Crack Identification by Artificial Neural Network

Current development in smart materials has improved the conventional composite materials in the ability of self-monitor during service. The massless sensor of micro-electro-mechanical system makes the work successful to embed the sensor into the composite material without changing the original system. For an ideal on-line identification, the measured values of sensors are obtained during service not from experiment. Due to this important reason, the study of the crack identification by using the static deformation as the input is gradually an interested research which will be different from the conventional non-destructive techniques such as, ultrasonics, magnetic flux leakage, X-rays, penetrant, eddy current, etc. To achieve the on-line identification, the artificial neural network is considered instead of the usual nonlinear optimization technique. An artificial neural network is a parallel, distributed information processing structure consisting of processing elements interconnected with weights. In this paper, a most popular learning scheme called the back-propagation neural network (BPN) will be applied for implementing the crack identification. A typical architecture of BPN contains three basic layers input, output and hidden layers. According to Kolmogorov's mapping theorem, any continuous function could be mapped exactly by a three-layer feedforward neural networks. The input pattern is propagated forward, and the calculated responses are obtained. The errors between the desired outputs and the calculated outputs then propagate backward through the network, providing vital information for weight adaptation. It is known that the crack effects are localized which may not be clearly reflected