Uncertainty Quantification for Predicting Low-Temperature Crack Resistance of Asphalt Mixture Base on Bayesian Neural Network

Developing models for predicting the low-temperature cracking resistance of asphalt mixtures is a complex process with a wide variety and complex influence mechanisms of variables, leading to higher uncertainty in the prediction results. Several models have been developed in this regard. This study developed a Bayesian neural network (BNN) model for predicting the fracture energy of low-temperature semi-circular bending (SCB) tests based on pavement condition measurements, traffic, climate, and basic parameters of the material. The model was trained and evaluated using low-temperature SCB test data from in-situ pavement core samples, and the results showed that the coefficient of determination (R2) of the BNN model was greater than 0.8 for both the training and testing sets. The variable importance scores showed that the decrease of transverse crack rating index (TCEI) and gradation were the most important factor affecting low-temperature fracture energy and that the ambient temperature was relatively least important. The uncertainty of the BNN model variables was quantified using epistemic uncertainty and aleatoric uncertainty. The results of the uncertainty analyses showed that the epistemic uncertainty was less than or equal to the aleatoric uncertainty for most of the variables used in this study, which indicated that the uncertainty caused by the model parameters was less than that caused by the noise in the original data, that corroborated the reliability of the BNN model used in this study.