UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Post-Hurricane Recovery in the United States: A Multi-Scale Approach(2019) Kerr, Siobhan Elizabeth; Patwardhan, Anand; Public Policy; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)As we increasingly consider resilience as a central strategy for addressing climate change, recovery emerges as an important dimension that is often the focus of public policy. The progression of global climate change will cause an increase in the scale and magnitude of disasters, so it is more important than ever to understand how we can not only prevent impacts, but also recover from them. This research was carried out with the primary goal of examining recovery at multiple scales, while simultaneously considering the social and economic forces and community behaviors that influence recovery outcomes. This dissertation proposes new ways of conceptualizing and quantifying recovery and analyzes the way that neighborhood characteristics and community engagement influence the recovery process. The findings emphasize the importance of assessing recovery progress on multiple timescales and highlight the opportunities that emerge as a result of community engagement with local government throughout the recovery process. The first analytical chapter considers the interaction between vulnerability and recovery by studying power outages and restoration following Hurricane Isaac in Louisiana. This approach uses power restoration as a metric by which to better understand short-term recovery of a specific infrastructure system, building a model for recovery that takes into account antecedent conditions, impact, hazard and prioritization. The next chapter considers 311 requests in Houston TX as a potential proxy measure for civic engagement and social capital. This chapter analyzes 311 contact volumes across the City of Houston and identifies the neighborhood characteristics that influence proclivity to call. Finally, the 311 data is used to better understand system-level recovery and community engagement in the recovery process in Houston TX following Hurricane Harvey in 2017. The chapter compares neighborhood-level use of 311 services prior to Hurricane Harvey to the way it was used for storm-related concerns in the weeks directly following the storm.Item Design for Disaster Displacement(2014) Kandigian, Christine; Bovill, Carl; Architecture; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Due to the increase in occurrence of natural disasters, it is imperative for our society to learn to maintain resiliency, while also preparing for the aftermath of a disaster. The major tasks of this proposal include providing emergency and permanent housing, within a condensed timeframe to a medium density while providing communal spaces and activities for long term use. New York City, the epicenter of the region and the country, can be catastrophically damaged by an earthquake or hurricane, particularly because of the density of population and lack of awareness of seismic risk. The quality of pre-disaster planning immediately results in a more successful post-disaster reconstruction, which directly impacts the future resiliency of the community. In order to decrease the timeframe between the disaster, emergency response, the relief phase, and the recovery of the community, a new building assembly system must be developed to solve this problem.Item IMPROVING RESILIENCE OF RAIL-BASED INTERMODAL FREIGHT TRANSPORTATION SYSTEMS(2013) Zhang, Xiaodong; Miller-Hooks, Elise; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the increasing natural and human-made disasters, the risk of an event with potential to cause major disruption to our transportation systems and their components also increases. It is of paramount importance that transportation systems could be effectively recovered, thus economic loss due to the disasters can be minimized. This dissertation addresses the optimization problems for transportation system performance measurement, decision-making on pre-disaster preparedness and post-event recovery actions planning and scheduling to achieve the maximum network resilience level. In assessing a network's potential performance given possible future disruptions, one must recognize the contributions of the network's inherent ability to cope with disruption via its topological and operational attributes and potential actions that can be taken in the immediate aftermath of such an event. A two-stage stochastic program is formulated to solve the problem of measuring a network's maximum resilience level and simultaneously determining the optimal set of preparedness and recovery actions necessary to achieve this level under budget and level-of-service constraints. An exact methodology, employing the integer L-shaped method and Monte Carlo simulation, is proposed for its solution. In this dissertation, a nonlinear, stochastic, time-dependent integer program is proposed, from operational perspective, to schedule short-term recovery activities to maximize transportation network resilience. Two solution methods are proposed, both employing a decomposition approach to eliminate nonlinearities of the formulation. The first is an exact decomposition with branch-and-cut methodology, and the second is a hybrid genetic algorithm that evaluates each chromosome's fitness based on optimal objective values to the time-dependent maximum flow subproblem. Algorithm performance is also assessed on a test network. Finally, this dissertation studies the role of network topology in resilience. 17 specific network topologies were selected for network resilience analysis. Simple graph structures with 9~10 nodes and larger network with 100 nodes are assessed. Resilience is measured in terms of throughput and connectivity and average reciprocal distance. The integer L-shaped method is applied again to study the performance of the network structure with respect to all three resilience measures. The relationships between resilience and average degree, diameter, and cyclicity are also investigated.