基于人工智能的高速铁路站台门结构变形的预测
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1.中国铁道科学研究院研究生部;2.中国铁道科学研究院集团有限公司电子计算技术研究所

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[U-9]

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轨道交通分布式数据共享计算方法研究


Prediction of platform door structure deformation of high-speed railway based on artificial intelligence
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Graduate Department, China Academy of Railway Sciences

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    摘要:

    为了解决运营线路站台门结构变形难以实时监控的问题,精准预测列车过站时高速铁路站台门的结构变形数据,本文采用基于人工智能的神经网络方法,选取210种不同车长、阻塞比、安装距离和车速的站台门结构变形数据作为网络模型训练样本,运用CNN和基于K-Fold的GRNN两种神经网络模型,建立了不同工况下的高速铁路站台门结构变形的预测模型,并与剩余样本数据进行对比验证。研究表明,两种模型均可有效预测铁路站台门结构运维数据,在预测精度上,基于K-Fold优化的GRNN模型优于CNN模型,基于K-Fold优化的GRNN模型的预测均方差能够维持在0.22之内,均方根误差维持在0.27之内,处于研究领域的领先水平。基于K-Fold优化的GRNN模型能够较好预测列车过站时的站台门结构形变量,为高速铁路站台门的设计与运维提供数据参考。

    Abstract:

    In order to solve the problem that it is difficult to monitor the structural deformation of platform doors of operating lines in real time and accurately predict the structural deformation data of platform doors of high-speed railway when trains pass the station, this paper adopts artificial intelligent-based neural network method and selects 210 kinds of platform door structural deformation data with different train length, congestion ratio, installation distance and speed as network model training samples. By using two neural network models, CNN and GRNN based on K-Fold, the prediction models of platform door structure deformation under different working conditions of high-speed railway were established, and the comparison and verification were carried out with the remaining sample data. The research shows that both models can effectively predict the operation and maintenance data of railway platform door structure. In terms of prediction accuracy, the GRNN model based on K-Fold optimization is superior to the CNN model. The mean square error of prediction of the GRNN model based on K-Fold optimization can be maintained within 0.22, and the root mean square error is maintained within 0.27. At the leading level in the field of research. The GRNN model based on K-Fold optimization can better predict the structural shape variables of platform doors when trains pass the station, and provide data reference for the design and operation and maintenance of platform doors of high-speed railway.

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杨博璇,王志飞,李樊,等. 基于人工智能的高速铁路站台门结构变形的预测[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-05-11
  • 最后修改日期:2024-06-15
  • 录用日期:2024-07-09
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