Prediction of concrete strength using Multilayer Perceptron Neural Network-based utilizing sustainable waste materials

This paper presents a laboratory investigation on optimum level of Vitrified polish waste (VPW) and Ground Granulated Blast Furnace Slag (GGBS) as a partial replacement of cement to study the strength characteristics of concrete. Ordinary Portland cement was partially replaced by 5%, 10%, 15%, and 20% mix of Vitrified polish waste and GGBFS. The water to cementitious materials ratio was maintained at 0.38 for all mixes. The strength characteristics of the concrete were evaluated by conducting compressive test, strength test, splitting tensile strength test and flexural strength test. The compression strength test was conducted for 7 and 28 days of curing, while the split tensile strength test and flexural strength test were performed on an M30, M35 and M40 grade concrete. The mix proportion M30, M35 and M40 was found to be 1:1.615:3.427, 1:1.50:3.25, and 1:1.40:3.15 respectively. The test results proved that the compressive strength, split tensile strength and flexural strength of concrete mixtures containing GGBFS and VPW increases as the amount of GGBS and VPW increase. A Multilayer Perceptron (MLP) Neural Network was utilized to evaluate the concrete strength, with the predicted values aligning closely with the observed data. The results indicate that an optimum point, at 15% of GGBFS and VPW of the total binder content, the further addition of GGBFS and VPW does not improve the compressive strength, split tensile strength and flexural strength.