Optimization Algorithm of Road and Bridge Engineering Construction Management Based on Ant Colony Neural Network

Highway bridge construction involves many links, with relatively large investment and high-quality requirements, which requires that the on-site construction management should be done well, and the whole project must be done well on the premise of ensuring quality. The quality of on-site construction management of highway and bridge projects directly affects the quality and later use of the whole highway and bridge project. In this paper, ant colony algorithm is used to optimize the parameters of neural network, and a parameter feature fusion model based on ant colony neural network is proposed, and the corresponding program is developed by using the network toolbox. The prediction results show that the maximum relative error of the improved ant colony algorithm test results is 1.22%, and the standard deviation of the relative error is 0.72%, indicating that the model has strong learning ability. Strong robustness, high prediction accuracy and fast speed. very good Therefore, this paper provides a new research idea for highway bridge construction management