Crumb rubber use in asphalt mixtures using wet process technology has been in practice for years in the United States with good performance history; however, it has some drawbacks that include the need for special blending equipment, high rubber-binder temperatures, and longer waiting time at mixing plants. Pre-treated crumb rubber technologies are emerging as a new method to produce asphalt rubber mixtures in the field. A new crumb rubber modifier known as Reacted and Activated Rubber (RAR) is one such technology. RAR (industrially known as “RARX”) acts like an Enhanced Elastomeric Asphalt Extender to improve the engineering properties of the binder and mixtures. It is intended to be used in a dry mixing process with the purpose of simplifying mixing at the asphalt plant. The objective of this research study was first to perform a Superpave mix design for determination of optimum asphalt content with 35% RAR by weight of binder; and secondly, analyse the performance of RAR modified mixtures prepared using the dry process against Crumb Rubber Modified (CRM) mixtures prepared using the wet process by conducting various laboratory tests. Performance Grade (PG) 64-22 binder was used to fabricate RAR and CRM mixtures and Performance Grade (PG) 70-10 was used to fabricate Control mixtures for this study. Laboratory tests included: Dynamic Modulus Test, Flow Number Test, Tensile Strength Ratio, Axial Cyclic Fatigue Test and C* Fracture Test. Observations from test results indicated that RAR mixes prepared through the dry process had excellent fatigue life, moisture resistance and cracking resistance compared to the other mixtures.

Road networks are valuable assets that deteriorate over time and need to be preserved to an acceptable service level. Pavement management systems and pavement condition assessment have been implemented widely to routinely evaluate the condition of the road network, and to make recommendations for maintenance and rehabilitation in due time and manner. The problem with current practices is that pavement evaluation requires qualified raters to carry out manual pavement condition surveys, which can be labor intensive and time consuming. Advances in computing capabilities, image processing and sensing technologies has permitted the development of vehicles equipped with such technologies to assess pavement condition. The problem with this is that the equipment is costly, and not all agencies can afford to purchase it. Recent researchers have developed smartphone applications to address this data collection problem, but only works in a restricted set up, or calibration is recommended. This dissertation developed a simple method to continually and accurately quantify pavement condition of an entire road network by using technologies already embedded in new cars, smart phones, and by randomly collecting data from a population of road users. The method includes the development of a Ride Quality Index (RQI), and a methodology for analyzing the data from multi-factor uncertainty. It also derived a methodology to use the collected data through smartphone sensing into a pavement management system. The proposed methodology was validated with field studies, and the use of Monte Carlo method to estimate RQI from different longitudinal profiles. The study suggested RQI thresholds for different road settings, and a minimum samples required for the analysis. The implementation of this approach could help agencies to continually monitor the road network condition at a minimal cost, thus saving millions of dollars compared to traditional condition surveys. This approach also has the potential to reliably assess pavement ride quality for very large networks in matter of days.