On-line identification of holes/cracks in composite structures

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Abstract
Generally, the on-line identification of holes or cracks in a structure is a pressing task of non-destructive identification. In this paper, the method used is different from that generally studied previously: the detectors of the inverse problem are the static strains simply measured by strain gauges, and the system of on-line identification is accomplished through an artificial neural network (ANN). It is more and more feasible and accurate to on-line measure the static strains by applying highly developed smart materials. To express the complex relationship between the strains and the parameters of holes or cracks, a network of ANN with two hidden layers is designed. Not only the size but also the location and orientation of a hole/crack in a composite plate can be identified on-line. The weights and thresholds in the networks can be updated based upon the well-trained values if new training data are added. Consequently, the training time will be saved. To perform the optimal learning efficiency and accuracy, many numerical results are provided in this paper.