Deterioration Modeling of Pavement Performance in Cold Regions Using Probabilistic Machine Learning Method

Accurate and reliable modeling of pavement deterioration is critical for effective infrastructure management. This study proposes a probabilistic machine learning framework using Bayesian-optimized Natural Gradient Boosting (BO-NGBoost) to predict the International Roughness Index (IRI) of asphalt pavements in cold climates. A dataset only for cold regions was constructed from the Long-Term Pavement Performance (LTPP) database, integrating multiple variables related to climate, structure, materials, traffic, and constructions. The BO-NGBoost model was evaluated against conventional deterministic models, including artificial neural networks, random forest, and XGBoost. Results show that BO-NGBoost achieved the highest predictive accuracy (R2 = 0.897, RMSE = 0.184, MAE = 0.107) while also providing uncertainty quantification for risk-based maintenance planning. BO-NGBoost effectively captures long-term deterioration trends and reflects increasing uncertainty with pavement age. SHAP analysis reveals that initial IRI, pavement age, layer thicknesses, and precipitation are key factors, with freeze–thaw cycles and moisture infiltration driving faster degradation in cold climates. This research contributes a scalable and interpretable framework that advances pavement deterioration modeling from deterministic to probabilistic paradigms and provides practical value for more uncertainty-aware infrastructure decision-making.