Optimization of bridges' parameters based on bp neural network and genetic algorithm

During the construction control of bridges, one of the main causes about the differences between the measured elevation and the predicted elevation is that the parameters in the computation of the predicted elevation are not sufficiently identical with the practical ones. Therefore the effective correction of the computational parameters becomes a key issue in modern construction control. In this paper, the key parameters affecting the elevation differences were analyzed and determined first. A BP neural network model based on the FEM analysis was set up relating the elevation to the key parameters, which may substitute the FEM to predict the elevation when the parameters take value in given ranges. This can reduce the computational complexity significantly. Based on the BP model, a floating-point encoding genetic algorithm was used to optimize objective function for the optimized key parameters. An example from a practical engineering problem showed that the determined key parameters have clear physics meanings. Their corrections based on the suggested methods in the paper can enhance the prediction accuracy of the elevations effectively and the optimized methods recommended in the paper could be good reference for the analysis of similar bridges.