Roadway systems in the United States have become huge assets that need massive resources to maintain and operate. To meet the long-term performance goal, government agencies developed pavement management systems (PMSs) to help them manage roadway assets effectively with limited resources. Currently, some PMSs in the United States have been designed for two types of pavements: asphalt and concrete. The composite pavement, another pavement type, which is the result of concrete pavement rehabilitations and constructed with an asphalt surface layer over a concrete base, was treated as asphalt. However, the literature review indicates that compared to asphalt pavements, composite pavements perform differently and have different dominant distresses. In addition, as the amount of composite pavements increases, it is necessary to investigate them independently to incorporate more accurate information into the PMS. Therefore, the goal of this research is to improve and to expand the PMS with an additional pavement type: composite pavements. To achieve this goal, the PMS managed by the North Carolina Department of Transportation (NCDOT) was used as a case study, and several objectives were accomplished in this research: 1) to identify composite pavements and generate the raw data based on the construction history; 2) to clean the raw data and mitigate errors using statistical methods and engineers’ experiences; 3) to develop nonlinear models to describe dominant distresses and pavement performances; 4) to propose quantile regression (QR) models to predict pavement performances; and 5) to investigate the pavement treatment effectiveness by exploring performance index jumps.
Based on findings of this research, it was concluded that the automated data were more consistent with engineers’ experience and revealed more information than the windshield data; longitudinal cracking and transverse cracking were found to be the dominant distresses in composite pavements, followed by alligator cracking and raveling; Interstate composite pavements deteriorated faster than both US and NC composite pavements, and NC composite pavements had the slowest deterioration rate; QR models can be used as a new prediction method of pavement performances at both the project and the network levels; in general the “Resurfacing” treatment was more effective than the “Chip Seal” treatment; and The average service life of asphalt and composite pavements were similar, but composite pavements have a smaller variation of service lives than that of asphalt pavements.
It was recommended that the automated data should be used in future PMS related research projects, due to its better data quality, and because of the robust performance of QR models at both network and project levels, QR models should be incorporated in the future PMS.
In summary, this research expanded the existing NCDOT PMS with composite pavements, proposed systematic methods to improve the quality of performance data, enriched the diversity of prediction models by exploring potentials of QR models, and investigated the effectiveness of pavement treatments. Essentially, transportation agencies can use the findings of this research to make informative investment decisions.
|Commitee:||Hildreth, John, Lim, Churlzu, Nicholas, Thomas, Wu, Jy S.|
|School:||The University of North Carolina at Charlotte|
|Department:||Infrastructure and Environmental Systems|
|School Location:||United States -- North Carolina|
|Source:||DAI-B 78/09(E), Dissertation Abstracts International|
|Subjects:||Civil engineering, Information science|
|Keywords:||Composite pavements, Distress model, Performance model|
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