Predictive Modeling of Aggregate Polished Stone Value from Mineralogical and Chemical Composition

The polished stone value (PSV) is a key parameter for assessing the resistance of aggregates to polishing in the laboratory. It is included in technical specifications and serves as both a regulatory and contractual criterion for selecting aggregates for wearing courses. Its determination requires non-negligible amounts of material, long testing durations, and skilled operators. This study aims to develop a predictive modeling approach to estimate the polished stone value (PSV) from the mineralogical and chemical composition of aggregates. A curated database was compiled from the peer-reviewed literature, and compositional data were transformed using Isometric Log-Ratio (ILR) to generate physically interpretable balances and avoid constant-sum artifacts. Machine learning algorithms, including Gradient Boosting, CatBoost, and Multivariate Adaptive Regression Splines (MARS), were trained and evaluated using repeated 10 × 2 K-Fold cross-validation with preprocessing embedded within the loop. CatBoost achieved the highest accuracy, with 90.4% of predictions within ±20% of the measured PSV. Model interpretability using permutation feature importance and SHAP analysis identified meaningful drivers, highlighting the roles of CO2/SO3 versus the major-oxide framework, and silica-rich oxides versus CaO/MgO, consistent with petrographic expectations. The proposed workflow provides a practical and interpretable approach for predicting PSV from compositional data. It offers a time- and resource-efficient alternative to conventional laboratory tests, while also providing insight into the material factors that control aggregate polishing resistance. Limitations related to dataset size and inter-source variability are discussed.