Structural optimization of closed built-up cold-formed steel columns

Abstract

Cold-formed steel (CFS) members are commonly found in single sections, whereas built-up CFS members are usually composed of existing single sections. There is a lack of designed shapes for single sections targeting high structural performance built-up members, i.e., maximizing their resistance-to-weight ratio. Furthermore, traditionally, design optimization methods have a solid mathematical background (gradient-based methods for instance) and use design guidelines for the search of the optimal solution. The construction industry is responsible for a large share of the worldwide consumption of natural resources, and structural optimization plays an important role in improving the sustainability of the sector and reducing the impact of climate change. Non-traditional search optimization methods such as evolutionary algorithms are growing popularity in engineering optimization problems due to i) their nature-inspired technique, ii) their easy way (for the user) of solving complex real-world optimization problems, and iii) the current advanced computing machines, which provide sufficient computational speed to generate solutions to difficult problems in reasonable time. Therefore, optimized structural solutions for closed built-up CFS columns under compression are proposed by using the particle swarm optimization (PSO) algorithm and the finite element method (FEM). Design predictions for the optimum columns based on European Code and North American Specification are also given and compared with the numerical ones. Finally, the findings of this research work show how some parameters (including steel thickness, cross-section height and column length) can affect the optimum solution of the studied objective function (resistance-to-weight ratio), especially the steel thickness.