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 fa...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.展开更多
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).
文摘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.
