Among the existing researches on structural damage identification that use CNN network to extract features,problems such as low accuracy and low recognition efficiency can be found when only 1D-CNN and 2D-CNN are used to extract features for damage identi-fication.Therefore,this paper proposes a structural damage identification method based on generalized S-transform and parallel neural network.In order to enrich the feature dimen-sions of the input signal,the filtered signal is converted into a time-frequency diagram by u-sing the generalized S-transform.At the same time,the one-dimensional acceleration re-sponse signal and the two-dimensional time-frequency diagram are input into 1D-CNN and 2D-CNN respectively for time-frequency and time-frequency feature extraction,and the char-acteristics are spliced in the convergence layer.Then,the damage identification results are classified through FC layer and Softmax layer.The proposed parallel network model is veri-fied by the second-stage test data of IASC-ASCE SHM Benchmark structure.The results show that the proposed network model has higher identification accuracy and efficiency than other similar methods.
NETL
NSTL
维普
万方数据
