Abstract:In order to improve the performance of the CNN model for structural damage identification, a CNN model based on multi-head self-attention was proposed, which takes structural vibration acceleration signal as input. The acceleration signal of structural vibration was firstly fed into the model and processed with one-dimensional CNN to extract local features. The multi-head self-attention was then utilized to attend to important information in different positions and different representation subspaces of the input data to learn global features. All the learned features were finally used for structural damage pattern recognition. The results of numerical and shaking table tests of cantilever beam show that compared with CNN model, CNN-LSTM joint model and CNN-BiLSTM joint model, the CNN model based on multi-head self-attention has lower complexicy, easier training, higher damage detecting accuracy, stronger anti-noise capacity and better ability to distinguish damage modes with similar characteristics.
Keywords: deep learning; multi-head self-attention; convolutional neural networks; structural damage identification
张健飞,黄朝东,王子凡. 基于多头自注意力机制和卷积神经网络的结构损伤识别研究[J]. 振动与冲击, 2022, 41(24): 60-71.
ZHANG Jianfei,HUANG Chaodong,WANG Zifan. Research on structural damage identification based on multi-head self-attention and convolutional neural networks. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(24): 60-71.
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