Prediction of aerodynamic pressure amplitude in tunnel based on PSO-BP neural network

被引：2
|
作者：
Cui F. [1 ]
Wang H. [1 ,2 ]
Shu Z. [1 ]
机构：
[1] School of Civil Engineering, Central South University, Changsha
[2] National Engineering Research Center for High-speed Railway Construction Technology, Central South University, Changsha
来源：
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2023年 / 54卷 / 09期
基金：
中国国家自然科学基金;
关键词：
aerodynamic pressure amplitude; cross-validation; high-speed train; PSO-BP neural network model; tunnel;
D O I：
10.11817/j.issn.1672-7207.2023.09.034
中图分类号：
学科分类号：
摘要：
The BP neural network technology was used to predict the amplitude of aerodynamic pressure change in tunnel, and the particle swarm optimization(PSO) was introduced to optimize it, and the PSO-BP neural network model was constructed. In order to verify the accuracy and reliability of the models, the collected data samples were used to train the models, and cross-validation was introduced to evaluate the performance of the two models. The results show that the PSO-BP neural network can accurately predict the aerodynamic pressure amplitude under different conditions, and the average relative error, the average absolute error, the root-mean-square error and the determination coefficient of samples are better than those of the unoptimized BP neural network. therefore, it has higher prediction accuracy. Based on the PSO-BP pressure amplitude prediction model. The variation of pressure amplitude under different conditions is obtained. © 2023 Central South University of Technology. All rights reserved.
引用
收藏
页码：3752 / 3761
页数：9
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