In recent years, average temperatures in Canada have been continuously increasing, owing to changes in the global climate. This can be attributed to a surge in the concentration of greenhouse gases in the atmosphere. Climate scientists predict the trend to further aggravate in the near future. Pavement performance models show that changing climate will result in accelerated pavement deterioration. To mitigate pavement deterioration, various adaptation strategies have been suggested in the recent literature. One of these adaptation strategies is upgrading the superpave asphalt binder grade. It is well known that asphalt binder is highly sensitive to climate factors such as temperature and percent sunshine. Hence, reviewing asphalt binder grade is a vital step, and that can help decelerate pavement deterioration. The goal of this research is to determine new asphalt binder grades across Canada based on the projected climate data. To achieve this goal, the analysis was carried out in four phases. In the first phase, statistically downscaled climate change models were gathered from the Climate Change model database. Then in the second phase, python code is written to extract the maximum and minimum temperatures from the climate change models for a particular location. Later in the third phase, from the extracted maximum and minimum temperature, average seven-day maximum pavement temperature and minimum pavement temperature are determined using the LTPP pavement temperature prediction model. Lastly, high-temperature grade (XX) and low temperature grade (YY) of an asphalt binder (PG XX – YY) are estimated using the average seven-day maximum and minimum pavement temperature respectively and tabulated in an easy-to-use format for application by the transportation agencies in Canada. Note this paper presents a very brief summary of this research project and five climate models used in the analysis. Finally, this paper also presents the revised asphalt binder grades for ten different locations, each from one of the provinces.

Contributors: Swarna S T, Hossain K.