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electromechanical admittance-based automatic damage assessment in plate structures via one-dimensional CNN-based deep learning models

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FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING 2024年第11期18卷 1730-1751页

作者： Huynh, Thanh-Canh Hoang, Nhat-Duc Pham, Quang-Quang Truong, Gia Toai Nguyen, Thanh-Truong Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Civil Engn Da Nang 550000 Vietnam Danang Architecture Univ Bridge & Rd Dept Da Nang 550000 Vietnam Dong A Univ Fac Civil Engn & Technol Da Nang 550000 Vietnam Ho Chi Minh City Univ Technol HCMUT Ind Maintenance Training Ctr Ho Chi Minh City 700000 Vietnam

The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location, especially when damage introduces nonlinearities in admittance features. This study proposes a novel automated damage localization method for plate-like structures based on deep learning of raw admittance signals. A one-dimensional (1D) convolutional neural network (CNN)-based model is designed to automate processing of raw admittance response and prediction of damage probabilities across multiple locations in a monitored structure. Raw admittance data set is augmented with white noise to simulate realistic measurement conditions. Stratified K-fold cross-validation technique is employed for training and testing the network. The experimental validation of the proposed method shows that the proposed method can accurately identify the state and damage location in the plate with an average accuracy of 98%. Comparing with established 1D CNN models reveals superior performance of the proposed method, with significantly lower testing error. The proposed method exhibits the ability to directly handle raw electromechanical admittance responses and extract optimal features, overcoming limitations associated with traditional piezoelectric admittance approaches. By eliminating the need for signal preprocessing, this method holds promise for real-time damage monitoring of plate structures.

关键词： convolutional neural network electromechanical admittance electromechanical impedance piezoelectric transducer damage localization plate structure deep learning structural health monitoring

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