Optimal combinations of parameters for seismic response prediction of high-speed railway bridges using machine learnings

Abstract

This study aims to determine the optimal number and combination of input parameters from machine learning (ML) techniques, encompassing both earthquake and bridge parameters, for effectively and efficiently predicting the seismic response of high-speed railway bridges (HSRBs). 14 earthquake parameters and 37 bridge parameters were carefully selected. To mitigate multicollinearity among earthquake parameters and identify the most influential bridge parameters, correlation matrix analysis and the “Weight” method were employed, respectively. ML models were trained using data obtained from finite element analyses of 1000 bridge-ground motion pairs. The forward floating selection algorithm sequentially (FFSAS) and the backward floating selection algorithm sequentially (BFSAS) were utilized to determine the optimal combination of ML inputs for HSRBs. The analysis results demonstrate that the ML model, when utilizing the optimal input combination, yields accurate predictions of seismic response for HSRBs under various ground motion types. The predictive performance of machine learning models improves with more input parameters, but this particular model achieves stability with only six parameters. The optimization of this parameter combination is significantly influenced by factors such as the model type, evaluation criteria, and characteristics of the bridge components. FFSAS and BFSAS methods are both applicable for identifying the optimal combination of input parameters in machine learning models.