Structural Design Optimization of All-Steel Buckling-Restrained Braces Using Intelligent Optimizers

This study aims to introduce a novel optimal structural design framework for buckling-restrained braces (BRBs) in multi-story buildings. Five artificial intelligence (AI) algorithms, including particle swarm optimization (PSO), shuffled frog-leaping algorithm (SFLA), interior search algorithm (ISA), hybrid of bat and particle swarm optimization (BAT-PSO), and political optimizer, which is recently proposed, are adopted for the optimum design of BRB systems. In the proposed optimization process, the BRB cross-sectional area is taken as the objective function considering the stiffness-strength criteria. As a result, by optimizing the BRB cross-sectional area, the weight of BRBs will reduce. Two mostly-used cross-sectional profiles for all-steel BRBs (circular and rectangular) are considered. In general, the optimized solutions using AI algorithms were more cost effective and exhibited considerably better structural performance in terms of global buckling requirements in comparison to other conventional BRB designs. The results showed that BAT-PSO worked the best in terms of objective function value and computational time. The design solutions obtained using BAT-PSO were lighter (35% for circular profiles and 20% for rectangular profiles), and had superior performance in terms of both stiffness and strength in comparison with the conventional BRB designs. It was also shown that using circular profile can reduce the weight of BRB elements by around 15% compared to rectangular profile. The results of this study should prove useful in more efficient design of BRB systems in common practice.