Development of performance prediction models in flexible pavement using regression analysis method

Abstract The capability to forecast future pavement condition has been questions of common interest for the economic reason for pavement management systems and the need to develop an intelligent prioritization schedule became ever more important for the sake of efficiency. If the pavement performance prediction model can be developed based on the past pavement performance data, the remaining service lives for pavements can be forecasted. It would help to optimize the scheduling of the rehabilitation activities and to determine the funding level required to achieve a predetermined level of performance. However, the results of the previous attempts to develop general pavement condition forecasting models have not been satisfied reliable because of the difficulties of collection pavement performance data, complexity of the pavement construction situation and different properties of pavement materials. The Georgia Department of Transportation (GDOT) has used the Pavement Condition Evaluation System (PACES) to evaluate the pavement conditions for the entire highway system in Georgia annually for the past 15 years. In this paper, the asphalt pavement performance prediction models for the state highways and the interstate highways have been developed applying simple and multiple regression analysis methods using the PACES data and PACES rating. The multiple linear regression model is effective to forecast pavement performance when ratings with various AADT. If this pavement performance prediction model using multiple linear regression analysis is implemented into the Pavement Management System, it could play an important role in the decision making process for the asphalt pavement management system