Comparison of artificial neural network (ANN) and response surface methodology (RSM) in predicting the compressive and splitting tensile strength of concrete prepared with glass waste and tin (Sn) can fiber

Abstract

Amidst a world of never-ending waste production and waste disposal crises, scientists have been working their way to come up with solutions to serve the earth better. Two such commonly found trash deteriorating the environment are glass and tin can waste. This study aims to investigate the comparative suitability of response surface methodology (RSM) and artificial neural network (ANN) in predicting the mechanical strength of concrete prepared with fine glass aggregate (GFA) and condensed milk can (tin) fibers (CMCF). An experimental scheme has been designed in this study with two input variables as GFA and CMCF, and two output variables compressive and splitting tensile strength. The results show that both variables influenced the compressive and splitting tensile strength of concrete at 7, 28, and 56 days (p < 0.01). The maximum compressive and splitting tensile strength was found at 20% GFA with 1% CMCF and 10% GFA with 0.5% CMCF, respectively. The model predicted values in both techniques were in close agreement with corresponding experimental values in all cases. The results of different statistical parameters in terms of coefficient of correlation, coefficient of determination, chi-square, mean square error, root mean square error, mean absolute error, and standard error prediction indicate the functionality of both modeling approaches for concrete strength prediction. However, RSM models yield better accuracy in simulating the compressive and splitting tensile strength of concrete than ANN models.

Keywords

• Glass waste
• Condensed milk can (tin)
• Compressive strength
• Splitting tensile strength
• Response surface methodology
• Artificial neural network