Interpretable Machine Learning for One-Part Fly-Ash/Slag Geopolymer Strength Prediction: Toward Multifunctional Binder Design

Portland cement production accounts for roughly 8% of anthropogenic CO2 emissions, driving interest in low-carbon geopolymer binders. One-part (“just-add-water”) geopolymers, which replace hazardous liquid activators with a dry, pre-blended solid activator, are especially suited to field deployment where handling safety and logistics are decisive. However, their formulation space is combinatorially vast, and trial-and-error development cannot efficiently navigate it. This paper reviews one-part geopolymer science, presents a new comparative and interpretable ML analysis of a published 80-mixture one-part fly-ash/GGBS geopolymer dataset from twelve studies, and proposes an AI-assisted design framework. The ML demonstration targets 28-day compressive strength only. Under leave-one-source-out (LOSO) cross-validation—the appropriate test for a literature-pooled dataset—gradient-boosted trees achieved R2 = 0.61, well above a linear baseline (0.36), suggesting that non-linear structure transfers across studies; a random split gives a higher but less reliable R2 = 0.90 on only 16 test mixtures. Because fly-ash and GGBS contents are near-perfectly anti-correlated (r=−0.99), we model the precursor axis as a single slag-fraction descriptor; SHAP then identifies this precursor balance and the activator’s Na2O dosage as the dominant, mechanistically coherent strength levers. Demonstrated for strength, the framework offers a transferable route toward multifunctional low-carbon binders for protective and infrastructure applications.