Fly ash-based geopolymer concrete (GPC) is a sustainable alternative to conventional cementitious materials; however, its compressive strength is governed by complex and highly correlated mixture parameters, making experimental optimization expensive and data-driven modeling challenging. While machine learning (ML) techniques have been widely applied to predict GPC strength, most studies prioritize predictive accuracy without explicitly addressing multicollinearity among input variables, which can distort feature importance, reduce model stability, and limit engineering interpretability. This study proposes a multicollinearity-integrated and interpretable ML framework that systematically embeds correlation diagnostics and structured feature screening within the modeling pipeline rather than treating interpretability as a post-processing step. Multiple conventional and ensemble learning algorithms were comparatively evaluated using cross-validation to ensure generalization robustness. The proposed framework achieved a maximum coefficient of determination (R2) of 0.96 with low prediction error, outperforming baseline regression models while demonstrating improved stability under correlated input conditions. Unlike existing studies that rely solely on black-box optimization, the integrated interpretability analysis revealed physically consistent dominance of curing temperature, alkali content, and water-related parameters in governing strength development. By explicitly coupling predictive performance with multicollinearity mitigation and engineering-oriented interpretability, this work advances beyond accuracy-driven ML applications and provides a robust and transparent decision-support tool for sustainable geopolymer mix design.
Ahadian et al. (Wed,) studied this question.