Bridge Safety Monitoring Method Based on GA Optimized Neural Network

To scientifically perform health monitoring and safety evaluation of bridges, a new bridge safety evaluation method is proposed based on genetic algorithm and BP neural network. We analyze the implementation principle of neural network technology in engineering structure damage detection, and focus on the functional characteristics and sample set composition of neural network suitable for bridge damage identification. Then, GA is used to optimize the weight and threshold of BP neural network, and the specific implementation process is provided. Finally, referring to the actual highway bridge data, the large-scale complex finite element model is established by using numerical simulation object, and the scheme is simulated and tested in MATLAB. The case analysis results also show that the algorithm has fast convergence speed and high prediction accuracy, which provides a new idea for bridge safety monitoring.