Data-Driven Framework for Predicting Airborne Sound Insulation of Recycled Rubber–Polyurethane Composite Panels

The increasing accumulation of end-of-life tires has motivated the development of sustainable construction materials incorporating recycled rubber for acoustic insulation applications. This study proposes a data-driven framework for predicting the weighted airborne sound reduction index (Rw) of recycled rubber–polyurethane composite panels based on a limited experimental dataset. Specimens with varying granulometric composition, material density, and polyurethane adhesive dosage were evaluated in accordance with EN ISO 10140-2:2010 and EN ISO 717-1:2013. To address data scarcity, a regression-oriented SMOTE strategy was applied exclusively to the training set to preserve statistical representativeness and avoid data leakage. Test set representativeness was ensured by systematically evaluating numerous data splits and adopting the one that maximized multivariate statistical consistency. A hierarchical modeling approach was adopted, ranging from classical regression models to tree-based ensemble methods and multigene symbolic regression. Model performance was evaluated using R2, RMSE, MAE, and MAPE on an independent test set. The highest accuracy and robustness were obtained using symbolic regression, with R2 values close to 0.99 and minimal prediction errors. Shapley value analysis and PDP/ICE plots identified material density as the dominant predictor of Rw, followed by polyurethane adhesive dosage, while granulometric composition exhibited a weaker influence. The proposed framework provides an accurate and interpretable tool for the preliminary design and optimization of recycled rubber acoustic panels.