基于PSO-LSTM的重载铁路车轨桥系统随机振动响应预测方法

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中文摘要：在车桥系统随机振动分析中,随机动力响应是评价行车安全性的关键因素之一,而现有的响应计算方法存在耗时长、成本高的问题.能够快速准确预测车-轨-桥系统的动力响应对重载铁路桥梁的状态评估和运维养维具有重要意义.本文提出了一种基于粒子群优化(Particle Swarm Optimization,PSO)长短期记忆(Long Short-term Memory,LSTM)神经网络模型的重载车桥系统随机振动响应预测方法.该方法以车桥随机参数与轨道随机不平顺激励为输入,以桥梁动力响应为输出构造代理模型.首先,基于商业软件MATLAB平台构建PSO-LSTM网络模型;其次,通过建立的车-轨-桥系统随机振动分析模型计算初始样本集对应的随机动态响应,并进行模型训练,同时利用PSO算法优化LSTM结构参数;最后,使用训练好的PSO-LSTM模型对桥梁动态响应进行预测.为了验证本算法的优越性和鲁棒性,以朔黄重载铁路实测数据为例,对比本算法与BP(Back Propagation)神经网络、GRU(Gated Recurrent Unit)神经网络和LSTM神经网络的预测效率,并讨论不同车速下的预测情况,开展本模型与实测数据及有限元分析数据的对比分析.研究结果表明:在PSO优化下,LSTM模型预测结果得到一定的改善,PSO-LSTM模型拟合相关性系数可以达到0.97,其他评价误差值也均小于BP神经网络、GRU神经网络模型,本文模型可更高效准确地预测桥梁随机动力响应,可为进一步发展车-轨-桥系统随机振动响应预测理论提供技术支持.

外文标题：Predicting random vibration response of heavy-haul train-track-bridge system based on PSO-LSTM

外文摘要：In the stochastic vibration analysis of train-bridge systems,the stochastic dynamic response is one of the key factors in evaluating the safety of traveling,and the existing response calculation methods have the problems of time-consuming and high cost.The ability to quickly and accurately predict the dynamic response of the train-track-bridge system is of great significance to the condition assessment and operation and maintenance of heavy-haul railroad bridges.In this paper,a stochastic vibration response prediction method for the heavy-haul train-bridge system based on Particle Swarm Optimization (PSO) Long Short-term Memory (LSTM) neural network model was proposed.The method took the random parameters of the train-bridge and the random irregularity excitation of the track as inputs and constructs an agent model with the dynamic response of the bridge as output.Firstly,the PSO-LSTM network model was constructed based on the commercial software MATLAB platform.Secondly,the stochastic dynamic response corresponding to the initial sample set was calculated by the established stochastic vibration analysis model of the train-track-bridge system,and the model was trained,while the PSO algorithm was utilized to optimize the structural parameters of the LSTM.Finally,the trained PSO-LSTM model was used to predict the bridge dynamic response.To verify the superiority and robustness of the present algorithm,the prediction efficiency of the present algorithm was compared with that of BP (Back Propagation) neural network,GRU (Gated Recurrent Unit) neural network and LSTM neural network by taking the measured data of Shuohuang heavy-haul Railway as an example.The prediction under different speeds was discussed,and the comparison analysis between the present model and the measured data and the Comparative analysis of this model with the finite element analysis data was carried out.The results are shown as follows.Under the optimization of PSO,the prediction results of the LSTM model have been improved,the correlation coefficient of the PSO-LSTM model can reach 0.97,and the other evaluation error values are smaller than those of BP neural network and GRU neural network model.The model in this paper can predict the random dynamic response of the bridge more efficiently and accurately,and provide technical support for the further development of the theory of predicting the random dynamic response of the train-track-bridge system.

外文关键词：

random vibrationresponse predictionPSO algorithmLSTM neural networktrain-track-bridge system

作者：

毛建锋、李铮、伍军、余志武、胡连军

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作者单位：

中南大学土木工程学院,湖南长沙 410075

高速铁路建造技术国家工程中心,湖南长沙 410075

中国中铁股份有限公司,北京 100036

中铁二院工程集团有限责任公司,四川成都 610031

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关键词：

随机振动响应预测 PSO算法 LSTM神经网络车轨桥系统

基金：

国家重点研发计划国家自然科学基金资助项目中国中铁股份有限公司科技研究开发计划项目中国中铁股份有限公司科技研究开发计划项目湖南省自然科学基金资金项目

项目编号：

2023YFB4302500520784852021-重大-162021-专项-082024JJ5427

出版年：

2024

DOI：

10.19713/j.cnki.43-1423/u.T20232004

铁道科学与工程学报

中南大学中国铁道学会

铁道科学与工程学报

CSTPCD北大核心EI

影响因子：0.837

ISSN：1672-7029

年,卷(期)：2024.21(9)

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