Numerical Model Updating and Validation of a Truss Railway Bridge considering Train-Track-Bridge Interaction Dynamics

Abstract

Vibration-based structural health monitoring (SHM) is crucial in assessing the integrity of large civil infrastructures. Developing an accurate fnite element (FE) model that efectively refects the dynamics of a full-scale structure is essential for the success of vibration-based SHM. Tis study introduces a comprehensive framework for FE model updating and validation of a truss railway bridge. Te framework integrates operational modal analysis, a sensitivity-based FE model updating approach, and the dynamics of train-track-bridge interaction (TTBI). Te research focused on a 36-meter-long steel truss railway bridge, which underwent testing under both static and dynamic loads caused by train transit. For modal identifcation, the study utilized the covariancedriven stochastic subspace identifcation (Cov-SSI) method in conjunction with hierarchical density-based spatial clustering of applications with noise (HDBSCAN). Results indicated that the sensitivity-based FE model updating signifcantly enhanced model accuracy, reducing the average frequency errors from 11% to much lower 3%. In addition, the FE model was validated through static load tests using an EMD GT26 locomotive. In the dynamic validation phase, the vertical TTBI was modelled. Comparatively, the acceleration time histories from the TTBI model, exhibiting a normalized mean absolute error (nMAE) of 36%, aligned more closely with actual measurements than those from the basic moving load model, which had a higher nMAE of 45%. Tis framework provides an accurate numerical platform for load rating and SHM activities.