Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest

Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) gene expression programming (GEP), are used this study to develop an empirical model for prediction compressive strength FGPC. A widespread, reliable, consistent database FGPC set up via comprehensive literature review. The consists 298 data points. influential parameters considered as input variables modelling curing temperature T , time id="M2"> t age specimen id="M3"> A molarity NaOH solution id="M4"> M percent SiO2 solids water ratio id="M5"> % S / W sodium silicate (Na2SiO3) solution, volume total aggregate ( id="M6"> G ), fine id="M7"> F oxide (Na2O) id="M8"> N Na2SiO3 alkali or activator FA id="M9"> L id="M10"> s o plasticizer id="M11"> P extra added id="M12"> E % . RFR ensemble algorithm gives outburst performance compared GEP. However, GEP proposed can be estimate accuracy both models evaluated statistical error checks, external validation considered. equation sensitivity analysis parametric then with nonlinear linear expressions.