Nonlinear structural damage detection using support vector machines

Author(s):  
Li Xiao ◽  
Wenzhong Qu
2010 ◽  
Vol 20-23 ◽  
pp. 1365-1371 ◽  
Author(s):  
Jian Hong Xie

Structural damage detection and health monitoring is very important in many applications, and a key related issue is the method of damage detection. In this paper, Fuzzy Least Square Support Vector Machine (FLS-SVM) is constructed by combining Fuzzy Logic with LS-SVM, and a real-coded Quantum Genetic Algorithm (QGA) is applied to optimize parameters of FLS-SVM. Then, the method of FLS-SVM integrated QGA is used to detect damages for fiber smart structures. The testing results show FLS-SVM possesses the higher detecting accuracy and the bitter dissemination ability than LS-SVM under the same conditions, and the parameters of FLS-SVM can be effectively optimized by the real-coded QGA. The proposed method of FLS-SVM integrated QGA is effective and efficient for structural damage detection.


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