Mapping model between lateral responses of bridges under random traffic loads based on data-driven approach

Abstract

Abstract The lateral distribution characteristic of a bridge is one of the important features reflecting its in-service state. Conventional deterministic indicators often struggle to consider this time-varying feature. In this study, a data-driven approach is employed to establish the mapping model among responses at the different lateral positions using the eXtreme Gradient Boosting model optimized by the Bayesian optimization algorithm. The proposed method is validated based on both data from a bridge health monitoring system equipped on an actual bridge and data from numerical simulation. At different lateral positions of the actual bridge, the overall coefficient of determination (R2) of the strain response mapped by the model is above 0.991, and the overall root mean squared error (RMSE) of the strain response mapped by the model are below 1.159 με. The numerical simulation method is used to consider a variety of working conditions with different road surface roughness and different traffic densities. Under different working conditions, the overall R2 of the deflection response mapped by the model is still above 0.961, and the overall RMSE of the deflection response mapped by the model is below 0.249 mm. These indicate that the proposed model can consider time-varying mapping relationships among responses at different lateral positions, and has good accuracy and applicability.