Research on numerical solution algorithm for real-time hybrid simulation of high-speed railway on suspension bridge

被引:4
|
作者
Guo, Wei [1 ,2 ]
Zhang, Rui [1 ,2 ]
Hu, Jingyi [1 ,2 ]
Wang, Yang [1 ,2 ]
机构
[1] Cent South Univ, Sch Civil Engn, Changsha 410075, Peoples R China
[2] Natl Engn Res Ctr High Speed Railway Construct Tec, Changsha, Peoples R China
来源
EARTHQUAKE ENGINEERING AND RESILIENCE | 2022年 / 1卷 / 03期
基金
中国国家自然科学基金;
关键词
long-span railway suspension bridge; LSTM; numerical solution algorithm; real-time hybrid simulation; train-track-bridge coupling vibrations; STABILITY;
D O I
10.1002/eer2.29
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Real-time hybrid simulation combines experimental substructure and numerical substructure and is an effective method to study the dynamic response of coupled vibrations on high-speed railway. This paper constructs a semi-active suspension-train-suspension bridge coupling real-time hybrid framework and a real-time hybrid simulation method based on a neural network is proposed, which uses a long-short-term memory model instead of a complex train-bridge structure model, evaluates the prediction performance of the trained network models through time history, regression analysis, and normalization error distribution. Finally, the network model is applied to a real-time hybrid simulation, the accuracy and real-time ability of the method are verified, and the results show that the proposed method can effectively improve the accuracy and efficiency of solving the numerical substructure. In this paper, long-short-term memory (LSTM) is applied to the coupling vibration study of the train-bridge system, and an RTHS framework for semi-active vibration damping device-train-suspension bridge coupling is established to illustrate the test logic and feasibility, and a dynamic response solution method of numerical substructure based on LSTM is proposed. Then, the feasibility of the network models in RTHS was analyzed, and the predicted performance of the trained network models was evaluated by time course comparison, regression analysis, and normalized error distribution. Finally, the LSTM was applied to a train-bridge RTHS, verify its accuracy and real-time performance, and the results show that the proposed method can effectively improve the accuracy and efficiency of solving the numerical substructure. image
引用
收藏
页码:336 / 349
页数:14
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