Metaheuristically optimized nano-MgO additive in freeze-thaw resistant concrete: a charged system search-based approach

Abstract

With progressive advances in the synthesis, characterization, and commercialization of nanoparticles and nanomaterials, these modern engineered materials are becoming an ingredient of innovative structural materials for various applications in civil and construction engineering. In this research, MgO nanoparticles were systematically added to normal concrete samples in order to investigate the effect of these nanomaterials on the durability of the samples under freeze and thaw conditions. The compressive and tensile strengths as well as the permeability of concrete samples containing nanoparticles were measured and compared with the corresponding values of control samples without nanoparticles. The curing time of the concrete samples, the amount of nanoparticles, and the water-cement ratios (w/c) were the variables of the experiments. Moreover, data clustering and the Charged System Search (CSS) algorithm were utilized as the numerical analysis and optimization methods. The regression analysis before clustering and after clustering proved the process of clustering is a prerequisite of regression analysis. Furthermore, the CSS optimization method showed that the optimum amount of nano MgO is 1% of the weight of cement, which can increase the compressive strength of concrete by 9.12% more than plain samples over 34 days.