Estimating greenhouse gas emissions of highway construction and rehabilitation to support pavement life cycle assessment

Highway infrastructure plays a significant role in society. The building and upkeep of America’s highways provide society the necessary means of transportation for goods and services needed to develop as a nation. However, as a result of economic and social development, vast amounts of greenhouse gas emissions (GHG) are emitted into the atmosphere contributing to global climate change. In recognizing this, future policies may mandate the monitoring of GHG emissions from public agencies and private industries in order to reduce the effects of global climate change. To effectively reduce these emissions, there must be methods that agencies can use to quantify the GHG emissions associated with constructing and maintaining the nation’s highway infrastructure. Current methods for assessing the impacts of highway infrastructure include methodologies that look at the economic impacts (costs) of constructing and maintaining highway infrastructure over its life cycle. This is known as Life Cycle Cost Analysis (LCCA). With the recognition of global climate change, transportation agencies and contractors are also investigating the environmental impacts that are associated with highway infrastructure construction and rehabilitation. A common tool in doing so is the use of Life Cycle Assessment (LCA). Traditionally, LCA is used to assess the environmental impacts of products or processes. LCA is an emerging concept in highway infrastructure assessment and is now being implemented and applied to transportation systems. This research focuses on life cycle GHG emissions associated with the construction and rehabilitation of highway infrastructure using a LCA approach. Life cycle phases of the highway section include; the material acquisition and extraction, construction and rehabilitation, and service phases. Departing from traditional approaches that tend to use LCA as a way to compare alternative pavement materials or designs based on estimated inventories, this research proposes a shift to a context sensitive process-based approach that uses actual observed construction and performance data to calculate greenhouse gas emissions associated with highway construction and rehabilitation. The goal is to support strategies that reduce long-term environmental impacts. Ultimately, this thesis outlines techniques that can be used to assess GHG emissions associated with construction and rehabilitation operations to support the overall pavement LCA.

CONVERSION OF DOMAIN TYPE ENFORCEMENT LANGUAGE TO THE JAVA SECURITY MANAGER

With today's prevalence of Internet-connected systems storing sensitive data and the omnipresent threat of technically skilled malicious users, computer security remains a critically important field. Because of today's multitude of vulnerable systems and security threats, it is vital that computer science students be taught techniques for programming secure systems, especially since many of them will work on systems with sensitive data after graduation. Teaching computer science students proper design, implementation, and maintenance of secure systems is a challenging task that calls for the use of novel pedagogical tools. This report describes the implementation of a compiler that converts mandatory access control specification Domain-Type Enforcement Language to the Java Security Manager, primarily for pedagogical purposes. The implementation of the Java Security Manager was explored in depth, and various techniques to work around its inherent limitations were explored and partially implemented, although some of these workarounds do not appear in the current version of the compiler because they would have compromised cross-platform compatibility. The current version of the compiler and implementation details of the Java Security Manager are discussed in depth.

EFFECTIVE FUNCTION CHOICE IN THE R SCRIPTING LANGUAGE

This project examines the current available work on the explicit and implicit parallelization of the R scripting language and reports on experimental findings for the development of a model for predicting effective points for automatic parallelization to be performed, based upon input data sizes and function complexity. After finding or creating a series of custom benchmarks, an interval based on data size and time complexity where replacement becomes a viable option was found; specifically between O(N) and O(N3) exclusive. As data size increases, the benefits of parallel processing become more apparent and a point is reached where those benefits outweigh the costs in memory transfer time. Based on our observations, this point can be predicted with a fair amount of accuracy using regression on a sample of approximately ten data sizes spread evenly between a system determined minimum and maximum size.