Mixture-of-Experts Learning for Mixture-Response Interpretation and Screening of PE-ECC

Featuring considerable tensile ductility and multiple cracking behavior, polyethylene fiber-reinforced engineered cementitious composites (PE-ECCs) are promising cement-based materials for engineering construction. However, establishing accurate design models for evaluating the mechanical properties of PE-ECC is a challenging task owing to the complex material components. This study presents an interpretable data-driven framework for predicting the mechanical properties of PE-ECC using mixture-of-experts (MoE) learning. A database comprising 383 deduplicated material-level records from 90 verified literature sources was compiled for modeling the compressive strength, ultimate tensile strain, ultimate tensile strength and first-cracking tensile strength of PE-ECC. An MoE prediction model was developed by integrating XGBoost, LightGBM, CatBoost, WDBPANN and TabPFN through out-of-fold stacking and learned gating. The model achieved coefficient of determination (R²) values of 0.971, 0.950, 0.970 and 0.954 for the four mechanical properties, respectively. Shapley additive explanations (SHAP), accumulated local effects (ALE) and response maps were used to examine the fitted nonlinear associations between the reported mixture variables and each target property. Based on these relationships, support-filtered virtual screening was conducted within the database-supported design space to identify candidate mixtures for subsequent experimental verification. The framework links target-specific prediction with mixture-response interpretation and confines screening to regions supported by reported PE-ECC mixtures.