Predicting Asphalt Pavement Deterioration Under Climate Change Uncertainty Using Bayesian Neural Network

Uncertainty in climate change poses challenges in obtaining accurate and reliable prediction models for future pavement performance. This study aimed to develop an advanced prediction model specifically for flexible pavements, incorporating uncertainty quantification through a Bayesian Neural Network (BNN). Focusing on predicting the International Roughness Index (IRI) and rut depth of asphalt pavement, BNN model was applied to different climate regions, using long-term pavement performance (LTPP) data from 1989 to 2021. The Tree-structured Parzen Estimators (TPE) algorithm was used to optimize model hyperparameters. The impact of climate change on IRI and rut depth was analyzed. Results showed that the proposed BNN model surpasses Artificial Neural Network (ANN), providing predictions with confidence intervals that account for uncertainty in climate data and model parameters. Compared to historical climate data, increases in IRI and rut depth were more significant when based on projected climate data. Relying only on historical climate data would underestimate pavement deterioration. Climate change appeared to have a more significant impact on rut depth than on IRI. Rut depth was particularly sensitive to climate change, increasing by more than 40%. Considering the uncertainty, rutting depth could increase by up to 85.6%. This highlights the importance of considering regional differences in climate change when developing reliable prediction models. The main contributions of this study include the quantification of uncertainty, the impact analysis of climate change and regional sensitivity analysis. It helps adapt to future climate change and supports informed decision-making in transportation infrastructure management.