Optimum Cost Design of Reinforced Concrete Columns Using Genetic Algorithms

Abstract

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