Explainable AutoML with Uncertainty Quantification for CO2-Cured Concrete Compressive Strength Prediction

The cement and concrete industry is one of the primary sources of anthropogenic carbon dioxide (CO2) emissions globally, responsible for nearly 8% of total emissions, making the need for a low-carbon transition urgent. CO2 curing provides both strength enhancement and carbon sequestration, yet the compressive strength of such concrete remains challenging to predict due to limited and strongly coupled experimental factors. This study developed an explainable Automated Machine Learning (AutoML) framework with integrated uncertainty quantification to predict the 28-day compressive strength of CO2-cured concrete. The framework was built using 198 standardized experimental data and trained with four algorithms—Random Forest (RF), Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), and the transformer-based Tabular Prior-Data Fitted Network (TabPFN). To enhance model accuracy and efficiency, stratified cross-validation, hyperparameter optimization, and bootstrap-based uncertainty analysis were applied during training. The results show that TabPFN achieves the highest predictive accuracy (test R2 = 0.959) and maintains a stable 95% prediction interval. SHapley Additive exPlanations (SHAP) indicates that cement content, aggregate composition, water–binder (W/B) ratio, and CO2 curing time are the dominant factors, with an optimal W/B ratio near 0.40. Interaction analysis further reveals synergistic effects between cement content and W/B, and a strengthening coupling between curing time and CO2 concentration at longer durations. The framework enhances predictive reliability and explainability, supporting mixture design and curing optimization for low-carbon concrete development.