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

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.