Structural damage identification based on graph convolutional neural network under strong noise and small sample conditions
Structural damage identification based on deep learning are mainly realized by capturing the characteristics and internal rules of data.Insufficient training samples and noise interference may lead to the failure of mining effective features and internal laws.It is particularly important to mine information as much as possible from the data for damage identification.To solve these problems,a structural damage identification method based on graph convolutional network(GCN)is proposed.In order to extract more features,considering the correlation between different position sensors and the characteristics of each sensor data,one-dimensional vibration data was converted into graph data by the graph construction method.Subsequently,GCN was used to extract the data features of the graph samples and achieve rapid classification to achieve the purpose of damage identification.The feasibility and reliability of the proposed method were verified by the Qatar University grandstand simulator structure,and the effects of noise level,number of samples,the method of graph construction and convolutional network parameters on the recognition results were discussed.The results show that,compared with 1 dimensional convolutional neural network,the GCN model has higher damage identification accuracy in the case of strong noise and small samples.The method of graph construction and pooling have certain influence on the identification results.The identification results of Path graph and Topk pooling are stable and higher than those of other combination forms.
structural health monitoringdamage identificationvibration responsedeep learninggraph convolutional neural network
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