Sparse Bayesian Identification of Temperature-Displacement Model for Performance Assessment and Early Warning of Bridge Bearings
Publication: Journal of Structural Engineering
Volume 148, Issue 6
Abstract
Bearings usually play numerous important functionalities such as deformation regulation, load transfer, and seismic isolation in bridges. A better mastery of their service performance is increasingly desired for bridge owners. In the present study, a novel sparse Bayesian temperature-displacement relationship (TDR) model is proposed to characterize and predict the bearing displacement responses induced by temperature actions in a probabilistic manner, based on the use of long-term structural health monitoring (SHM) data. Compared with the traditional deterministic TDR model, the newly proposed model can deal with two critical problems: (1) most of temperature difference terms barely have effects on bearing displacement responses, leading to the sparsity of model parameters; and (2) uncertainties will inevitably arise from factors such as measurement noise and inherent randomness, resulting in the uncertainty of model parameters. Therefore, it enables to account for the uncertainty associated with the predictions of temperature-induced bearing displacement responses. By combining the probabilistic prediction results with the reliability and anomaly analysis principles, a reliability index is adopted to assess the service performance of bearings subjected to extreme temperature actions. In addition, an anomaly index is defined to determine whether there are performance degradations and then trigger early warnings for the degraded bearings. The long-term SHM data from an in-service long-span railway bridge is employed for effectiveness verifications. The results show that the sparse Bayesian TDR model can achieve effective probabilistic predictions for temperature-induced bearing displacement responses and the reliability and anomaly indices are favorable for bearing performance assessment and early warning.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code 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 (Grant Nos. 51908184 and 51978128) and the Natural Science Foundation of Hebei Province, China (Grant No. E2021202182).
References
Baisthakur, S., and A. Chakraborty. 2021. “Experimental verification for load rating of steel truss bridge using an improved Hamiltonian Monte Carlo-based Bayesian model updating.” J. Civ. Struct. Health 11 (4): 1093–1112. https://doi.org/10.1007/s13349-021-00495-8.
Bhowmik, B., T. Tripura, B. Hazra, and V. Pakrashi. 2019. “First-order eigen-perturbation techniques for real-time damage detection of vibrating systems: Theory and applications.” Appl. Mech. Rev. 71 (6): 060801. https://doi.org/10.1115/1.4044287.
Bhowmik, B., T. Tripura, B. Hazra, and V. Pakrashi. 2020. “Robust linear and nonlinear structural damage detection using recursive canonical correlation analysis.” Mech. Syst. Sig. Process. 136 (Feb): 106499. https://doi.org/10.1016/j.ymssp.2019.106499.
Brownjohn, W., A. Raby, S. K. Au, Z. Zhu, and D. D’Ayala. 2019. “Bayesian operational modal analysis of offshore rock lighthouses: Close modes, alignment, symmetry and uncertainty.” Mech. Syst. Sig. Process. 133 (Nov): 106306. https://doi.org/10.1016/j.ymssp.2019.106306.
Chen, Z. H., X. W. Liu, G. D. Zhou, H. Liu, and Y. X. Fu. 2021. “Damage detection for expansion joints of a combined highway and railway bridge based on long-term monitoring data.” J. Perform. Constr. Facil. 35 (4): 04021037. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001608.
Cheung, S. H., and S. Bansal. 2017. “A new Gibbs sampling based algorithm for Bayesian model updating with incomplete complex modal data.” Mech. Syst. Sig. Process. 92 (Aug): 156–172. https://doi.org/10.1016/j.ymssp.2017.01.015.
Dubbs, N. C., and F. L. Moon. 2015. “Comparison and implementation of multiple model structural identification methods.” J. Struct. Eng. 141 (11): 04015042. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001284.
Feng, D. C., S. Z. Chen, M. R. Azadi Kakavand, and E. Taciroglu. 2021. “Probabilistic model based on Bayesian model averaging for predicting the plastic hinge lengths of reinforced concrete columns.” J. Eng. Mech. 147 (10): 04021066. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001976.
Frangopol, D. M. 2011. “Life-cycle performance, management, and optimisation of structural systems under uncertainty: Accomplishments and challenges.” Struct. Infrastruct. Eng. 7 (6): 389–413. https://doi.org/10.1080/15732471003594427.
Fuentes, R., C. Mineo, S. G. Pierce, K. Worden, and E. J. Cross. 2019. “A probabilistic compressive sensing framework with applications to ultrasound signal processing.” Mech. Syst. Sig. Process. 117 (Feb): 383–402. https://doi.org/10.1016/j.ymssp.2018.07.036.
Garcia-Sanchez, D., A. Fernandez-Navamuel, D. Z. Sánchez, D. Alvear, and D. Pardo. 2020. “Bearing assessment tool for longitudinal bridge performance.” J. Civ. Struct. Health 10 (5): 1023–1036. https://doi.org/10.1007/s13349-020-00432-1.
Huang, H. B., T. H. Yi, H. N. Li, and H. Liu. 2018. “New representative temperature for performance alarming of bridge expansion joints through temperature-displacement relationship.” J. Bridge Eng. 23 (7): 04018043. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001258.
Huang, H. B., T. H. Yi, H. N. Li, and H. Liu. 2020. “Strain-based performance warning method for bridge main girders under variable operating conditions.” J. Bridge Eng. 25 (4): 04020013. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001538.
Kim, S. H., H. S. Mha, and S. W. Lee. 2006. “Effects of bearing damage upon seismic behaviors of a multi-span girder bridge.” Eng. Struct. 28 (7): 1071–1080. https://doi.org/10.1016/j.engstruct.2005.11.015.
Krishnan, M., B. Bhowmik, B. Hazra, and V. Pakrashi. 2018. “Real time damage detection using recursive principal components and time varying auto-regressive modeling.” Mech. Syst. Sig. Process. 101 (Feb): 549–574. https://doi.org/10.1016/j.ymssp.2017.08.037.
Kromanis, R., and P. Kripakaran. 2014. “Predicting thermal response of bridges using regression models derived from measurement histories.” Comput. Struct. 136 (May): 64–77. https://doi.org/10.1016/j.compstruc.2014.01.026.
Kromanis, R., P. Kripakaran, and B. Harvey. 2016. “Long-term structural health monitoring of the Cleddau bridge: Evaluation of quasi-static temperature effects on bearing movements.” Struct. Infrastruct. Eng. 12 (10): 1342–1355. https://doi.org/10.1080/15732479.2015.1117113.
Kuok, S. C., and K. V. Yuen. 2019. “Bayesian nonparametric modeling of structural health indicators under severe typhoons.” J. Aerosp. Eng. 32 (4): 04019036. https://doi.org/10.1061/(ASCE)AS.1943-5525.0001023.
Lye, A., A. Cicirello, and E. Patelli. 2021. “Sampling methods for solving Bayesian model updating problems: A tutorial.” Mech. Syst. Sig. Process. 159 (Oct): 107760. https://doi.org/10.1016/j.ymssp.2021.107760.
Lyngdoh, G. A., M. A. Rahman, and S. K. Mishra. 2019. “Bayesian updating of structural model with a conditionally heteroscedastic error distribution.” J. Eng. Mech. 145 (12): 04019091. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001668.
MacKay, D. J. 1992. “Bayesian interpolation.” Neural Comput. 4 (3): 415–447. https://doi.org/10.1162/neco.1992.4.3.415.
Magalhães, F., A. Cunha, and E. Caetano. 2012. “Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection.” Mech. Syst. Sig. Process. 28 (Apr): 212–228. https://doi.org/10.1016/j.ymssp.2011.06.011.
Moser, P., and B. Moaveni. 2011. “Environmental effects on the identified natural frequencies of the Dowling hall footbridge.” Mech. Syst. Sig. Process. 25 (7): 2336–2357. https://doi.org/10.1016/j.ymssp.2011.03.005.
Ni, Y. Q., X. G. Hua, K. Y. Wong, and J. M. Ko. 2007. “Assessment of bridge expansion joints using long-term displacement and temperature measurement.” J. Perform. Constr. Facil. 21 (2): 143–151. https://doi.org/10.1061/(ASCE)0887-3828(2007)21:2(143).
Ni, Y. Q., Y. W. Wang, and C. Zhang. 2020. “Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data.” Eng. Struct. 212 (Jun): 110520. https://doi.org/10.1016/j.engstruct.2020.110520.
Prakash, G. 2021. “Probabilistic model for remaining fatigue life estimation of bridge components.” J. Struct. Eng. 147 (10): 04021147. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003114.
Prakash, G., S. Narasimhan, and R. Al-Hammoud. 2019. “A two-phase model to predict the remaining useful life of corroded reinforced concrete beams.” J. Civ. Struct. Health 9 (2): 183–199. https://doi.org/10.1007/s13349-019-00327-w.
Rostami, P., M. Mahsuli, S. Farid Ghahari, and E. Taciroglu. 2021. “Bayesian joint state-parameter-input estimation of flexible-base buildings from sparse measurements using Timoshenko beam models.” J. Struct. Eng. 147 (10): 04021151. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003095.
Straub, D., and I. Papaioannou. 2015. “Bayesian updating with structural reliability methods.” J. Eng. Mech. 141 (3): 04014134. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000839.
Tipping, M. E. 2001. “Sparse Bayesian learning and the relevance vector machine.” J. Mach. Learn. Res. 1 (Apr): 211–244. https://doi.org/10.1162/15324430152748236.
Tipping, M. E., and A. C. Faul. 2003. “Fast marginal likelihood maximization for sparse Bayesian models.” In Proc., 9th Int. Workshop on Artificial Intelligence and Statistics, edited by C. M. Bishop and B. J. Frey, 1–13. Key West, FL: Miketipping.
Toufigh, V., and A. Jafari. 2021. “Developing a comprehensive prediction model for compressive strength of fly ash-based geopolymer concrete (FAGC).” Constr. Build. Mater. 277 (Mar): 122241. https://doi.org/10.1016/j.conbuildmat.2021.122241.
Wang, G. X., Y. L. Ding, Y. S. Song, L. Y. Wu, Q. Yue, and G. H. Mao. 2016. “Detection and location of the degraded bearings based on monitoring the longitudinal expansion performance of the main girder of the Dashengguan Yangtze Bridge.” J. Perform. Constr. Facil. 30 (4): 04015074. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000820.
Worden, K., and E. J. Cross. 2018. “On switching response surface models, with applications to the structural health monitoring of bridges.” Mech. Syst. Sig. Process. 98 (Jan): 139–156. https://doi.org/10.1016/j.ymssp.2017.04.022.
Wu, G. M., D. H. Yang, T. H. Yi, H. N. Li, and H. Liu. 2020. “Sliding life prediction of sliding bearings using dynamic monitoring data of bridges.” Struct. Control Health 27 (Apr): e2515. https://doi.org/10.1002/stc.2515.
Wu, G. M., T. H. Yi, D. H. Yang, H. N. Li, and H. Liu. 2021. “Early warning method for bearing displacement of long-span bridges using a proposed time-varying temperature-displacement model.” J. Bridge Eng. 26 (9): 04021068. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001763.
Yu, B., S. Liu, and B. Li. 2019. “Probabilistic calibration for shear strength models of reinforced concrete columns.” J. Struct. Eng. 145 (5): 04019026. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002307.
Yuen, K. V., and K. Huang. 2018. “Identifiability-enhanced Bayesian frequency-domain substructure identification.” Comput.-Aided Civ. Infrastruct. 33 (Sep): 800–812. https://doi.org/10.1111/mice.12377.
Yuen, K. V., and G. A. Ortiz. 2018. “Multiresolution Bayesian nonparametric general regression for structural model updating.” Struct. Control Health 25 (3): e2077. https://doi.org/10.1002/stc.2077.
Zhao, H. W., Y. L. Ding, S. Nagarajaiah, and A. Q. Li. 2019. “Behavior analysis and early warning of girder deflections of a steel-truss arch railway bridge under the effects of temperature and trains: Case study.” J. Bridge Eng. 24 (1): 05018013. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001327.
Information & Authors
Information
Published In
Journal of Structural Engineering
Volume 148 • Issue 6 • June 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 12, 2021
Accepted: Feb 1, 2022
Published online: Mar 25, 2022
Published in print: Jun 1, 2022
Discussion open until: Aug 25, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Guang Qu, Mingming Song, Limin Sun, Real-Time Bridge Deflection Prediction Based on a Novel Bayesian Dynamic Difference Model and Nonstationary Data, Journal of Bridge Engineering, 10.1061/JBENF2.BEENG-6710, 29, 9, (2024).
- Guang-Ming Wu, Bing Zhang, Sheng-Li Li, Ting-Hua Yi, Xiu-Dao Mei, Ya-Fei Wang, Neutral Axis-Based Anomaly Detection for Local Damage to Girders Affected by Nonlinear Temperature Gradients, Journal of Bridge Engineering, 10.1061/JBENF2.BEENG-6469, 29, 3, (2024).
- Wei Zhang, Hongyin Yang, Hongyou Cao, Xiucheng Zhang, Aixin Zhang, Nanhao Wu, Zhangjun Liu, Separation of Temperature-Induced Response for Bridge Long-Term Monitoring Data Using Local Outlier Correction and Savitzky–Golay Convolution Smoothing, Sensors, 10.3390/s23052632, 23, 5, (2632), (2023).
- De-Cheng Feng, Yue Li, Abdollah Shafieezadeh, Ertugrul Taciroglu, Advances in Data-Driven Risk-Based Performance Assessment of Structures and Infrastructure Systems, Journal of Structural Engineering, 10.1061/JSENDH.STENG-12434, 149, 5, (2023).
- Zhen Wang, Ting-Hua Yi, Dong-Hui Yang, Hong-Nan Li, Data-Driven Modal Equivalent Standardization for Early Damage Detection in Bridge Structural Health Monitoring, Journal of Engineering Mechanics, 10.1061/JENMDT.EMENG-6778, 149, 1, (2023).
- Renhe Yao, Hongkai Jiang, Chunxia Yang, Hongxuan Zhu, Chaoqiang Liu, An integrated framework via key-spectrum entropy and statistical properties for bearing dynamic health monitoring and performance degradation assessment, Mechanical Systems and Signal Processing, 10.1016/j.ymssp.2022.109955, 187, (109955), (2023).
- Guang-Ming Wu, Ting-Hua Yi, Dong-Hui Yang, Hong-Nan Li, Hua Liu, Friction anomaly alarm for bridge sliding bearings under operating environmental conditions, Engineering Structures, 10.1016/j.engstruct.2022.115481, 278, (115481), (2023).
- Zhen Wang, Ting-Hua Yi, Dong-Hui Yang, Hong-Nan Li, Guan-Hua Zhang, Ji-Gang Han, Early Anomaly Warning of Environment-Induced Bridge Modal Variability through Localized Principal Component Differences, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 10.1061/AJRUA6.0001269, 8, 4, (2022).
- Yuan Ren, Qiaowei Ye, Xiang Xu, Qiao Huang, Ziyuan Fan, Chong Li, Weijie Chang, An anomaly pattern detection for bridge structural response considering time-varying temperature coefficients, Structures, 10.1016/j.istruc.2022.10.020, 46, (285-298), (2022).
- Hai-Bin Huang, Wei Zhang, Zhi-Guo Sun, Dong-Sheng Wang, Development of probabilistic FRP-to-concrete bond strength models for externally-bonded reinforcement on grooves: Bayesian approach, Construction and Building Materials, 10.1016/j.conbuildmat.2022.128857, 350, (128857), (2022).
- See more