Interpretable machine learning-based analysis of hydration and carbonation of carbonated reactive magnesia cement mixes

This study explored the influence of different input variables on the hydration and carbonation degree of carbonated reactive magnesia cement (RMC) system by employing six machine learning algorithms. These included support vector machine (SVM), particle swarm optimization-based SVM (PSO-SVM), extreme learning machine (ELM), grey wolf optimizer-based SVM (GWO-SVM), kernel extreme learning machine (KELM), and extreme gradient boosting (XGBoost). The followed approach enabled the deep learning of the relevant database to achieve parameter prediction. Two feature analysis methodologies, i.e. partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP), were applied to uncover the operating laws underpinning the black box operation characteristics of machine learning models. Results revealed that GWO-SVM and XGBoost outperformed all other models in predicting the hydration and carbonation degree of the complete database set (R2 of the total database set was 0.9470/0.9775 and 0.9663/0.9727 for hydration and carbonation degree, respectively). Factors such as carbonation duration, CO2 concentration, pre-curing temperature, and w/b directly influenced the degree of hydration and carbonation. Among them, carbonation duration and CO2 concentration were two of the most influential factors, resulting in the promotion of brucite consumption and accelerating the formation of carbonation products.