Bayesian Updating of Nonlinear Constitutive Models for Steel–Concrete Composite Girder Bridges Using Large-Scale Load Test Data
Publication: Journal of Bridge Engineering
Volume 30, Issue 1
Abstract
Establishing an accurate finite-element (FE) model that faithfully replicates the nonlinear behavior of steel–concrete composite girder bridges is crucially important for effective model-based structural health monitoring (SHM) for this specific bridge type. While Bayesian model updating methodology has gained widespread acclaim for its application in data-driven FE models, apprehensions have emerged regarding the rationality of the updated model parameter values. These concerns predominantly stem from the use of oversimplified FE beam models in model updating studies. This study addressed these concerns by introducing nonlinear constitutive models for the steel–concrete composite girder bridge, as well as comprehensive and detailed three-dimensional (3D) FE model updates. Key parameters for nonlinear constitutive models were estimated based on structural responses from a large-scale static loading test. Bayes’ theorem was employed to infer posterior probability density functions (PDFs) for the model parameters. A transitional Markov chain Monte Carlo sampler was used as a computational tool to generate samples for representing the posterior PDFs. A two-step model updating approach designed to achieve a balance between computational efficiency and simulation accuracy was proposed in the context of nonlinear model updating. Initially, deflection and neutral axis height data were used to update the linear segment of the constitutive model. Load–deflection curves were then used to update the nonlinear segment. Following the model updating with deflection and strain data, the nonlinear simulation results showed improved comparability to the measured data, indicating a significant improvement in the model accuracy. Furthermore, the model update effectiveness was cross verified successfully by comparing load–strain curves of concrete and reinforcing bars obtained during the experiment. The updated model showcases its capability for structural performance evaluation and its potential application in the domain of SHM.
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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 study was partially sponsored by the Japanese Society for the Promotion of Science (JSPS) grant-in-aid for Scientific Research (B) under Project No. 22H01576. That financial support is gratefully acknowledged. The first author expresses sincere gratitude for the financial support from the China Scholarship Council under Grant No. 202106260039.
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Published In
Journal of Bridge Engineering
Volume 30 • Issue 1 • January 2025
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jan 5, 2024
Accepted: Aug 27, 2024
Published online: Oct 17, 2024
Published in print: Jan 1, 2025
Discussion open until: Mar 17, 2025
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