Testing Wisconsin asphalt mixtures for the 2002 AASHTO mechanistic design procedure

There has been a continuous evolutionary process in asphalt pavement design. In the beginning it was crude and based on past experience. Through research, empirical methods were developed based on materials response to specific loading at the AASHO Road Test. Today, pavement design has progressed to a mechanistic-empirical method. This methodology takes into account the mechanical properties of the individual layers and uses empirical relationships to relate them to performance. The mechanical tests that are used as part of this methodology include dynamic modulus and flow number, which have been shown to correlate with field pavement performance. This thesis was based on a portion of a research project being conducted at Michigan Technological University (MTU) for the Wisconsin Department of Transportation (WisDOT). The global scope of this project dealt with the development of a library of values as they pertain to the mechanical properties of the asphalt pavement mixtures paved in Wisconsin. Additionally, a comparison with the current associated pavement design to that of the new AASHTO Design Guide was conducted. This thesis describes the development of the current pavement design methodology as well as the associated tests as part of a literature review. This report also details the materials that were sampled from field operations around the state of Wisconsin and their testing preparation and procedures. Testing was conducted on available round robin and three Wisconsin mixtures and the main results of the research were: The test history of the Superpave SPT (fatigue and permanent deformation dynamic modulus) does not affect the mean response for both dynamic modulus and flow number, but does increase the variability in the test results of the flow number. The method of specimen preparation, compacting to test geometry versus sawing/coring to test geometry, does not statistically appear to affect the intermediate and high temperature dynamic modulus and flow number test results. The 2002 AASHTO Design Guide simulations support the findings of the statistical analyses that the method of specimen preparation did not impact the performance of the HMA as a structural layer as predicted by the Design Guide software. The methodologies for determining the temperature-viscosity relationship as stipulated by Witczak are sensitive to the viscosity test temperatures employed. The increase in asphalt binder content by 0.3% was found to actually increase the dynamic modulus at the intermediate and high test temperature as well as flow number. This result was based the testing that was conducted and was contradictory to previous research and the hypothesis that was put forth for this thesis. This result should be used with caution and requires further review. Based on the limited results presented herein, the asphalt binder grade appears to have a greater impact on performance in the Superpave SPT than aggregate angularity. Dynamic modulus and flow number was shown to increase with traffic level (requiring an increase in aggregate angularity) and with a decrease in air voids and confirm the hypotheses regarding these two factors. Accumulated micro-strain at flow number as opposed to the use of flow number appeared to be a promising measure for comparing the quality of specimens within a specific mixture. At the current time the Design Guide and its associate software needs to be further improved prior to implementation by owner/agencies.

MATURE FINE TAILINGS (MFTs): A STUDY OF COMPRESSIVE STRENGTH AND RHEOLOGICAL PROPERTIES OF ATHABASCA OIL SANDS PETROLEUM MINING WASTE APPLIED IN CONCRETE MIXTURES

This study investigates the compressive properties of concrete incorporating Mature Fine Tailings (MFTs) waste stream from a tar sands mining operation. The objectives of this study are to investigate material properties of the MFT material itself, as well as establish general feasibility of the utilization of MFT material in concrete mixtures through empirical data and visual observations. Investigations undertaken in this study consist of moisture content, materials finer than No. 200 sieve, Atterburg Limits as well as visual observations performed on MFT material as obtained. Control concrete mixtures as well as MFT replacement mixture designs (% by wt. of water) were guided by properties of the MFT material that were experimentally established. The experimental design consists of compression testing of 4”-diameter concrete cylinders of a control mixture, 30% MFT, 50% MFT and 70% MFT replacement mixtures with air-entrainer additive, as well as a control mixture and 30% MFT replacement mixture with no air-entrainer. A total of 6 mixtures (2 control mixtures, 4 replacement mixtures) moist-cured in lime water after 24 hours initial curing were tested for ultimate compressive strength at 7 days and 28 days in accordance to ASTM C39. The test results of fresh concrete material show that the addition of air-entrainer to the control mixture increases slump from 4” to 5.5”. However, the use of MFT material in concrete mixtures significantly decreases slump as compared to controls. All MFT replacement mixtures (30%, 50%, and 70%) with air-entrainer present slumps of 1”. 30% MFT with no air-entrainer presents a slump of 1.5”. It was found that 7-day ultimate compressive stress was not a good predictor of 28-day ultimate compressive stress. 28-day results indicate that the use of MFT material in concrete with air-entrainer decreases ultimate compressive stress for 30%, 50% and 70% MFT replacement amounts by 14.2%, 17.3% and 25.1% respectively.

Bayesian change-point analysis in linear regression model with scale mixtures of normal distributions

In this thesis, we consider Bayesian inference on the detection of variance change-point models with scale mixtures of normal (for short SMN) distributions. This class of distributions is symmetric and thick-tailed and includes as special cases: Gaussian, Student-t, contaminated normal, and slash distributions. The proposed models provide greater flexibility to analyze a lot of practical data, which often show heavy-tail and may not satisfy the normal assumption. As to the Bayesian analysis, we specify some prior distributions for the unknown parameters in the variance change-point models with the SMN distributions. Due to the complexity of the joint posterior distribution, we propose an efficient Gibbs-type with Metropolis- Hastings sampling algorithm for posterior Bayesian inference. Thereafter, following the idea of [1], we consider the problems of the single and multiple change-point detections. The performance of the proposed procedures is illustrated and analyzed by simulation studies. A real application to the closing price data of U.S. stock market has been analyzed for illustrative purposes.