Model Updating Using Hierarchical Bayesian Strategy Employing B-WIM Calibration Data
Publication: Journal of Bridge Engineering
Volume 27, Issue 5
Abstract
Bridge weigh-in-motion (B-WIM) systems are employed for monitoring traffic weights, providing useful information for management decisions. Many applications were proposed based on the information collected, such as calculation of influence lines and damage detection. In this work, an additional application is addressed, to perform model updating of structural parameters from information collected during the calibration of B-WIM systems. The goal of model updating techniques is to adjust the model parameters in order to achieve better agreement between predicted and experimental responses. Therefore, the resulting updated model is able to provide valuable information for decision makers. For many civil engineering applications, the updated parameters may have an inherent variability during the execution of the experimental procedure, since some external effects, such as environmental conditions, may change considerably along the process. To account for this inherent variability properly, a hierarchical Bayesian strategy is adopted. Results for both numerically simulated signals and a real engineering calibration procedure indicate that the proposed hierarchical Bayesian model updating approach is able to perform suitable estimates.
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Data Availability Statement
The following data that support the findings of this study are available from the corresponding author upon reasonable request: the dataset of numerical simulations for bridge response and codes for performing the hierarchical Bayesian model updating.
Acknowledgments
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (CAPES) (finance code 001) and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ) (Grant No. 307133/2020-6).
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Journal of Bridge Engineering
Volume 27 • Issue 5 • May 2022
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© 2022 American Society of Civil Engineers.
History
Received: Apr 5, 2021
Accepted: Jan 22, 2022
Published online: Mar 10, 2022
Published in print: May 1, 2022
Discussion open until: Aug 10, 2022
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Cited by
- Xinyu Jia, Wang-Ji Yan, Costas Papadimitriou, Ka-Veng Yuen, An analytically tractable solution for hierarchical Bayesian model updating with variational inference scheme, Mechanical Systems and Signal Processing, 10.1016/j.ymssp.2022.110060, 189, (110060), (2023).