Optimization Design of Bridge Inspection Vehicle Boom Structure based on Improved Genetic Algorithm

self-weight of bridge inspection vehicles increases the safety risk of the inspected bridge structures. In this study, a bridge inspection vehicle arm structure self-weight optimization design model is proposed to improve the efficiency and safety of bridge structure inspection. The model uses a finite element model of the arm structure to generate force data to validate and train a back propagation (BP) neural network-based self-weight prediction model of the arm structure, and uses an improved genetic algorithm to assist the prediction model in searching for the optimal solution. The experimental results show that the maximum stress and maximum deformation of the optimal solution from the optimization model designed in this study are lower than the allowable values of the material, and the total weight of the structure from the optimal solution is the lowest, 4687.5 kg. The computational time of the optimization model designed in this study is lower than all the comparison models. The experimental data show that the optimized model for the self-weight optimization of the bridge inspection vehicle arm structure designed in this study has good optimization effect and has some application potential.