Determining the defect locations and sizes in elastic plates by using the artificial neural network and boundary element method

In this work, a new approach for solving the inverse problem of the defect location in an elastic plate is developed by combining the artificial neural network (ANN) and the boundary element method (BEM). The inverse problem is formulated as a regression problem, which extracts the solution of highest probability through machine learning (ML) from a large amount of data. The efficiency and accuracy of data generation is guaranteed by using the BEM, which solves for strain values on the boundaries of plates with circular defects and creates the ML datasets. The ANNs, which are constructed by a fully connected multi-layer perceptron, are trained using these datasets to predict the center coordinates and radii of the circular defects and can achieve about 98% accuracy in the detection. Compared with the Lamb wave based structural health monitoring (SHM) techniques, which in general require signal generation, collection and processing, the proposed approach only requires the data of boundary strains of the considered structures as the input data and a simple training process. Therefore, it is much easier to implement and has great potential in applications of SHM.