Structural damage identification based on multi-label convolution neural network
Accurate identification of structural multi-site damage has always been a difficult problem in structural damage identification.In order to improve the accuracy of structural multi-site damage identification,a multi-label classification method based on convolution neural network(CNN-MLC)was proposed for structural damage identification.In this method,the multi-site damage identification of the structure was transformed into a multi-label classification problem,and each site damage is represented by a separate label.Using the strong feature extraction ability of CNN,the correlation of common damage site between different damage conditions was deeply mined,and the multi-site damage identification was realized.The CNN-MLC method was verified by multi-site damage identification of a four-story frame structure and a railway continuous beam bridge,and the identification results were compared with those of CNN-MCC and InsDif-MLC.The results show that under two-sites and three-sites damage conditions,the recognition accuracy of CNN-MLC is 2.50%and 9.64%higher than that of CNN-MCC,and 17.50%and 29.28%higher than that of InsDif-MLC.For the two-sites damage and three-sites damage of railway continuous beam bridges,the recognition accuracy of CNN-MLC is 1.63%and 6.85%higher than that of CNN-MCC,and 4.18%and 18.49%higher than that of InsDif-MLC.With the increase of the number of damage sites,the recognition accuracy of CNN-MLC is significantly improved.
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