 239-251; Fan, W., Qiao, P., Vibration-based Damage Identification Methods: A Review and Comparative Study (2011) Structural Health Monitoring, 10, pp. 83-111; Mei, Q., Gül, M., (2019) A Cost Effective Solution for Road Crack Inspection using Cameras and Deep Neural Networks, , arXiv:1907.06014v2 [cs.CV]; Zhang, X., Rajan, D., Story, B., Concrete crack detection using context-aware deep semantic segmentation network (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (11), pp. 951-971; Nayyeri, F., Hou, L., Zhou, J., Guan, H., Foreground-background separation technique for crack detection (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (6), pp. 457-470; Ni, F., Zhang, J., Chen, Z., Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (5), pp. 367-384; Cha, Y., Choi, W., Büyüköztürk, O., Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks (2018) Computer-Aided Civil and Infrastructure Engineering, 32 (5), pp. 361-378; Li, S., Zhao, X., Zhou, G., Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (7), pp. 616-634; Bang, S., Park, S., Kim, H., Kim, H., Encoder-decoder network for pixel-level road crack detection in black-box images (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (8), pp. 713-727; Rafiei, M., Adeli, H., A novel machine learning-based algorithm to detect damage in high-rise building structures (2017) Smart Materials and Structures, 26 (18); Zhang, M. Y., Schmidt, R., Markert, B., Structural damage detection methods-based on the correlation functions (2014) Proceedings of the 9th International Conference on Structural Dynamics, , EURODYN, Porto, Portugal; Yang, Z., Wang, L., Wang, H., Ding, Y., Dang, X., Damage Detection in Composite Structures Using Vibration Response under Stochastic Excitation (2009) Journal of Sound and Vibration, 325 (4), pp. 14-16; Huo, L.-S., Li, X., Yan, Y.-B., Li, H.-N., Damage Detection of Structures for Ambient Loading-Based on Cross-Correlation Function Amplitude and SVM (2016) Shock and Vibration, 2016, pp. 1-12; Yang, Z., Yu, Z., Sun, H., On the Cross-Correlation Function Amplitude Vector and its Application to Structural Damage Detection (2007) Mechanical Systems and Signal Processing, 21, pp. 2918-2932; Kunisch, K., Volkwein, S., Galerkin proper orthogonal decomposition methods for parabolic problem (2001) Numerische Mathematik, 90, pp. 117-148; Grepl, M. A., Maday, Y., Nguyen, N. C., Patera, A. T., Efficient reduced-basis treatment of nonaffine and nonlinear partial differential equations (2007) ESAIM Mathematical Modelling and Numerical Analysis, 41 (3), pp. 575-605; Bhouri, M. A., Patera, A. T., (2020) A two-level parameterized model-order reduction approach for time-domain elastodynamics, , arXiv:2002.11084v2 [math.NA, CS.NA]; Bhouri, M.A., (2020) A two-step port-reduced reduced-basis component method for time domain elastodynamic pde with application to structural health monitoring, , Ph.D. thesis, Massachusetts Institute of Technology, phD Thesis, Published; Huynh, D., Knezevic, D., Patera, A., A static condensation reduced basis element method: Approximation and a posteriori error estimation (2013) ESAIM Mathematical Modelling and Numerical Analysis, 47 (1), pp. 213-251; Huynh, D., Knezevic, D., Patera, A., A static condensation reduced basis element method: Complex problems (2013) Computer Methods in Applied Mechanics and Engineering, 259, pp. 197-216; Eftang, J. L., Patera, A., Port Reduction in Component-Based Static Condensation for Parametrized Problems: Approximation and a Posteriori Error Estimation (2013) International Journal for Numerical Methods in Engineering, 96 (5), pp. 269-302; Eftang, J. L., Patera, A., A port-reduced static condensation reduced basis element method for large component-synthesized structures: approximation and a posteriori error estimation (2014) Advanced Modeling and Simulation in Engineering Sciences, 1 (1), p. 3; Smetana, K., A new certification framework for the port reduced static condensation reduced basis element method (2015) Computer Methods in Applied Mechanics and Engineering, 283, pp. 352-383; Smetana, K., Patera, A. T., Optimal local approximation spaces for component-based static condensation procedures (2016) SIAM Journal on Scientific Computing, 38 (5), pp. A3318-A3356; Antoulas, A. C., Beattie, C. A., Gugercin, S., Interpolatory Model Reduction of Large-Scale Dynamical Systems (2010) Efficient Modeling and Control of Large-Scale Systems, pp. 3-58. , J. Mohammadpour, K. Grigoriadis (Eds), Springer, Boston, MA; Beattie, C., Gugercin, S., (2014) Model Reduction by Rational Interpolation, , arXiv:1409.2140v1[math.NA]; Yu, M., Wu, G., Kong, L., Tang, Y., Tire-Pavement Friction Characteristics with Elastic Properties of Asphalt Pavements (2017) Applied Sciences, 7, p. 1123; Yap, P., Truck tire types and road contact pressures (1989) Proceedings of the 2nd International Symposium on Heavy Vehicle Weights and Dimensions, , The Roads and Transport Association of Canada, Canada; Feng, M. Q., Lee, S. C., (2009) Determining the effective system damping of highway bridges, , Tech. Rep. CA-UCI-2009-001, California Department of Transportation, Sacramento, CA; Musiał, M., Grosel, J., Determining the young's modulus of concrete by measuring the eigenfrequencies of concrete and reinforced concrete beams (2016) Construction and Building Materials, 121, pp. 44-52. , Elsevier; Culmo, M. P., (2009) Connection Details for Prefabricated Bridge Elements and Systems, , Tech. Rep. FHWA-IF-09-010, Federal Highway Administration, Washington, DC; Barth, F., Frosch, J. R., Abou-Zeid, M., Allen, H. J., Barlow, J. P., Brander, M. E., Carlson, K., Zielinski, Z. A., (2001) Control of Cracking of Concrete Structures, , Tech. Rep. ACI 224R-01, American Concrete Institute, Farmington Hills, MI; Balakumaran, S. S. G., Weyers, R. E., Brown, M. C., (2018) Linear Cracking in Bridge Decks, , Tech. Rep. FHWA/VTRC18-R13, Virginia Transportation Research Council, Charlottesville, VA; Logan, A., Choi, W., Mirmiran, A., Rizkalla, S., Zia, P., Short-Term Mechanical Properties of High-Strength Concrete (2009) ACI Materials Journal, 106, pp. 413-418; Gindy, M., Nassif, H. H., Velde, J., Bridge Displacement Estimates from Measured Acceleration Records (2007) Transportation Research Record, 2028 (1), pp. 136-145; Jo, B. W., Lee, Y. S., Jo, J. H., Computer Vision-based Bridge Displacement Measurements using Rotation-Invariant Image Processing Technique (2018) Sustainability, 10 (6), pp. 1-16. , K. R. M. A","Bhouri, M.A.; Massachusetts Institute of TechnologyUnited States",,"Computers and Information in Engineering Division;Design Engineering Division","American Society of Mechanical Engineers (ASME)","33rd Conference on Mechanical Vibration and Sound, VIB 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021","17 August 2021 through 19 August 2021",,174204,,9780791885475,,,"English","Proc. ASME Des. Eng. Tech. Conf.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85119984148
"Shu J., Zandi K., Zhao W.","55654267000;57192681171;7403942725;","Automated generation of FE mesh of concrete structures from 3D point cloud using computer vision technology",2021,"Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations - Proceedings of the 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020",,,,"3300","3303",,,"10.1201/9780429279119-448","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117603784&doi=10.1201%2f9780429279119-448&partnerID=40&md5=af700aaf4f9ffedaace9675f389dcfef","Department of Civil Engineering and Architecture, Zhejiang University, China; Department of Architecture and Civil Engineering, Chalmers University of Technology, Sweden","Shu, J., Department of Civil Engineering and Architecture, Zhejiang University, China; Zandi, K., Department of Architecture and Civil Engineering, Chalmers University of Technology, Sweden; Zhao, W., Department of Civil Engineering and Architecture, Zhejiang University, China","To achieve real-time structural health monitoring (SHM), a concept of digital twin - a digital copy of a structure has been brought up and investigated. It provides an up-to-date virtual model of structures, with the integration of physical as well as data information. The goal of this research is to provide faster and more accurate procedures to capture the spatial information required by a digital twin of a concrete structure using 3D point cloud data. Given that the method is intended for real-scale structures, such as bridges, the work can be divided to 3 steps: (1) to segment and extract geometric information for structural components; (2) to convert the geometry information to FE mesh with consideration of element types; (3) to assign material property as well as boundary conditions based on extracted components type. Linear FE analyses have been carried out to evaluate the structural performance based on the FE model created from the point cloud. The automation of such a process is an essential part of the creation of a digital twin of infrastructures. © 2021 Taylor & Francis Group, London",,"Computer vision; Concrete buildings; Concrete construction; Concretes; Structural health monitoring; 3D point cloud; Automated generation; Computer vision technology; Data informations; Digital copy; Physical information; Point cloud data; Real- time; Spatial informations; Virtual models; Mesh generation",,,,,"Stanford University, SU; H2020 Marie Skłodowska-Curie Actions, MSCA: 754412","The research was conducted during the visiting time in Structures and Composites Laboratory (SACL) at Stanford University. The authors acknowledge the support from Marie Skłodowska-Curie Actions (Grant Agreement Nr. 754412) and also thank Dr. Cruz Carlos in Concrete Lab Manager at The University of California, Berkeley, Prof. Hana Mori Böttger and Mohammad Shooshtarian at The University of San Francisco for providing the cracked specimens to collect point cloud data and experimental data.",,,"Pauly, M., Point primitives for interactive modeling and processing of 3D geometry (2003) Hartung-Gorre, (15134), pp. 1-168. , https://doi.org/10.3929/ethz-a-004612876; Söderkvist, I., (2009) Using SVD for some fitting problems, (2), pp. 2-5. , https://www.ltu.se/cms_fs/1.51590!/svd-fitting.pdf, Retrieved from; (2019) Diana Finite Element Analysis, , TNO DIANA BV. TNO DIANA BV; Zhou, Q.-Y., Park, J., Koltun, V., (2018) Open3D: A Modern Library for 3D Data Processing, , http://arxiv.org/abs/1801.09847, ArXiv: 1801.09847. Retrieved from",,"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-s2.0-85117603784
"Shao S., Deng G., Zhou Z.","57215084079;57201408119;15830628600;","Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor",2021,"Lecture Notes in Civil Engineering","128",,,"3","13",,,"10.1007/978-3-030-64908-1_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102276416&doi=10.1007%2f978-3-030-64908-1_1&partnerID=40&md5=68acbcfe0f191dcc52bd95831ef40ec8","College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China; College of Civil Engineering, Chongqing Jiaotong University, Chongqing, China; State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing, China","Shao, S., College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China, College of Civil Engineering, Chongqing Jiaotong University, Chongqing, China, State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing, China; Deng, G., College of Civil Engineering, Chongqing Jiaotong University, Chongqing, China, State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing, China; Zhou, Z., College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China","Full-field noncontact structural geometry morphology monitoring can be used to achieve a breakthrough in the fields of structural safety monitoring and digital twins owing to its advantages of economy, credibility, high frequency, and holography. Moreover, such type of monitoring can improve the precision and efficiency of the structural health monitoring technology and theory of large-scale structures. This study validated the performance of a proposed holographic visual sensor and algorithms in computer vision-based, full-field, noncontact displacement and vibration measurement. On the basis of the temporal and spatial characteristics of the measured series data, denoising, and the disturbance-rejection algorithm, the microscopy algorithm of subpixel motion and the extracting algorithm of motion information were respectively constructed for weak high-order displacement components and the holographic measurement of high-quality geometric morphology. Moreover, an intelligent perception method optimized for holographic-geometric and operational-modal shapes were used to extract morphological features from a series of holographic transient responses under excitation. Experimental results showed that the holographic visual sensor and the proposed algorithms can extract an accurate holographic displacement signal and factually and sensitively accomplish vibration measurement while accurately reflecting the actual change in structural properties under various damage/action conditions. The accuracy and efficiency of the system in the structural geometry monitoring for dense full-field displacement measurement and smooth operational modal shape photogrammetry were confirmed in the experiments. The proposed method could serve as a foundation for further research on digital twins for large-scale structures, structural condition assessment, and intelligent damage identification methods. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Dense full-field displacement measurement; Digital twins; Holographic visual sensor; Operational modal shapes photogrammetry; Structural geometry morphology monitoring; System identification","Damage detection; Digital twin; Displacement measurement; Disturbance rejection; Efficiency; Geometry; Holography; Monitoring; Morphology; Transient analysis; Vibration measurement; Displacement components; Disturbance rejection algorithm; Holographic measurement; Intelligent perception; Large scale structures; Morphological features; Structural health monitoring technology; Structural safety monitoring; Structural health monitoring",,,,,,,,,"Feng, D.M., Feng, M.Q., Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection-a review (2018) Eng. Struct., 156, pp. 105-117; Review on China’s bridge engineering research: 2014. China (2014) J. Highw. Transp, 27 (5), pp. 1-96; Shao, S., Zhou, Z.X., Deng, G.J., Wang, S.R., Experiment of structural morphology monitoring for bridges based on non-contact remote intelligent perception method (2019) China J. Highw. Transp., 32 (11), pp. 91-102; Sun, L.M., Shang, Z.Q., Xia, Y., Development and prospect of bridge structural health monitoring in the context of big data (2019) China J. Highw. Transp., 32 (11), pp. 1-20; Ye, X.W., Dong, C.Z., Review of computer vision-based structural displacement monitoring (2019) China J. Highw. Transp., 32 (11), pp. 20-39; Bao, Y.Q., Li, H., Ou, J.P., Emerging data technology in structural health monitoring: Compressive sensing technology (2012) J. Civ. Struct. Health Monit., 4 (2), pp. 77-90; Bao, Y.Q., Yu, Y., Li, H., Mao, X.Q., Jiao, W.F., Zou, Z.L., Ou, J.P., Compressive sensing based lost data recovery of fast-moving wireless sensing for structural health monitoring (2015) Struct. Control Health Monit., 22 (3), pp. 433-448; Javh, J., Slavič, J., Boltežar, M., The subpixel resolution of optical-flow-based modal analysis (2017) Mech. Syst. Signal Process., 88, pp. 89-99; Guo, J., Zhu, C.A., Dynamic displacement measurement of large-scale structures based on the Lucas-Kanade template tracking algorithm (2016) Mech. Syst. Signal Process., 66, pp. 425-436; Yang, Y.C., 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. Signal Process., 85, pp. 567-590; Xu, Y., Brownjohn, J., Kong, D.L., A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge (2018) Struct. Control Health Monit., 25 (5); Cha, Y.J., Chen, J.G., Büyüköztürk, O., Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters (2017) Eng. Struct., 132, pp. 300-313; Feng, D.M., Feng, M.Q., Model updating of railway bridge using in situ dynamic displacement measurement under trainloads (2015) J. Bridge Eng., 20; Cha, Y.J., Trocha, P., Büyüköztürk, O., Field measurement-based system identification and dynamic response prediction of a unique MIT building (2016) Sensors, 16, p. 1016; Mei, Q.P., 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; Shao, S., Zhou, Z.X., Deng, G.J., Du, P., Jian, C.Y., Yu, Z.Y., Experiment of structural geometric morphology monitoring for bridges using holographic visual sensor (2020) Sensors, 20, p. 1187","Zhou, Z.; College of Civil and Transportation Engineering, China; email: zhixiangzhou@szu.edu.cn","Rizzo P.Milazzo A.",,"Springer Science and Business Media Deutschland GmbH","10th European Workshop on Structural Health Monitoring, EWSHM 2020","1 July 2022 through 1 July 2022",,255199,23662557,9783030649074,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85102276416
"Ye C., Middleton C., Kuok S.-C., Butler L.","57216481422;7005340597;36015370900;55795448200;","Challenges of implementing bridge model updating in industry practice",2020,"IABSE Congress, Christchurch 2020: Resilient Technologies for Sustainable Infrastructure - Proceedings",,,,"464","470",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104791751&partnerID=40&md5=b803a6eb674a8c376bfdcc3006494a81","University of Cambridge, Cambridge, United Kingdom; University of Macau, Macau; York University, Toronto, Canada","Ye, C., University of Cambridge, Cambridge, United Kingdom; Middleton, C., University of Cambridge, Cambridge, United Kingdom; Kuok, S.-C., University of Macau, Macau; Butler, L., York University, Toronto, Canada","Model updating aims to update an analysis model (e.g. a finite element model) of an engineering structure in order to closely represent the true condition and performance of the physical structure. Model updating of bridges has been an active research field for more than two decades, yet the confidence and practical usefulness of bridge model updating results may be subject to questioning. While model updating may have worked well for many other engineering applications, it has found to be challenging and problematic to implement such practice on bridge structures. More recently, there has been a vision of developing bridge digital twins which can automatically update the model in near real time as new monitoring data become available. This paper aims to elaborate on the critical issues that have not been addressed properly to enable real-world implementation of bridge model updating. A series of industry facing semi-structured interviews have been conducted with 19 bridge professionals (owners, operators and consultants) to aid in investigating the technical and practical challenges of implementing bridge model updating in practice. It is envisioned that the outcomes of this paper will inform future research regarding model updating and digital twin development for bridge applications. © 2020 IABSE Congress, Christchurch 2020: Resilient Technologies for Sustainable Infrastructure - Proceedings. All rights reserved.","Bridge; Implementation; Model updating; Structural health monitoring","Digital twin; Bridge applications; Bridge structures; Engineering applications; Engineering structures; Industry practices; Physical structures; Real-world implementation; Semi structured interviews; Bridges",,,,,"Engineering and Physical Sciences Research Council, EPSRC","The authors would like to thank the 19 bridge professionals for participating in the industry facing interviews. The first author would also like to thank the EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment for providing travel fund for the interviews.",,,"Glisic, B., (2015) Branko Glisic Home Page, , http://glisicstructuralhealthmonitoring.princeton.edu/, [Internet]. [cited 2020 Jan 2]; Webb, G.T., Vardanega, P.J., Middleton, C.R., Categories of SHM Deployments: Technologies and Capabilities (2014) ASCE Journal of Bridge Engineering, 20 (11), p. 04014118; Webb, G.T., (2014) Structural Health Monitoring of Bridges, , University of Cambridge; Aghagholizadeh, M., Catbas, F.N., A Review of Model Updating Methods for Civil Infrastructure Systems (2015) Computational Techniques for Civil and Structural Engineering, pp. 83-99. , Stirlingshire, UK: Saxe-Coburg Publications; Simoen, E., De Roeck, G., Lombaert, G., Dealing with uncertainty in model updating for damage assessment: A review (2015) Mechanical Systems and Signal Processing, 56-57, pp. 123-149; Theofanous, M., Gardner, L., Testing and numerical modelling of lean duplex stainless steel hollow section columns (2009) Engineering Structures, 31 (12), pp. 3047-3058; Xu, J., Butler, L.J., Elshafie, M.Z.E.B., Experimental and numerical investigation of the performance of self-sensing concrete sleepers (2020) Structural Health Monitoring, 19 (1), pp. 66-85; Bentz, E.C., Hoult, N.A., Bridge model updating using distributed sensor data (2017) Proceedings of the Institution of Civil Engineers - Bridge Engineering, 170 (1), pp. 74-86; Daniell, W.E., Macdonald, J.H.G., Improved finite element modelling of a cable-stayed bridge through systematic manual tuning (2007) Engineering Structures, 29 (3), pp. 358-371; 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; Lea, F.C., Middleton, C.R., (2002) Reliability of Visual Inspection of Highway Bridges, , University of Cambridge; Moore, M., Phares, B., Graybeal, B., Rolander, D., Washer, G., (2001) Reliability of Visual Inspection for Highway Bridges, Volume I: Final Report, , Federal Highway Administration; (2019) CS 454 Assessment of Highway Bridges and Structures, , Highways England; (2018) UK Network Rail Standards, , Network Rail","Ye, C.; University of CambridgeUnited Kingdom; email: cy273@cam.ac.uk","Abu A.","Arup;Aurecon;Granor Rubber and Engineering;Sika;TJAD;WSP","International Association for Bridge and Structural Engineering (IABSE)","IABSE Congress Christchurch 2020: Resilient Technologies for Sustainable Infrastructure","3 February 2021 through 5 February 2021",,168364,,9783857481703,,,"English","IABSE Congress, Christchurch: Resilient Technol. Sustain. Infrastr. - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85104791751