Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis

Abstract

(standard = 1) of 0.987, 0.973, and 0.986 and adequate precision (standard = 7) of 129.7, 85.3, and 123.5, for the Fc, Ff, and Ft, respectively. In addition, the RSM as a symbolic regression learning system proposed closed-form equations with which the model can be applied manually to design the production of SCGPC performing at optimal strength with optimized application of the industrial waste materials especially the most influential components, which are the GGBS, FA and NaOH. The NB came least in performance. Overall, the studied ensemble-based ML techniques applied in this present research paper outperformed the techniques used in previous literatures, except the poorly performed NB.