Study on structural damage identification based on generalized S-transform and parallel neural network
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
万方数据
维普
