Compressive strength prediction for SCM-based concretes using a multiple machine learning models and datasets approach

• ML models for predicting SCM concretes compressive strength developed • Multiple datasets devised from a global dataset were used for training • Multiple ML models used in conjunction with multiple datasets for best results • Accurate prediction results obtained for all SCM concrete types studied • Prediction uncertainty bounds established by conformal prediction analysis Supplementary cementitious materials (SCM) used as partial replacements of cement can reduce carbon emissions and construction costs. The amount and types of SCM used directly influence the compressive strength of concrete, which is a crucial indicator of concrete performance. However, predicting compressive strength is challenging for SCM-based concretes due to their complex compositions, especially when multiple types of SCM are used. This study used five machine learning (ML) models, namely artificial neural network (ANN), random forest (RF), extra trees (ET), extreme gradient boosting (XGB), and adaptive boosting (AdaBoost) to predict the 28-day compressive strength of SCM concretes incorporating fly ash (FA), silica fume (SF), and ground granulated blast furnace slag (GGBFS). Firstly, a global dataset comprising 1456 data points was organized into seven individual and six combined datasets for training of the five ML models. After this, the performances of the ML models’ predictions using individual and combined datasets were compared using root mean squared error (RMSE) and coefficient of determination (R 2 ) to identify the optimal ML model-dataset configurations for each type of SCM-based concrete. It is found that in general, XGB models developed using the combined datasets demonstrated superior prediction accuracy, achieving an R² value exceeding 0.8. Both Shapley Additive Explanation (SHAP) and partial dependence plots (PDPs) were employed to illustrate the effects of input variables on compressive strength. Finally, uncertainty quantification using the Jackknife method combined with a normalized nonconformity measure was employed to provide accurate uncertainty bounds for compressive strength predictions.