Innovative approaches for predicting the elastic modulus and tensile strength of green concrete using soft computing methods

In this study, interpretable and semi-interpretable soft computing techniques, including Group Method of Data Handling (GMDH), Gene Expression Programming (GEP), and Response Surface Methodology (RSM), were employed to develop predictive relationships for estimating the elastic modulus (E) and splitting tensile strength (STS) of concrete containing waste foundry sand (WFS). A sensitivity analysis was subsequently conducted to evaluate the influence of various parameters on these mechanical properties. The input variables considered were the ratio of waste foundry sand to cement (WFS/C), the ratio of waste foundry sand to fine aggregate (WFS/FA), the ratio of fine aggregate to total aggregate (FA/TA), the ratio of water to cement (W/C), the ratio of coarse aggregate to cement (CA/C), the ratio of superplasticizer to cement (1000SP/C) and the age of the sample. The results revealed that the GMDH model achieved the highest correlation coefficient (R) among all methods in predicting STS, exhibiting the lowest root mean square error (RMSE = 0.533) and mean absolute error (MAE = 0.434). Thus, the GMDH model demonstrated superior performance in predicting STS based on all statistical indicators. For predicting E, the RSM method provided the most accurate results, with the highest R = 0.978 and the lowest errors, RMSE = 1.372 and MAE = 1.088. Sensitivity analysis indicated that CA/C had the most significant effect on STS, while W/C had the greatest influence on E. Other parameters, WFS/C, CA/C, and FA/TA, showed relatively minor impacts on E.