Detection of cracks using neural networks and computational mechanics

An inverse analysis method is proposed to simulate the A-scan ultrasonic nondestructive testing by means of back-propagation neural networks and computational mechanics. Both direct problem and inverse problem are considered in this study. In the direct problem, the frequency responses of a cracked medium subjected to an impact loading are calculated by the computational mechanics combining the finite element method with the boundary integral equation. The transient responses are obtained using fast Fourier transform. In the inverse problem, the back-propagation neural networks are trained by the characteristic parameters extracted from the various surface responses obtained from the direct problem. These surface responses carry a great deal of information about the structure of the medium with or without cracks. The trained neural networks are then utilized for the classification and identification of the crack in the medium to determine the type, location, and length of the crack.