A Data-Driven Approach for Estimation and Multi-Objective Optimization of Concrete Mix Design

Abstract

The study focuses on establishing the optimum concrete mix of ratios through a comprehensive analysis of experimental results. For this purpose, 62 numbers of concrete mixtures have been considered by varying the level of key ingredients- cement, water, fine aggregate and coarse aggregate. Using experimental data, Genetic Expression Programming (GEP) has been used to develop predictive equations for compressive strength and slump with cement, water, and coarse aggregates and fine aggregates as inputs. These equations are useful to estimate compressive strength and workability of concrete for particular ingredients. Moreover, mathematical multi objective optimization has been conducted by Genetic Algorithm (GA) using these equations as basic functions and optimum content of cement, water, fine aggregate and coarse aggregate have been determined for obtaining maximum compressive strength, maximum slump at lowest cost. Further, multi objective optimizations of different grades of concrete with slump and cost separately have also been carried out to determine these ingredients. Thus, by implementing the present results a more accurate number of mixed proportions with desired compressive strength, and slump can be obtained at minimum cost