Identification of Crack Shape Hidden in Solid by Means of Neural Network and Computational Mechanics

This paper describes an application of the hierarchical neural network to the identification of a crack hidden in solid using the electric potential drop method. The present method consists of three subprocesses. First, a number of sample data of identification parameters vs. electric potential values are calculated by the finite element method. Second, the error-back-propagation neural network is trained using the sample data. Finally, the trained network is utilized for crack identification.