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Pavement management systems and performance prediction modeling tools are essential for maintaining an efficient and cost effective roadway network. One indicator of pavement performance is the International Roughness Index (IRI), which is a measure of ride quality and also impacts road safety. Many transportation agencies use IRI to allocate annual

Pavement management systems and performance prediction modeling tools are essential for maintaining an efficient and cost effective roadway network. One indicator of pavement performance is the International Roughness Index (IRI), which is a measure of ride quality and also impacts road safety. Many transportation agencies use IRI to allocate annual maintenance and rehabilitation strategies to their road network.

The objective of the work in this study was to develop a methodology to evaluate and predict pavement roughness over the pavement service life. Unlike previous studies, a unique aspect of this work was the use of non-linear mathematical function, sigmoidal growth function, to model the IRI data and provide agencies with the information needed for decision making in asset management and funding allocation. The analysis included data from two major databases (case studies): Long Term Pavement Performance (LTPP) and the Minnesota Department of Transportation MnROAD research program. Each case study analyzed periodic IRI measurements, which were used to develop the sigmoidal models.

The analysis aimed to demonstrate several concepts; that the LTPP and MnROAD roughness data could be represented using the sigmoidal growth function, that periodic IRI measurements collected for road sections with similar characteristics could be processed to develop an IRI curve representing the pavement deterioration for this group, and that pavement deterioration using historical IRI data can provide insight on traffic loading, material, and climate effects. The results of the two case studies concluded that in general, pavement sections without drainage systems, narrower lanes, higher traffic, or measured in the outermost lane were observed to have more rapid deterioration trends than their counterparts.

Overall, this study demonstrated that the sigmoidal growth function is a viable option for roughness deterioration modeling. This research not only to demonstrated how historical roughness can be modeled, but also how the same framework could be applied to other measures of pavement performance which deteriorate in a similar manner, including distress severity, present serviceability rating, and friction loss. These sigmoidal models are regarded to provide better understanding of particular pavement network deterioration, which in turn can provide value in asset management and resource allocation planning.

ContributorsBeckley, Michelle Elizabeth (Author) / Kaloush, Kamil (Thesis advisor) / Underwood, Benjamin S (Committee member) / Mamlouk, Michael S. (Committee member) / Arizona State University (Publisher)
Created2016
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Highway safety is a major priority for the public and for transportation agencies. Pavement distresses directly affect ride quality, and indirectly contribute to driver distraction, vehicle operation, and accidents. In this study, analysis was performed on highways in the states of Arizona, North Carolina and Maryland for years

Highway safety is a major priority for the public and for transportation agencies. Pavement distresses directly affect ride quality, and indirectly contribute to driver distraction, vehicle operation, and accidents. In this study, analysis was performed on highways in the states of Arizona, North Carolina and Maryland for years between 2013 and 2015 in order to investigate the relationship between accident rate and pavement roughness and rutting. Two main types of data were collected: crash data from the accident records and roughness and rut depth data from the pavement management system database in each state. Crash rates were calculated using the U.S. Department of Transportation method, which is the number of accidents per vehicle per mile per year multiplied by 100,000,000. The variations of crash rate with both International Roughness Index (IRI) and rut depth were investigated. Linear regression analysis was performed to study the correlation between parameters. The analysis showed positive correlations between road roughness and rut depth in all cases irrespective of crash severity level. The crash rate data points were high for IRI values above 250-300 inches/mile in several cases. Crash road segments represent 37-48 percent of the total length of the network using 1-mile segments. Roughness and rut depth values for crash and non-crash segments were close to each other, suggesting that roughness and rutting are not the only factors affecting number of crashes but possibly in combination with other factors such as traffic volume, human factors, etc. In summary, it can be concluded that both roughness and rut depth affect crash rate and highway maintenance authorities should maintain good pavement condition in order to reduce crash occurrences.
ContributorsVinayakamurthy, Mounica (Author) / Mamlouk, Michael S. (Thesis advisor) / Underwood, Benjamin (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2017
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Description

United States Air Force airfield PAVER pavement management system enterprise data was reviewed for 67 networks. The distress survey extents and severity fields were joined with treatment costs estimated using RSMeans to determine the costliest distress. In asphalt surfaced pavements Longitudinal/transverse cracking, weathering, and block cracking resulted in the most

United States Air Force airfield PAVER pavement management system enterprise data was reviewed for 67 networks. The distress survey extents and severity fields were joined with treatment costs estimated using RSMeans to determine the costliest distress. In asphalt surfaced pavements Longitudinal/transverse cracking, weathering, and block cracking resulted in the most pavement condition index (PCI) deducts while the costliest distresses are weathering, block cracking and longitudinal cracking. In portland cement concrete surfaced pavements linear cracking, joint seal damage, and joint spalling resulted in the most PCI deducts while the costliest distresses are joint seal damage, linear cracking, and corner spalling. The results of this data were then compared to airfield attributes: Pavement Temperature Group, Dominant American Association of State Highway and Transportation Officials (AASHTO) Soil Classification, Pavement- Transportation Computer Assisted Structural Engineering (PCASE) Climate Zone, and years since last maintenance. Maps showing the Pavement Temperature Group, Dominant AASHTO Soil Classification, and PCASE Climate Zone are included in Appendix A. Alligator cracking is most prevalent at the airfields with PTG 64-34 (Ellsworth, Fairchild, Hill, and Offutt) and 58-22 (Niagara and Vandenberg). Rutting is most prevalent at PTG 64-34 (Ellsworth, Fairchild, Hill, and Offutt). An increasing trend of joint spalling, corner spalling, and corner break with decreasing soil quality (AASHOTO A-1 to A-8 soils). The PCASE Climate Zone Cost Indices the cost index for weathering is approximately double in the moist region over the dry region. The cost index for block cracking is approximately double in the cold region over the hot region. It is recommended that the agency review its pavement performance modeling in the pavement management system to increase the recommendation of pavement preservation treatments and review the use of higher quality materials for pavement maintenance treatments.

ContributorsThevenot, Ronald (Author) / Kaloush, Kamil (Thesis advisor) / Mamlouk, Michael S. (Thesis advisor) / Ozer, Hasan (Committee member) / Arizona State University (Publisher)
Created2020