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,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
"Jiang F., Ding Y., Song Y., Geng F., Wang Z.","57204694266;55768944900;55494118800;36637279300;36723167900;","Digital Twin-driven framework for fatigue life prediction of steel bridges using a probabilistic multiscale model: Application to segmental orthotropic steel deck specimen",2021,"Engineering Structures","241",,"112461","","",,18,"10.1016/j.engstruct.2021.112461","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105352890&doi=10.1016%2fj.engstruct.2021.112461&partnerID=40&md5=368175e567688c9bcbb3720458bb938c","Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing, 210096, China; School of Architecture Engineering, Jinling Institute of Technology, Nanjing, 211169, China; School of Architecture Engineering, Nanjing Institute of Technology, Nanjing, 211167, China; Shenzhen Express Engineering Consulting Co. Ltd, Shenzhen, 518000, China","Jiang, F., Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing, 210096, China; Ding, Y., Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing, 210096, China; Song, Y., School of Architecture Engineering, Jinling Institute of Technology, Nanjing, 211169, China; Geng, F., School of Architecture Engineering, Nanjing Institute of Technology, Nanjing, 211167, China; Wang, Z., Shenzhen Express Engineering Consulting Co. Ltd, Shenzhen, 518000, China","Accurate fatigue life prediction facilitates the fatigue maintenance of steel bridges. Since Digital Twin can simulate the lifecycle for physical objects at various scales, this study aims to provide a Digital Twin-driven framework for non-deterministic fatigue life prediction of steel bridges. A probabilistic multiscale model was developed to depict the fatigue evolution throughout the bridge lifecycle. The small crack initiation period was well described by the modified Fine and Bhat model considering microstructure uncertainties. After obtaining the critical model parameter via crystal plastic finite element simulation, the modified model was further calibrated using the assumed historical fatigue data in Digital Twin database. Based on the initiated half-penny-shaped small crack, the small crack initiation period was connected to the macrocrack extension period. Given the uncertainties of macrocrack propagation, the Paris’ law with random growth parameters was adopted. The Bayesian inference of the growth parameters realized the real-time calibration of the macrocrack growth model using Markov chain Monte Carlo simulation. The feasibility of the proposed framework was demonstrated through fatigue tests on a segmental steel deck specimen with mixed-mode deformed U-rib to diaphragm welded joints. The results show that the predicted fatigue initiation life and residual fatigue life are in good agreement with the experimentally observed life results. In summary, the proposed framework enhances our understanding of the fatigue evolution mechanism throughout the bridge lifecycle and provides an entirely new approach to accurately predict the fatigue life of steel bridges under various sources of uncertainties. © 2021 Elsevier Ltd","Crack initiation; Crack propagation; Digital Twin; Fatigue life prediction; Multiscale model; Steel bridges","Bayesian networks; Crack initiation; Crack propagation; Cracks; Fatigue of materials; Fatigue testing; Inference engines; Intelligent systems; Life cycle; Markov processes; Monte Carlo methods; Steel bridges; Welding; Welds; Cracks initiations; Cracks propagation; Fatigue life prediction; Growth parameters; Macro-cracks; Model application; Multiscale models; Probabilistics; Small crack; Uncertainty; Forecasting; bridge; crack propagation; fatigue; finite element method; growth modeling; microstructure; steel structure",,,,,"BK20190013; National Natural Science Foundation of China, NSFC: 51978154","This work was supported by the Distinguished Young Scientists of Jiangsu Province [grant number BK20190013] and the National Natural Science Foundation of China [grant number 51978154].",,"Lin, W., Yoda, T., Steel bridges (2017) Bridge engineering, pp. 111-136. , Elsevier Cambridge; Maier, P., Kuhlmann, U., Popa, N., Willms, R., Optimizing bridge design by improved deterioration models through fatigue tests (2012) Bridge maintenance, safety, management, resilience and sustainability, pp. 1816-1821. , F. Biondini D.M. Frangopol Taylor & Francis Group London; Lukić, M., Al-Emrani, M., Aygül, M., Bokesjö, M., Urushadze, S., Frýba, L., Bridge fatigue guidance: meeting sustainable design and assessment (2013), Publications Office of the European Union Luxembourg; Grieves, M., Vickers, J., Digital Twin: mitigating unpredictable, undesirable emergent behavior in complex systems (2017) Transdisciplinary perspectives on complex systems, pp. 85-113. , F. Kahlen S. Flumerfelt A. Alves Springer International Publishing Cham; Leser, P.E., Warner, J.E., Leser, W.P., Bomarito, G.F., Newman, J.A., Hochhalter, J.D., A digital twin feasibility study (Part II): non-deterministic predictions of fatigue life using in-situ diagnostics and prognostics (2020) Eng Fract Mech, 229; Božić, Ž., Schmauder, S., Mlikota, M., Hummel, M., Multiscale fatigue crack growth modelling for welded stiffened panels (2014) Fatigue Fract Eng Mater Struct, 37, pp. 1043-1054; Tanaka, K., Mura, T., A dislocation model for fatigue crack initiation (1981) J Appl Mech, 48, pp. 97-103; Mura, T., Nakasone, Y., A theory of fatigue crack initiation in solids (1990) J Appl Mech, 57, pp. 1-6; Lin, M.R., Fine, M.E., Mura, T., Fatigue crack initiation on slip bands: theory and experiment (1986) Acta Metall, 34, pp. 619-628; Fine, M.E., Bhat, S.P., A model of fatigue crack nucleation in single crystal iron and copper (2007) Mater Sci Eng, A, 468-470, pp. 64-69; Voothaluru, R., Richard, L.C., Determination of lattice level energy efficiency for fatigue crack initiation (2013) Fatigue Fract Eng Mater Struct, 36, pp. 670-678; Li, L., Shen, L., Proust, G., Fatigue crack initiation life prediction for aluminium alloy 7075 using crystal plasticity finite element simulations (2015) Mech Mater, 81, pp. 84-93; Yeratapally, S.R., Leser, P.E., Hochhalter, J.D., Leser, W.P., Ruggles, T.J., A digital twin feasibility study (Part I): non-deterministic predictions of fatigue life in aluminum alloy 7075–T651 using a microstructure-based multi-scale model (2020) Eng Fract Mech, 228; Zhao, Z., Haldar, A., Breen, F.L., Fatigue-reliability evaluation of steel bridges (1994) J Struct Eng, 120, pp. 1608-1623; Nagy, W., van Bogaert, P., de Backer, H., LEFM based fatigue design for welded connections in orthotropic steel bridge decks (2015) Procedia Eng, 133, pp. 758-769; Wang, B., Nagy, W., De Backer, H., Chen, A., Fatigue process of rib-to-deck welded joints of orthotropic steel decks (2019) Theor Appl Fract Mech, 101, pp. 113-126; Glaessgen, E., Stargel, D., The Digital Twin paradigm for future NASA and U.S. air force vehicles (2012), pp. 1-14. , American Institute of Aeronautics and Astronautics Honolulu (HI). Reston (VA); Frangopol, D.M., Kim, S., Life-cycle of structures under uncertainty: emphasis on fatigue-sensitive civil and marine structures (2019), CRC Press Boca Raton; Cheng, B., Cao, X., Ye, X., Cao, Y., Fatigue tests of welded connections between longitudinal stringer and deck plate in railway bridge orthotropic steel decks (2017) Eng Struct, 153, pp. 32-42; Luo, P., Zhang, Q., Bao, Y., Bu, Y., Fatigue performance of welded joint between thickened-edge U-rib and deck in orthotropic steel deck (2019) Eng Struct, 181, pp. 699-710; Huang, Y., Zhang, Q., Bao, Y., Bu, Y., Fatigue assessment of longitudinal rib-to-crossbeam welded joints in orthotropic steel bridge decks (2019) J Constr Steel Res, 159, pp. 53-66; Zhang, Q., Bu, Y., Li, Q., Review on fatigue problems of orthotropic steel bridge deck (2017) China J Highw Transp, 30, pp. 14-30. , (in Chinese); (2003), CEN. EN 1991-2: EUROCODE 1: Actions on structures - Part 2: Traffic loads on bridges; Bhat, S.P., Fine, M.E., Fatigue crack nucleation in iron and a high strength low alloy steel (2001) Mater Sci Eng, A, 314, pp. 90-96; Schönecker, S., Li, X., Johansson, B., Kwon, S.K., Vitos, L., Thermal surface free energy and stress of iron (2015) Sci Rep, 5, p. 14860; Aqra, F., Ayyad, A., Surface energies of metals in both liquid and solid states (2011) Appl Surf Sci, 257, pp. 6372-6379; Zhang, K.-S., Ju, J.W., Li, Z., Bai, Y.-L., Brocks, W., Micromechanics based fatigue life prediction of a polycrystalline metal applying crystal plasticity (2015) Mech Mater, 85, pp. 16-37; Smith, M., ABAQUS/standard user's manual, version 6.9. Providence (2009) Dassault Systèmes Simulia Corp; Wu, G.C., Li, Y.F., Pan, X.D., Wang, G.L., Numerical simulation of fatigue damage and shape instability behavior of steel 40Cr by the damage-coupled crystal plastic model (2017) Strength Mater, 49, pp. 118-124; Wang, J., Jiang, W., Numerical assessment on fatigue damage evolution of materials at crack tip of CT specimen based on CPFEM (2020) Theor Appl Fract Mech, 109; Zhang, X., Wu, Y., Tang, L., Liu, Z., Jiang, Z., Liu, Y., Modeling and computing parameters of three-dimensional Voronoi models in nonlinear finite element simulation of closed-cell metallic foams (2018) Mech Adv Mater Struct, 25, pp. 1265-1275; Zhang, X., Wang, Y., Yang, J., Qiao, Z., Ren, C., Chen, C., Deformation analysis of ferrite/pearlite banded structure under uniaxial tension using digital image correlation (2016) Opt Lasers Eng, 85, pp. 24-28; Taylor, G.I., Plastic strain in metals (1938) J Inst Met, 62, pp. 307-324; Asaro, R.J., (1983) Crystal plasticity. J Appl Mech, 50, pp. 921-934; Hutchinson, J.W., Bounds and self-consistent estimates for creep of polycrystalline materials (1976) Proc R Soc London A Math Phys Sci, 348, pp. 101-127; Bassani, J.L., Wu, T., Latent hardening in single crystals II: analytical characterization and predictions (1991) Proc R Soc London Ser A Math Phys Sci, 435, pp. 21-41; (2013), p. 815. , Inverse transform method. In: Gass SI, Fu MC, editors. Encyclopedia of operations research and management science. Boston: Springer; Schijve, J., Fatigue of structures and materials (2009), Springer Dordrecht; Paris, P., Erdogan, F., A critical analysis of crack propagation laws (1963) J Basic Eng, 85, pp. 528-533; (2005), BSI. BS 7910:2005: Guide to methods for assessing the acceptability of flaws in metallic structures; Erdogan, F., Sih, G.C., On the crack extension in plates under plane loading and transverse shear (1963) J Basic Eng, 85, pp. 519-525; Atkinson, K.E., An introduction to numerical analysis (1989), 2nd ed. John Wiley & Sons New York","Ding, Y.; Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, China; email: civilding@seu.edu.cn",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","",Scopus,2-s2.0-85105352890
"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
"Blomfors M., Lundgren K., Zandi K.","57156650200;7005462844;57192681171;","Incorporation of pre-existing longitudinal cracks in finite element analyses of corroded reinforced concrete beams failing in anchorage",2020,"Structure and Infrastructure Engineering",,,,"1","17",,6,"10.1080/15732479.2020.1782444","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087878859&doi=10.1080%2f15732479.2020.1782444&partnerID=40&md5=e4d641f85b10506ccd8d896bd6aae922","Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden","Blomfors, M., Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden; Lundgren, K., Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden; Zandi, K., Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden","Transportation infrastructure is of fundamental importance and must be regularly assessed to ensure its safety and serviceability. The assessment of ageing reinforced concrete bridge stock may need to consider corrosion and cracks, as the likelihood of deterioration increases with age. This work accordingly investigates the incorporation of pre-existing anchorage zone corrosion cracks into the finite element modelling of reinforced concrete beam structural behaviour. Three methods of accounting for cracks were applied: (1) modifying the bond stress–slip relation, (2) weakening elements at the position of the crack, and (3) weakened discrete crack elements. The results show that modifying the bond stress–slip relation results in accurate predictions of the ultimate capacity when one-dimensional reinforcement bars are used in the model. Weakening elements at the position of the crack provides reasonable results when the anchorage is modelled with three-dimensional reinforcement bars and a frictional bond model. The implementation of discrete cracks was found to be unsuitable for the studied load situation, as compressive stresses formed perpendicular to the crack. It was concluded that the capacity of the studied case could be well estimated based on visual measurements, without knowledge of the exact corrosion level. © 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","Concrete–steel bond slip; digital twin modelling; nonlinear finite element analysis; pre-existing crack modelling; reinforced concrete; reinforcement anchorage zone","Anchorage zones; Anchorages (foundations); Concrete beams and girders; Concrete construction; Corrosion; Deterioration; Finite element method; Rebar; Accurate prediction; Corroded reinforced concrete beams; Finite element modelling; Reinforced concrete beams; Structural behaviour; Three-dimensional reinforcement; Transportation infrastructures; Visual measurements; Reinforced concrete",,,,,"Svenska Forskningsrådet Formas: 2017-01668","The work was supported by FORMAS under Grant number 2017-01668. The FE analyses were performed on resources provided by Chalmers Centre for Computational Science and Engineering (C3SE).",,"Arneth, A., Barbosa, H., Benton, T., Calvin, K., Calvo, E., Connors, S., Zommers, Z., (2019) Climate change and land: Summary for policymakers. an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, , …; Berdica, K., An introduction to road vulnerability: What has been done, is done and should be done (2002) Transport Policy, 9, pp. 117-127; Biondini, F., Vergani, M., Deteriorating beam finite element for nonlinear analysis of concrete structures under corrosion (2015) Structure and Infrastructure Engineering, 11, pp. 519-532; Blomfors, M., Zandi, K., Lundgren, K., Coronelli, D., Engineering bond model for corroded reinforcement (2018) Engineering Structures, 156, pp. 394-410; Bradley, D., Hehenberger, P., (2016) Mechatronic futures: Challenges and solutions for mechatronic systems and their designers, , Springer International Publishing, &, Switzerland; Cavaco, E.S., Neves, L.A.C., Casas, J.R., On the robustness to corrosion in the life cycle assessment of an existing reinforced concrete bridge (2018) Structure and Infrastructure Engineering, 14, pp. 137-150; Christidis, P., Leduc, G., Longer and heavier vehicles for freight transport (2009) JRC Scientific and Technical Reports, 40. , EUR 23933; Cornelissen, H.A.W., Hordijk, D.A., Reinhardt, H.W., Experimental determination of crack softening characteristics of normalweight and lightweight (1986) Heron, 32, pp. 45-56; Coronelli, D., Gambarova, P., Structural assessment of corroded reinforced concrete beams: Modeling guidelines (2004) Journal of Structural Engineering, 130 (8), pp. 1214-1224; (2017) Fe-Software DIANA 10, p. 2. , Delft, The Netherlands; Feenstra, P.H., (1993) Computational aspects of biaxial stress in plain and reinforced concrete, , Delft University of Technology; (2013) Model Code 2010. FIB model code for concrete structures 2010, , Lausanne, Switzerland; Gálvez, J.C., Červenka, J., Cendón, D.A., Saouma, V., A discrete crack approach to normal/shear cracking of concrete (2002) Cement and Concrete Research, 32, pp. 1567-1585; Hanjari, Z., Kettil, P., Lundgren, K., Analysis of mechanical behavior of corroded reinforced concrete structures (2012) ACI Structural Journal, 108, pp. 532-541; Hendriks, M.A.N., de Boer, A., Belletti, B., Guidelines for Nonlinear Finite Element Analysis of Concrete Structures (2017) Rijkswaterstaat Centre for Infrastructure, , Report RTD:1016-1:2017; Jansson, A., Lofgren, I., Lundgren, K., Gylltoft, K., Bond of reinforcement in self-compacting steel-fibre-reinforced concrete (2012) Magazine of Concrete Research, 64, pp. 617-630; Jiradilok, P., Nagai, K., Matsumoto, K., Meso-scale modeling of non-uniformly corroded reinforced concrete using 3D discrete analysis (2019) Engineering Structures, 197, p. 109378; Jiradilok, P., Wang, Y., Nagai, K., Matsumoto, K., Development of discrete meso-scale bond model for corrosion damage at steel-concrete interface based on tests with/without concrete damage (2020) Construction and Building Materials, 236, p. 117615; Lundgren, K., Bond between ribbed bars and concrete (2005) Magazine of Concrete Research, 57, pp. 371-382; Lundgren, K., Kettil, P., Zandi Hanjari, K., Schlune, H., Roman, A.S.S., Analytical model for the bond-slip behaviour of corroded ribbed reinforcement (2012) Structure and Infrastructure Engineering, 8, pp. 157-169; Malm, R., Holmgren, J., Cracking in deep beams owing to shear loading (2008) Magazine of Concrete Research, 60, pp. 381-388; Muttoni, A., Fernández Ruiz, M., The levels-of-approximation approach in MC 2010: Application to punching shear provisions (2012) Structural Concrete, 13 (1), pp. 32-41; Nasr, A., Björnsson, I., Honfi, D., Larsson Ivanov, O., Johansson, J., Kjellström, E., A review of the potential impacts of climate change on the safety and performance of bridges (2019) Sustainable and Resilient Infrastructure, pp. 1-21; Ng, P.L., Ma, F.J., Kwan, A.K.H., Crack analysis of reinforced concrete members with and without crack queuing algorithm (2019) Structural Engineering and Mechanics, 70 (1), pp. 43-54; Rots, J.G., (1988) Computational modelling of concrete fracture, , Delft University; Rots, J.G., Blaauwendraad, J., Crack models for concrete: Discrete or smeared? Fixed Multi-Directional or Rotating? (1989) Heron, 34 (1), pp. 3-59; Saether, I., Bond deterioration of corroded steel bars in concrete (2011) Structure and Infrastructure Engineering, 7, pp. 415-429; Shu, J., Shear assessment of a reinforced concrete bridge deck slab according to level-of-approximation approach (2018) Structural Concrete, 19, pp. 1838-1850; Tahershamsi, M., Fernandez, I., Zandi, K., Lundgren, K., Four levels to assess anchorage capacity of corroded reinforcement in concrete (2017) Engineering Structures, 147, pp. 434-447; Vecchio, F., Collins, M., Compression response of cracked reinforced concrete (1993) Journal of Structural Engineering, 119, pp. 3590-3610; Wittmann, F.H., Rokugo, K., Brühwiler, E., Mihashi, H., Simonin, P., Fracture energy and strain softening of concrete as determined by means of compact tension specimens (1988) Materials and Structures, 21 (1), pp. 21-32; Zandi, K., Corrosion-induced cover spalling and anchorage capacity (2015) Structure and Infrastructure Engineering, 11, pp. 1518-1547; Zandi, K., Boubitsas, D., Fahimi, S., Johansson, M., Spetz, J., Flansbjer, M., (2019), Gothenburg:, &, Autonomous automated non-intrusive condition assessment of concrete structures,. Report ACE 2019:5; Zandi, K., Ransom, E.H., Topac, T., Chen, R., Beniwal, S., Blomfors, M., Chang, F.-K., A framework for digital twin of civil infrastructure - Challenges and opportunities (2019) The 12th International Workshop on Structural Health Monitoring, Stanford, California, USA, p. 7. , Lancaster, PA:, …, September 10-12, 2019 (p., : DEStech Publications, Inc","Blomfors, M.; Department of Architecture and Civil Engineering, Sweden; email: blomfors@chalmers.se",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85087878859
"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. Mag., 59 (2), pp. 10-15. , Feb; Xu, Y., A digital-twin-assisted fault diagnosis using deep transfer learning (2019) Ieee Access, 7, pp. 19990-19999; Wang, J., Ye, L., Gao, R.X., Li, C., Zhang, L., Digital twin for rotating machinery fault diagnosis in smart manufacturing (2019) Int. J. Prod. Res., 57 (12), pp. 3920-3934; Revetria, R., Tonelli, F., Damiani, L., Demartini, M., Bisio, F., Peruzzo, N., A real-time mechanical structures monitoring system based on digital twin, IoT and augmented reality (2019) Proc. Spring Simul. Conf., pp. 1-10; Knezevic, D., Predictive digital twins for structural integrity management and asset life extension-jip concept and results (2019) Proc. Spe Offshore Europe Conf. Exhib., pp. 1-6; Shim, C.-S., Dang, N.-S., Lon, S., Jeon, C.-H., Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model (2019) Struct. Infrastruct. Eng., 15 (10), pp. 1319-1332; Liao, Y., Snowfort: An open source wireless sensor network for data analytics in infrastructure and environmental monitoring (2014) Ieee Sensors J., 14 (12), pp. 4253-4263. , Dec; Zhang, Y., O'Connor, S.M., Linden Der Van, G.W., Prakash, A., Lynch, J.P., Senstore: A scalable cyberinfrastructure platform for implementation of data-to-decision frameworks for infrastructure health management (2016) J. Comput. Civil Eng., 30 (5). , Art. no. 04016012; Khan, A., Shahid, F., Maple, C., Ahmad, A., Jeon, G., Towards smart manufacturing using spiral digital twin framework and twinchain (2020) Ieee Trans. Ind. Informat., , to be published; (2020) Ge Digital Twin: Analytic Engine for the Digital Power Plant, , https://www.ge.com/digital/, Accessed: Apr. 3; (2020) Optimizing Fpso Inspection ROIWith Akselos Digital Twin, , https://akselos.com/knowledgebase/, Akselos, Accessed: Apr. 3; Boschert, S., Heinrich, C., Rosen, R., Next generation digital twin (2018) Proc. TMCE. Las Palmas, pp. 209-217; Tao, F., Zhang, H., Liu, A., Nee, A.Y.C., Digital twin in industry: Stateof-the-art (2019) Ieee Trans. Ind. Informat., 15 (4), pp. 2405-2415. , Apr; Callaham, J.L., Koch, J.V., Brunton, B.W., Kutz, J.N., Brunton, S.L., Learning dominant physical processes with data-driven balance models (2021) Nat. Commun., 12 (1), pp. 1-10; Tran-Ngoc, H., Khatir, S., De Roeck, G., Bui-Tien, T., Nguyen-Ngoc, L., AbdelWahab, M., Model updating for nam O bridge using particle swarm optimization algorithm and genetic algorithm (2018) Sensors, 18 (12), pp. 4131-4150; Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Parizi, R.M., Choo, K.-K.R., Fog data analytics: A taxonomy and process model (2019) J. Netw. Comput. Appl., 128, pp. 90-104; Darwin, D., Dolan, C.W., Nilson, A.H., (2016) Design of Concrete Structures, , New York, NY, USA: McGraw-Hill Education; Kennedy, M.C., O'Hagan, A., Bayesian calibration of computer models (2001) J. Roy. Stat. Soc., Ser. B. (Stat. Methodol.), 63 (3), pp. 425-464; Kaur, K., Container-as-a-service at the edge: Trade-off between energy efficiency and service availability at fog nano data centers (2017) Ieee Wireless Commun., 24 (3), pp. 48-56. , Jun; Mahmoud, M.M., Towards energy-aware fog-enabled cloud of things for healthcare (2018) Comput. Elect. Eng., 67, pp. 58-69; Chaudhary, R., Kumar, N., Zeadally, S., Network service chaining in fog and cloud computing for the 5G environment: Data management and security challenges (2017) Ieee Commun. Mag., 55 (11), pp. 114-122. , Nov; Genez, T.A., Bittencourt, L.F., Madeira, E.R., Workflow scheduling for SAAS/PAAS cloud providers considering two SLA levels (2012) Proc. Ieee Netw. Oper. Manage. Symp., pp. 906-912; Dang, H.V., Tran-Ngoc, H., Nguyen, T.V., Bui-Tien, T., Roeck, G., Nguyen, H.X., Data-driven structural health monitoring using feature fusion and hybrid deep learning (2020) Ieee Trans. Automat. Sci. Eng., 18 (4), pp. 2087-2103. , Oct; Systèmes, D., Abaqus Analysis User's Manual (2007) Simulia Corp, , Providence, RI, USA; Gupta, A., Gurrala, G., Sastry, P., An online power system stability monitoring system using convolutional neural networks (2019) Ieee Trans. Power Syst., 34 (2), pp. 864-872. , Mar; Tai, Y., Qian, K., Huang, X., Zhang, J., Jan, M.A., Yu, Z., Intelligent intraoperative haptic-AR navigation forCOVID-19 lung biopsy using deep hybrid model (2021) Ieee Trans. Ind. Informat., 17 (9), pp. 6519-6527. , Sep; Ince, T., Kiranyaz, S., Eren, L., Askar, M., Gabbouj, M., Real-timemotor fault detection by 1-D convolutional neural networks (2016) Ieee Trans. Ind. Electron., 63 (11), pp. 7067-7075. , Nov; Kiranyaz, S., Ince, T., Gabbouj, M., Real-time patient-specific ECG classification by 1-D convolutional neural networks (2016) Ieee Trans. Biomed. Eng., 63 (3), pp. 664-675. , Mar; He, K., Zhang, X., Ren, S., Sun, J., Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015) Proc. Ieee Int. Conf. Comput. Vis., pp. 1026-1034; Qian, P., Zhang, D., Tian, X., Si, Y., Li, L., A novel wind turbine condition monitoring method based on cloud computing (2019) Renewable Energy, 135, pp. 390-398; Jeong, S., Hou, R., Lynch, J.P., Sohn, H., Law, K.H., A scalable cloud-based cyberinfrastructure platform for bridge monitoring (2019) Struct. Infrastruct. Eng., 15 (1), pp. 82-102","Nguyen, H.X.; London Digital Twin Research Centre, United Kingdom; email: H.Nguyen@mdx.ac.uk",,,"IEEE Computer Society",,,,,15513203,,,,"English","IEEE Trans. Ind. Inf.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85115797070
"Zhao H., Tan C., Obrien E.J., Zhang B., Uddin N., Guo H.","55652511100;56144513100;57218648462;57196052582;7003593965;57343858000;","Developing Digital Twins to Characterize Bridge Behavior Using Measurements Taken under Random Traffic",2022,"Journal of Bridge Engineering","27","1","04021101","","",,3,"10.1061/(ASCE)BE.1943-5592.0001814","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119283909&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001814&partnerID=40&md5=e4eecbd322cfaf2c2e1f7686cccb11a3","Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan Univ., Changsha, 410082, China; Dept. of Civil Engineering, Univ. College Dublin, Dublin, D04 V1W8, Ireland; Dept. of Civil, Construction, and Environmental Engineering, Univ. of Alabama at Birmingham, 1075 13th St S, Birmingham, AL  35205, United States","Zhao, H., Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan Univ., Changsha, 410082, China; Tan, C., Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan Univ., Changsha, 410082, China; Obrien, E.J., Dept. of Civil Engineering, Univ. College Dublin, Dublin, D04 V1W8, Ireland; Zhang, B., Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan Univ., Changsha, 410082, China; Uddin, N., Dept. of Civil, Construction, and Environmental Engineering, Univ. of Alabama at Birmingham, 1075 13th St S, Birmingham, AL  35205, United States; Guo, H., Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan Univ., Changsha, 410082, China","This paper presents a method of developing digital twins (DTs) of road bridges directly from field measurements taken under random traffic loading. In a physics-based approach, the full three-dimensional behavior of the bridge is represented using response functions and distribution factors. In contrast to conventional finite-element analysis, this approach focuses on the relationship between the applied loads and the measured responses, given the limitations on the information about the applied loads due to random passing traffic. At the same time, it takes advantage of some key features of bridge traffic loading that are consistent, regardless of the weights of the passing vehicles. The nature of traffic loading is that axles travel from one end of a bridge to the other and the response is a linear combination of axle weights and ordinates of the same influence line function, adjusted for relative axle locations. Small/medium span concrete slab-girder decks are the target structures of the study. The three-dimensional nature of such structures is a particular challenge, especially in the case of multiple vehicle presence. While these bridges are strongly orthotropic, there is a significant degree of load distribution between the girders immediately under the passing vehicle and girders under adjacent lanes. This is addressed using an iterative approach that uses transverse distribution factors. The proposed DT model is verified using both numerical simulation and field tests. © 2021 American Society of Civil Engineers.","Axle weight; Digital twin; Influence line; Multiple presence; Response function; Signal separation; Transverse load distribution","Axles; Concrete slabs; Distribution functions; Electric power plant loads; Highway bridges; Iterative methods; Traffic surveys; Applied loads; Axle weights; Distribution factor; Influence lines; Multiple presence; Response functions; Road bridge; Signal separation; Traffic loading; Transverse load distributions; Vehicles",,,,,"National Natural Science Foundation of China, NSFC: 52008162; China Postdoctoral Science Foundation: 2020M680114; Guangxi Key Research and Development Program: 2019SK2172; Science and Technology Program of Hunan Province: 2020RC2018","This research was supported by the National Natural Science Foundation of China (Grant No. 52008162), the Key Research and Development Program of Hunan Province (Grant No. 2019SK2172), the Science and Technology Innovation Program of Hunan Province (Grant No. 2020RC2018), and the Fellowship of China Postdoctoral Science Foundation (Grant No. 2020M680114).",,"(2012) Bridge Design Specifications, , AASHTO. 8th ed. Washington, DC: AASHTO; Chen, S.-Z., Wu, G., Feng, D.-C., Development of a bridge weigh-in-motion method considering the presence of multiple vehicles (2019) Eng. Struct., 191, pp. 724-739. , https://doi.org/10.1016/j.engstruct.2019.04.095; Chen, Z.-W., Zhu, S., Xu, Y.-L., Li, Q., Cai, Q.-L., Damage detection in long suspension bridges using stress influence lines (2015) J. Bridge Eng., 20 (3), p. 05014013. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000681; Deng, L., Cai, C.S., Identification of parameters of vehicles moving on bridges (2009) Eng. Struct., 31 (10), pp. 2474-2485. , https://doi.org/10.1016/j.engstruct.2009.06.005; Gregory, A., Lau, F.D.-H., Girolami, M., Butler, L.J., Elshafie, M.Z., The synthesis of data from instrumented structures and physics-based models via Gaussian processes (2019) J. Comput. Phys., 392, pp. 248-265. , https://doi.org/10.1016/j.jcp.2019.04.065; Grieves, M., (2014) Digital Twin: Manufacturing Excellence through Virtual Factory Replication, pp. 1-7. , White Paper 1. Melbourne, FL: Florida Institute of Technology; Heitner, B., Schoefs, F., Obrien, E.J., Znidarič, A., Yalamas, T., Using the unit influence line of a bridge to track changes in its condition (2020) J. Civ. Struct. Health Monit., 10, pp. 667-678; Hester, D., González, A., A wavelet-based damage detection algorithm based on bridge acceleration response to a vehicle (2012) Mech. Syst. Sig. Process., 28 (C), pp. 145-166. , https://doi.org/10.1016/j.ymssp.2011.06.007; Hughs, E., Idriss, R., Live-load distribution factors for prestressed concrete, spread box-girder bridge (2006) J. Bridge Eng., 11 (5), pp. 573-581. , https://doi.org/10.1061/(ASCE)1084-0702(2006)11:5(573); Inkoom, S., Sobanjo, J., Chicken, E., Performance assessment of deteriorating bridge channels in the presence of hurricane failures: The competing risks scenario (2021) J. Infrastruct. Syst., 27 (1), p. 04020041. , https://doi.org/10.1061/(ASCE)IS.1943-555X.0000573; Jacob, B., O'Brien, E.J., Newton, W.J., Assessment of the accuracy and classification of weigh-in-motion systems. Part 2: European specification (2000) Int. J. Heavy Veh. Syst., 7 (23), pp. 153-168. , https://doi.org/10.1504/IJHVS.2000.004831; Liu, M., Fang, S., Dong, H., Xu, C., Review of digital twin about concepts, technologies, and industrial applications (2021) J. Manuf. Syst., 58 (B), pp. 346-361; Lu, Q., Parlikad, A.K., Woodall, P., Don Ranasinghe, G., Xie, X., Liang, Z., Konstantinou, E., Schooling, J., Developing a digital twin at building and city levels: Case study of West Cambridge campus (2020) J. Manage. Eng., 36 (3), p. 05020004. , https://doi.org/10.1061/(ASCE)ME.1943-5479.0000763; McNulty, P., O'Brien, E.J., Testing of bridge weigh-in-motion system in a sub-Arctic climate (2003) J. Test. Eval., 31 (6), pp. 497-506; Moses, F., Weigh-in-motion system using instrumented bridges (1979) Transp. Eng., 3 (105), pp. 233-249; Obrien, E.J., Quilligan, M.J., Karoumi, R., Calculating an influence line from direct measurements (2006) Proc. Inst. Civ. Eng.- Bridge Eng., 159 (1), pp. 31-34. , https://doi.org/10.1680/bren.2006.159.1.31; Reiff, A.J., Sanayei, M., Vogel, R.M., Statistical bridge damage detection using girder distribution factors (2016) Eng. Struct., 109, pp. 139-151. , https://doi.org/10.1016/j.engstruct.2015.11.006; Richardson, J., Jones, S., Brown, A., O'Brien, E., Hajializadeh, D., On the use of bridge weigh-in-motion for overweight truck enforcement (2014) Int. J. Heavy Veh. Syst., 21 (2), pp. 83-104. , https://doi.org/10.1504/IJHVS.2014.061632; Shim, C.-S., Dang, N.-S., Lon, S., Jeon, C.-H., Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model (2019) Struct. Infrastruct. Eng., 15 (10), pp. 1319-1332. , https://doi.org/10.1080/15732479.2019.1620789; Shim, C., Kang, H., Dang, N.S., Lee, D., Development of BIM-based bridge maintenance system for cable-stayed bridges (2017) Smart Struct. Syst., 20 (6), pp. 697-708; Sofia, H., Anas, E., Faïz, O., Mobile mapping, machine learning and digital twin for road infrastructure monitoring and maintenance: Case study of Mohammed VI Bridge in Morocco (2020) 2020 IEEE Int. Conf. Of Moroccan Geomatics (Morgeo), , New York: IEEE; Tan, C., Uddin, N., Hilbert transform based approach to improve extraction of 'drive-by' bridge frequency (2020) Smart Struct. Syst., 25 (3), pp. 265-277; Wang, N.-B., He, L.-X., Ren, W.-X., Huang, T.-L.J.E.S., Extraction of influence line through a fitting method from bridge dynamic response induced by a passing vehicle (2017) Eng. Struct., 151, pp. 648-664. , https://doi.org/10.1016/j.engstruct.2017.06.067; Yang, S.-I., Frangopol, D.M., Neves, L.C., Optimum maintenance strategy for deteriorating bridge structures based on lifetime functions (2006) Eng. Struct., 28 (2), pp. 196-206. , https://doi.org/10.1016/j.engstruct.2005.06.024; Yang, Y.B., Yau, J., Yao, Z., Wu, Y., (2004) Vehicle-bridge Interaction Dynamics: With Applications to High-speed Railways, , Singapore: World Scientific; 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) Proc. 12th Int. Workshop on Structural Health Monitoring 2019, , Stanford, CA: Stanford Univ; Yu, Y., Cai, C., Deng, L., Nothing-on-road bridge weigh-in-motion considering the transverse position of the vehicle (2018) Struct. Infrastruct. Eng., 14 (8), pp. 1108-1122. , https://doi.org/10.1080/15732479.2017.1401095; Zhao, H., Tan, C., Obrien, E.J., Uddin, N., Zhang, B., Wavelet-based optimum identification of vehicle axles using bridge measurements (2020) Appl. Sci., 10 (21), p. 7485. , https://doi.org/10.3390/app10217485; Zhao, H., Uddin, N., O'Brien, E., Shao, X., Zhu, P., Identification of vehicular axle weights with a bridge weigh-in-motion system considering transverse distribution of wheel loads (2014) J. Bridge Eng., 19 (3), pp. 6-10. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000533; Znidarič, A., Influence of number and quality of weigh-in-motion data on evaluation of load effects on bridges (2017) Ph.D. Thesis, , Univerza v Ljubljani, Fakulteta za gradbeništvo in geodezijo; Znidaric, A., Lavric, I., Kalin, J., The next generation of bridge weigh-in-motion systems (2002) Proc. 3rd Int. Conf. On Weigh-in-Motion, , Ames, IA: Iowa State Univ","Tan, C.; Key Laboratory for Wind and Bridge Engineering of Hunan Province, China; email: tchj0219@hnu.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85119283909
"Gawade V., Singh V., Guo W.“.","57194425152;57226743277;57217496903;","Leveraging simulated and empirical data-driven insight to supervised-learning for porosity prediction in laser metal deposition",2022,"Journal of Manufacturing Systems","62",,,"875","885",,3,"10.1016/j.jmsy.2021.07.013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112571515&doi=10.1016%2fj.jmsy.2021.07.013&partnerID=40&md5=d01cd5a515d0334fbe3ae9345b0ef2b3","Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Rd, Piscataway, NJ  08854, United States","Gawade, V., Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Rd, Piscataway, NJ  08854, United States; Singh, V., Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Rd, Piscataway, NJ  08854, United States; Guo, W.“., Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Rd, Piscataway, NJ  08854, United States","The advent of digital-twin manufacturing in additive manufacturing (AM) is to integrate the physical world of real-time 3D printing with the digital world of a simulated print. This paper contributes to digital-twin manufacturing in laser-based additive manufacturing by combining melt pools’ simulated thermal behavior via finite element analysis (FEA) and melt pools’ empirical thermal behavior via pyrometry-based sensors. Studying the thermal behavior of melt pools based on heat transfer characteristics determines melt pools’ porosity and part quality. FEA uses Godak's moving heat flux to capture the melt pools’ physically bound temperature profile in three dimensions. Simulated data helps to mitigate the influence of measuring errors from real-world data and provides non-observable data such as gradient changes of thermal behavior at the curvature of the 3D melt pool. The pyrometer captures empirical temperature behavior, including uncertainty and randomness introduced to the process. A significant knowledge gap exists when predicting melt pool porosity accurately with theoretical FEA and empirical in situ evidence alone. The gap is bridged by combining the data sources, specifically, feature engineering via functional principal component analysis (empirical data source) and capturing the melt pool's 3-D temperature shape profile via FEA (simulated data source). A hybrid model predicts melt pool porosity by capturing the strengths of prior simulated and posterior in situ empirical data by matching simulated melt pools to real-world empirical melt pools. Moreover, comparing predicted porosity labels with true porosity labels of Ti–6Al–4V thin-wall structure from laser metal deposition verified the proposed interpretable and robust supervised-learning model's validity. This methodology can apply to other materials and part shapes printed under various additive-manufactured printers. © 2021 The Society of Manufacturing Engineers","Additive manufacturing; Machine learning; Porosity prediction","Additives; Deposition; Digital twin; Heat flux; Heat transfer; Lakes; Porosity; Pyrometers; Pyrometry; Supervised learning; Empirical temperatures; Functional principal component analysis; Heat transfer characteristics; Laser metal deposition; Laser-based additive manufacturing; Porosity predictions; Temperature profiles; Thin-wall structures; 3D printers",,,,,"Mississippi State University, MSU; University College London, UCL","The authors would like to thank Dr. Linkan Bian of Mississippi State University, Dr. Yuebin Guo of Rutgers University, and Dr. Yuanchang Liu of University College London for their valuable input to this paper. This work is partially supported by the International Collaborative Research Grant from Rutgers Global at Rutgers, The State University of New Jersey. Conflict of interest: None declared.","The authors would like to thank Dr. Linkan Bian of Mississippi State University, Dr. Yuebin Guo of Rutgers University, and Dr. Yuanchang Liu of University College London for their valuable input to this paper. This work is partially supported by the International Collaborative Research Grant from Rutgers Global at Rutgers, The State University of New Jersey.","Wang, Q., Jiao, W., Zhang, Y., Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control (2020) J Manuf Syst, 57, pp. 429-439; Psarommatis, F., A generic methodology and a digital twin for zero defect manufacturing (zdm) performance mapping towards design for zdm (2021) J Manuf Syst, 59, pp. 507-521; Liu, M., Fang, S., Dong, H., Xu, C., Review of digital twin about concepts, technologies, and industrial applications (2020) J Manuf Syst; Zhang, L., Chen, X., Zhou, W., Cheng, T., Chen, L., Guo, Z., Digital twins for additive manufacturing: a state-of-the-art review (2020) Appl Sci, 10 (23), p. 8350; Liu, C., Le Roux, L., Körner, C., Tabaste, O., Lacan, F., Bigot, S., Digital twin-enabled collaborative data management for metal additive manufacturing systems (2020) J Manuf Syst; Yi, L., Gläßner, C., Aurich, J.C., How to integrate additive manufacturing technologies into manufacturing systems successfully: a perspective from the commercial vehicle industry (2019) J Manuf Syst, 53, pp. 195-211; Gisario, A., Kazarian, M., Martina, F., Mehrpouya, M., Metal additive manufacturing in the commercial aviation industry: a review (2019) J Manuf Syst, 53, pp. 124-149; B.T, An engineering review of surfacing technologies in 3d printing for orthopedic implants (2020) Appl Sci; Jin, Y., Du, J., He, Y., Optimization of process planning for reducing material consumption in additive manufacturing (2017) J Manuf Syst, 44, pp. 65-78; Gardan, N., Schneider, A., Topological optimization of internal patterns and support in additive manufacturing (2015) J Manuf Syst, 37, pp. 417-425; Ransikarbum, K., Ha, S., Ma, J., Kim, N., Multi-objective optimization analysis for part-to-printer assignment in a network of 3d fused deposition modeling (2017) J Manuf Syst, 43, pp. 35-46; Saadlaoui, Y., Milan, J.-L., Rossi, J.-M., Chabrand, P., Topology optimization and additive manufacturing: comparison of conception methods using industrial codes (2017) J Manuf Syst, 43, pp. 178-186; Li, Z., Li, B.-Q., Bai, P., Liu, B., Wang, Y., Research on the thermal behaviour of a selectively laser melted aluminium alloy: simulation and experiment (2018) Materials, 11 (7), p. 1172; Portolés, L., Jordá, O., Jordá, L., Uriondo, A., Esperon-Miguez, M., Perinpanayagam, S., A qualification procedure to manufacture and repair aerospace parts with electron beam melting (2016) J Manuf Syst, 41, pp. 65-75; Guo, S., Guo, W., Bain, L., Hierarchical spatial-temporal modeling and monitoring of melt pool evolution in laser-based additive manufacturing (2020) IISE Trans, 52 (9), pp. 977-997; Khanzadeh, M., Chowdhury, S., Marufuzzaman, M., Tschopp, M.A., Bian, L., Porosity prediction: supervised-learning of thermal history for direct laser deposition (2018) J Manuf Syst, 47, pp. 69-82; Menon, A., Póczos, B., Feinberg, A.W., Washburn, N.R., Optimization of silicone 3d printing with hierarchical machine learning (2019) 3D Printing Addit Manuf, 6 (4), pp. 181-189; Ansari, M.J., Nguyen, D.-S., Park, H.S., Investigation of slm process in terms of temperature distribution and melting pool size: modeling and experimental approaches (2019) Materials, 12 (8), p. 1272; Goldak, J., Chakravarti, A., Bibby, M., A new finite element model for welding heat sources (1984) Metall Trans B, 15 (2), pp. 299-305; Ning, J., Wang, W., Zamorano, B., Liang, S.Y., Analytical modeling of lack-of-fusion porosity in metal additive manufacturing (2019) Appl Phys A, 125 (11), pp. 1-11; Liu, J., Liu, C., Bai, Y., Rao, P., Williams, C.B., Kong, Z., Layer-wise spatial modeling of porosity in additive manufacturing (2019) IISE Trans, 51 (2), pp. 109-123; Zhang, Z., Gu, G.X., Physics-informed deep learning for digital materials (2021) Theoret Appl Mech Lett, p. 100220; Zhao, X., Rosen, D.W., A data mining approach in real-time measurement for polymer additive manufacturing process with exposure controlled projection lithography (2017) J Manuf Syst, 43, pp. 271-286; Paul, R., Anand, S., Optimal part orientation in rapid manufacturing process for achieving geometric tolerances (2011) J Manuf Syst, 30 (4), pp. 214-222; Liu, R., Liu, S., Zhang, X., A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing (2021) Int J Adv Manuf Technol, 113 (7), pp. 1943-1958; Montazeri, M., Nassar, A.R., Dunbar, A.J., Rao, P., In-process monitoring of porosity in additive manufacturing using optical emission spectroscopy (2020) IISE Trans, 52 (5), pp. 500-515; Liu, C., Law, A.C.C., Roberson, D., Kong, Z.J., Image analysis-based closed loop quality control for additive manufacturing with fused filament fabrication (2019) J Manuf Syst, 51, pp. 75-86; Decker, N., Wang, Y., Huang, Q., Efficiently registering scan point clouds of 3d printed parts for shape accuracy assessment and modeling (2020) J Manuf Syst, 56, pp. 587-597; Khanzadeh, M., Rao, P., Jafari-Marandi, R., Smith, B.K., Tschopp, M.A., Bian, L., Quantifying geometric accuracy with unsupervised machine learning: using self-organizing map on fused filament fabrication additive manufacturing parts (2018) J Manuf Sci Eng, 140 (3); Khanzadeh, M., Tian, W., Yadollahi, A., Doude, H.R., Tschopp, M.A., Bian, L., Dual process monitoring of metal-based additive manufacturing using tensor decomposition of thermal image streams (2018) Addit Manuf, 23, pp. 443-456; Lu, Y., Wang, Y., Monitoring temperature in additive manufacturing with physics-based compressive sensing (2018) J Manuf Syst, 48, pp. 60-70; He, H., Yang, Y., Pan, Y., Machine learning for continuous liquid interface production: printing speed modelling (2019) J Manuf Syst, 50, pp. 236-246; Kapusuzoglu, B., Mahadevan, S., Physics-informed and hybrid machine learning in additive manufacturing: application to fused filament fabrication (2020) JOM, 72 (12), pp. 4695-4705; Jafari-Marandi, R., Khanzadeh, M., Tian, W., Smith, B., Bian, L., From in-situ monitoring toward high-throughput process control: cost-driven decision-making framework for laser-based additive manufacturing (2019) J Manuf Syst, 51, pp. 29-41; Snow, Z., Diehl, B., Reutzel, E.W., Nassar, A., Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning (2021) J Manuf Syst, 59, pp. 12-26; Wang, T., Kwok, T.-H., Zhou, C., Vader, S., In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing (2018) J Manuf Syst, 47, pp. 83-92; Tian, Q., Guo, S., Melder, E., Bian, L., Guo, W., Deep learning-based data fusion method for in situ porosity detection in laser-based additive manufacturing (2021) J Manuf Sci Eng, 143 (4); Gaikwad, A., Yavari, R., Montazeri, M., Cole, K., Bian, L., Rao, P., Toward the digital twin of additive manufacturing: integrating thermal simulations, sensing, and analytics to detect process faults (2020) IISE Trans, 52 (11), pp. 1204-1217; Tian, Q., Guo, S., Guo, Y., A physics-driven deep learning model for process-porosity causal relationship and porosity prediction with interpretability in laser metal deposition (2020) CIRP Ann, 69 (1), pp. 205-208; Marshall, G.J., Thompson, S.M., Shamsaei, N., Data indicating temperature response of ti-6al-4v thin-walled structure during its additive manufacture via laser engineered net shaping (2016) Data Brief, 7, pp. 697-703; Ramsay, J., Ramsay, J., Silverman, B., Media, S.S., Silverman, H., Functional Data Analysis, Springer Series in Statistics (2005), Springer; Welsch, G., Boyer, R., Collings, E., Materials Properties Handbook: Titanium Alloys (1993), ASM International; De Boor, C., De Boor, C., A Practical Guide to Splines, vol. 27 (1978), Springer-Verlag New York; Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009), Springer Science & Business Media","Guo, W.“.; Department of Industrial and Systems Engineering, 96 Frelinghuysen Rd, United States; email: wg152@soe.rutgers.edu",,,"Elsevier B.V.",,,,,02786125,,JMSYE,,"English","J Manuf Syst",Article,"Final","",Scopus,2-s2.0-85112571515
"Ghahari F., Malekghaini N., Ebrahimian H., Taciroglu E.","57444742200;57445607700;57112070500;6602889035;","Bridge Digital Twinning Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements",2022,"Sensors","22","3","1278","","",,2,"10.3390/s22031278","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124304449&doi=10.3390%2fs22031278&partnerID=40&md5=a94857cf5d412b0ac40c919eebdda28a","Department of Civil & Environmental Engineering, University of California, Los Angeles, CA  90095, United States; Department of Civil & Environmental Engineering, University of Nevada, Reno, NV  89557, United States","Ghahari, F., Department of Civil & Environmental Engineering, University of California, Los Angeles, CA  90095, United States; Malekghaini, N., Department of Civil & Environmental Engineering, University of Nevada, Reno, NV  89557, United States; Ebrahimian, H., Department of Civil & Environmental Engineering, University of Nevada, Reno, NV  89557, United States; Taciroglu, E., Department of Civil & Environmental Engineering, University of California, Los Angeles, CA  90095, United States","Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emer-gency response management and recovery. This can be facilitated using digital technologies to re-move the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agree-ment between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The described process is a practice of digital twinning and the updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","Bayesian inference; Damage diagnosis; Digital twin; Finite element model updating; Foundation input motion; Rapid post-earthquake assessment; Soil-structure interaction; Structural health monitoring","Bayesian networks; Damage detection; Decision making; Earthquakes; Finite element method; Inference engines; Soils; Structural health monitoring; Bayesian inference; Bayesian model updating; Damage diagnosis; Finite element modelling (FEM); Finite-element model updating; Foundation input motion; Model updating techniques; Rapid post-earthquake assessment; Soil-structure interaction; Uncertainty; Soil structure interactions; acceleration; adult; article; California; digital twin; earthquake; finite element analysis; human; motion; soil structure; uncertainty",,,,,"1014-963; California Department of Transportation, CT: 65A0450","The work presented in this manuscript was funded, in part, by the California Geological Survey (Contract No. 1014-963) and by the California Department of Transportation (Grant No. 65A0450). Any opinions, findings, conclusions, or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of the sponsors.",,"Karoumi, R., Some modeling aspects in the nonlinear finite element analysis of cable supported bridges (1999) Comput. Struct, 71, pp. 397-412. , https://doi.org/10.1016/S0045-7949(98)00244-2; Shamsabadi, A., Rollins, K.M., Kapuskar, M., Nonlinear soil–abutment–bridge structure interaction for seismic performance-based design (2007) J. Geotech. Geoenvironmental Eng, 133, pp. 707-720. , https://doi.org/10.1061/(ASCE)1090-0241(2007)133:6(707); Johnson, N., Saiidi, M.S., Sanders, D., Nonlinear earthquake response modeling of a large-scale two-span concrete bridge (2009) J. Bridg. Eng, 14, pp. 460-471. , https://doi.org/10.1061/(asce)be.1943-5592.0000009; Ebrahimian, H., Astroza, R., Conte, J.P., Extended Kalman filter for material parameter estimation in nonlinear structural finite element models using direct differentiation method (2015) Earthq. Eng. Struct. Dyn, 44, pp. 1495-1522. , https://doi.org/10.1002/eqe.2532; Ebrahimian, H., Astroza, R., Conte, J.P., Papadimitriou, C., Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures (2018) Struct. Control Health Monit, 25, p. e2128. , https://doi.org/10.1002/stc.2128; Ghahari, S.F., Abazarsa, F., Ghannad, M.A., Taciroglu, E., Response-only modal identification of structures using strong motion data (2013) Earthq. Eng. Struct. Dyn, 42, pp. 1221-1242. , https://doi.org/10.1002/eqe.2268; Jaishi, B., Ren, W.-X., Structural finite element model updating using ambient vibration test results (2005) J. Struct. Eng, 131, pp. 617-628. , https://doi.org/10.1061/(ASCE)0733-9445(2005)131:4(617); Ghahari, S.F., Abazarsa, F., Taciroglu, E., Blind modal identification of non-classically damped structures under non-stationary excitations (2016) Struct. Control Health Monit, 24, p. e1925. , https://doi.org/10.1002/stc.1925; Abazarsa, F., Nateghi, F., Ghahari, S.F., Taciroglu, E., Extended blind modal identification technique for nonstationary excita-tions and its verification and validation (2016) J. Eng. Mech, 142, p. 04015078. , https://doi.org/10.1061/(ASCE)EM.1943-7889.0000990; Moaveni, B., He, X., Conte, J.P., Restrepo, J.I., Damage identification study of a seven-story full-scale building slice tested on the UCSD-NEES shake table (2010) Struct. Saf, 32, pp. 347-356. , https://doi.org/10.1016/j.strusafe.2010.03.006; Shakal, A.F., Ragsdale, J.T., Sherburne, R.W., CSMIP strong-motion instrumentation and records from transportation struc-tures—Bridges (1984) Lifeline Earthquake Engineering: Performance, Design and Construction, pp. 117-132. , ASCE: San Fransisco, CA, USA; Malhotra, P.K., Huang, M.J., Shakal, A.F., Seismic interaction at separation joints of an instrumented concrete bridge (1995) Earthq. Eng. Struct. Dyn, 24, pp. 1055-1067. , https://doi.org/10.1002/eqe.4290240802; Arici, Y., Mosalam, K.M., System identification of instrumented bridge systems (2003) Earthq. Eng. Struct. Dyn, 32, pp. 999-1020. , https://doi.org/10.1002/eqe.259; Zhang, J., Makris, N., Seismic response analysis of highway overcrossings including soil-structure interaction (2002) Earthq. Eng. Struct. Dyn, 31, pp. 1967-1991. , https://doi.org/10.1002/eqe.197; Wolf, J.P., Deeks, A.J., (2004) Foundation Vibration Analysis: A Strength of Materials Approach, , https://www.elsevier.com/books/foundation-vibration-analysis/wolf/978-0-7506-6164-5, 1st ed.; Butterworth-Heinemann: Oxford, UK, (accessed on 20 December 2021); Iguchi, M., Luco, J.E., Dynamic response of flexible rectangular foundations on an elastic half-space (1981) Earthq. Eng. Struct. Dyn, 9, pp. 239-249. , https://doi.org/10.1002/eqe.4290090305; Mahsuli, M., Ghannad, M.A., The effect of foundation embedment on inelastic response of structures (2009) Earthq. Eng. Struct. Dyn, 38, pp. 423-437. , https://doi.org/10.1002/eqe.858; Luco, J.E., Mita, A., Response of circular foundation to spatially random ground motion (1987) J. Eng. Mech, 113, pp. 1-15. , https://doi.org/10.1061/(ASCE)0733-9399(1987)113:1(1); Stewart, J.P., Fenves, G.L., Seed, R.B., Seismic soil-structure interaction in buildings. I: Analytical methods (1999) J. Geotech. Geoenvi-ronmental Eng, 125, pp. 26-37. , https://doi.org/10.1061/(ASCE)1090-0241(1999)125:1(26); Wolf, J., (1985) Dynamic Soil-Structure Interaction, , Prentice Hall, Inc.: Hoboken, NJ, USA; Astroza, R., Ebrahimian, H., Li, Y., Conte, J.P., Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures (2017) Mech. Syst. Signal Process, 93, pp. 661-687. , https://doi.org/10.1016/j.ymssp.2017.01.040; Al-hussein, A., Asce, A.M., Haldar, A., Asce, D.M., Novel Unscented Kalman filter for health assessment of structural systems with unknown input (2015) J. Eng. Mech, 141, p. 04015012. , https://doi.org/10.1061/(ASCE)EM.1943-7889.0000926; Song, M., Astroza, R., Ebrahimian, H., Moaveni, B., Papadimitriou, C., Adaptive Kalman filters for nonlinear finite element model updating (2020) Mech. Syst. Signal Process, 143, p. 106837. , https://doi.org/10.1016/j.ymssp.2020.106837; 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, 141, p. 04014149. , https://doi.org/10.1061/(ASCE)EM.1943-7889.0000851; Julier, S., Uhlmann, J., Durran-Whyte, H., A new method for the nonlinear transformation of means and covariances in filters and estimators (2000) IEEE Trans. Automat. Contr, 45, p. 477. , https://doi.org/10.1109/TAC.1973.1100244; Julier, S.J., Uhlmann, J.K., New extension of the Kalman filter to nonlinear systems (1997) Proceedings of the SPIE, pp. 182-193. , https://doi.org/10.1117/12.280797, Orlando, FL, USA, 20–25 April; Ebrahimian, H., Kohler, M., Massari, A., Asimaki, D., Parametric estimation of dispersive viscoelastic layered media with application to structural health monitoring (2018) Soil Dyn. Earthq. Eng, 105, pp. 204-223. , https://doi.org/10.1016/j.soildyn.2017.10.017; Ebrahimian, H., Astroza, R., Conte, J.P., Bitmead, R.R., Information-theoretic approach for identifiability assessment of nonlinear structural finite-element models (2019) J. Eng. Mech, 145, p. 04019039. , https://doi.org/10.1061/(ASCE)EM.1943-7889.0001590; (2022), https://www.strongmotioncenter.org/NCESMD/pho-tos/CGS/lllayouts/ll25749.pdf, (accessed on 20 December 2021); (2022), https://www.cesmd.org, (accessed on 20 December 2021); McKenna, F., OpenSees: A framework for earthquake engineering simulation (2011) Comput. Sci. 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
"Choi J.-S., Kim C.-M., Jang H.-I., Kim E.-J.","57201666011;57221217100;57192402749;55477775700;","Detailed and fast calculation of wall surface temperatures near thermal bridge area",2021,"Case Studies in Thermal Engineering","25",,"100936","","",,2,"10.1016/j.csite.2021.100936","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102854363&doi=10.1016%2fj.csite.2021.100936&partnerID=40&md5=bd7a83781b687622b3750beba4f9b358","Department of Architectural Engineering, Inha University, Incheon, 22212, South Korea; Institute of Green Building and New Technology, Mirae Environment Plan, Seoul, 01905, South Korea","Choi, J.-S., Department of Architectural Engineering, Inha University, Incheon, 22212, South Korea; Kim, C.-M., Institute of Green Building and New Technology, Mirae Environment Plan, Seoul, 01905, South Korea; Jang, H.-I., Institute of Green Building and New Technology, Mirae Environment Plan, Seoul, 01905, South Korea; Kim, E.-J., Department of Architectural Engineering, Inha University, Incheon, 22212, South Korea","The popularity of digital-twin technology has increased the demand for a fast and accurate model for prompt analysis. This work proposes a low-order but accurate numerical model for the thermal analysis of wall-window joint surfaces. A simple method to develop such a model is presented. An ABAQUS-based extraction method was developed to quasi-automatically define a state-space model for the target cases used, which may reduce the amount of elaborate programming work required. Then, an order reduction technique was applied to the state-space model. The results showed that the state-space model obtained from ABAQUS describes almost the same thermal responses as the reference ABAQUS simulation model. After reduction, the proposed model with an order of 10, equivalent to 10 equations, sufficiently described the dynamics of temperature variations, with an error of less than 1%. The model conversion to state-space formulism and reduction technique significantly decreased the CPU time by more than 30,000 times (from 79.5 s to 0.002 s). © 2021 The Authors.","FEM; Numerical model; Reduction technique; State-space model","Digital twin; Thermoanalysis; ABAQUS simulations; Accurate modeling; Extraction method; Order reduction techniques; Reduction techniques; State - space models; Temperature variation; Wall surface temperature; State space methods",,,,,"Ministry of Land, Infrastructure and Transport, MOLIT: 20CTAP-C152248-02; Korea Agency for Infrastructure Technology Advancement, KAIA","This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20CTAP-C152248-02 ).",,"Lydon, G.P., Caranovic, S., Hischier, I., Schlueter, A., Coupled simulation of thermally active building systems to support a digital twin (2019) Energy Build., 202, p. 109298; Lu, Q., Xie, X., Parlikad, A.K., Schooling, J.M., Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance (2020) Autom. ConStruct., 118, p. 103277; Cui, P., Yang, H., Fang, Z., Numerical analysis and experimental validation of heat transfer in ground heat exchangers in alternative operation modes (2008) Energy Build., 40 (6), pp. 1060-1066; Kim, D., Braun, J.E., Reduced-order building modeling for application to model-based predictive control (2012) Proc. SimBuild, 5 (1), pp. 554-561; Hudobivnik, B., Pajek, L., Kunic, R., Košir, M., FEM thermal performance analysis of multi-layer external walls during typical summer conditions considering high intensity passive cooling (2016) Appl. Energy, 178, pp. 363-375; Miasik, P., Licholai, L., The influence of a thermal bridge in the corner of the walls on the possibility of water vapour condensation (2018) E3S Web of Conferences, 49, p. 00072. , EDP Sciences; Santos, P., Gonçalves, M., Martins, C., Soares, N., Costa, J.J., Thermal transmittance of lightweight steel framed walls: Experimental versus numerical and analytical approaches (2019) J. Build Eng., 25, p. 100776; Nagy, B., Stocker, G., Numerical analysis of thermal and moisture bridges in insulation filled masonry walls and corner joints (2019) Period. Polytech. Civ. Eng., 63 (2), pp. 446-455; Ascione, F., Bianco, N., De Masi, R.F., Mauro, G.M., Musto, M., Vanoli, G.P., Experimental validation of a numerical code by thin film heat flux sensors for the resolution of thermal bridges in dynamic conditions (2014) Appl. Energy, 124, pp. 213-222; Hibbitt, H., Karlsson, B., Sorensen, P., (2011) Abaqus Analysis User's Manual Version 6.10, , Dassault Systèmes Simulia Corp. Providence, RI, USA; Marshall, S.A., An approximate method for reducing the order of a linear system (1966) Control, 10, pp. 642-643; Gao, Y., Roux, J.J., Teodosiu, C., Zhao, L.H., Reduced linear state model of hollow blocks walls, validation using hot box measurements (2004) Energy Build., 36 (11), pp. 1107-1115; Gao, Y., Roux, J.J., Zhao, L.H., Jiang, Y., Dynamical building simulation: A low order model for thermal bridges losses (2008) Energy Build., 40 (12), pp. 2236-2243; Kim, E.J., Plessis, G., Hubert, J.L., Roux, J.J., Urban energy simulation: Simplification and reduction of building envelope models (2014) Energy Build., 84, pp. 193-202; Moore, B., Principal component analysis in linear systems: Controllability, observability, and model reduction (1981) IEEE Trans. Automat. Contr., 26 (1), pp. 17-32; Laub, A., Heath, M.T., Paige, C., Ward, R., Computation of system balancing transformations and other applications of simultaneous diagonalization algorithms (1987) IEEE Trans. Automat. Contr., 32 (2), pp. 115-122; Kim, E.J., Roux, J.J., Bernier, M.A., Cauret, O., Three-dimensional numerical modeling of vertical ground heat exchangers: Domain decomposition and state model reduction (2011) HVAC R Res., 17 (6), pp. 912-927; Kim, E.J., He, X., Roux, J.J., Johannes, K., Kuznik, F., Is it possible to use a single reduced model for a number of buildings in urban energy simulation (2015) 14th Conference of International Building Performance, , 2015, December; Kim, E.J., He, X., Roux, J.J., Johannes, K., Kuznik, F., Fast and accurate district heating and cooling energy demand and load calculations using reduced-order modelling (2019) Appl. Energy, 238, pp. 963-971","Kim, E.-J.; Department of Architectural Engineering, South Korea; email: ejkim@inha.ac.kr",,,"Elsevier Ltd",,,,,2214157X,,,,"English","Case Stud. Therm. Eng.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102854363
"Baron J.M., Salinas G., Mo X., Vergara F., Arnaiz P.J., Alou P., Vasic M.","57222720098;57204116610;57222725277;57222723293;57222725872;56249124100;24922369500;","Methodology for multi-die package semiconductor Thermal Model in a Dynamic Environment",2021,"2021 28th International Workshop on Electric Drives: Improving Reliability of Electric Drives, IWED 2021 - Proceedings",,,"9376341","","",,1,"10.1109/IWED52055.2021.9376341","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103847804&doi=10.1109%2fIWED52055.2021.9376341&partnerID=40&md5=e3ce38414beccc59873dbcfcac961b1c","Centro de Electrónica Industrial, Polytechnic University of Madrid, Madrid, Spain; European Space Agency, Noordwijk, Netherlands; Fagor Automation Mondragón, Spain","Baron, J.M., Centro de Electrónica Industrial, Polytechnic University of Madrid, Madrid, Spain; Salinas, G., European Space Agency, Noordwijk, Netherlands; Mo, X., Centro de Electrónica Industrial, Polytechnic University of Madrid, Madrid, Spain; Vergara, F., Fagor Automation Mondragón, Spain; Arnaiz, P.J., Fagor Automation Mondragón, Spain; Alou, P., Centro de Electrónica Industrial, Polytechnic University of Madrid, Madrid, Spain; Vasic, M., Centro de Electrónica Industrial, Polytechnic University of Madrid, Madrid, Spain","This paper provides a detailed methodology for the development of a dynamic thermal model based on the finite element method and statistical modelling. Finite element method based thermal modelling, consisting of a nonphysical model, is improved by means of statistical correlations to obtain accurate temperature estimates in response to dynamic boundary conditions, in contrast to the classical thermal models which are very dependent on the boundary conditions such as Cauer and Foster network, thus allowing it application for digital twinning and device failure reporting. The dynamic thermal model has been developed for a TO-247 IGBT device and, later, benchmarked and verified against measurements obtained from an experimental platform, composed of three IGBT half-bridge mounted on a single heatsink with forced air cooling. © 2021 IEEE.","Digital Twin; Finite element analysis; Semiconductor devices; Statistical analysis; Thermal analysis","Boundary conditions; Electric drives; Insulated gate bipolar transistors (IGBT); Thermography (temperature measurement); Dynamic boundary conditions; Dynamic environments; Dynamic thermal modeling; Experimental platform; Forced air cooling; Statistical correlation; Statistical modelling; Thermal modelling; Finite element method",,,,,,,,"Wang, Z., Tian, B., Qiao, W., Qu, L., Real-time aging monitoring forigbt modules using case temperature (2015) Ieee Transactions on IndustrialElectronics, 63 (2), pp. 1168-1178; Drofenik, U., Kolar, J.W., Teaching thermal design of powerelectronic systems with web-based interactive educational software (2003) Eighteenth Annual Ieee Applied Power Electronics Conference and Exposition, 2003. APEC'03., 2, pp. 1029-1036; Cengel, Y., (2020) Heat and Mass Transfer: Fundamentalsand Applications, , McGraw-Hill Higher Education; Bergman, T.L., Lavine, A.S., Incropera, F.P., Dewitt, D.P., (2012) Fundamentals of Heat and Mass Transfer 7th Ed, , John Wiley & SonsNew York; Sabatino, D., Yoder, K., Pyrolytic graphite heat sinks: A study of cir-cuit board applications (2014) Ieee Transactions on Components, Packagingand Manufacturing Technology, 4 (6), pp. 999-1009; Culham, J., Teertstra, P., Yovanovich, M., The role of spreadingresistance on effective conductivity in laminated substrates (2000) FutureCircuits, 6, pp. 73-78; Li, H., Hu, Y., Liu, S., Li, Y., Liao, X., Liu, Z., An improved thermalnetwork model of the igbt module for wind power converters consideringthe effects of base-plate solder fatigue (2016) Ieee Transactions on Deviceand Materials Reliability, 16 (4), pp. 570-575; Bahman, A.S., Ma, K., Ghimire, P., Iannuzzo, F., Blaabjerg, F., A3-d-lumped thermal network model for long-term load profiles analysisin high-power igbt modules (2016) Ieee Journal of Emerging and SelectedTopics in Power Electronics, 4 (3), pp. 1050-1063; Salinas, G., (2020) Thermal Modelling of High-frequency Magnetic Componentsfor Power Electronics by Finite Element Analysis, , Ph.D. dissertation, UPM ETSII Madrid; Wu, R., Iannuzzo, F., Wang, H., Blaabjerg, F., (2014) Fast and Accurateicepak-pspice Co-simulation of Igbts under Short-circuit with An Ad-vanced Pspice Model; Alavi, O., Abdollah, M., Viki, A.H., Thermal optimization ofigbt modules based on finite element method and particle swarmoptimization (2017) Journal of Computational Electronics, 16 (3), pp. 930-938; Jiang, M., Fu, G., Ceccarelli, L., Du, H., Fogsgaard, M.B., Bahman, A.S., Yang, Y., Iannuzzo, F., Finite element modelling of igbt modules to explore the correlation between electric parameters and damage inbond wires (2019) 2019 Ieee Energy Conversion Congress and Exposition (ECCE). Ieee, pp. 839-844; Maheswari, L., Anand, M., (2009) Analog Electronics, , PHI Learning Pvt.Ltd; Poppe, A., Zhang, Y., Wilson, J., Farkas, G., Szabo, P., Parry, J., Rencz, M., Szekely, V., Thermal measurement and modelling of multi-die pack-ages (2009) Ieee Transactions on Components and Packaging Technologies, 32 (2), pp. 484-492",,,,"Institute of Electrical and Electronics Engineers Inc.","28th International Workshop on Electric Drives: Improving Reliability of Electric Drives, IWED 2021","27 January 2021 through 29 January 2021",,168036,,9780738144436,,,"English","Int. Workshop Electr. Drives: Improv. Reliab. Electr. Drives, IWED - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85103847804
"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
"Kusimba B.A., Rinzin T., Banno Y., Kinoshita K.","57979532100;57697436000;57208163754;57772942600;","Condition Assessment and Adaptation of Bailey Bridges as a Permanent Structures",2022,"Applied Sciences (Switzerland)","12","22","11673","","",,,"10.3390/app122211673","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142476503&doi=10.3390%2fapp122211673&partnerID=40&md5=d1bf6f72faca4b2884e20ff32bd9654a","Department of Civil Engineering, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan; Department of Roads, Ministry of Works and Human Settlement, Zhemgang, Gomphu, 34001, Bhutan","Kusimba, B.A., Department of Civil Engineering, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan; Rinzin, T., Department of Roads, Ministry of Works and Human Settlement, Zhemgang, Gomphu, 34001, Bhutan; Banno, Y., Department of Civil Engineering, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan; Kinoshita, K., Department of Civil Engineering, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan","The present study assessed the Bailey Bridge’s condition and investigated its adaptation as a permanent structure, targeted the Acrow Bailey Bridge in Japan. Field diagnostic loading experiments were performed under various loading conditions, such as dynamic and static loading tests. The onsite data were obtained using a transducer, friction strain gauge, target measurements for the image processing approach, and accelerometer. From the field measurements, the deflection and stresses of the bridge were found to operate within the linear elastic region. The bridge was then accurately modeled based on the in situ geometric configuration of the bridge, and Finite Element Analysis was performed. The model’s accuracy was validated with the onsite data under the linear elastic domain. The model was deployed to check for resistance of critical members. A nonlinear analysis based on the linear and nonlinear buckling method was performed to determine the subject bridge’s Serviceability Limit State and Ultimate Limit State. The results showed that the first out-of-plane eigenvalue buckling analysis could monolithically assess bridge members. Further, the study established digital twin models resolve for historical data through in situ modeling measurements. Therefore, the findings obtained in this study highlight the bridge’s Structural Health Condition, bearing capacity, and propose a framework for adaptation as a permanent structure. © 2022 by the authors.","bailey bridge; bearing capacity; bending moment; deflection; deformation; historical data; static and dynamic loading",,,,,,,,,"Parivallal, S., Narayanan, T., Ravisankar, K., Kesavan, K., Maji, S., Instrumentation and Response Measurement of a Double-Lane Bailey Bridge during Load Test (2005) Strain, 41, pp. 25-30; Khounsida, T., Takafumi, N., Shozo, N., Toshihiro, O., Khampaseuth, T., Study on Static and Dynamic Behavior of Bailey Bridge (2019) Adv. Struct. Eng. Mech, 46, pp. 9425-9428; Bailey Panel Bridge System—Triple Truss Single Storey Class 100, , https://www.sindorf.nl/Portals/0/BAILEYBRIDGES.pdf, Available online; Yi, P., Vaghela, G., Andrew, B., Condition Assessment And Load Rating of Arched Bailey Bridge Proceedings of the Austroads Bridge Conference, pp. 1-11. , Sydney, NSW, Australia, 22–24 October 2014; King, W.S., Wu, S.M., Duan, L., Laboratory Load Tests and Analysis of Bailey Bridge Segments (2013) J. Bridg. Eng, 18, pp. 957-968; Khounsida, T., Nishikawa, T., Nakamura, S., Okumatsu, T., Thepvongsa, K., Experimental and Analytical Study on Dynamic Behavior of Bailey Bridge (2020) Proc. Constr. Steel, 28, pp. 771-777; https://www.nacinc.jp/analysis/software/movias-neo/?add_list=MOVIAS+Neo, Available online; Jatmiko, J., Psimoulis, P., Deformation Monitoring of a Steel Structure Using 3D Terrestrial Laser Scanner (TLS) Proceedings of the 24th International Workshop on Intelligent Computing in Engineering, 2017, pp. 168-177. , Nottingham, UK, 12 July 2017; (2020) Precise and Flexible Strain Gauges, p. 96. , Tokyo Measuring Instruments Ltd., Tokyo, Japan; (1997) Fatigue of Steel Bridge, pp. 47-309. , Japan Road Association, Tokyo, Japan; Godoi, F.C., Prakash, S., Bhandari, B.R., (2005) Prefabricated Steel Bridge Systems, 1, pp. 1-261. , FHWA SOLICITATION NO. DTFH61-03-R-00113, Structure Design and Rehabilitation, Inc., Gaithersburg, MD, USA; Masatoshi, N., Yuki, C., Ichiro, A., Topological Optimum Shape of a Fundamental Module of the Periodic Structure and Cantilever Bridge (2021) J. Struct. Eng, 67A, pp. 90-98; Joiner, C.J.H., The Story of the Bailey Bridge (2011) Proc. Inst. Civ. Eng.-Eng. Hist. Herit, 164, pp. 65-72; (1986) Bailey Bridge, pp. 3-349. , Field Manual—TM 5-277, Department of the Army, Washington, DC, USA; Gómez-Martínez, R., Sánchez-García, R., Escobar-Sánchez, J.A., Arenas-García, L.M., Mendoza-Salas, M.A., Rosales-González, O.N., Monitoring Two Cable-Stayed Bridges during Load Tests with Fiber Optics (2021) Structures, 33, pp. 4344-4358; Umekawa, Y., Suganuma, H., Bridge Displacement Monitoring Using Acceleration Measurement and Development of Efficient Bridge Management System Proceedings of the IABSE Symp. Nantes 2018 Tomorrow’s Megastructures, , Nantes, France, 19–21 September 2018; Lichti, D.D., Gordon, S.J., Stewart, M.P., Franke, J., Tsakiri, M., Comparison of Digital Photogrammetry and Laser Scanning (2002) ISPRS J. Photogramm. Remote Sens, pp. 39-44; https://tml.jp/e/product/transducers/displacement_high.html, Available online; https://ttes.co.jp/service/integral/, Available online; Umekawa, Y., Hisatada, S., Kinoshita, K., Ono, T., A Study on Displacement Monitoring of Bridges Using Acceleration Data Associated with Vehicle Traffic Proceedings of the 72nd Annual Conference of Japan Society of Civil Engineers JSCE, 8, pp. 699-700. , Kyushu, Japan, 11 September 2017; Shimosato, T., Tai, M., Hisatada, S., Umekawa, Y., Hiyama, Y., Deflection Measurement of Bridge Using TWM System Proceedings of the Japan Society of Civil Engineers Western Branch Okinawa Meeting, 7th Technical Research Conference, pp. 146-147. , JSCE, Okinawa, Japan, 7–11 May 2018; Ma, M.J., Dynamic Load Test Analysis for Continuous Steel Bridge (2013) Appl. Mech. Mater, 275–277, pp. 1078-1081; Paeglite, I., Paeglitis, A., Smirnovs, J., Dynamic Amplification Factor for Bridges With Span Length From 10 To 35 Meters (2015) Eng. Struct. Technol, 6, pp. 151-158; Bruls, A., Calgaro, J.A., Mathieu, H., Prat, M., (1996) ENV1991–Part 3: The Main Models of Traffic Loads on Bridges: Background Studies, pp. 215-228. , IABSE Colloquim, Delft, The Netherlands; Ghavami, P., (1956) Mechanics of Materials: An Introduction to Engineering Technology, 78. , Springer, New York, NY, USA; Gross, D., Ehlers, W., Wriggers, P., Schröder, J., Müller, R., (2016) Mechanics of Materials-Formulas and Problems: Engineering Mechanics 2, 2, pp. 1-212. , Springer, Berlin/Heidelberg, Germany; Maros, H., Juniar, S., (2016) Inspection and Maintenance of Steel Girders, , 3rd ed., Indian Railways Institute of Civil Engg, Pune, India; (2010) AASHTO LRFD Bridge Design Specifications, , American Association of State Highway and Transportation Officials, Washington, DC, USA; Yuji, I., Nakamura, S., Kusaba, T., Nishikawa, T., Field Measurement and Structural Analysis of Bailey Bridge for Understanding Fundamental Load-Bearing Configuration (2022) Proceedings of the Japan Society of Civil Engineering-West, pp. 45-46. , I-023, JSCE, Tokyo, Japan; Benčat, J., Kohár, R., Bridges Subjected to Dynamic Loading (2018) Bridge Engineering, , Intech, Rijeka, Croatia; Kalin, J., Žnidarič, A., Anžlin, A., Kreslin, M., Measurements of Bridge Dynamic Amplification Factor Using Bridge Weigh-in-Motion Data (2021) Struct. Infrastruct. Eng, 18, pp. 1164-1176; Umekawa, Y., Hisatada, S., Kinoshita, K., Ono, T., A Study on Displacement Response Monitoring Using Acceleration Response (2017) Proceedings of the JSCE Western Branch Okinawa Meeting, 6th Technical Research Presentation, 8, pp. 88-89. , Okinawa, Japan, 11–13 September 2017, JSCE, Okinawa, Japan; (2015) National Structural Code of the Philippines, pp. 1-1008. , Nscp C101-15, Association of Structural Engineers of the Philiines, Quezon City, Philiines; (2016) Garden Reach Shipbuilders and Engineers Limited Kolkata—India, 4, pp. 1-93. , GRSE—Teschnical Manual, GRSE, Kolkata, India; (2011) Design of Steel Structures—Part 1-1: General Rules and Rules for Buildings, , CEN, Brussels, Belgium; (2003) Eurocode 1: Actions on Structures—Part 2: Traffic Loads on Bridges on Bridges, , CEN, Brussels, Belgium; Tian, Q., Hang, C., Wan, Z., Zou, Y., Local Optimization Analysis of Bailey Beam Bracket Based on Multiscale Model (2019) Key Eng. Mater, 815, pp. 229-234; Holmes, J.D., (2018) Wind Loading of Structures, , 2nd ed., Spon Press, New York, NY, USA; Çiftçioğlu, A.Ö., Yildizel, S.A., Yildirim, M.S., Doğan, E., Wind Load Design of Hangar-Type Closed Steel Structures with Different Roof Pitches Using Abaqus CAE Software (2017) TEM J, 6, pp. 336-341; https://www.fhwa.dot.gov/bridge/lrfd/us_ds8.cfm#designstep86_5, Available online; Poddaeva, O., Fedosova, A., Gribach, J., The Study of Wind Effects on the Bridge Constructions (2019) E3S Web Conf, 97, p. 03030; Gorenc, B.E., Tinyou, R., Syam, A.A., (2005) Steel Designers’ Handbook, , 7th ed., UNSW Press, Sydney, Australia; Ochshorn, J., (2009) Structural Elements for Architects and Builders: Design of Columns, Beams, and Tension Elements in Wood, Steel, and Reinforced Concrete, , Elsevier, New York, NY, USA; (2003) American Society of Civil Engineers Design Loads on Structures During Construction American Society of Civil Engineers Design Loads on Structures During Construction, , Amer Society of Civil Engineers, Reston, VA, USA; Kumamoto, T., Seismic Hazard Maps of Japan and Computational Differences in Models and Parameters (1999) Geogr. Rev. Jpn. Ser. B, 72, pp. 135-161; Dassault Systèmes (2012) Abaqus CAE User’s Manual, pp. 1-1174. , Simulia, Johnston, RI, USA; Chakrabarty, J., (2006) Theory of Plasticity, , 3rd ed., Elsevier Butterworth-Heinemann, Bington, Singapore; Sun, S., Lei, G., Sun, Z., Dynamic and Static Load Tests on a Large-Span Rigid-Frame Bridge (2019) Math. Model. Eng. Probl, 6, pp. 409-414; Miki, C., (2000) Civil Engineering: Steel Structures, , 10th ed., Kyoritsu Publishing, Tokyo, Japan; (2001) Structural Use of Steel Work in Buildings Part 1, , BSI, London, UK","Kusimba, B.A.; Department of Civil Engineering, 1-1 Yanagido, Japan; email: z3921007@edu.gifu-u.ac.jp",,,"MDPI",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85142476503
"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; Frangopol, D.M., Strauss, A., Kim, S., Bridge reliability assessment based on monitoring (2008) J Bridge Eng, 13 (3), pp. 258-270; Deng, Y., Ding, Y., Li, A., Zhou, G., Fatigue reliability assessment for bridge welded details using long-term monitoring data (2011) Sci China Ser E, 54 (12), pp. 3371-3381; Ni, Y.Q., Ye, X.W., Ko, J.M., Monitoring-based fatigue reliability assessment of steel bridges: analytical model and application (2010) J Struct Eng, 136 (12), pp. 1563-1573; Guo, T., Chen, Y.W., Field stress/displacement monitoring and fatigue reliability assessment of retrofitted steel bridge details (2011) Eng Fail Anal, 18 (1), pp. 354-363; Mao, J.X., Wang, H., Feng, D.M., Tao, T.Y., Zheng, W.Z., Investigation of dynamic properties of long-span cable-stayed bridges based on one-year monitoring data under normal operating condition[J] (2018) Struct Control Health Monit, 25 (5); Deng, Y., Liu, Y., Feng, D.M., Li, A.Q., Investigation of fatigue performance of welded details in long-span steel bridges using long-term monitoring strain data (2015) Struct Control Health Monit, 22 (11), pp. 1343-1358; Huang, J., Li, D., Li, H., Song, G., Liang, Y., Damage identification of a large cable-stayed bridge with novel cointegrated Kalman filter method under changing environments Struct Control Health Monit, 25 (5); Guo, T., Frangopol, D.M., Chen, Y., Fatigue reliability assessment of steel bridge details integrating weigh-in-motion data and probabilistic finite element analysis (2012) Comput Struct, 112, pp. 245-257; Chen, Z.W., Xu, Y.L., Wang, X.M., Fatigue reliability analysis of long span bridges with structural health monitoring systems (2009) 4th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2009; Liu, M., Frangopol, D.M., Kwon, K., Fatigue reliability assessment of retrofitted steel bridges integrating monitored data (2010) Struct Saf, 32 (1), pp. 77-89; Yan, F., Chen, W., Lin, Z., Prediction of fatigue life of welded details in cable-stayed orthotropic steel deck bridges (2016) Eng Struct, 127, pp. 344-358; Farreras-Alcover, I., Chryssanthopoulos, M.K., Andersen, J.E., Regression models for structural health monitoring of welded bridge joints based on temperature, traffic and strain measurements (2015) Struct Health Monit, 14 (6), pp. 648-662; Yu, S., Ye, Z., Ou, J., Data-based models for fatigue reliability assessment and life prediction of orthotropic steel deck details considering pavement temperature and traffic loads (2019) J Civ Struct Health, 9 (4), pp. 579-596; Liu, Y., Zhang, H., Liu, Y., Deng, Y., Jiang, N., Lu, N., Fatigue reliability assessment for orthotropic steel deck details under traffic flow and temperature loading (2017) Eng Fail Anal, 71, pp. 179-194; Webster, G.A., Ezeilo, A.N., Residual stress distributions and their influence on fatigue lifetimes (2001) Int J Fatigue, 23, pp. 375-383; Cheng, X., Fisher, J.W., Prask, H.J., Gnäupel-Herold, T., Yen, B.T., Roy, S., Residual stress modification by post-weld treatment and its beneficial effect on fatigue strength of welded structures (2003) Int J Fatigue, 25 (9-11), pp. 1259-1269; Barsoum, Z., Barsoum, I., Residual stress effects on fatigue life of welded structures using LEFM (2009) Eng Fail Anal, 16 (1), pp. 449-467; Yu, S., Ou, J., Fatigue life prediction for orthotropic steel deck details with a nonlinear accumulative damage model under pavement temperature and traffic loading (2021) Eng Fail Anal, 126; Cui, C., Zhang, Q., Bao, Y., Bu, Y., Ye, Z., Fatigue damage evaluation of orthotropic steel deck considering weld residual stress relaxation based on continuum damage mechanics (2018) J Bridge Eng, 23 (10); Cui, C., Zhang, Q., Luo, Y., Hao, H., Li, J., Fatigue reliability evaluation of deck-to-rib welded joints in OSD considering stochastic traffic load and welding residual stress (2018) Int J Fatigue, 111, pp. 151-160; Cao, B.Y., Ding, Y.L., Song, Y.S., Zhong, W., Fatigue life evaluation for deck-rib welding details of orthotropic steel deck integrating mean stress effects (2019) J Bridge Eng, 24 (2); Glaessgen, E., Stargel, D., The digital twin paradigm for future NASA and US Air Force vehicles (2012) 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA; Wang, F.Y., Xu, Y.L., Sun, B., Zhu, Q., Updating multiscale model of a long-span cable-stayed bridge (2018) J Bridge Eng, 23 (3); Ernst, J.H., Der E-Modul von Seilen unter berucksichtigung des Durchhanges (1965) Der Bauingenieur, 40 (2), pp. 52-55; Zhang, Q., Bu, Y., Li, Q., Review on fatigue problems of orthotropic steel bridge deck [J] (2017) China Journal of Highway and Transport, 30 (3), pp. 14-30. , 39. (in Chinese); Crespo-Minguillon, C., Casas, J.R., A comprehensive traffic load model for bridge safety checking (1997) Struct Saf, 19 (4), pp. 339-359; Zheng, Y., (2009) The Temperature Adjustments for Dynamic Deflection and Back-calculated Moduli of AC Pavement, , Ph.D. dissertation., Dalian, China, Dalian University of Technology at Dalian; Zhao, Q., Wu, C., Numerical analysis of welding residual stress of U-rib stiffened plate Engineering Mechanics, 29 (8), pp. 262-268. , (in Chinese); Nagel, K., Schreckenberg, M., A cellular automaton model for freeway traffic (1992) Journal de Physique I, 2 (12), pp. 2221-2229; Maerivoet, S., de Moor, B., Cellular automata models of road traffic (2005) Phys Rep, 419 (1), pp. 1-64; Li, Z.X., Chan, T.H., Ko, J.M., Fatigue analysis and life prediction of bridges with structural health monitoring data—Part I: methodology and strategy (2001) Int J Fatigue, 23 (1), pp. 45-53; (2005) Design of Steel Structures. 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
"Červenka J., Rymeš J., Pukl R.","7103036677;57192986869;6507525719;","Advanced modelling of concrete bridges",2022,"IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report",,,,"1321","1328",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133548836&partnerID=40&md5=71c795c78912fd5fbe081bba01f07b21","Červenka Consulting, s.r.o., Prague, Czech Republic","Červenka, J., Červenka Consulting, s.r.o., Prague, Czech Republic; Rymeš, J., Červenka Consulting, s.r.o., Prague, Czech Republic; Pukl, R., Červenka Consulting, s.r.o., Prague, Czech Republic","The combination of non-linear finite element analysis with structural monitoring can considerably improve the prognosis of bridge behaviour, deterioration, and damage. Recently, a digital twin concept is utilized in which a digital replica of a real structure is developed. Based on the data obtained from the monitoring system installed on the Wonka bridge, Czech Republic, a computational model was calibrated. Then, advanced deterioration models accounting for chloride-induced reinforcement corrosion were applied to assess the long-term development of the structure's load-bearing capacity. It is shown that the proposed integrated system can be used as a tool for the ageing management of concrete structures ensuring their long-term safe operation. © 2022 IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report. All rights reserved.","ageing management; bridge monitoring; durability assessment; finite element method (FEM); non-linear analysis","Chlorine compounds; Concrete bridges; Concretes; Deterioration; Electrochemical corrosion; Monitoring; Advanced modeling; Ageing management; Bridge monitoring; Digital replicas; Durability assessment; Finite element method; Non-linear analysis; Nonlinear finite element analyses (FEA); Real structure; Structural monitoring; Finite element method",,,,,"Technologická Agentura České Republiky: TF06000016","The durability modelling presented in this study was supported by the Czech Technological Agency under the project TF06000016 “ Advanced system for monitoring, diagnosis and reliability assessment of large-scale concrete infrastructures” .",,"(2021) Infrastructure maintenance (indicator), , OECD; Haag, S, Anderl, R., Digital twin - Proof of concept (2018) Manuf Lett, 15, pp. 64-66. , Jan 1; Červenka, V, Jendele, L, Červenka, J., ATENA Program Documentation: Part 1 Theory, p. 360. , Prague; Červenka, J, Červenka, V, Eligehausen, R., Fracture-plastic material model for concrete, application to analysis of powder actuated anchors (1998) Proceedings FRAMCOS, (3), pp. 1107-1116; Červenka, J, Papanikolaou, VK., Three dimensional combined fracture-plastic material model for concrete (2008) Int J Plast, 24 (12), pp. 2192-2220. , Dec 1; Hájková, K, Šmilauer, V, Jendele, L, Červenka, J., Prediction of reinforcement corrosion due to chloride ingress and its effects on serviceability (2018) Eng Struct, 174, pp. 768-777. , (February); Vergoossen, RPH, Wolfert, ARM, Koenders, EAB., Objective risk based structural assessment of existing concrete structures (2017) Life-Cycle Eng Syst Emphas Sustain Civ Infrastruct - 5th Int Symp Life-Cycle Eng IALCCE 2016, pp. 1406-1413. , (February 2017); (2000) Probabilistic performance based durability design of concrete structures: Final technical report of Duracrete project; Luping, T, Boubitsas, D, Utgenannt, P, Abbas, Z., (2018) Chloride Ingress and Reinforcement Corrosion - After 20 years' field exposure in a highway environment, , Borås; Hartt, WH, Lee, S-K., (2018) Corrosion Forecasting and Failure Projection of Post-Tension Tendons in Deficient Cementitious Grout; (2002) Eurocode 1: Actions on structures - Part 1-1: General actions - Densities, self-weight, imposed loads for buildings; González, JA, Andrade, C, Alonso, C, Feliu, S., Comparison of rates of general corrosion and maximum pitting penetration on concrete embedded steel reinforcement (1995) Cem Concr Res, 25 (2), pp. 257-264. , Feb 1; (2012) ISO 9223:2012 Corrosion of metals and alloys - Corrosivity of atmospheres - Classification, determination and estimation, , ISO/TC 156","Červenka, J.; Červenka Consulting, Czech Republic; email: jan.cervenka@cervenka.cz",,"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-s2.0-85133548836
"Koddenbrock M., Heimann J., Herfert D., Pehe J., Wargulski L.","57192205263;57205287274;56707386100;57405570600;57405570700;","WaveImage Bridges the Gap Between Measurement and Simulation. An Application Example of How to Create a Modal Digital Twin Using FE Model Updating",2022,"Conference Proceedings of the Society for Experimental Mechanics Series",,,,"39","48",,,"10.1007/978-3-030-77348-9_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122520101&doi=10.1007%2f978-3-030-77348-9_6&partnerID=40&md5=ae53d2e394e8ee9095b4a9cd9dd926e5","Society for the Advancement of Applied Computer Science, Berlin, Germany; gfai tech GmbH, Berlin, Germany; GFaI e.V., Berlin, Germany","Koddenbrock, M., Society for the Advancement of Applied Computer Science, Berlin, Germany; Heimann, J., Society for the Advancement of Applied Computer Science, Berlin, Germany; Herfert, D., Society for the Advancement of Applied Computer Science, Berlin, Germany; Pehe, J., gfai tech GmbH, Berlin, Germany; Wargulski, L., GFaI e.V., Berlin, Germany","In this paper, a best-practice example of a digital twin is presented. For this purpose, the authors choose a test model of a machine frame with a rotating motor to simulate a situation from an industrial context. During the manufacturing process, this type of frame undergoes excitation from the imbalance forces of a single-speed drive. In order to avoid a failure of a structure when operating conditions change, it is important to be able to simulate the impact accurately. This accurate simulation requires an adequate digital twin. In this context, a good digital twin is one that reproduces the modal characteristics of the structure properly. In this paper, the modal parameters of the machine frame are determined by experimental modal analysis. Afterward, model updating is performed to approximate the simulated modal parameters to the ones obtained from the real structure experiment. The full process is executed using the software WaveImage, which provides an easy-to-use modular kit for experimental modal analysis, finite element analysis, and finite element model updating. © 2022, The Society for Experimental Mechanics, Inc.","Digital twin; Experimental modal analysis; Finite element simulation; Model updating","Failure (mechanical); Modal analysis; Structural dynamics; Application examples; Best practices; Experimental modal analysis; FE model updating; Finite elements simulation; Measurement and simulation; Modal parameters; Model updating; Rotating motors; Test models; Finite element method",,,,,,,,"Leurs, W., Deblauwe, F., Lembregts, F., (1993) Modal Parameter Estimation Based on Complex Mode Indicator Functions; Dong, X., Wang, Y., (2018) Formulation and Optimization Algorithm Comparison for the FE Model Updating of Large-Scale Structures; Gollnick, M., Herfert, D., Heimann, J.: 9. Automatic modal parameter identification with methods of artificial intelligence (2021) Topics in Modal Analysis & Testing, Vol. 8. Springer International Publishing; Allemang, R., Brown, D., (2006) A Complete Review of the Complex Mode Indicator Function (CMIF) with Applications; Ovako Material data sheet S235JR: https://steelnavigator.ovako.com/steel-grades/s235/pdf?variantIDs=701","Herfert, D.; Society for the Advancement of Applied Computer ScienceGermany; email: herfert@gfai.de","Mao Z.",,"Springer","39th IMAC, A Conference and Exposition on Structural Dynamics, 2021","8 February 2021 through 11 February 2021",,264509,21915644,9783030773472,,,"English","Conf. Proc. Soc. Exp. Mech. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85122520101
"Cervenka J., Jendele L., Zalsky J., Pukl R., Novak D.","7103036677;6507138049;57222347260;6507525719;7103231214;","Digital twin approach for durability and reliability assessment of bridges",2020,"fib Symposium",,,,"1840","1848",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134807069&partnerID=40&md5=26db7e81805c9080dfb8a42232b3581f","Cervenka Consulting s r o, Prague, Czech Republic; Klokner’s Institute, Czech Technical University, Prague, Czech Republic; Dep of Civil Engineering, Technical University of Brno, Brno, Czech Republic","Cervenka, J., Cervenka Consulting s r o, Prague, Czech Republic; Jendele, L., Cervenka Consulting s r o, Prague, Czech Republic; Zalsky, J., Klokner’s Institute, Czech Technical University, Prague, Czech Republic; Pukl, R., Cervenka Consulting s r o, Prague, Czech Republic; Novak, D., Dep of Civil Engineering, Technical University of Brno, Brno, Czech Republic","Digital twin is a modern concept, in which a digital replica of a real product and structure is developed, and a simulation is performed to test the product behaviour under service conditions. In the presented paper the digital twin method is used for making assessments of safety, durability and reliability of bridge structures. Although some numerical modelling is often done when an existing bridge is evaluated, it usually does not involve the simulation of real behaviour under service and environmental loads including chloride ingress, reinforcement corrosion and assessment of ultimate load carrying capacity. The digital twin concept in addition includes an important aspect of the digital twin calibration and validation using the real monitoring data. The paper presents a chemo-mechanical model covering initiation and propagation of chlorides or carbonation. This model is combined with the nonlinear modelling of cracking, bond failure and reinforcement yielding (Cervenka and Papanikolaou (2008). The paper extents the previously developed model by the authors Hájková et al. (2019), Jendele, Šmilauer and Červenka (2014). The models were implemented in ATENA software and are validated on experimental data. The developed models can be efficiently used in large scale analysis of real engineering problems as demonstrated on applications to an existing bridge structures in Germany. The example simulation using the digital twin concept show time development of reinforcement corrosion due to chloride ingress, and their impact on the evolution of structural safety and reliability. © fédération internationale du béton (fib).","Chloride ingress; Concrete bridges; Corrosion; Durability; Finite element analysis","Chlorine compounds; Concrete buildings; Concrete construction; Durability; Electrochemical corrosion; Load limits; Reinforcement; Reliability; Calibration and validations; Chemo-mechanical model; Chlorides or carbonations; Initiation and propagation; Non-linear modelling; Reinforcement corrosion; Reliability assessments; Ultimate load-carrying capacity; Digital twin",,,,,,,,"Crisfield, M.A., An Arc-Length Method Including Line Search and Accelerations (1983) International Journal for Numerical Methods in Engineering, 19, pp. 1269-1289; Červenka, J., Červenka, V., Eligehausen, R., Fracture-Plastic Material Model for Concrete, Application to Analysis of Powder Actuated Anchors (1998) Proc. FRAMCOS, 3 (1998), pp. 1107-1116; Červenka, V., Jendele, L., Červenka, J., (2020) Atenaprogram Documentation - Part1 - Theory, , www.cervenka.cz, Praha: Cervenka Consulting; Červenka, J., Papanikolaou, V.K., (2008), Three dimensional combined fracture-plastic material model for concrete (2008) Int. J. Plast, 24, pp. 2192-2220; Darmawan, M.S., Stewart, M.G., Effect of Pitting Corrosion on Capacity of Prestress-ing Wires (2007) Magazine of Concrete Research, 59 (2), pp. 131-139; (2006) Model Code for Service Life Design, , Fédération Internationale du Béton (fib), Lausanne, Switzerland; Gonzales, J.A., Rade, C., Alonso, C., Feliu, S., Comparison of Rates of General Corrosion and Maximum Pitting Penetration on Concrete Embedded Steel Reinforcement (1995) Cement and Concrete Research, 25 (2), pp. 257-264; Hájková, K., Šmilauer, V., Jendele, L., Červenka, J., Prediction of reinforcement corrosion due to chloride ingress and its effects on serviceability (2019) Engineering Structures, 174, pp. 768-777; Jendele, L., Šmilauer, V., Červenka, J., Multiscale hydro-thermo-mechanical model for early-age and mature concrete structures (2014) Adv. Eng. Software, p. 2014; Kwon, S.J., Na, U.J., Park, S.S., Jung, S.H., Service life prediction of concrete wharves with early-aged crack: Probabilistic approach for chloride diffusion (2009) Struct Safety, 31 (1), pp. 75-83; Liu, Y., (1996) Modelling the Time-To-Corrosion Cracking of the Cover Concrete in Chloride Contaminated Reinforced Concrete Structures, , Virginia: Polytechnic Institute; Liu, T., Weyers, R.W., Modelling the dynamic corrosion process in chloride contaminated concrete structures (1998) Cem Concr Res, 28 (3), pp. 365-367","Cervenka, J.; Cervenka Consulting s r oCzech Republic; email: jan.cervenka@cervenka.cz","Zhao B.Lu X.",,"fib. The International Federation for Structural Concrete","International fib Symposium on Concrete structures for resilient society, 2020","22 November 2020 through 24 November 2020",,267619,26174820,9782940643042,,,"English","fib. Symp.",Conference Paper,"Final","",Scopus,2-s2.0-85134807069
"Cervenka J., Jendele L., Zalsky J., Pukl R., Novak D.","7103036677;6507138049;57222347260;6507525719;7103231214;","Digital twin approach for durability and reliability assessment of bridges",2020,"Proceedings of the fib Symposium 2020: Concrete Structures for Resilient Society",,,,"1840","1848",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102411016&partnerID=40&md5=701f028050f7d458648fc048f6069e47","Prague, Czech Republic; Klokner's Institute, Czech Technical University, Prague, Czech Republic; Dep. of Civil Engineering, Technical University of Brno, Brno, Czech Republic","Cervenka, J., Prague, Czech Republic; Jendele, L., Prague, Czech Republic; Zalsky, J., Klokner's Institute, Czech Technical University, Prague, Czech Republic; Pukl, R., Prague, Czech Republic; Novak, D., Dep. of Civil Engineering, Technical University of Brno, Brno, Czech Republic","Digital twin is a modern concept, in which a digital replica of a real product and structure is developed, and a simulation is performed to test the product behaviour under service conditions. In the presented paper the digital twin method is used for making assessments of safety, durability and reliability of bridge structures. Although some numerical modelling is often done when an existing bridge is evaluated, it usually does not involve the simulation of real behaviour under service and environmental loads including chloride ingress, reinforcement corrosion and assessment of ultimate load carrying capacity. The digital twin concept in addition includes an important aspect of the digital twin calibration and validation using the real monitoring data. The paper presents a chemo-mechanical model covering initiation and propagation of chlorides or carbonation. This model is combined with the nonlinear modelling of cracking, bond failure and reinforcement yielding (Cervenka and Papanikolaou (2008). The paper extents the previously developed model by the authors Hájková et al. (2019), Jendele, Šmilauer and Cervenka (2014). The models were implemented in ATENA software and are validated on experimental data. The developed models can be efficiently used in large scale analysis of real engineering problems as demonstrated on applications to an existing bridge structures in Germany. The example simulation using the digital twin concept show time development of reinforcement corrosion due to chloride ingress, and their impact on the evolution of structural safety and reliability. © Proceedings of the fib Symposium 2020: Concrete Structures for Resilient Society. All rights reserved.","Chloride ingress; Concrete bridges; Corrosion; Durability; Finite element analysis","Chlorine compounds; Concrete buildings; Concrete construction; Durability; Electrochemical corrosion; Load limits; Reinforcement; Reliability; Calibration and validations; Chemo-mechanical model; Chlorides or carbonations; Initiation and propagation; Non-linear modelling; Reinforcement corrosion; Reliability assessments; Ultimate load-carrying capacity; Digital twin",,,,,"Grantová Agentura České Republiky, GA ČR; Technologická Agentura České Republiky: TF06000016","The monitoring program was part of an international Eurostars-2 project E! 10925 “cyberBridge”. The application of global safety formats was supported by the project from Czech Grant Agency 20-01781S Uncertainty modelling in safety formats of concrete structures. The optimization of material parameters was using the tools and methods developed under the project supported by Technological Agency of Czech Republic - TF06000016, Advanced system for monitoring, diagnosis and reliability assessment of large-scale concrete infrastructures.",,"Crisfield, M.A., An Arc-Length Method Including Line Search and Accelerations (1983) International Journal for Numerical Methods in Engineering, 19, pp. 1269-1289; Cervenka, J., Cervenka, V., Eligehausen, R., Fracture-Plastic Material Model for Concrete, Application to Analysis of Powder Actuated Anchors (1998) Proc. FRAMCOS, 3, pp. 1107-1116. , 1998; Cervenka, V, Jendele, L, Cervenka, J., (2020) ATENAProgram documentation - Part1 - Theory, , www.cervenka.cz, Praha: Cervenka Consulting; Cervenka, J., Papanikolaou, V.K., Three dimensional combined fracture-plastic material model for concrete (2008) Int. J. Plast, 24, pp. 2192-2220. , 2008; Darmawan, M.S., Stewart, M.G., Effect of Pitting Corrosion on Capacity of Prestress-ing Wires (2007) Magazine of Concrete Research, 59 (2), pp. 131-139; (2006) Model code for service life design, , fib Bulletin 34 Fédération Internationale du Béton (fib), Lausanne, Switzerland; Gonzales, J. A., Andrade, C., Alonso, C., Feliu, S., Comparison of Rates of General Corrosion and Maximum Pitting Penetration on Concrete Embedded Steel Reinforcement (1995) Cement and Concrete Research, 25 (2), pp. 257-264; Hájková, K., Šmilauer, V., Jendele, L., Cervenka, J., Prediction of reinforcement corrosion due to chloride ingress and its effects on serviceability (2019) Engineering Structures, 174, pp. 768-777; Jendele, L., Šmilauer, V., Cervenka, J., Multiscale hydro-thermo-mechanical model for early-age and mature concrete structures (2014) Adv. Eng. Software 2014; Kwon, SJ, Na, UJ, Park, SS, Jung, SH., Service life prediction of concrete wharves with early-aged crack: probabilistic approach for chloride diffusion (2009) Struct Safety, 31 (1), pp. 75-83; Liu, Y., (1996) Modelling the Time-to-corrosion Cracking of the Cover Concrete in Chloride Contaminated Reinforced Concrete Structures, , Virginia: Polytechnic Institute; Liu, T, Weyers, RW., Modelling the dynamic corrosion process in chloride contaminated concrete structures (1998) Cem Concr Res, 28 (3), pp. 365-367; (2011), Model Code 2010 fib Lausanne. Ernst & Sohn: Switzerland, ISBN 978-3-433-03061-5; Muthena, A, Andrade, C, Nilsson, L-O, Edvardsen, C, (2000) DuraCrete, , Final technical report. Tech. rep.2000; Petschacher, M., (2010) Bridge-Weigh-in-Motion, , ISSN 0379-1491. FSV, Wien; Tang, L, Utgenannt, P, Boubitsas, D., Durability and service life prediction of reinforced concrete structures (2015) J Chin Ceram Soc, 43 (10), pp. 1408-1419","Cervenka, J.Czech Republic; email: jan.cervenka@cervenka.cz","Zhao B.Lu X.","ALLPLAN;Liuzhou OVM Machinery Co., Ltd.","International Federation for Structural Concrete","2020 fib Symposium: Concrete Structures for Resilient Society","22 November 2020 through 24 November 2020",,167100,,9782940643042,,,"English","Proc. fib Symp.: Concrete Struct. Resilient Soc.",Conference Paper,"Final","",Scopus,2-s2.0-85102411016