Multi-Objective Reliability Optimization of Telescopic Boom for Special Vehicles Based on RSM-RBFNN Hybrid Surrogate Model

To achieve the co-optimization of dynamic–static performance and lightweight design for the telescopic boom, we proposed an integrated approach within a multi-objective optimization framework by using hybrid surrogate model, combined weighting-TOPSIS method, and Monte Carlo simulation (MCS). First, a parametric model of the telescopic boom under extreme working conditions is established, and its dynamic and static performance is analyzed through finite element analysis. Then, using the cross-sectional parameters of the telescopic boom as input variables, design variables with significant influence on the output responses are identified via Spearman correlation analysis. Subsequently, based on sample points obtained by optimal Latin hypercube design, a high-precision RSM-RBFNN hybrid surrogate model is constructed. On this basis, the NSGA-II algorithm is applied to perform multi-objective deterministic optimization of the telescopic boom, and the combined weighting-TOPSIS method is employed to extract optimal solutions from the Pareto solution set. Finally, considering uncertainties in the design variables, 3-Sigma reliability optimization of the telescopic boom is carried out using Monte Carlo simulation. The results show that, while meeting all design requirements, the mass of the telescopic boom is reduced by 12.5%, the second-order natural frequency is improved by 9.4%, and all performance metrics achieved the 3σ level. This study provides practical guidance for the structural optimization design of the telescopic boom.