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.
Crumb rubber use in asphalt mixtures by means of wet process technology has been in place for several years in the United States with good performance record; however, it has some shortcomings such as maintaining high mixing and compaction temperatures in the field production. Organosilane (OS), a nanotechnology chemical substantially improves the bonding between aggregate and asphalt by modifying the aggregate structure from hydrophilic to hydrophobic contributing to increased moisture resistance of conventional asphalt mixtures. Use of Organosilane also reduces the mixing and compaction temperatures and facilitates similar compaction effort at lower temperatures. The objective of this research study was first to perform a Superpave mix design for Crumb Rubber Modified Binder (CRMB) gap-graded mixture with and without Organosilane; and secondly, analyse the performance of CRMB mixtures with and without Organosilane by conducting various laboratory tests. Performance Grade (PG) 64-22 binder was used to create the gap-graded Hot Mix Asphalt (HMA) mixtures for this study. Laboratory tests included rotational viscometer binder test and mixtures tests: dynamic modulus, flow number, tensile strength ratio, and C* fracture test. Results from the tests indicated that the addition of Organosilane facilitated easier compaction efforts despite reduced mixing and compaction temperatures. Organosilane also modestly increased the moisture susceptibility and resistance to crack propagation yet retaining equal rutting resistance of the CRMB mixtures.
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.
There is a growing need to quantify the project performance and financial benefits of PPP. This dissertation fills this gap in knowledge by performing a comprehensive quantitative analysis of PPP project performance and financial sources for transportation projects in the U.S. This study’s specific research objectives are:
(1) Develop a solid baseline for comparison, comprised of non-PPP projects;
(2) Quantify PPP project cost and schedule performance; and
(3) Quantify private versus public financing sources of PPP.
A thorough literature review led to the development of a structured data collection process for PPP and comparable non-PPP projects. Financing data was collected and verified for a total of 133 ongoing and completed projects; while performance data was verified for a subset of 81 completed projects. Data analysis included regression analysis, descriptive statistics, inferential statistics and non-parametric statistical tests.
The results provide benchmarks for PPP project performance and financing sources. For the performance results, non-PPP projects have an average cost change of 8.46 percent and an average schedule change of -0.22 percent. PPP projects have an average cost change of 3.04 percent and average schedule change of 1.38 percent. Statistical analysis showed cost change for PPP projects were superior to that of non-PPP; however, schedule change differences were not significant. For the financing results, private financing totaled 44.5 percent while public financing totaled 55.5 percent. This result shows private financing can be used to leverage public financing with close to a one-to-one ratio and that PPP has the potential to double the amount of infrastructure delivered to the public.
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.