Study on the Damage Identification of Long-Span Cable-Stayed Bridge Based on Support Vector Machine

Method of support vector machine (SVM) as a new machine learning algorithm has shown its superiority of the ability of regression in the fields of damage identification. Through setting variation displacement of mode shape to the feature parameters of damage identification, the method of the damage identification of long-span cable-stayed bridge based on SVM is presented. The method of least square support vector machine is used to cable-stayed 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 BP neural network, the precision of the method in this paper is verified.