ABSTRACT
Environmental conditions including temperature and moisture vary over the life of flexible pavements. Their combined effects can have long-term impacts on layer stiffness, stress and strain responses, and damage of the pavements. Climate change can change the temperature and moisture profiles of pavements, accelerating damage and reducing the service life. This research aims to introduce a method to assess the impacts of climate change on damage of flexible pavements. A supervised machine learning algorithm was adopted to train Artificial Neural Networks (ANN), using data including climatic factors, traffic, maintenance, and Falling Weight Deflectometer (FWD) back-calculated layer stiffness. The trained ANN was used to predict layer stiffness under future climate. Critical strain, the number of load repetition to failure and accumulation of damage was evaluated using layered elastic analysis to determine the additional damage that can be attributed to climate change. A case study was performed based on a Long Term Pavement Performance (LTPP) road section in Minnesota to demonstrate the method. For this case study, projected changes in climate will double the speed of rutting damage accumulation and correspondingly reduces pavement service life up to 50% under 2100 climate.
