LAUSR

Abstract The impact of aging on asphalt binder depends on multiple factors including binder type and environmental conditions. In a previous study, Shalaby proposed two empirical equations to investigate the effects of extended short-term aging on the shear modulus and phase angle of asphalt binders. The objective of this study was to use a robust mathematical approach based on an artificial neural network (ANN) to validate the empirical approach proposed by Shalaby to model short-term aging of asphalt binders. Both methods were compared in terms of percent error, root mean squared error, and plots of predicted versus measured values for four different binders. The results showed that Shalaby’s equations exhibited sufficient accuracy to predict the effect of aging duration and temperature on the shear modulus and phase angle of asphalt binder. Furthermore, this study showed that Shalaby’s approach can be employed to estimate the increase in high-temperature true grade as a result of extended aging. Therefore, the approach provides designers with a tool to make informed decisions regarding binder type selection. In particular, such a method can be utilized when extended aging durations resulting from construction delays or extended hauling distances are expected in the field.