In this research, different amounts of nano-MgO were added to normal concrete samples, and the effect of these particles on the durability of the samples under freeze and thaw conditions was investigated. The compressive and tensile strength as well as the permeability of concrete containing nanoparticles were measured and compared to those of plain samples (without nanoparticles). The age of concrete samples, percentage of nanoparticles, and water-to-binder ratio are the variables of the current research. Based on the results, the addition of 1% nano-MgO to the normal concrete with a water-to-binder ratio of 0.44 can reduce the permeability up to 63% and improve the compressive and tensile strengths by 9.12% and 10.6%, respectively. Gene Expression Programming (GEP) is applied, and three formulations are derived for the prediction of mechanical properties of concrete containing nano-MgO. In this method, 80% of the dataset is used randomly for the training process and 20% is utilized for testing the formulation. The results obtained by GEP showed acceptable accuracy.

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.