Ensemble Learning and SHAP Interpretation for Predicting Tensile Strength and Elastic Modulus of Basalt Fibers Based on Chemical Composition

Tensile strength and elastic modulus are key mechanical properties for continuous basalt fibers, which are inherently sustainable materials derived from naturally occurring volcanic rock. This study employs five ensemble learning models, including Extra Tree Regression, Random Forest, Extreme Gradient Boosting, Categorical Gradient Boosting, and Light Gradient Boosting Machine, to predict the tensile strength and elastic modulus of basalt fibers based on chemical composition. Model performance was evaluated using the coefficient of determination (R 2 ), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Following hyperparameter optimization, the Extreme Gradient Boosting model demonstrated superior performance for tensile strength prediction (R 2 = 0.9152, MSE = 0.2867, RMSE = 0.5354, and MAE = 0.6091), while CatBoost excelled in elastic modulus prediction (R 2 = 0.9803, MSE = 0.1209, RMSE = 0.3478, and MAE = 0.2692). SHapley Additive exPlanations (SHAP) analysis identified CaO and SiO 2 as the most significant features, with dependency analysis further revealing optimal ranges of critical variables that enhance mechanical performance. This approach enables rapid data-driven basalt selection, reduces energy-intensive trials, lowers costs, and aligns with sustainability by minimizing resource use and emissions. Integrating machine learning with material science advances eco-friendly fiber production, supporting the circular economy in construction and composites.