Intelligent optimization method for seismic risk transfer parameters of high-speed railway track-bridge system

Abstract

Due to the widespread use of high-speed railway simply supported beam bridges in areas prone to high-risk earthquakes, the challenge of risk control for these bridges under seismic activity has become increasingly significant. Therefore, this study develops a finite element model to analyze the seismic response characteristics of the high-speed railway track-bridge system. It proposes a seismic risk transfer system for the high-speed railway track-bridge system. Then, an adaptive hybrid optimization algorithm is constructed, combining Particle Swarm Optimization (PSO) and Simulated Annealing (SA). This algorithm is used to create a neural network-based intelligent optimization method for configuring the parameters of the risk transfer device. In addition, the efficiency of the optimized risk transfer device is evaluated under various earthquake scenarios. The results indicated that the optimized seismic risk transfer device significantly enhances the seismic performance of the high-speed railway track-bridge system. The intelligent optimization method for the seismic risk transfer device parameters substantially reduces the stiffness requirements of the device, yielding economic benefits. The risk transfer device successfully mitigates the seismic risk to the bridge by transferring it to a more easily repairable embankment structure, thus reducing post-earthquake maintenance time and costs. These research results provide novel insights into the seismic design and optimization of bridges.