Optimization of Concrete Gravity Dam Section using New Election Meta-heuristic Algorithm

Abstract

According to design criteria, site specifications, and required height, different dimensional options are considered for geometric parameters, while design gravity dams. Among options, an option that provides sustainability and low costs is considered as an executive option. In this study, the Koyna concrete gravity dam section in India and the Kalat Zavin concrete gravity dam in Khorasan Razavi province were optimized using the election meta-heuristic algorithm (EA) concerning the stability conditions. In this model, geometric parameters were considered as decision variables and dam weight as an objective function. The model obtains geometric parameters in such a way that the dam weight is minimized. To evaluate the proposed method, the Koyna dam and Kalat Zavin dam data set were used and the results were compared with Genetic (GA), Honey Bee Mating Optimization (HBMO), and Imperialist Competitive (ICA) algorithms. The results of the EA method indicate a 9.87% and 10.40% decrease in the volume of concrete consumption and a reduction in area for Koyna and Kalat Zavin dams. Based on the comparisons, the EA algorithm showed better performance in optimizing the dam section than the genetic algorithm (GA), honey bee mating algorithm (HBMO), and imperialist competitive algorithm (ICA) optimization models. optimization models. Also, the improvement rate of the EA algorithm in comparison with ICA, HBMO, and GA algorithms is equal to 8%, 6%, and 11%, respectively.