Hyper-tuning gene expression programming to develop interpretable prediction models for the strength of corncob ash-modified geopolymer concrete

Abstract

Geopolymer concrete has been offered as an alternative to traditional concrete due to the environmental consequences of cement manufacture. It is also unable to make adjustments if the concrete's strength after casting does not meet the required strength. Because of this, it is highly beneficial to make strength predictions before pouring concrete. This study proposes applying the gene expression programming (GEP) method to forecast the mechanical strength of slag and corncob ash-based geopolymer concrete (SCA-GPC). The SHapley Additive exPlanations (SHAP) approach was used to identify the significance of parameters used for modeling. Best-fitted simulations and exceptional prediction performance were produced by the GEP (R2 = 0.96, MAE = 1.876 MPa, MAPE = 6.00%, RMSE = 2.417 MPa, and NSE = 0.954), (R2 = 0.94, MAE = 0.178 MPa, MAPE = 3.50%, RMSE = 0.236 MPa, and NSE = 0.942), and (R2 = 0.94, MAE = 0.126 MPa, MAPE = 3.60%, RMSE = 0.154 MPa, and NSE = 0.935) for compressive, flexural, and split tensile strengths models of SCA-GPC, respectively. Error and efficiency-based statistical checks confirmed the accuracy of the created models. The SHAP research also found that the mechanical strength of SCA-GPC was mostly controlled by slag quantity, curing age, water quantity, and fine aggregate quantity in the mix proportion. This research showed that the mechanical strength of SCA-GPC could be effectively predicted using the GEP method. The implementation of such methods will significantly improve the process of ensuring the quality of geopolymer concrete.