Dynamic vehicle load identification method based on LSTM network

被引：0
|
作者：
Lu Z. [1 ,2 ]
Dongming F. [1 ,2 ]
Gang W. [1 ,2 ]
机构：
[1] Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing
[2] National and Local Joint Engineering Research Center for Intelligent Construction and Maintenance, Southeast University, Nanjing
来源：
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2023年 / 53卷 / 02期
关键词：
acceleration responses; dynamic vehicle load identification; long short-term memory (LSTM) network; structural health monitoring; vehicle-bridge interaction system;
D O I：
10.3969/j.issn.1001-0505.2023.02.001
中图分类号：
学科分类号：
摘要：
To identify the dynamic vehicle load, a method based on long short-term memory (LSTM) network was proposed. The bridge acceleration response was used as the input in the method, and the dynamic vehicle load can be identified based on the finite data sets. The verification was carried out based on a vehicle-bridge interaction model. Taking 60 groups of bridge acceleration responses as the input and the corresponding vehicle dynamic load as the output, the vehicle dynamic load inversion was realized by training the LSTM network. The influence of ambient noise and road roughness on the identification was discussed. The results show that the average vehicle dynamic load identification error of the test set is less than 5 %. The vehicle dynamic load identification error does not change with the noise level, and the average error is less than 5 %. The vehicle dynamic identification error increases slightly with the increase of road roughness, and the average error is less than 5 %. The LSTM network can be used to identify vehicle dynamic loads under different noise and roughness levels. © 2023 Southeast University. All rights reserved.
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页码：187 / 192
页数：5
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