Temperature predictions for asphalt pavement with thick asphalt layer

Abstract

Temperature is one of the most important factors affecting functional as well as structural performance of asphalt pavements with thick asphalt layer (>30 cm). For a successful pavement design, it is vital to accurately predict the pavement temperatures at various depths. However, most previous researches focused on the temperature predictions for conventional asphalt pavements, of which the asphalt thickness is less than 30 cm. This suggests their proposed models are applicable in top layers, but may not be so effective for temperature predictions at deeper depths. As a result, the primary objective of this research was to develop a statistical model to predict temperatures at deep depths. Three test sites were selected, and they were instrumented with a number of sensors and a data logger to record the pavement temperature hourly. Also, all test sections can provide meteorological monitoring to collect hourly air temperatures and hourly total solar radiation. The recorded meteorological conditions were found to have cumulative effect on the measured pavement temperatures at various depths. On basis of their relationship, a statistical regression was performed, and the temperature prediction model was determined as a function of depth, average air temperature and total solar radiation calculated in the cumulative time. For an improvement in applicability, historical mean monthly air temperatures were also incorporated into the mode. The accuracy and applicability of the improved model were validated by applying it to additional sites for which the measured pavement temperatures and meteorological data were available. Also, by comparing with existing models, the developed model was testified to be more effective for asphalt pavements with thick asphalt layer, promising the model's potential use.