Chemistry-informed data-driven models for predicting strength of ambient-cured slag and fly ash geopolymer concrete

Abstract

Geopolymer concrete (GPC) is a sustainable alternative to Portland cement concrete; however, complex geopolymerization mechanisms and nonlinear strength development under ambient curing make mixture optimization challenging. This study develops a chemistry-informed data-driven framework to predict the 28-day compressive strength of ambient-cured slag/fly ash–based GPC. A dataset of 151 mixtures was compiled incorporating eight input parameters, including key precursor oxide ratios (SiO₂/CaO, SiO₂/Al₂O₃, and CaO/Al₂O₃), which are rarely considered in existing predictive models. Artificial Neural Network (ANN) and Gene Expression Programming (GEP) models were developed and compared. The optimal ANN model (8–2–2–1 architecture) achieved superior predictive accuracy (R² = 0.93, MAE = 2.82), while the GEP model (R² = 0.77, MAE = 5.55) produced an explicit mathematical equation suitable for practical applications. Model reliability was verified experimentally using four new mix designs. Sensitivity analysis identified the SiO₂/CaO ratio as the most influential parameter governing strength development in ambient-cured GPC.