Research on Cost-Effectiveness Analysis of Concrete Materials Based on Optimization Algorithm

Abstract

Concrete, as an important construction material in modern construction projects, is widely used in various fields of engineering and plays a great role in it, and different types of concrete have different impacts on project costs. The study adopts Box-Behnken experimental design method in response surface methodology to establish a response surface test model with water-cement ratio, recycled brick aggregate substitution rate, and polypropylene fiber volume fraction as factors. Meanwhile, an objective function considering economic cost and carbon emission is constructed, and an improved non-dominated sequential genetic algorithm (I-NSGA-II) is proposed for the optimal design method of concrete proportion. The results show that the testing model is well fitted with high prediction accuracy; for flexural strength, split tensile strength and compressive strength, the corresponding optimal BF admixtures are 0.2%, 0.1% and 0.2%, respectively. After testing, the improved algorithm significantly improves the speed of convergence and the uniformity of the distribution of optimal solutions. Finally, the multi-objective optimization model proposed in this paper is applied to concrete cost analysis, and 10 sets of optimal mixes are found among 200 sets of mixes by Topsis method. The objective function range is used as a constraint to establish a constrained multi-objective optimization model to find 10 groups of optimal fit ratios by the same method, and comparing the two types of optimal fit ratios, the multi-objective optimization model in this paper can obtain a higher Topsis score.