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ABSTRACT Pre-treated crumb rubber technologies are emerging as a new method to produce asphalt rubber mixtures in the field. A new crumb rubber modifier industrially known as "RuBind" is one such technology. RuBindTM is a "Reacted and Activated Rubber" (RAR) that acts like an 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 objectives of this research study were to evaluate the rheological and aging properties of binders modified with RuBindTM and its compatibility with warm mix technology. Two binders were used for this study: Performance Grade (PG) 70-10 and PG 64-22, both modified with 25% by weight of asphalt binder. Laboratory test included: penetration, softening point, viscosity, Dynamic Shear Rheometer (DSR) and Bending Beam Rheometer (BBR). Tests were conducted under original, short and long -term aging conditions. Observations from the test results indicated that there is a better improvement when RuBindTM is added to a softer binder, in this case a PG 64-22. For short-term aging, the modified binder showed a similar aging index compared to the control. However, long term aging was favorable for the modified binders. The DSR results showed that the PG 64-22 binder high temperature would increase to 82 °C, and PG 70-10 would be increased to 76 °C, both favorable results. The intermediate temperatures also showed an improvement in fatigue resistance (as measured by the Superpave PG grading parameter |G*|sinä). Test results at low temperatures did not show a substantial improvement, but the results were favorable showing reduced stiffness with the addition of RuBindTM. The evaluation of warm mix additive using EvothermTM confirmed the manufacturer information that the product should have no negative effects on the binder properties; that is the modified binder can be used in a warm mix process. These results were encouraging and the recommendation was to continue with a follow up study with mixture tests using the RuBindTM modified binders.
The activity-based approach to travel demand analysis and modeling, which has been developed over the past 30 years, has received tremendous success in transportation planning and policy analysis issues, capturing the multi-way joint relationships among socio-demographic, economic, land use characteristics, activity participation, and travel behavior. The development of synthesizing population with an array of socio-demographic and socio-economic attributes has drawn remarkable attention due to privacy and cost constraints in collecting and disclosing full scale data. Although, there has been enormous progress in producing synthetic population, there has been less progress in the development of population evolution modeling arena to forecast future year population. The objective of this dissertation is to develop a well-structured full-fledged demographic evolution modeling system, capturing migration dynamics and evolution of person level attributes, introducing the concept of new household formations and apprehending the dynamics of household level long-term choices over time. A comprehensive study has been conducted on demography, sociology, anthropology, economics and transportation engineering area to better understand the dynamics of evolutionary activities over time and their impacts in travel behavior. This dissertation describes the methodology and the conceptual framework, and the development of model components. Demographic, socio-economic, and land use data from American Community Survey, National Household Travel Survey, Census PUMS, United States Time Series Economic Dynamic data and United States Center for Disease Control and Prevention have been used in this research. The entire modeling system has been implemented and coded using programming language to develop the population evolution module named `PopEvol' into a computer simulation environment. The module then has been demonstrated for a portion of Maricopa County area in Arizona to predict the milestone year population to check the accuracy of forecasting. The module has also been used to evolve the base year population for next 15 years and the evolutionary trend has been investigated.
in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as
collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when
comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.
Nevertheless, some organizations understand the importance of risk management practices and have begun to measure their risk maturity in order to identify weaknesses and improve risk management practices. Risk maturity measures the organization's ability and perceptions towards risk management. It is possible that many of the barriers to improving risk management would not exist if increased risk maturity was found to have a positive correlation with successful project performance.
The comprehensive hypothesis of the research is that increased risk maturity improves project performance. An exploratory study was conducted on data collected to identify measurable benefits with risk management. Quantitative and qualitative data was collected on 266 construction projects over a seven year period. Multiple statistical analyses were performed on the data and found a positive correlations between risk maturity and project performance. A positive correlations was found between customer satisfaction and contractors risk maturity. Additional findings from the recorded data included the increased ability to predict risks during construction projects within an organization. These findings provide clear reasoning for organizations to devote additional resources in which improve their risk management practices.
