Ensemble machine learning models for predicting the CO2 footprint of GGBFS-based geopolymer concrete

Abstract

While geopolymer concrete (GPC) has gained popularity for its environmentally friendly attributes compared to ordinary Portland cement, the absence of a prediction model for the carbon footprint of its constituents presents challenges for optimization within the evolving concrete industry. This study offers a thorough CO2 footprint prediction for ground granulated blast-furnace slag (GGBFS)-based GPC, utilizing advanced AI techniques, including a combination of machine learning models and stacking ensembles. This research statistically examines crucial parameters responsible for CO2 emissions in GGBFS-based GPC production, identifying factors like superplasticizer content, initial curing temperature, and NaOH (dry) content as significant contributors. Emphasizing sustainability, the study advocates optimizing concrete mixtures by considering factors like the NaOH ratio to other activator materials. After rigorously evaluating 12 machine learning models, including ensemble techniques, this study identified M4—a stacking model of Support Vector Regression (SVR) and Neural Network (NN)—as weak models, and Decision Tree (DT) as a meta-model, as the most effective ensemble model for predicting CO2 footprints. The choice of M4 is supported by various performance metrics such as the lowest Mean Squared Error of 88.8 and Root Mean Squared Error of 9.42, alongside the highest R2, Adjusted R2, and Explained Variance scores, all approximately 0.95. Additional analyses, such as Euclidean distance and Taylor diagrams, further substantiate the selection of M4. The findings have practical implications for sustainable and cleaner concrete production, enabling businesses to optimize the CO2 footprint of GGBFS-based GPC.