市政技术2024，Vol.42Issue(1)：68-72,5.DOI:10.19922/j.1009-7767.2024.01.068
新型无监督聚类算法监测与评估桥梁结构健康状况
A Novel Unsupervised Clustering Algorithm for Monitoring and Evaluating Bridge Structural Health
摘要
Abstract
In recent years,accidents of urban elevated bridge collapse have occurred frequently due to vehicle over-loading,structural design defects,construction quality issues and other problems.Therefore,an efficient method has been proposed that can monitor the operation status of elevated bridges in real time,conduct real-time detection of structural damage by improved K-means clustering algorithm,and detect data-driven structural damage for the first time.This method mainly collects vibration data of steel structure bridge models under intact structural condi-tions.The effective structural damage sensitive feature values are extracted from these data by deep research.Finally,an improved unsupervised clustering algorithm is used to train the singular value detection model.The experimental results show that damage sensitive characteristic values under intact bridge structures serve as training data to train the mathematical model.It can effectively detect and identify the test results of bridge structures under various damage conditions.This new detection method can monitor real-time the structural health of urban elevated bridges during long-term operation.关键词
桥梁/结构损伤检测/数据驱动/K-means算法/损伤敏感特征/无监督聚类Key words
bridge/structural damage detection/data-driven/K-means algorithm/damage sensitive feature/un-supervised clustering分类
交通运输引用本文复制引用
王子龙..新型无监督聚类算法监测与评估桥梁结构健康状况[J].市政技术,2024,42(1):68-72,5.基金项目
江苏省建设系统科技项目(2018ZD012):基于健康监测技术的市政桥梁综合状况评估研究 （2018ZD012）
