Concrete production is one of the main sources of CO2 emissions. The primary reason for this is the high clinker content of Portland cement. To mitigate this problem, supplementary cementitious materials (SCMs) such as fly ash, silica fume, Ground granulated blast furnace slag (GGBFS), rice husk ash, and natural pozzolans are increasingly being used. These materials are used as partial replacements for cement. SCMs not only reduce the environmental impact of concrete but can also improve its long-term mechanical and durability properties. The aim of this study is to develop a machine learning framework that can accurately predict the compressive strength of concrete containing SCMs. The framework includes the training and evaluation of several machine learning models. Nested cross-validation and Bayesian hyperparameter optimization were used to explore the full capacity of the models and ensure reliable evaluation. Permutation significance testing and learning curve analysis were applied to verify that the models learn meaningful patterns rather than memorize the data. Also, feature importance and SHapley Additive exPlanations analyses were performed and the key variables that influence the prediction of the compressive strength of SCM concrete were identified. The optimized XGBoost model achieved the best generalization performance with a holdout R2 of 0.8398. It confirms the effectiveness of the proposed statistically rigorous machine learning framework for reliable compressive strength prediction of SCM-blended concrete.
Işıkdağ et al. (Thu,) studied this question.