DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Assessment of Seismic Damage of Buildings and Related Environmental Impacts

Sustainable development has only recently started examining the existing infrastructure, and a key aspect of this is hazard mitigation. To examine buildings under a sustainable perspective requires an understanding of a building's life-cycle environmental costs, including the consideration of associated environmental impacts induced by earthquake damage. Damage repair costs lead to additional material and energy consumption, leading to harmful environmental impacts. Merging results obtained from a seismic evaluation and life-cycle analysis for buildings will give a novel outlook on sustainable design decisions. To evaluate the environmental impacts caused by buildings, long-term impacts accrued throughout a building's lifetime and impacts associated with damage repair need to be quantified. A method and literature review for completing this examination has been developed and is discussed. Using software Athena and HAZUS-MH, this study evaluated the performance of steel and concrete buildings considering their life-cycle assessments and earthquake resistance. It was determined that code design-level greatly effects a building repair and damage estimations. This study presented two case study buildings and found specific results that were obtained using several premade assumptions. Future research recommendations were provided to make this methodology more useful in real-world applications. Examining cost and environmental impacts that a building has through, a cradle-to-grave analysis and seismic damage assessment will help reduce material consumption and construction activities from taking place before and after an earthquake event happens.

Life cycle assessment of trayless dining

Universities in the United States are applying more sustainable approaches to their dining service operations. "The increase in social consciousness and environmental stewardship on college campuses has spurred an array of new and innovative sustainability programs"(ARAMARK Higher Education 2008). University residence dining is typically cafeteria style, with students using trays to carry food. Studies report that food served without trays substantially reduces food waste and water and electrical consumption associated with washing trays. Commonly, these reported results are estimates and not measurements taken under actual operating conditions. This study utilizes measurements recorded under actual dining service conditions in student residence halls at Michigan Technological University to develop the following: 1) operational-specific data on the issues and potential savings associated with a conversion to trayless dining and 2) life cycle assessment (LCA) cost and environmental impact analyses comparing dining with and without trays. For the LCA, the entire life cycle of the system is considered, from the manufacturing to the usage and disposal phases. The study shows that trayless dining reduces food waste because diners carry less food. The total savings for the diner shifts when not using trays for the standard academic year (205 days), with an average number of 700 diners, is 7,032 pounds of food waste from the pre-rinse area (33% reduction) and 3,157 pounds of food waste from the pan washing area (39% reduction). In addition, for each day of the study, the diners consumed more food during the trayless portion of the experiment. One possible explanation for the increased food consumption during this short duration study could be that the diners found it more convenient to eat the extra food on their plate rather than carrying it back for disposal. The trayless dining experiment shows a reduction in dishwasher water, steam, and electrical consumption for each day of the study. The average reduction of dishwasher water, steam, and electrical consumption over the duration of the study were 10.7%, 9.5%, and 6.4% respectively. Trayless dining implementation would result in a decrease of 4,305 gallons of consumption and wastewater discharge, 2.87 mm BTU of steam consumption, and 158 kWh of electrical consumption for the dinner shift over the academic year. Results of the LCA indicate a total savings of $190.4 when trays are not used during the dinner shift. Trayless dining requires zero CO2 eq and cumulative energy demand in the manufacturing stage, reductions of 1005 kg CO2 eq and 861 MJ eq in the usage phase, and reductions of 6458 kg CO2 eq and 1821 MJ eq in the end of the life cycle.

A REGIONAL ASSESSMENT OF GROUNDWATER AVAILABILITY AS CONSTRAINED BY LOCAL HYDROGEOLOGY AND ENVIRONMENTAL LIMITS TO STREAMFLOW DEPLETION

Groundwater pumping from aquifers in hydraulic connection with nearby streams is known to cause adverse impacts by decreasing flows to levels below those necessary to maintain aquatic ecosystems. The recent passage of the Great Lakes--St. Lawrence River Basin Water Resources Compact has brought attention to this issue in the Great Lakes region. In particular, the legislation requires the Great Lakes states to enact measures for limiting water withdrawals that can cause adverse ecosystem impacts. This study explores how both hydrogeologic and environmental flow limitations constrain groundwater availability in the Great Lakes Basin. A methodology for calculating maximum allowable pumping rates is presented. Groundwater availability across the basin is shown to be constrained by a combination of hydrogeologic yield and environmental flow limitations varying over both local and regional scales. The results are sensitive to factors such as pumping time and streamflow depletion limits as well as streambed conductance. Understanding how these restrictions constrain groundwater usage and which hydrogeologic characteristics and spatial variables have the most influence on potential streamflow depletions has important water resources policy and management implications.

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.

FLOOD RISK ASSESSMENT UNDER HISTORICAL AND PREDICTED LAND USE CHANGE USING CONTINUOUS HYDROLOGIC MODELING

Current procedures for flood risk estimation assume flood distributions are stationary over time, meaning annual maximum flood (AMF) series are not affected by climatic variation, land use/land cover (LULC) change, or management practices. Thus, changes in LULC and climate are generally not accounted for in policy and design related to flood risk/control, and historical flood events are deemed representative of future flood risk. These assumptions need to be re-evaluated, however, as climate change and anthropogenic activities have been observed to have large impacts on flood risk in many areas. In particular, understanding the effects of LULC change is essential to the study and understanding of global environmental change and the consequent hydrologic responses. The research presented herein provides possible causation for observed nonstationarity in AMF series with respect to changes in LULC, as well as a means to assess the degree to which future LULC change will impact flood risk. Four watersheds in the Midwest, Northeastern, and Central United States were studied to determine flood risk associated with historical and future projected LULC change. Historical single framed aerial images dating back to the mid-1950s were used along with Geographic Information Systems (GIS) and remote sensing models (SPRING and ERDAS) to create historical land use maps. The Forecasting Scenarios of Future Land Use Change (FORE-SCE) model was applied to generate future LULC maps annually from 2006 to 2100 for the conterminous U.S. based on the four IPCC-SRES future emission scenario conditions. These land use maps were input into previously calibrated Soil and Water Assessment Tool (SWAT) models for two case study watersheds. In order to isolate effects of LULC change, the only variable parameter was the Runoff Curve Number associated with the land use layer. All simulations were run with daily climate data from 1978-1999, consistent with the 'base' model which employed the 1992 NLCD to represent 'current' conditions. Output daily maximum flows were converted to instantaneous AMF series and were subsequently modeled using a Log-Pearson Type 3 (LP3) distribution to evaluate flood risk. Analysis of the progression of LULC change over the historic period and associated SWAT outputs revealed that AMF magnitudes tend to increase over time in response to increasing degrees of urbanization. This is consistent with positive trends in the AMF series identified in previous studies, although there are difficulties identifying correlations between LULC change and identified change points due to large time gaps in the generated historical LULC maps, mainly caused by unavailability of sufficient quality historic aerial imagery. Similarly, increases in the mean and median AMF magnitude were observed in response to future LULC change projections, with the tails of the distributions remaining reasonably constant. FORE-SCE scenario A2 was found to have the most dramatic impact on AMF series, consistent with more extreme projections of population growth, demands for growing energy sources, agricultural land, and urban expansion, while AMF outputs based on scenario B2 showed little changes for the future as the focus is on environmental conservation and regional solutions to environmental issues.