Experimental analysis and gene expression programming optimization of sustainable concrete containing mineral fillers

Abstract

Rapid urbanization has led to a high demand for concrete, causing a significant depletion of vital natural resources, notably river sand, which is crucial in the manufacturing process of concrete. As a result, there is a growing need for environmentally sustainable alternatives to fine aggregate in concrete. Quarry dust (QD) has evolved as a viable and ecologically friendly substitute in response to this demand. In the past, limited experimental investigations and only conventional modeling techniques were used to promote sustainable mineral fillers in concrete. This study proposed a robust soft computing technique using gene-expression programming (GEP) to enhance the usability of sustainable alternatives in concrete. Initially, an experimental study was carried out to examine the feasibility and mechanical characteristics of concrete made from materials including quarry dust and superplasticizer as a partial replacement for fine aggregate. Ten mixed proportions with various proportions (0%, 20%, 40%, and 60%) of quarry dust were used to make M15 and M20 grades of concrete. A series of experimental tests, such as workability, compressive strength (CS), and tensile strength (TS), were conducted to examine the fresh and hardened properties of modified concrete. The established database from the experimental investigations was then used to develop machine learning (ML) models using GEP. The outcomes of the GEP models were validated by comparing them with multi-linear regression (MLR) models and using various statistical metrics such as root mean squared error (RMSE), performance index (PI), correlation coefficient (R), and external validation methods. Finally, sensitivity analysis was performed to investigate the influence of ingredients such as mineral fillers, superplasticizers, and others on the mechanical properties of concrete. To enhance the practical usage of the study, a graphical user interface (GUI) was also created. The study revealed that 40% of the replacement of fine aggregates with mineral filler and superplasticizer shows the optimum properties. GEP models outperformed MLR, achieving R² values of 0.96 in CS and 0.92 in TS, compared to MLR’s lower values of 0.85 in CS and 0.81 in TS. The proposed GEP equations and user-friendly GUI can be used to develop the pre-mix design of concrete using quarry dust and superplasticizers.