Lateral Collision Model and Parameter Analysis of Bridge Structure Based on Sensor Array

After the bridge is built and used, the structural data under different climates, environments and various dynamic and static loads are collected by setting the sensor array. Using artificial intelligence algorithms for analysis and processing is an important technical measure to ensure bridges' long-term safe and effective operation. The processing and analysis of various parameters required for constructing the lateral collision model of the bridge structure collected by the sensor array is a process for solving nonlinear complex problems. Therefore, this paper considers the lateral impact of collisions as it involves complex nonlinear dynamic behavior and uncertainty. A genetic algorithm is an optimization algorithm based on the principles of biological evolution, suitable for solving complex and nonlinear problems. By improving the cross probability and mutation probability equations, you can better adjust the algorithm's performance to adapt to the problems of bridge structure lateral collision models. The simulation experiment proves that the execution time of the optimized adaptive genetic algorithm increases steadily when the number of previous tasks is small. Suppose the data size of the later acquisition increases, and the bridge environment becomes complex. In that case, the global optimal solution can be searched in the iteration according to the optimization of the algorithm model. Besides, the horizontal collision model of the constructed bridge structure has a reasonable degree of fitting and can meet the needs of on-site safety management. This study provides good technical support for optimizing the lateral collision model of bridge structures.