Dynamic Calibrating of Multiscale Bridge Model Using Long-Term Stochastic Vehicle-Induced Responses
Publication: Journal of Bridge Engineering
Volume 29, Issue 9
Abstract
The traditional multiscale model static updating method for long-span bridges requires load tests to obtain the correspondence between load and response, which leads to prolonged traffic interruption, with poor timeliness and low efficiency. Therefore, an efficient multiscale model dynamic calibrating framework based on stochastic vehicle-induced responses is proposed in this paper. The multiscale model is calibrated by monitoring data, and the dynamic calibrating efficiency is improved through the substructure–refined model combination modeling. First, the relationship between the structure and its corresponding response statistical characteristics is derived under stochastic traffic loads, and a statistical-based calibrating objective function of the multiscale model is established. Second, the framework for efficient multiscale model dynamic calibrating based on long-term monitoring data is presented, including efficient multiscale model establishment and dynamic calibrating based on stochastic vehicle-induced responses. Finally, the effectiveness of the proposed method is verified by its application to a long-span steel box girder suspension bridge. Comparison with the traditional load test method demonstrates that the proposed method effectively achieves multiscale model dynamic calibrating based on monitoring data during bridge operation, improving calibrating efficiency while ensuring multiscale modeling accuracy.
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Data Availability Statement
All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research work was jointly supported by the National Natural Science Foundation of China (Grants Nos. 52250011 and 52078102).
References
AASHTO (American Association of State Highway and Transportation Officials). 2018. The manual for bridge evaluation. 3rd ed. Farmington Hills, MI: AASHTO.
CEN (European Committee for Standardization). 2005. Design of steel structures—Part 1–9: Fatigue. Eurocode 3. EN1993-1-9. Brussels, Belgium: CEN.
Cui, C., Y.-L. Xu, and Q.-H. Zhang. 2021. “Multiscale fatigue damage evolution in orthotropic steel deck of cable-stayed bridges.” Eng. Struct. 237: 112144. https://doi.org/10.1016/j.engstruct.2021.112144.
Deng, F., S. Y. Wei, X. W. Jin, Z. C. Chen, and H. Li. 2023. “Damage identification of long-span bridges based on the correlation of probability distribution of monitored quasi-static responses.” Mech. Syst. Signal Proc. 186: 109908. https://doi.org/10.1016/j.ymssp.2022.109908.
Dhiman, H. S., D. Deb, and J. M. Guerrero. 2019. “Hybrid machine intelligent SVR variants for wind forecasting and ramp events.” Renewable Sustainable Energy Rev. 108: 369–379. https://doi.org/10.1016/j.rser.2019.04.002.
Di, J., X. Z. Ruan, X. H. Zhou, J. Wang, and X. Peng. 2021. “Fatigue assessment of orthotropic steel bridge decks based on strain monitoring data.” Eng. Struct. 228: 111437. https://doi.org/10.1016/j.engstruct.2020.111437.
Du, Y.-L., T.-H. Yi, X.-J. Li, X.-L. Rong, L.-J. Dong, D.-W. Wang, G. Yang, and Z. Leng. 2023. “Advances in intellectualization of transportation infrastructures.” Engineering 24: 239–252. https://doi.org/10.1016/j.eng.2023.01.011.
Dujc, J., B. Brank, and A. Ibrahimbegovic. 2010. “Multi-scale computational model for failure analysis of metal frames that includes softening and local buckling.” Comput. Methods Appl. Mech. Eng. 199 (21–22): 1371–1385. https://doi.org/10.1016/j.cma.2009.09.003.
Guan, Z.-X., D.-H. Yang, T.-H. Yi, W.-J. Li, and C. Li. 2024. “Bridge finite element model updating using stochastic vehicle-induced static response monitoring data.” Eng. Struct. 301: 117280. https://doi.org/10.1016/j.engstruct.2023.117280.
Li, Z. X., T. Q. Zhou, T. H. T. Chan, and Y. Yu. 2007. “Multi-scale numerical analysis on dynamic response and local damage in long-span bridges.” Eng. Struct. 29 (7): 1507–1524. https://doi.org/10.1016/j.engstruct.2006.08.004.
Lin, S.-W., Y.-L. Du, T.-H. Yi, and D.-H. Yang. 2022. “Model updating using bridge influence lines based on an adaptive metamodel global optimization method.” J. Bridge Eng. 27 (3): 04022003. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001839.
Lin, S.-W., Y.-L. Du, T.-H. Yi, S.-H. Zhang, and D.-H. Yang. 2023. “A multiscale modeling and updating framework for suspension bridges based on modal frequencies and influence lines.” J. Bridge Eng. 28 (7): 04023042. https://doi.org/10.1061/jbenf2.beeng-6148.
Lu, Q., J. Zhu, and W. Zhang. 2020. “Quantification of fatigue damage for structural details in slender coastal bridges using machine learning-based methods.” J. Bridge Eng. 25 (7): 04020033. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001571.
Martini, A., E. M. Tronci, M. Q. Feng, and R. Y. Leung. 2022. “A computer vision-based method for bridge model updating using displacement influence lines.” Eng. Struct. 259: 114129. https://doi.org/10.1016/j.engstruct.2022.114129.
Mashayekhi, M., and E. Santini-Bell. 2020. “Fatigue assessment of a complex welded steel bridge connection utilizing a three-dimensional multiscale finite element model and hotspot stress method.” Eng. Struct. 214: 110624. https://doi.org/10.1016/j.engstruct.2020.110624.
MOT (Ministry of Transport of the People’s Republic of China). 2011. Specifications for inspection and evaluation of loadbearing capacity of highway bridges. JTG/T-J21-2011. Beijing: China Communications Press.
Qin, S. Q., Y. G. Yuan, S. Han, and S. W. Li. 2023. “A novel multiobjective function for finite-element model updating of a long-span cable-stayed bridge using in situ static and dynamic measurements.” J. Bridge Eng. 28 (1): 04022131. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001974.
Schinazi, R. B. 2012. Transformations of random variables and random vectors, probability with statistical applications, 201–268. Boston: Birkhäuser Boston.
Sun, L. M., Y. X. Li, and W. Zhang. 2020. “Experimental study on continuous bridge-deflection estimation through inclination and strain.” J. Bridge Eng. 25 (5): 04020020. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001543.
Tan, Z. X., D. P. Thambiratnam, T. H. T. Chan, and H. Abdul Razak. 2017. “Detecting damage in steel beams using modal strain energy based damage index and Artificial Neural Network.” Eng. Fail Anal. 79: 253–262. https://doi.org/10.1016/j.engfailanal.2017.04.035.
Wang, F.-Y., Y.-. Xu, B. Sun, and Q. Zhu. 2018. “Updating multiscale model of a long-span cable-stayed bridge.” J. Bridge Eng. 23 (3): 04017148. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001195.
Wang, X., J. Zhang, Y. Sun, Z. Wu, N. F. C. Tchuente, and F. Yang. 2022. “Stiffness identification of deteriorated PC bridges by a FEMU method based on the LM-assisted PSO-Kriging model.” Structures 43: 374–387. https://doi.org/10.1016/j.istruc.2022.06.060.
Xiao, X., Y. L. Xu, and Q. Zhu. 2015. “Multiscale modeling and model updating of a cable-stayed bridge. II: Model updating using modal frequencies and influence lines.” J. Bridge Eng. 20 (10): 04014113. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000723.
Xu, L., L. Jiang, L. Shen, L. Gan, Y. Dong, and C. Su. 2023. “Adaptive hierarchical multiscale modeling for concrete trans-scale damage evolution.” Int. J. Mech. Sci. 241: 107955. https://doi.org/10.1016/j.ijmecsci.2022.107955.
Yang, D.-H., H.-L. Gu, T.-H. Yi, and H.-N. Li. 2023a. “Bridge cable anomaly detection based on local variability in feature vector of monitoring group cable forces.” J. Bridge Eng. 28 (6): 04023030. https://doi.org/10.1061/JBENF2.BEENG-6084.
Yang, D.-H., Z.-X. Guan, T.-H. Yi, H.-N. Li, and Y.-S. Ni. 2022. “Fatigue evaluation of bridges based on strain influence line loaded by elaborate stochastic traffic flow.” J. Bridge Eng. 27 (9): 04022082. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001929.
Yang, S.-H., T.-H. Yi, C.-X. Qu, S.-H. Zhang, and C. Li. 2023b. “Adaptive sampling-based Bayesian model updating for bridges considering substructure approach.” ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A: Civ. Eng. 9 (3): 0402302. https://doi.org/10.1061/AJRUA6.RUENG-1077.
Zhang, W., Y. X. Li, and L. M. Sun. 2021. “SHM-oriented hybrid modeling for stress analysis of steel girder bridge.” J. Bridge Eng. 26 (6): 05021002. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001710.
Zhao, Y., J. J. Zhang, D. S. Li, D. C. Zhou, and D. B. Xin. 2023. “Finite element model updating of bridge structures based on improved response surface methods.” Struct. Control Health Monit. 2023: 2488951. https://doi.org/10.1155/2023/2488951.
Zheng, X., T.-H. Yi, J.-W. Zhong, and D.-H. Yang. 2022. “Rapid evaluation of load-carrying capacity of long-span bridges using limited testing vehicles.” J. Bridge Eng. 27 (4): 04022008. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001838.
Zhu, Q., Y. L. Xu, and X. Xiao. 2015. “Multiscale modeling and model updating of a cable-stayed bridge. I: Modeling and influence line analysis.” J. Bridge Eng. 20 (10): 04014112. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000722.
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© 2024 American Society of Civil Engineers.
History
Received: Nov 21, 2023
Accepted: Apr 30, 2024
Published online: Jun 27, 2024
Published in print: Sep 1, 2024
Discussion open until: Nov 27, 2024
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