Prediction and optimization of properties of concrete containing crushed stone dust and nylon fiber using response surface methodology

Over-extraction of aggregates from natural sources with rapid urbanization as well as massive waste generation in construction industry have imposed the need to utilize waste material as concrete constituent. Crushed Stone Dust (CSD) is such a supplementary material that can be utilized for the production of sustainable concrete. This study attempts to predict and optimize fresh and hardened properties of concrete utilizing CSD as a partial replacement of natural fine aggregate and Nylon Fiber (NF) as fiber reinforcement using Response Surface Methodology (RSM). A three-level factorial design of Box-Behnken was incorporated to investigate the effect of CSD, NF and W/C as three independent variables on compressive strength, splitting tensile strength, fresh density and workability of concrete as desired responses. All the developed probabilistic models were found to be significant in predicting the responses at 95% confidence level. Regression analysis in terms of correlation coefficient, coefficient of determination, coefficient of variation, adequate precision, chi-square, mean square error, root mean square error, and mean absolute error also indicated the accuracy and functionality of the developed models. The results reveal that both compressive and splitting tensile strength increase with increased NF content, but the rise in CSD percentages beyond a certain level has negative impact on strength of concrete. However, fresh density and workability of concrete show a declining trend with rise in both CSD and NF levels. From multi-objective optimization, 20% CSD, 0.75% NF and W/C of 0.49 have been found to be the optimum proportions for concrete mixture with a desirability of 0.915. Finally, an experimental validation was carried out with optimum mixture contents and relative error between the experimental and predicted optimized values was observed to be less than 5%.