Bayesian Updating of Nonlinear Constitutive Models for Steel–Concrete Composite Girder Bridges Using Large-Scale Load Test Data

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.