트리 기반 머신러닝 알고리즘을 활용한 가속 탄산화 양생 시멘트 복합재료의 압축강도 예측 모델 개발

In this study, tree-based machine learning models were developed to quantitatively predict the compressive strength development of CO₂-cured cementitious composites and to identify the key influencing factors based on experimental data collected from the literature. A dataset comprising 333 experimental results was compiled from 23 published studies, incorporating variables related to binder composition, mixture proportions, CO₂ curing conditions, environmental parameters, and curing age. The predictive performance of Random Forest and Gradient Boosting algorithms was evaluated, and the results showed that CatBoost and XGBoost achieved high prediction accuracy and stable generalization performance on the test dataset. SHAP-based sensitivity analysis revealed that CO₂ curing duration, coarse aggregate-to-binder ratio, and CO₂ concentration were the dominant variables governing compressive strength development, exhibiting non-monotonic and nonlinear influence characteristics. This study provides an interpretable, data-driven framework for understanding compressive strength development under CO₂ curing and offers a foundation for future multi-objective optimization studies that integrate carbon uptake efficiency and durability performance.