Model to Predict Pavement Temperature Profile: Development and Validation

Abstract

Flexible pavements comprise a majority of the primary highways in the United States. These primary highways are subjected to heavy loading that can cause significant damage to the hot-mix asphalt (HMA) pavements. As HMA is a viscoelastic material, the structural or load-carrying capacity of the pavement varies with temperature. Thus, to accurately determine in situ strength characteristics of flexible pavement, it is necessary to predict the temperature distribution within the HMA layers. The majority of previously published research on pavement temperature prediction has consisted of predicting the annual maximum or minimum pavement temperature so as to recommend a suitable asphalt binder performance grade. To determine the pavement temperature profile, the influence of ambient temperature and seasonal changes must be understood such that the effects of heating and cooling trends within the pavement structure can be quantified. Recent investigations have shown that it is possible to model daily pavement maxima and minima temperatures by knowing the maximum or minimum ambient temperatures, the depth at which the pavement temperature is desired, and the calculated solar radiation utilizing a linear relationship. This paper presents the verification that the pavement temperatures calculated using the daily solar radiation could be accurately applied to any location. The suggested location-independent model was successfully validated utilizing data from the Virginia Smart Road and two randomly selected long-term pavement performance seasonal monitoring program sites.