# Performance of Payement Temperature Prediction Models
## 1 Introduction  
It is important that the right asphalt binder is selected to ensure the longevity of the pavement with low maintenance and repair costs given the high cost of hot mix asphalt. The performance of asphalt binders on the road is affected by environmental factors such as air temperature and moisture. While moisture can be controlled through drainage provisions, the sensitivity of the pavement's performance to temperature [1] is of particular concern. Asphalt binders are selected based on their performance under expected pavement temperature extremes, i.e., the average seven-day maximum pavement temperature and the minimum pavement temperature, in the location of their intended use. The pavement temperatures are determined from air temperature. The maximum pavement temperature is determined at a depth of 20 mm below the pavement surface. Shear stresses arising from a combination of pavement temperature and traffic loading were determined to be critical at this depth [2]. At high temperatures, asphalt softens and pavement damage such as rutting will appear on the pavement, while at low temperatures asphalt stiffens and is subject to thermal cracking. The penetration grading system of measuring asphalt binder performance ensures that the asphalt binder used has sufficient stiffness to resist rutting and adequate resistance to thermal cracking. The correct selection of asphalt binders thus ensures that the asphalt binder chosen meets both the expected maximum and minimum pavement temperature requirements for the serviceability of the road under the given environmental conditions. Extensive research has been carried out across the world and in different climatological regions to formulate pavement temperature prediction models that can be used to select asphalt binder materials for asphalt roads [3; 4]. The models can be categorised as theoretical--based on the principles of heat transfer--or empirical models--based on statistical analysis of the measured environmental factors to determine pavement temperature. Theoretical models are widely acceptable, but they are more complex and require several input variables than the latter. This makes it more difficult to obtain accurate results when using theoretical models [5]. Empirical models, on the other hand, are simpler but require calibration. Their accuracy is highly dependent on the database used in developing the model. [PERSON] et al. [6] also found that calibration of the models improved the accuracy of the pavement temperature predicted values. The accuracy of predicted pavement temperature is also affected by the availability and accuracy of weather data, as well as variability of thermal properties of pavement materials [7]. According to [PERSON] et al. [8], pavement temperature is mainly affected by air temperature, but other factors such as geographical location play a major role. [PERSON] et al. and [PERSON] [9; 10] noted that geographical location significantly affects the accuracy of predicted pavement temperature. The effect of geographical location has been considered in most models through the inclusion of the latitude of the area under study. Several studies have been undertaken in different parts of the world (India, Pakistan, Iraq, Kuwait, Thailand, Egypt, Libya (cited from [PERSON] et al. [11]), Oman [12], Ghana [11] and Australia [13]) to determine the applicability of pavement prediction models in performance grade selection. [PERSON] et al. [13] observed good agreement in the predicted maximum pavement temperature for both internationally and locally developed models; however, they [13] noted more variability between model predictions for minimum pavement temperature. [PERSON] et al. [11] in their study identified the need for locally developed models. They [11] established that the Strategic Highway Research Program (SHRP) Superpave model did not perform reliability for performance binder selection in Ghana. Developing temperature prediction models would require resources in terms of time, finances and labour.