A Machine Learning Framework for Pavement Performance Prediction Under Extreme Climate Conditions

Accurate pavement performance prediction is critical for effective pavement management systems (PMS), enabling optimal maintenance and rehabilitation decisions. The Pavement Condition Index (PCI) is the most widely used performance indicator, yet reliable prediction requires models that capture full spectrum of deterioration drivers, including structural characteristics, traffic loads, and the increasingly impactful extreme climate events. While machine learning (ML) approaches have improved PCI prediction, most existing models overlook climate extremes. This study proposes a comprehensive ML-based PCI model that integrates extreme climate variables from the Expert Team on Climate Change Detection and Indices (ETCCDI). Eleven algorithms were evaluated on a dataset combining pavement age, structural characteristics, traffic loads, and extreme climate variables. Among the evaluated models, categorical boosting model achieved the lowest error values and the highest R2 (0. 81). Explainability analyses using feature importance and SHapley Additive exPlanations (SHAP) identified the number of icing days (ID), daily temperature range in December (DTRDec) and consecutive dry days (CDD) as the extreme climate indicators with the greatest negative predictive influence on PCI. Incorporating ETCCDI indices provided additional explanatory power beyond traditional annual average climatic variables, significantly improving both predictive accuracy and model interpretability. These findings highlight the importance of integrating standardized extreme climate indicators into PMS frameworks to support more resilient and sustainable pavement management under evolving climate conditions.