华东交通大学学报2026，Vol.43Issue(2)：28-37,10.
联合分布式宏应变与机器学习的铁路桥梁监测预警方法
Railway Bridge Monitoring and Early Warning Method Combining Distributed Macro-Strain and Machine Learning
摘要
Abstract
In order to investigate the early warning method for bridge assessment under random train loading with joint distributed macro-strain monitoring and machine learning,and to realize the distributed rapid assess-ment of railroad bridges,this study establish a three-dimensional refined finite element model of vehicle-rail-bridge coupled vibration.It is also apply load statistical analysis methods to construct a stochastic traffic flow model that is suitable for actual train operation,and based on the principle of distributed monitoring,propose a distributed macro-strain influence line area as the indicator design warning interval evaluation warning method for bridge warning;Furthermore,through simulation analysis of various stiffness degradation conditions,a dis-tributed macro-strain monitoring data sample library under random train loads was constructed to compare and study the accuracy of 4 machine learning methods in quantifying and locating bridge damage.The results show that all 4 types of machine learning are able to localize and quantify the localized damage of bridge structures with an average recognition accuracy of 90.0%,with the KNN model and the SVM model performing the best in the test of quantifying bridge damage,both with 95.0%recognition accuracy,and the SVM model performing the best in the test of locating the damage of the bridge structure,with a recognition accuracy of 98.3%.The joint distributed macro-strain monitoring and machine learning approach for bridge assessment has feasibility,SVM model performs best in the test of bridge structure damage localization,KNN model and SVM model perform best in the test of bridge damage quantification,and in the comprehensive analysis,SVM performs best in bridge damage localization and damage quantification analysis.关键词
分布式宏应变/桥梁健康监测/机器学习/安全预警/损伤识别Key words
distributed macro-strain/bridge health monitoring/machine learning/safety warning/damage identi-fication分类
交通工程引用本文复制引用
吴必涛,吴志鹏,樊小林,周珍伟,卢华喜..联合分布式宏应变与机器学习的铁路桥梁监测预警方法[J].华东交通大学学报,2026,43(2):28-37,10.基金项目
国家自然科学基金项目(52368042) （52368042）
江西省主要学科学术和技术带头人培养计划(20225BCJ23025) （20225BCJ23025）
江西省优秀青年基金项目(20242BAB22008) （20242BAB22008）
