1665055 - Development of Coastal Pavement Condition Prediction Model Considering Extreme Events

According to the 2023 UN Climate Report, the rise in sea levels due to extreme climatic conditions is projected to continue. With these rapid changes in climate, factors such as extreme temperatures (both high and low), humidity, solar radiation, and heavy rainfall significantly influence pavement conditions. Particularly, road submersion due to coastal flooding leads to drastic deterioration in pavement quality. Conventional pavement condition survey and prediction techniques face challenges in effectively managing road pavement assets that are rapidly changing due to extreme climatic conditions. As a result, there's a pressing need to develop a pavement condition prediction model considering the impacts of these climatic anomalies. The targeted area for this study is Georgia, located on the southeast coast of the United States, where recent sea temperatures and sea levels have been rising sharply. The research aims to collect data on dependent variables such as road pavement conditions, alongside influential factors like coastal road submersion, weather, and traffic conditions. By applying the collected data to machine learning techniques, the goal is to develop a pavement condition prediction model that considers the effects of extreme weather. Ultimately, using coastal road submersion prediction data and the developed pavement condition prediction model, the study intends to propose a road pavement asset management strategy for the coastal roads of Georgia.