Based on BP neural network forecast bridge temperature field and its effect on the behavior of bridge deflection

Engineering has attached more and more importance to bridge safety monitoring in recent years. However, financial resources and material resources as well as human resources of traditional means of monitoring were costly. Besides, traditional means of monitoring were low accuracy. This article takes the project for example, used neural network method, by using historical data of the bridge monitoring to construct the BP neural network model with dual hidden layer structure and forecast bridge temperature field and its effect on the behavior of bridge deflection. The fact indicates that the predicted biggest error is 3.06% of the bridge temperature field and the bridge deflection behavior under temperature field affected is 2.17% by the method of the BP neural network, fully satisfy the precision requirements of the construction, it has practical value.