铁道科学与工程学报2026，Vol.23Issue(7)：3047-3058,12.DOI:10.19713/j.cnki.43-1423/u.T20251352
基于反应谱嵌入的高速铁路轨道-简支梁桥系统地震响应预测模型
Response spectrum-embedded seismic response prediction model for high-speed railway track-simply supported bridge system
摘要
Abstract
Seismic-induced track deformation is one of the most critical factors that affect the post-earthquake safety and maintenance of high-speed railway bridges.To enable rapid prediction of seismic-induced displacement responses in high-speed railway track-simply supported bridge system,this study proposed a deep learning framework that combined temporal convolutional networks(TCN)and bidirectional long short-term memory(Bi-LSTM)networks.The proposed model was further optimized by embedding the seismic acceleration response spectrum and incorporating a self-attention mechanism to more accurately capture seismic wave features and temporal evolution,thereby significantly enhancing prediction accuracy and robustness.A detailed comparative analysis was conducted between the optimized and baseline models under ClassⅠ0 site conditions to assess the improvement in peak displacement prediction accuracy for the rail,sliding layer,and cement asphalt(CA)mortar layer.Different site classes and peak ground acceleration(PGA)levels were considered to systematically investigate the variation of the model's predictive performance under complex conditions.Using the optimized model,4 000 seismic records across different site classes and PGA levels were analyzed to predict rail alignment peak displacements.It further revealed the relationships between rail displacement amplitude,PGA,and site classification.The results show that the optimized model significantly can improve prediction accuracy for key component displacement responses under Class Ⅰ0 site conditions,with the maximum reduction in root mean square error(frmse)exceeding 35%.The optimized model can achieve high overall prediction accuracy across five site classes,with the coefficient of determination(R2)consistently ranging from 0.92 to 0.99,demonstrating strong robustness.The maximum alignment rail displacement can increase with the increase in PGA and the softening of site soils.The proposed research can offer a practical,robust,and efficient deep learning model for predicting seismic-induced displacement responses of high-speed railway track-simply supported bridge system.关键词
深度学习/高速铁路/轨道-简支梁桥系统/地震响应/峰值位移Key words
deep learning/high-speed railway/track-simply supported bridge system/seismic response/peak displacement分类
交通工程引用本文复制引用
周旺保,刘昊,彭康,彭梦颖,徐杰玉,秦浩然..基于反应谱嵌入的高速铁路轨道-简支梁桥系统地震响应预测模型[J].铁道科学与工程学报,2026,23(7):3047-3058,12.基金项目
中国中铁股份有限公司科技研究开发计划项目(2022-重大-17) （2022-重大-17）
国家自然科学基金资助项目(52178180) （52178180）
湖南省科技人才托举工程(2022TJ-Y10) （2022TJ-Y10）
中南大学前沿交叉研究项目(2023QYJC006) （2023QYJC006）
澳门特别行政区科学技术发展基金资助项目(SKL-IOTSC(UM)-2024-2026) （SKL-IOTSC(UM）
