Study on the Damage Identification of Long-span Arch Bridge Based on Support Vector Machine
LIU Chun-cheng1, 2; LIU Jiao1
(1.School of Civil and Architecture Engineering, Northeast Dianli University, Jilin, Jilin 132012, China;2. State Key Lab of Coastal and Offshore Engineering, Dalian University of Technology, Liaoning, Dalian 116023, China)
Abstract： As a new machine learning algorithm, the method of Support Vector Machine(SVM) has shown its superiority of the ability of regression in the fields of damage identification. Through setting variation ratio of curvature mode to the feature parameters of damage identification, the method of the damage identification of long-span arch bridge based on SVM is presented. At first, the variation ratio of curvature mode is used to carry on damage location identification, then, the training sample is reconstructed. After that the method of least square support vector machine is used to long-span arch bridge damage extent identification, and the identification results of this method which are very close to target are obtained under the condition of small sample. To compare with results from the RBF neural network, the precision of the method in this paper is verified.
刘春城;刘 佼. 基于支持向量机的大跨度拱桥损伤识别方法研究[J]. , 2010, 29(7): 174-178.
LIU Chun-cheng;;LIU Jiao. Study on the Damage Identification of Long-span Arch Bridge Based on Support Vector Machine. , 2010, 29(7): 174-178.
2010, Vol. 29 
