Optimum Cost Design of Reinforced Concrete Columns Using Genetic Algorithms

Abstract

The aim of this study is finding the optimum cost design of reinforced concrete columns with all loading conditions (axially, uniaxially and biaxially loaded) using the Genetic Algorithms GAs. Many design constraints were used to cover all the reliable design results, such as limiting the cross sectional dimensions, limiting the reinforcement ratio and even the behavior of the optimally designed sections. Each of the designed columns was handled by the GAs solver according to its loading condition specifications. The load contour method was used to design the biaxial sections with the adjustment of the plastic centroid. A long column constraint was introduced to limit the design procedure with the short columns only. The optimum results were compared with other published works, and a reduction in design cost of the biaxially loaded columns of about 26 % was achieved using the GAs design method while a small percent in the cost reduction ( 1 – 3 % ) was achieved for the uniaxially designed sections, while 50% was the cost savings in the axially loaded columns. It was proved that the genetic algorithm is capable for designing optimum columns sections despite the complex constraints that control the designing procedure