This report describes: (1) investigation of time-sequence roughness data collected at GPS test sections to study trends in development of roughness, (2) comparison between International Roughness Index and Ride Number, (3) development of models to predict changes in roughness, (4) investigation of roughness characteristics of new flexible and rigid pavements built for the SPS program, (5) investigation of roughness characteristics of flexible and rigid pavements that were subjected to different rehabilitation strategies under the SPS program, and (6) recommendations for quality assurance and profiling frequency for the test sections.

ABSTRACT: Roughness is an important indicator of pavement riding comfort and safety. It is a condition indicator that should be carefully considered when evaluating primary pavements. At the same time, the use of roughness measurements plays a critical role in the pavement management system. There are many devices used for roughness evaluation. The major tools used for road roughness quantify are the road profilers. In the thesis research, in order to obtain useful pavement surface condition data for pavement evaluation, an inertial based pavement roughness measuring system was developed with the combination of modern sensor technology and computer technology. The research will focus on the development of new method to get the profile in order to improve the repeatability of the inertial based pavement roughness system, the hardware design and the software development which is used for data sampling and data analysis. Finally maximum entropy spectral analysis method was used to evaluate the road profile spectrum. In order to get evaluate the accuracy and correction of the laser profiler system, different roughness devices (including Dipstick, direct type profiler and the laser profiler developed) were operated in the test sites. The research focused on several performance measures, such as repeatability (before and after new method analysis), impact of operating speed and sample interval, correlativity and etc. IRI from these devices were analyzed to evaluate the correlativity between these devices. Some regression models were developed in this research. Test results show that the new method can improve the repeatability of the profiler system. The laser profiler system has good repeatability and the operating speed and sample interval do not have a significant impact on the inertial based roughness measuring system. With the reliable results, the system is ready to be used in the field application within the speed and sample interval range. Through the spectrum analysis, it shows that the spectrum has a qualitative relation with pavement roughness conditions.

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.