A technique for damage diagnosis of bridge structure based on information fusion

Abstract

As the damage diagnosis of bridge structure is highly nonlinear in nature, it is difficult to develop a comprehensive model taking into account all of the independent variables, such as the structural and environmental properties, using conventional modeling techniques. In this study, a method was introduced for damage diagnosis of bridge structure by integration of BP artificial neural network (ANN) and information fusion based on D-S evidential theory. The basic probability assignment functions for data fusion were constructed according to the demand of the damage diagnosis and the real conditions of the bridge structure. And an application example of the proposed method was demonstrated. The results showed that the integration of the two strategy can remove the shortcoming of BP ANN with remaining of its advantages and promote the identified veracity of the whole diagnosis system.