Multi-objective optimizing high strength concrete using meta-heuristic genetic algorithm

Abstract

Abstract Reducing the cost of concrete construction as the most expensive building material reduces the overall cost of construction high-strength concrete (HSC). In the present study, to achieve this goal, the mix design of HSC is optimized in terms of strength and price using the meta-heuristic genetic algorithm (HGA). To do this, in the first step, a series of experimental data was considered as basic information and then, a mix design function was obtained to determine 28-day compressive strength and a slump calculation function using meta-HGA m in MATLAB software. In the next step, strength-price function was optimized using the meta HGA by changing the material ratios in the mix design, the price of the materials in each mix design and applying the required conditions of HSC including slump obtained from slump calculation function. Then, a comparison was performed between the results of this algorithm and the regression method, the results showed the better responses of the algorithm compared to regression for both factors of strength and price with the values of 10.2% and 6.5%, respectively. In addition, the construction of the mix design resulting from responses of the algorithm and regression in laboratory indicates that more than 97% of the strength was achieved at 28 days of age.