Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation estimate compressive strength fc? GPC made employing FA. To build a model, consistent, extensive and reliable data base is compiled through detailed review published research. The set comprised 298 experimental results. utmost dominant parameters counted explanatory variables, in other words, extra water added percent FA (%EW), percentage plasticizer (%P), initial curing temperature (T), age specimen (A), duration (t), fine aggregate total ratio (F/AG), volume ( %AG), SiO2 solids (% S/W) sodium silicate (Na2SiO3) solution, NaOH solution molarity (M), activator or alkali (AL/FA), oxide (Na2O) (N/W) for preparing Na2SiO3 (Ns/No). A GEP proposed with accuracy, generalization, prediction capability model was evaluated performing parametric analysis, applying statistical checks, then compared non-linear linear regression equations.