With the development of science technology and traffic enterprise, the continuous girder bridge ridge has been growing up rapidly in the recent years with it's unique predominance of handy construction, economical cost,reasonable internal force and comfortable traveling. But the theories and experience in it are not very perfect because of it's short appearance. There are still several questions in the construction.Relying on the Zhongxiang bridge, in this paper a genetic algorithm is applied in identifying the pre-stress loss of the long span bridge. A model with some parameters is made to assess the pre-stress loss. Based on several obvious characters on the disease of the bridge to determine the cause of the disease, the model parameters are identified by the genetic algorithm, and the pre-stress loss values of different sections are given. It is demonstrated that the genetic algorithm is efficient and the theoretical results fit well with the disease.The aptitudinal and predictable method based on combining Artificial Neural Networks (ANN) and Genetic Algorithm (GA) in structural damage detection are proposed. The procedure of identifying damage can be defined as a minimization problem. The optimum solution can be obtained effectively by using GA. Since GA usually needs a long analyzing and calculating process in use with the Finite Element Method (FEM). But a non-linear mapping function from multiple input data (structural damage parameters) to multiple output data (differences of response between damaged structure and intact structure calculated by FEM) is constructed within BP neural networks. The ability of constructing a non-linear mapping function within BP neural networks offer the strong calculation means which solve the problem of identification of the damages within Genetic Algorithm. The method and the approach about identification of the damages combined BP Neural Networks with Genetic Algorithm are given in this paper.