Intelligent design framework for compressive strength modeling of ultra-high-performance geopolymer concrete (UHPGC): grey wolf optimizer–integrated machine learning and experimental verification

Ultra-high-performance concrete (UHPC) provides superior strength and durability but suffers from high cost and environmental impact. As a sustainable alternative, Ultra-High-Performance Geopolymer Concrete (UHPGC) requires reliable tools for predicting compressive strength (CS), yet existing frameworks remain limited, especially those combining AI, explainability, and experimental verification. This paper develops a Grey Wolf Optimizer (GWO)-enhanced machine learning framework to predict the CS of UHPGC using 179 mixes compiled from the literature. Four GWO-ML models (CatBoost, GBM, RF, ETR) were trained, with GWO-CatBoost achieving the highest performance (R2 = 0.971), followed by GWO-GBM (R2 = 0.967). SHAP-based analysis identified age, fiber, SF, SFL, and Na2SiO3 as the most influential variables. ICE and PDPs provided optimal design ranges for engineering use. A user-friendly GUI was also developed to predict CS along with cost and carbon footprint. Experimental tests on 10 new mixtures confirmed strong generalization of the GWO-CatBoost model (R2 = 0.884).