The Structural Design and Optimization of Top-Stiffened Double-Layer Steel Truss Bridges Based on the Response Surface Method and Particle Swarm Optimization

Abstract

A lightweight design optimization algorithm is proposed to optimize the design parameters of stiffened double-layer steel girder bridges, the aim of which is to improve structural safety and reduce superstructure works. Taking a top-stiffened double-layer steel truss bridge as the reference project, a multiscale mixed-element model of the initial design parameters is established, and its computational accuracy is verified. Considering the structural configuration and loading characteristics of the bridge, the elastic modulus of steel, the deck plate thickness, the stiffening vertical bar height, and the relative distance between the double-layer main girders are selected as the design parameters for optimization. The mid-span vertical deflection, the axial forces in the stiffeners, the bottom plate of the deck, the compressed web tube at the pier top, and the quantity of superstructure works are chosen as the objective functions to be minimized. A lightweight calculation equation reflecting the relationship between the optimization parameters and the objective functions is established using the response surface method (RSM). Subsequently, an improved weighted particle swarm optimization (WPSO) model is employed to perform the multi-objective optimization of the design parameters for the bridge, and the results are compared with those obtained from the multi-objective genetic algorithm NSGA-II. The results show that the RSM accurately fits the numerical relationship between the optimization parameters and the objective response functions. When minimizing the quantity of superstructure works as the primary control objective and minimizing the mid-span vertical deflection and the axial forces in the compressed web tube at the pier top as secondary control objectives, the optimization results achieved by WPSO outperform those obtained by NSGA-II. The optimized results lead to reductions of 11.09%, 3.92%, 7.56%, 4.45%, and 8.38% in the respective objective function values of the structure. This method has important theoretical significance for the optimization of structural design parameters.