铁道科学与工程学报2025，Vol.22Issue(2)：734-747,14.DOI:10.19713/j.cnki.43-1423/u.T20240598
基于MSGWO-LSTM的车桥非线性系统地震响应预测研究
Research on earthquake response prediction of nonlinear vehicle bridge systems based on MSGWO-LSTM
摘要
Abstract
During strong earthquakes,high-speed railway(HSR)bridges are prone to enter the nonlinear phase,leading to significant difficulties in predicting system responses and potentially in threatening train running safety.Therefore,this study proposed a Multi-Strategy Gray Wolf Optimization-Long Short-Term Memory(MSGWO-LSTM)surrogate model to improve the accuracy of earthquake response prediction for the HSR nonlinear system.An OpenSees nonlinear model of the vehicle-bridge coupling system was established.Based on a large amount of seismic dynamic analysis,a database of bridge displacement,vehicle acceleration,and wheel-rail force response was constructed.Based on the traditional LSTM surrogate model,a Dropout layer was introduced to prevent overfitting during model training,and the Grey Wolf Optimization Algorithm(GWO)was introduced for automatic hyperparameter selection,thus constructing and training the GWO-LSTM surrogate model.By using multiple evaluation indicators and considering structural linearity or nonlinearity,different vehicle speeds,and other working conditions,the predictive performance of traditional LSTM and GWO-LSTM surrogate models was compared.It was found that GWO-LSTM did not satisfy the requirements in certain working conditions;therefore,a MSGWO-LSTM surrogate model was proposed by integrating multiple strategies,further improving the prediction accuracy of the model.The research results show that the R2 predicted by the GWO-LSTM surrogate model is stable between 0.95 and 0.99,and all other predictive indicators are close to 0.Moreover,the MAPE index is primarily controlled at around 1%,which is significantly better than the traditional LSTM surrogate model.This indicates that the GWO-LSTM model can significantly improve the accuracy of earthquake response prediction.In MIMO and nonlinear conditions,the prediction response of the GWO-LSTM model has a small number of predictors exceeding the 10%limit,while all prediction indicators of MSGWO-LSTM are below the limit,further improving the model's prediction accuracy and generalization ability.关键词
桥梁工程/地震响应预测/LSTM代理模型/高速铁路/OpenSeesKey words
bridge engineering/earthquake response prediction/LSTM surrogate model/high speed railway/OpenSees分类
交通工程引用本文复制引用
刘汉云,王子逸,韩艳,王力东,胡朋,国巍,余志武..基于MSGWO-LSTM的车桥非线性系统地震响应预测研究[J].铁道科学与工程学报,2025,22(2):734-747,14.基金项目
国家自然科学基金资助项目(52108433,52178452,52278546,52022113) （52108433,52178452,52278546,52022113）
湖南省科技创新计划资助项目(2021RC4031,2024RC3170) （2021RC4031,2024RC3170）
湖南省自然科学基金资助项目(2024JJ5018,2024JJ2002) （2024JJ5018,2024JJ2002）
高速铁路建造技术国家工程研究中心开放基金资助项目(HSR202004) （HSR202004）
