Theses and Dissertations from UMD
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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 give thesis/dissertation in DRUM
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Item Working in Reverse: Advancing Inverse Optimization in the Fields of Equilibrium and Infrastructure Modeling(2022) Allen, Stephanie Ann; Gabriel, Steven A; Dickerson, John P; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Transportation and infrastructure modeling allows us to pursue societal aims such as improved disaster management, traffic flow, and water allocation. Equilibrium programming enables us to represent the entities involved in these applications such that we can learn more about their dynamics. These entities include transportation users and market players. However, determining the parameters in these models can be a difficult task because the entities involved in these equilibrium processes may not be able to articulate or to disclose the parameterizations that motivate them. The field of inverse optimization (IO) offers a potential solution to this problem by taking observed equilibria to these systems and using them to parameterize equilibrium models. In this dissertation, we explore the use of inverse optimization to parameterize multiple new or understudied subclasses of equilibrium problems as well as expand inverse optimization's application to new infrastructure domains. In the first project of our dissertation, our contribution to the literature is to propose that IO can be used to parameterize cost functions in multi-stage stochastic programs for disaster management and can be used in disaster support systems. We demonstrate in most of our experiments that using IO to obtain the hidden cost parameters for travel on a road network changes the protection decisions made on that road network when compared to utilizing the mean of the parameter range for the hidden parameters (also referred to as ``uniform cost''). The protection decisions made under the IO cost parameterizations versus the true cost parameterizations are similar for most of the experiments, thus lending credibility to the IO parameterizations. In the second project of our dissertation, we extend a well-known framework in the IO community to the case of jointly convex generalized Nash equilibrium problems (GNEPs). We demonstrate the utility of this framework in a multi-player transportation game in which we vary the number of players, the capacity level, and the network topology in the experiments as well as run experiments assuming the same costs among players and different costs among players. Our promising results provide evidence that our work could be used to regulate traffic flow toward aims such as reduction of emissions. In the final project of our dissertation, we explore the general parameterization of the constant vector in linear complementarity problems (LCPs), which are mathematical expressions that can represent optimization, game theory, and market models (Gabriel et al., 2012). Unlike the limited previous work on inverse optimization for LCPs, we characterize theoretical considerations regarding the inverse optimization problem for LCPs, prove that a previously proposed IO solution model can be dramatically simplified, and handle the case of multiple solution data points for the IO LCP problem. Additionally, we use our knowledge regarding LCPs and IO in a water market allocation case study, which is an application not previously explored in the IO literature, and we find that charging an additional tax on the upstream players enables the market to reach a system optimal. In sum, this dissertation contributes to the inverse optimization literature by expanding its reach in the equilibrium problem domain and by reaching new infrastructure applications.Item DATA-DRIVEN ASSESSMENT FOR UNDERSTANDING THE IMPACTS OF LOCALIZED HAZARDS(2022) Ghaedi, Hamed; Reilly, Allison C.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Both the number of disasters in the U.S. and federal outlays following disasters are rising. Thus, evaluating the impact of varying natural hazards on the built environment and communities rapidly and at various spatial scales is of the utmost importance. Many hazards can cause significant and repetitive economic and social damages. This dissertation is a collection of studies that broadly evaluates resilience outcomes in urban areas using data-driven approaches. I do this over three chapters, each of which explores a unique aspect of hazards and their impact on society. The first two chapters are devoted to federal disaster programs aimed at supporting recovery and building resilience. I especially seek to understand how characteristics of hazards intersect with aspects of the physical and social environment to drive federal intervention. The final chapter explores the heterogenous impacts of natural hazards in urban communities and how disparities correlate with various socioeconomic and demographic characteristics. The first two studies examine two major federal disaster programs in the U.S. – FEMA Public Assistance (PA) and FEMA National Flood Insurance Program (NFIP) – at varying spatial and temporal scales. Both leverage parametric and non-parametric statistical learning algorithms to understand how measures of hazard intensity and local factors drive federal intervention. These studies could be used by federal/state-level resource managers for planning the level of aid that may be required after a disaster. This study can also potentially be useful for decision-makers to identify the potential causes of increased disaster spending over time. In the final chapter, I evaluate the links between public transit disruptions, socioeconomic characteristics, and precipitation. By analyzing the spatial distribution and clustering of infrastructure disruptions, I identify the area(s) susceptible to a disproportionate amount of disruptions. Additionally, spatial statistical models are developed to investigate the relationship between infrastructure disruptions and the characteristics of the communities by including variables related to socioeconomic, demographics, social vulnerability, traffic volume, transit system, road connectivity, and the built environment characteristics. For the decision-makers with the goal of improving the performance and resilience of the transit system, this study can provide insight to locate critical areas impacted by such disruptions.Item HYBRID RESILIENCE FRAMEWORK FOR SYSTEMS OF SYSTEMS INCORPORATING STAKEHOLDER PREFERENCES(2018) Emanuel, Roy Nelson; Ayyub, Bilal; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)From Presidential Policy Directive 21, to professional societies’ national meetings, to major United Nations initiatives, stakeholders recognize the value of achieving resilient systems. The literature clamors with methods to assess resilience of systems quantitatively and qualitatively. Resilience models typically focus on system performance and the threat to the system. Few models consider the preferences of the stakeholders of the systems. This course of study identified three gaps in the literature: first, the focus on system performance without considering the preferences of stakeholders; second, lack of resilience model-to-model comparison; and third, lack of a common framework for applying resilience models across domains and systems of systems. This course of study investigated the impact of incorporating stakeholder preferences into four existing resilience models: Resilience Factor, Quotient Resilience, Total Quotient Resilience, and Integral Resilience. The incorporated stakeholder preferences were time horizon, endogenous performance preference, and intertemporal substitutability of system performance. An analysis of the resultant eight illustrative models showed the models' comparative sensitivity to changes in system performance and stakeholder preferences using four fundamental system performance and stakeholder preference models. A deterministic system dynamics model of a city's critical infrastructure provided inputs to the eight models for an initial case study. The first phase identifies three stakeholder preference profiles for the water delivery infrastructure. The second phase assesses the impact of electrical outages on seven other critical infrastructures. The results of the sensitivity analysis and the initial case study led to selection of the Extended Integral Resilience model for additional demonstrations. Stochastic inputs for the system dynamics model showed a range of resilience outcomes for each stakeholders' infrastructure for five courses of action. The hybrid resilience model used Department of Energy reports on Puerto Rico's recovery from Hurricane Maria to generate a resilience value. A discrete event simulation of a fleet of aircraft used to train aviators provided the basis for the second set of case studies. The study considered the points of view of the Squadron Commanders which were limited to three year increments, and the program manager which considered a thirty-five year time horizon. The functional outputs of the model were graduates per quarter, aircraft ready to fly each day, and satisfied graduates per quarter. The case study introduced and demonstrated an event and time dependent intertemporal substitutability algorithm to be defined by the stakeholder.Item Using Social Media to Evaluate Public Acceptance of Infrastructure Projects(2018) Ding, Qinyi; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The deficit of infrastructure quality of the United States demands groundbreaking of more infrastructure projects. Despite the potential economic and social benefits brought by these projects, they could also negatively impact the community and the environment, which could in turn affect the implementation and operation of the projects. Therefore, measuring and monitoring public acceptance is critical to the success of infrastructure projects. However, current practices such as public hearings and opinion polls are slow and costly, hence are insufficient to provide satisfactory monitoring mechanism. Meanwhile, the development of state-of-the-art technologies such as social media and big data have provided people with unprecedented ways to express themselves. These platforms generate huge volumes of user-generated content, and have naturally become alternative sources of public opinion. This research proposes a framework and an analysis methodology to use big data from social media (e.g. the microblogging site Twitter) for project evaluation. The framework collects social media postings, analyzes public opinion towards infrastructure projects and builds multi-dimensional models around the big data. The interface and conceptual implementation of each component of the framework are discussed. This framework could be used as a supplement to traditional polls to provide a fast and cost-effective way for public opinion and project risk assessment. This research is followed by a case study applying the framework to a real-world infrastructure project to demonstrate the feasibility and comprehensiveness of the framework. The California High Speed Rail project is selected to be the object of study. It is an iconic and controversial large-scale infrastructure project that faced a lot of criticism, complaints and suggestions. Sentiment analysis, the most important type of analysis on the framework, is discussed concerning its application and implementation in the context of infrastructure projects. A public acceptance model for social media sentiment analysis is proposed and examined, and the best measurement of public acceptance is recommended. Moreover, the case study explores the driving force of the change in public acceptance: the social media events. Events are defined, evaluated, and an event influence quadrant is proposed to categorize and prioritize social media events. Furthermore, the individuals influencing the perceptions of these events, opinion leaders, are also modeled and identified. Three opinion leadership types are defined with top users in each type listed and discussed. A predictive model for opinion leader is also developed to identify opinion leaders using an a priori indicator. Finally, a user profiling model is established to describe social demographic characteristics of users, and each demographic feature is discussed in detail.Item Capital Development: Mandate Era Amman and the Construction of the Hashemite State, 1921-1946(2015) Guthorn, Harrison Brent; Wien, Peter; History; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation explores the modern history of Amman during the British Mandate and how the city’s development was closely tied to the evolution of the Hashemite state. This study explores the significant cultural and political hybridization of the local population in Amman because of the state’s centralization project. Few historians of the Middle East have examined in depth the formation of capital cities in nascent nation-states and even fewer have studied the city of Amman. The development of Amman must be understood in its regional context because it acts as a mirror for the development of the Jordanian state as a whole. This dissertation posits that Amman developed as a hybridized amalgam of Ottoman, Arab, and British characteristics. The Transjordanian state could not have existed if it had not borrowed countless Ottoman institutions and practices. The Anglo-Hashemite state used the Legislative Council of Transjordan to incorporate formerly autonomous elites into the machinery of the Jordanian state, transforming Amman into a Hashemite Versailles. By the end of the Mandate, Amman’s gilded cage both constrained and supported the elites within. The cage of Amman simultaneously limited elite influence and power, while protecting and reifying their muted authority as Transjordanian officials. Furthermore, Amman’s urban fabric was a reflection of its diverse heritage and cultural practices. The development of Amman as a “dual city,” divided between prosperous Westernized “West Amman” and the impoverished traditional “East Amman,” originated in the Mandate period. Finally, Amman’s central square, Feisal Square, became the figurative embodiment of the heart of Amman and the heart of the state.Item A Hybrid Testing Platform for Realistic Characterization of Infrastructure Sensor Technology(2011) Mercado, Michael William; Zhang, Yunfeng; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In America's transportation infrastructure, maintaining safe and serviceable bridges is of paramount importance to America's transportation officials. In order to meet the increasing demands for information-based maintenance and repair of civil infrastructures such as highway bridges, an increasing number of structural health monitoring sensors and other non-destructive evaluation (NDE) devices have begun to be implemented on these structures. Before these health monitoring sensors can be implemented on a large scale, they must first be validated and characterized in a controlled environment. This thesis proposes and demonstrates the use of a hybrid testing platform to create a more realistic testbed to evaluate these structural health monitoring sensors for steel bridges. The details of this hybrid testing platform are discussed including the effects of ramp time, stress level, complexity of the virtual model, fatigue, and high temperature testing. The accuracy and practical implementation of this hybrid testing platform are also addressed.