Efficient seismic response modeling for train-bridge systems: A time series mixer approach
Ping Xiang, Xiaonan Xie, Zhanjun Shao, Hongkai Ma, Peng Zhang, Han Zhao
Abstract
This paper introduces an innovative surrogate modeling approach for predicting seismic responses in train-bridge coupled (TBC) systems, utilizing the Time Series Mixer (TSMixer) neural network. The model features a multi-layered architecture with sliding time windows, ensuring continuous, real-time analysis. We refined key performance metrics to address limitations in traditional response evaluation, particularly in handling phase discrepancies. The TSMixer model demonstrates high accuracy across a variety of seismic waves and system parameters, making it a valuable tool for rapid prediction in urban seismic scenarios. By offering swift and precise assessments, this approach has significant implications for improving the design efficiency of high-speed rail systems under seismic conditions. This research advances the field by introducing a robust method capable of capturing the nonlinear dynamics of TBC systems, fulfilling modern engineering demands for speed and precision in seismic analysis.