Research on a Prediction Method for Maintenance Decision of Expressway Asphalt Pavement Based on Random Forest

This study predicts expressway asphalt pavement maintenance decisions using machine learning to overcome the information loss inherent in traditional composite indices like PQI and PCI. Using ten years of inspection data from the G3012 Expressway in Xinjiang, an interpretable Random Forest (RF) model was developed. The methodology integrates permutation-based feature selection, three imbalance mitigation strategies (Balanced Weighting, SMOTE, and Cost-Sensitive Learning), and a rigorous time-aware validation framework. Results indicate that raw distress features—specifically strip repairs, block cracking, transverse and longitudinal cracking—are the most influential predictors, significantly outperforming aggregated indices. The optimized model, using Balanced Weighting and mean imputation, achieved an accuracy of 0.826 and ROC-AUC of 0.853 under strict temporal validation, effectively identifying the minority “repair” class. This research demonstrates that leveraging raw distress data through an interpretable ensemble framework provides a robust, data-driven alternative to threshold-based planning, supporting the transition from reactive to preventive maintenance in complex infrastructure management.