A. James Clark School of Engineering

Permanent URI for this communityhttp://hdl.handle.net/1903/1654

The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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Now showing 1 - 6 of 6
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    Compositional Approach to Distributed System Behavior Modeling and Formal Validation of Infrastructure Operations with Finite State Automata: Application to Viewpoint-Driven Verification of Functionality in Waterways
    (MDPI, 2018-01-12) Austin, Mark A.; Johnson, John
    Now that modern infrastructure systems are moving toward an increased use of automation in their day-to-day operations, there is an emerging need for new approaches to the formal analysis and validation of system functionality with respect to correctness of operations. This paper describes a compositional approach to the multi-level behavior modeling and formal validation of large-scale distributed system operations with hierarchies and networks of finite state automata. To avoid the well-known state explosion problem, we develop a new procedure for viewpoint-action-process traceability, thereby allowing parts of a behavior model not relevant to a specific decision to be removed from consideration. Key features of the methodology are illustrated through the development of behavior models and validation procedures for polite conversation between two individuals, and lockset- and system-level concerns for ships traversing a large-scale waterway system.
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    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.
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    Utilization of Dynamic and Static Sensors for Monitoring Infrastructures
    (IntechOpen, 2018-12-12) Fu, Chung C.; Zhu, Yifan; Hou, Kuang-Yuan
    Infrastructures, including bridges, tunnels, sewers, and telecommunications, may be exposed to environmental-induced or traffic-induced deformation and vibrations. Some infrastructures, such as bridges and roadside upright structures, may be sensitive to vibration and displacement where several different types of dynamic and static sensors may be used for their measurement of sensitivity to environmental-induced loads, like wind and earthquake, and traffic-induced loads, such as passing trucks. Remote sensing involves either in situ, on-site, or airborne sensing where in situ sensors, such as strain gauges, displacement transducers, velometers, and accelerometers, are considered conventional but more durable and reliable. With data collected by accelerometers, time histories may be obtained, transformed, and then analyzed to determine their modal frequencies and shapes, while with displacement and strain transducers, structural deflections and internal stress distribution may be measured, respectively. Field tests can be used to characterize the dynamic and static properties of the infrastructures and may be further used to show their changes due to damage. Additionally, representative field applications on bridge dynamic testing, seismology, and earthborn/construction vibration are explained. Sensor data can be analyzed to establish the trend and ensure optimal structural health. At the end, five case studies on bridges and industry facilities are demonstrated in this chapter.
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    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.
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    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.
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    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.