Optimizing concrete performance through fine aggregate selection: experimental study and desirability-based modeling
Abstract
ABSTRACT This study applies mixture design, ANOVA, and desirability function analysis to optimize concrete performance with river (RS), crushed (CS), dune (DS) sands, and their combinations. Experimental validation confirms the reliability, practical applicability, and sustainability of optimized mixes, while highlighting key property relationships. Study optimized concrete mixes and developed predictive models using a multi-objective desirability approach. Twenty-one mixtures with varying RS, CS, and DS were designed via a three-factor, five-level simplex lattice method. Key properties including slump, 7 and 28-day compressive (CS) and flexural (FS) strengths, capillary absorption, and UPV were measured. ANOVA and statistical modeling in Design-Expert13 validated factor significance and interactions, enabling reliable mathematical models to predict concrete performance. ANOVA results showed strong predictive performance (R2 = 0.80–0.91). RS improves workability, while moderate amounts of CS and DS sands enhance strengths and reduce open porosity. Best 28-day CS (36 MPa) was recorded for the mix consisting of 0.4RS+0.6CS. Ternary mix M10 (20%RS+60%CS+20%DS) achieved the highest 28-day CS, though excessive dune sand (>40%) decreases performance. Model predictions deviated less than 9% from experiments, confirming reliability. Significant correlations (R2>0.91) among CS, open porosity, density, UPV, and dynamic elasticity modulus enable non-destructive assessment. Local sands (CS, DS) are viable sustainable alternatives to RS.