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- All Subjects: Pavements
- Creators: Mamlouk, Michael
- Creators: Beckley, Michelle Elizabeth
In recent years, an increase of environmental temperature in urban areas has raised many concerns. These areas are subjected to higher temperature compared to the rural surrounding areas. Modification of land surface and the use of materials such as concrete and/or asphalt are the main factors influencing the surface energy balance and therefore the environmental temperature in the urban areas. Engineered materials have relatively higher solar energy absorption and tend to trap a relatively higher incoming solar radiation. They also possess a higher heat storage capacity that allows them to retain heat during the day and then slowly release it back into the atmosphere as the sun goes down. This phenomenon is known as the Urban Heat Island (UHI) effect and causes an increase in the urban air temperature. Many researchers believe that albedo is the key pavement affecting the urban heat island. However, this research has shown that the problem is more complex and that solar reflectivity may not be the only important factor to evaluate the ability of a pavement to mitigate UHI. The main objective of this study was to analyze and research the influence of pavement materials on the near surface air temperature. In order to accomplish this effort, test sections consisting of Hot Mix Asphalt (HMA), Porous Hot Mix asphalt (PHMA), Portland Cement Concrete (PCC), Pervious Portland Cement Concrete (PPCC), artificial turf, and landscape gravels were constructed in the Phoenix, Arizona area. Air temperature, albedo, wind speed, solar radiation, and wind direction were recorded, analyzed and compared above each pavement material type. The results showed that there was no significant difference in the air temperature at 3-feet and above, regardless of the type of the pavement. Near surface pavement temperatures were also measured and modeled. The results indicated that for the UHI analysis, it is important to consider the interaction between pavement structure, material properties, and environmental factors. Overall, this study demonstrated the complexity of evaluating pavement structures for UHI mitigation; it provided great insight on the effects of material types and properties on surface temperatures and near surface air temperature.
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.
In this research effort, a reliability framework is developed using Monte Carlo simulation for predicting the fatigue life of AC material using the S-VECD model. The reliability analysis reveals that the fatigue life prediction is very sensitive to the uncertainty in the input variables. FAM testing in similar loading conditions as AC, and upscaling of AC modulus and damage response using FAM properties from a relatively simple homogenized continuum approach shows promising results. The FAM phase fatigue life prediction and upscaling of FAM results to AC show more reliable fatigue life prediction than the fatigue life prediction of AC material using its experimental data. To assess the sensitivity of fatigue life prediction model to uncertainty in the input variables, a parametric sensitivity study is conducted on the S-VECD model. Overall, the findings from this research show promising results both in terms of upscaling FAM to AC properties and the reliability of fatigue prediction in AC using experimental data on FAM.