Development of a Flexible Pavement Condition Rating Model Using Multi-attribute Utility Theory

Asphalt pavement surfaces deteriorate fast due to poor maintenance planning and implementation, use of inappropriate decision tools, and underestimating the effects of climate change in their design and maintenance. Excessive pavement surface distress results in increased Vehicle Operating Costs (VOC), many accidents, and generally reduced reliability of transport services. To prioritize pavement maintenance activities, Pavement Management System (PMS) presents several decision-making tools. These tools vary from simple ranking to complex optimization. Accordingly, a well-defined condition-rating system that can simulate the effects of climate, physical, and operational factors on asphalt pavements is highly needed. Therefore, this research presents a comprehensive asphalt pavement condition-rating model that integrates an extensive range of potential factors influencing flexible-pavement performance in the northwestern region of Nigeria. Experts from transportation ministries, departments, agencies, and various pavement condition surveys provided data for the development of the pavement condition rating model using Multi-attribute Utility Theory (MAUT). The model shows that rutting amount, with a weight of 26.85%, has the highest impact on the flexible-pavement condition, followed by longitudinal cracking, with a weight of 17.47%, and then subgrade type, with a weight of 17.44%. For the climate factors, rainfall amount has a more negative impact on flexible pavement condition rating with the weight of 10.41% than the air temperature which weighs 1.48%. The performance of the MAUT model and the traditional Pavement Condition Rating Index (PCI) was compared and the model shows 88% accuracy based on statistical validations. Finally, the model was applied to some case studies, and the results compared well with a widely used Pavement Condition Index (PCI) rating method. The MAUT model can assist transportation agencies in taking appropriate decisions for maintaining their road networks.