Explainable pavement surface condition classification using a TabNet-CatBoost hybrid machine learning framework

Accurate pavement condition assessment is critical for optimizing maintenance and ensuring transportation network safety, yet the opaque nature of advanced machine learning models often hinders their practical adoption. The primary objective of this study was to develop and validate a novel, explainable hybrid framework that can accurately classify pavement condition while remaining transparent. The framework integrates an attention-based TabNet model for intelligent feature extraction with a CatBoost classifier for robust prediction, using a suite of distress and roughness inputs including rutting, longitudinal and transverse cracking, alligator cracking, block cracking, edge cracking, potholes, and the international roughness index. The proposed TabNet-CatBoost model achieved superior performance, with a macro F1-score of 0.890 and a Quadratic Weighted Kappa of 0.917, significantly surpassing traditional baselines. The synergistic architecture proved particularly effective for the most challenging minority classes, more than doubling the recall for the critical failed condition and thereby enhancing its practical utility for risk management. A subsequent Shapley Additive Explanations (SHAP) analysis confirmed that the model’s logic aligns with engineering principles, identifying alligator cracking as the predominant predictor (22.7 % importance). The resulting framework provides a robust and highly interpretable tool for pavement condition classification, bridging the gap between advanced AI and the practical need for trustworthy decision support in infrastructure management.