Abstract
Timely rehabilitation and preservation of pavement systems are imperative to maximize benefits in terms of driver’s comfort and safety. However, the effectiveness of any treatment largely depends on the time of treatment and triggers governed by treatment performance models. This paper presents the development of rutting model for overlay treatment of composite pavement in the State of Louisiana. Various factors affecting the rutting of overlay treatment were identified. Regression analysis was conducted, and rut prediction model is generated. In order to better predict the pavement service life, the existing condition of the pavement was also utilized through the model. The developed models provided a good agreement between the measured and predicted rut values. It was found that the predictions were significantly improved, when existing pavement condition was incorporated. The resulting rutting model could be used as a good pavement management tool for timely pavement maintenance and rehabilitation actions to maximize LADOTD benefits and driver’s comfort and safety.
Highlights
Introduction and BackgroundRutting is considered as one of the major forms of distresses in hot mix asphalt (HMA) overlay of composite pavement
This paper presents the development of rutting model for overlay treatment of composite pavement in the State of Louisiana
The main objective of this study is to identify various parameters that affect the performance of overlay treatment and to develop rut prediction model for overlay treatment on composite pavements in the State of Louisiana
Summary
Rutting is considered as one of the major forms of distresses in HMA overlay of composite pavement. Rutting models based on statistical analysis have been developed by Long Term Pavement Performance (LTPP) program, Mississippi and Washington Department of Transportation (DOT) and other state agencies [6,7,8] All such models generally recognize that major factors contributing to the model are load characteristics, site factors, age of pavement, traffic loading, precipitation, temperature, freezing index, cooling index, and thickness of pavement layers [6,7,8]. Louisiana Department of Transportation and Development (LADOTD) is in the process of developing integrated and comprehensive PMS database that will include the pavement distresses and the climatic and pavement history and inventory data Such information is commonly used by most models [6]. This paper is the result of LTRC-initiated three-phase study that addresses such needs by developing rigorous treatment performance models
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