Estimation of defect in 2-D elastic wave fields using convolutional neural networks

Abstract

In the field of nondestructive evaluation, research on inverse analysis for determining a defect in structures and materials has been conducted since early times. In this paper, we extend the inverse analysis method using convolutional neural networks proposed by the first author for SH wave propagation to elastic wave propagation where P and S waves are coupled. First, we simulate the scattered waves from a defect using the convolution quadrature time-domain boundary element method (CQBEM). The obtained waveforms at receiver points are visualized and prepared for convolutional neural networks, and a deep learning model is created to estimate the position of a defect. Finally, by providing waveform data from an unknown defect to the created deep learning model, it is demonstrated that the developed deep learning model can accurately estimate the position and size of a defect.