Optimization of Steel Truss Using Genetic Algorithm

Abstract

In this paper, an optimization study is presented, focusing on steel trusses. The main goal of this study is to reduce the weight of truss structures using a Genetic Algorithm (GA), which is a widely acknowledged evolutionary-based method known for its efficiency in solving intricate optimization problems. The design problem formulation takes into account various constraints, such as displacement, tensile stress, and minimum size requirements. These constraints are implemented in MATLAB, utilizing the ANSI/AISC 360-16 Specification as a guideline for designing tension and compression members. To determine the optimal design, the approach involves considering discrete design variables. This is achieved by selecting sections from a database containing all available steel sections specified in the AISC Steel Construction Manual, ensuring practical and feasible design solutions. The efficiency of the algorithm is validated through its application to several plane truss types. Through a comparison of the outcomes obtained from the proposed algorithm with the results generated by CSI-ETABS software, it is demonstrated that this approach consistently yields better weight optimization. Overall, the study showcases the effectiveness of the GA-based algorithm in optimizing the weight of steel trusses. The results and implications of the findings are thoroughly discussed in the paper; this study has the potential to make a substantial contribution to the field of structural optimization and design.