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Application of genetic algorithm-support vector machine (GA-SVM) for damage identification of bridge. (English) Zbl 1251.68181

Summary: A support vector machine (SVM) optimized by genetic algorithm (GA)-based damage identification method is proposed in this paper. The best kernel parameters are obtained by GA from selection, crossover and mutation, and utilized as the model parameters of SVM. The combined vector of mode shape ratio and frequency rate is used as the input variable. A numerical example for a simply supported bridge with five girders is provided to verify the feasibility of the method. Numerical simulation shows that the maximal relative errors of GA-SVM for the damage identification of single, two and three suspicious damaged elements is 1.84%. Meanwhile, comparative analyzes between GA-SVM and radical basis function (RBF), back propagation networks optimized by GA (GA-BP) were conducted, the maximal relative errors of RBF and GA-BP are 6.91% and 5.52%, respectively. It indicates that GA-SVM can assess the damage conditions with better accuracy.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
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