Flaw classification and detection in thin‐plate structures based on scaled boundary finite element method and deep learning

Abstract

AbstractThe identification of internal structural flaws is an important research topic in structural health monitoring. At present, structural safety inspections based on nondestructive testing procedures mainly focus on qualitative analysis; hence, it is difficult to identify the scale of flaws quantitatively. In this article, an inversion model that can realize quantitative detection is proposed by combing the scaled boundary finite element method (SBFEM) with deep learning. First, the lamb wave propagation processes in thin structures containing flaws were simulated using the SBFEM, and the echo wave signal at an observation point was recorded and paired with the flaw information. Then, the paired SBFEM datasets were used to train the deep learning model employing a convolutional neural network. A flaw classification and identification model based on SBFEM datasets was established. For a thin structure with unknown flaw information, and by using the measured observation point signals, the established deep learning model can predict flaw information in real time. Finally, the model performance was verified using several numerical examples. The results show that the proposed model can accurately predict flaw information and is robust against noise; thus, it offers an advantage over existing nondestructive testing procedures and a development in the field of structural health monitoring.