Research on Two-Stage Damage Identification of Steel Frame Based on CNN and CMCM
In the field of structural damage identification,the Cross Model Cross Mode(CMCM)Method is constrained by the rank deficiency of the coefficient matrix when solving noise-inclusive damage identification problems,leading to a decrease in the accuracy of damage identification results.To address this issue,this study proposes a two-stage damage identification method aimed at reducing the redundant equations in the CMCM method's coefficient matrix,thereby enhancing the computational performance and noise robustness of the CMCM method.In the first stage,a Convolutional Neural Network(CNN)is employed for structural damage localization to eliminate the redundant equations in the coefficient matrix of the CMCM method.Subsequently,in the second stage,the reduced CMCM coefficient matrix equation is solved to obtain more accurate damage identification results.The effectiveness of the proposed two-stage damage identification method is validated through numerical and experimental studies.Compared with the traditional CMCM method,the method proposed in this paper significantly improves the accuracy of damage identification in solving noise-inclusive damage identification problems,demonstrating its superior performance.
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