Access Type

Open Access Dissertation

Date of Award

1-1-2010

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Civil and Environmental Engineering

First Advisor

Mumtaz Usmen

Abstract

Pavement rutting is one of the most important types of pavement distress that affect road safety and ride quality. Therefore, the primary objective of this study was to develop pavement rutting empirical models for different climate zones to predict pavement rutting on granular base based on LTPP data. Flexible pavements with granular base course were considered for this study. These models lead to better understanding of rutting phenomena and the factors that may have affect in pavement rutting. In addition, these models will help state and local transportation agencies make accurate decisions for maintenance, rehabilitation and reconstruction of pavement.

To develop a reliability-based methodology for pavement rutting prediction models, nine main steps were performed. These steps include reviewing previous studies, reviewing data sources of pavement performance, selecting the variables that may have effect pavement rutting, selecting the test sections at each climate zone, building the research database, verifying the data, analyzing the data, validating the models, and obtaining the final form of the models.

After the data were studied for missing and abnormal data, multiple regression analysis was performed to develop empirical models. Five models were developed based on the GPS-1 sections in wet freeze zone, dry freeze zone, wet no-freeze zone, dry no-freeze zone, and different climate zone combined.

The study indicated that traffic data was the most important factor in wet freeze zone model. The second significant factor in the model was SN followed by VTM, VMA, and Marshall stiffness. In dry freeze zone model, VMA is the most significant factor affecting pavement rutting. Traffic loads are the second significant factor affecting pavement rutting followed by Marshall stiffness and freeze index. The contributing factors in wet no-freeze zone model are VTM which is the most significant factor, SN, and traffic loads. In dry no-freeze zone, the developed model includes traffic loads as the most important factor that affect pavement rutting followed by number of days above 32 °C. As in wet no-freeze zone, VTM is the most significant factor that has affect in pavement rutting in the proposed model developed based on combined data from different climate zones. The model also includes traffic loads as the second significant factors affecting pavement rutting and SN.

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