Optimization of reinforced concrete structures using population-based metaheuristic algorithms

Abstract

For many industrial activities, ideal projects are achieved by comparing the solution of alternative projects with those already executed. The feasibility of solutions plays an important role in these activities. For example, the underlying objective (cost, profit, etc.) estimated for each project solution is calculated and the best solution is adopted. This is the usual procedure followed by many constructors due to time and resource limitations. However, in many cases, this method is followed simply by a lack of knowledge of existing optimization procedures. In this context, a comparative study of population-based metaheuristic algorithms applied to a case study of a reinforced concrete beam design reinforced with a polymer matrix with carbon fibers will be presented. Evolutionary algorithms have the ability to determine the optimal values of the design variables without disregarding the restrictions on ACI-318 and ACI-440 standards while minimizing the reinforcement area for each beam (cost). The comparative study shows that not all presented algorithms violated design constraints. In addition, it can be said that the values found for the design variables present a low dispersion around the mean value of the objective function.