铁道标准设计2026，Vol.70Issue(7)：74-81,8.DOI:10.13238/j.issn.1004-2954.202408290005
基于卷积神经网络与注意力机制的铁路基床翻浆病害识别研究
Study on Identification of Railway Subgrade Mud Pumping Based on Convolutional Neural Network and Attention Mechanism
摘要
Abstract
[Objective]Due to the concealed nature of mud pumping diseases in ballastless track subgrade,the detection and identification performance of track inspection vehicles and ground-penetrating radar systems is currently not significant.[Methods]Based on the measured vibration response data of subgrade mud pumping,a simple and efficient method for identifying mud pumping diseases in ballastless track subgrade,which combined an attention mechanism with a one-dimensional convolutional neural network,was proposed.[Results]The convolutional neural network incorporating the attention mechanism showed an average accuracy improvement of 30.4%,21.33%,31.25%,and 20%on the track slab acceleration validation set compared to traditional convolutional neural network and machine learning methods such as random forest,support vector machine,and gradient boosting tree.To identify the most suitable dataset for the model to recognize subgrade mud pumping diseases and enhance the overall performance of practical identification,six datasets—acceleration,dynamic displacement,and velocity of the base plate and the track slab—were utilized as model inputs.Additionally,the changes in accuracy and loss value with training epochs were analyzed.[Conclusion]The results show that the fitting process of the base plate dynamic displacement dataset is more stable,and the average accuracy on the validation set reaches 98.75%.Therefore,it is recommended to collect the base plate dynamic displacement dataset during on-site testing and utilize it as the training dataset for identifying subgrade mud pumping diseases to achieve better performance.关键词
无砟轨道基床/卷积神经网络/注意力机制/基床翻浆/病害识别/动测试验Key words
ballastless track subgrade/convolutional neural network/attention mechanism/subgrade mud pumping/disease identification/dynamic test分类
交通工程引用本文复制引用
朱瑞龙,乔浩宇,李婷,高煜钦,刘小宇,黄叶萱..基于卷积神经网络与注意力机制的铁路基床翻浆病害识别研究[J].铁道标准设计,2026,70(7):74-81,8.基金项目
国家自然科学基金项目(52208358) （52208358）
石家庄铁道大学土木工程学院自主课题(TMXN2207) （TMXN2207）
