Interpretable ensemble learning for predicting flexural and compressive strength of 3D-printable concrete: Linking mixture design, fiber characteristics, and aggregate effects

The mechanical performance of three-dimensional concrete printing (3DCP) mixtures is governed by a complex interaction between mixture composition, fiber characteristics, aggregate properties, and layer-wise deposition effects. Although compressive strength (CS) has been widely investigated, flexural strength (FS) is equally critical because printed elements are vulnerable to crack propagation, interlayer weakness, and direction-dependent behaviour. This study develops interpretable machine learning models for the prediction of FS and CS of 3D-printable concrete using literature-derived experimental datasets. Extra Trees (ET), XGBoost, and Random Forest (RF) models were trained using selected mixture, binder, admixture, fiber, and aggregate descriptors. Recursive Feature Elimination and model hyperparameter optimization were integrated within a random search procedure with 10-fold cross-validation and applied separately for each model and target variable. Model performance was assessed using R 2 , MAE, RMSE, MAPE, actual-predicted plots, Taylor diagrams, and residual analysis. SHAP analysis was then used to interpret the relative and directional effects of the selected variables. All three models provided accurate predictions, with XGBoost showing the best cross-validation performance for both FS and CS. XGBoost achieved independent-test R 2 values of 0.9856 and 0.9781 for FS and CS, respectively, with the corresponding MAE values of 0.5211 MPa and 2.8523 MPa. SHAP results showed that FS predictions were influenced most strongly by water-reducing admixture content, water-to-binder ratio, fiber ratio, aggregate mass, and fiber density. In contrast, CS predictions were influenced primarily by cement mass, water-to-binder ratio, fiber ratio, and fiber density.