Structural damage localization using probabilistic neural networks

In this paper, the structural damage localization on a simple composite plate specimen is identified using probabilistic neural networks. First, the category to be identified is defined according to the structural location, and the number of categories is reduced by grouping neighboring elements to one category. Second, the state data of damaged structure are collected by a data collection system, and are utilized as feature vectors for the probabilistic neural network. Finally, the smoothing parameter in the probabilistic neural network is studied. When this trained network is subjected to the measured response, it should be able to locate existing damage. The effectiveness of the proposed method is demonstrated.