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.

Asphalt binder is a complex viscoelastic hydrocarbon, whose performance depends upon interaction between its physical and chemical properties, both of which are equally important to the successful understanding of the material. Researchers have proposed various models linking linear viscoelastic (LVE) and microstructural parameters. However, none of these parameters provide insight into the relationship in the non- linear viscoelastic NLVE domain. The main goals of this dissertation are two fold. The first goal is to utilize the technique of Laser Desorption Mass Spectroscopy (LDMS) to relate the molecular structure of asphalt binders to its viscoelastic properties. The second goal of the study is to utilize different NLVE characterization tools and analysis procedures to get a clear understanding of the NLVE behavior of the asphalt binders. The goals of the study are divided into four objectives; 1) Performing the LDMS test on asphalt binder to develop at the molecular weight distributions for different asphalts, 2) Characterizing LVE properties of Arizona asphalt binders, 3) Development of relationship between molecular structure and linear viscoelasticity, 4) Understanding NLVE behavior of asphalt binders through three different characterization methods and analysis techniques.

In this research effort, a promising physico-chemical relationship is developed between number average molecular weight and width of relaxation spectrum by utilizing the data from LVE characterization and the molecular weight distribution from LDMS. The relationship states that as the molecular weight of asphalt binders increase, they require more time to relax the developed stresses. Also, NLVE characterization was carried out at intermediate and high temperatures using three different tests, time sweep fatigue test, repeated stress/strain sweep test and Multiple Stress Creep and Recovery (MSCR) test. For the intermediate temperature fatigue tests, damage characterization was conducted by applying the S-VECD model and it was found that aged binders possess greater fatigue resistance than unaged binders. Using the high temperature LAOS tests, distortion was observed in the stress-strain relationships and the data was analyzed using a Fourier transform based tool called MITlaos, which deconvolves stress strain data into harmonic constituents and aids in identification of non-linearity by detecting higher order harmonics. Using the peak intensities observed at higher harmonic orders, non-linearity was quantified through a parameter termed as “Q”, which in future applications can be used to relate to asphalt chemical parameters. Finally, the last NLVE characterization carried out was the MSCR test, where the focus was on the scrutiny of the Jnrdiff parameter. It was found that Jnrdiff is not a capable parameter to represent the stress-sensitivity of asphalt binders. The developed alternative parameter Jnrslope does a better job of not only being a representative parameter of stress sensitivity but also for temperature sensitivity.

The built environment is responsible for a significant portion of global waste generation.

Construction and demolition (C&D) waste requires significant landfill areas and costs

billions of dollars. New business models that reduce this waste may prove to be financially

beneficial and generally more sustainable. One such model is referred to as the “Circular

Economy” (CE), which promotes the efficient use of materials to minimize waste

generation and raw material consumption. CE is achieved by maximizing the life of

materials and components and by reclaiming the typically wasted value at the end of their

life. This thesis identifies the potential opportunities for using CE in the built environment.

It first calculates the magnitude of C&D waste and its main streams, highlights the top

C&D materials based on weight and value using data from various regions, identifies the

top C&D materials’ current recycling and reuse rates, and finally estimates a potential

financial benefit of $3.7 billion from redirecting C&D waste using the CE concept in the

United States.

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.