Rao Wenbi1£¬Tan Huaijiang1£¬Bostrom Henrik2
(1.School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430070,China;2.Department of Computer and Systems Sciences,Stockholm University and Swedish Royal Institute of Technology,Stockholm SE-16440,Sweden)
Abstract:Inductive learning methods were used for the detection of structural damage. Firstly,a inductive learning method (RAC) that is more efficient and effective,was developed by combining the basic decision tree learning algorithm with sequential covering algorithm and then improved;bagging method was used to generate several classification methods for predicting. Then the radial basis function (RBF) neural network was trained by orthogonal least squares (OLS) algorithm which selects the radial basis function centers by using information”Mcontribution rule. At the end, the inductive learning methods mentioned above were used for the location detection of a beam structural damage. The detecting results show that the precision can be more than 90£„ for both RAC and bagging methods with the ensemble sizes of 10 or 50 when the noise level of test samples is within 100£„. And the same identify precision can be kept for RBF neural network only if noise level of test samples is within 70£„.
Keywords:structural damage detection; rule inductive learning; bagging learning algorithm; neural network