Investigation on identification of structural anomalies from polluted data sets using an unsupervised learning method  

作　　者：Junchen YE Zhixin ZHANG Ke CHENG Xuyan TAN Bowen DU Weizhong CHEN 

机构地区：[1]School of Transportation Science and Engineering,Beihang University,Beijing 100191,China [2]CCSE Lab,Beihang University,Beijing 100191,China [3]State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics Chinese Academy of Sciences,Wuhan 430071,China [4]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Frontiers of Structural and Civil Engineering》2024年第10期1479-1491,共13页结构与土木工程前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.51991395,51991391,and U1811463);the S&T Program of Hebei,China(No.225A0802D).

摘　　要：Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters,so monitoring is required.Data collected by structural health monitoring(SHM)systems are easily affected by many factors,such as temperature,sensor fluctuation,sensor failure,which can introduce a lot of noise,increasing the difficulty of structural anomaly identification.To address this problem,this paper designs a new process of structural anomaly identification under noisy conditions and offers Civil Infrastructure Denoising Autoencoder(CIDAE),a denoising autoencoder-based deep learning model for SHM of civil infrastructure.As a case study,the effectiveness of the proposed model is verified by experiments on deformation stress data of the Wuhan Yangtze River Tunnel based on finite element simulation.Investigation of the circumferential weld and longitudinal weld data of the case study is also conducted.It is concluded that CIDAE is superior to traditional methods.

关 键 词：structural health monitoring deep learning anomaly detection 

分 类 号：TM24[一般工业技术—材料科学与工程]