Abstract
Stone arch bridge was divided into three substructures. They were main arch, vertical wall and vice arch or carriageway board. Probabilistic neural network was applied to substructural damage identification. Static displacement and low-order frequencies were taken as input parameters of the network training. A numerical model was developed to simulate the process of substructural damage identification of stone arch bridge. The effects of noise data to training and recognition were researched. The results show that it is feasible and effective to use probabilistic neural network in substructural damage identification of stone arch bridge.
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