Civil & Environmental Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2753
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Item ENHANCING RESILIENCE OF COMPLEX NETWORKS: WASHINGTON D.C. URBAN RAIL TRANSIT AS A CASE STUDY(2020) Saadat, Yalda; Ayyub, Bilal BA; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)According to the United Nation’s Department of Economic and Social Affairs Population Division, 66% of the world’s population will reside in urban areas by 2050; a boost from 30 % in 1950. Urbanization has indeed triumphed and its speed has brought innovation and economic growth. Its synergies within infrastructure systems are undeniable and have increased the demand for such systems. However, urbanization is one reason infrastructure systems are knocked out of equilibrium and show complex dynamical behavior. Most infrastructure systems have been designed without planning for this magnitude of potential demographic changes; thus redesigns are long overdue. Also, climate change looms. Resource scarcity and host of other factors leave their impacts; all pose some incidence of perturbation in the state of the infrastructure system. These perturbations can affect the system’s resilience, which is a defining property of each system for remaining functional in the midst of disruption from an adverse event. Therefore, it is essential to develop appropriate metrics and methods to enhance the resilience of infrastructures at the network level. Such enhancements are critical for sustainable infrastructure development that is capable of performing satisfactorily through intentional and/or stochastic disruptions. A resilience evaluation of a network typically entails assessing vulnerability and robustness as well as identifying strategies to increasing network efficiency and performance and offering recovery strategies ideally taken in a cost-effective manner. This dissertation uses complex network theory (CNT) as the theoretic basis to enhance the resilience of large-scale infrastructure networks, such as urban rail transit systems. Urban rail transit infrastructures are heterogeneous, complex systems consisting of a large number of interacting nodes and links, which can imitate a network paradigm. Any adverse event leading to a disruption in the interaction and connectivity of network components would dramatically affect the safety and wellbeing of commuters, as well as the direct and indirect costs associated with performance loss. Therefore, enhancing their resilience is necessary. Using the Washington D.C. Urban rail transit as a case study, this dissertation develops a methodology to analyze network topology, compute its efficiency, vulnerability, and robustness in addition to provide a unified metric for assessing the network resilience. The steps of methodology are applied to two models of weighted and unweighted networks. For the weighted model two novel algorithms are proposed to capture the general pattern of ridership in the network, and to reflect the weights on assessing network efficiency, respectively. This dissertation then proposes an effective strategy to increase the network resilience prior to a disruptive event, e.g., a natural disaster, by adding several loop lines in the network for topological enhancement. As such, adding a loop line can create redundancy to the vulnerable components and improve network resilience. Expanding on this, the dissertation offers comparative recovery strategies and cost model in the case of disruption. An effective recovery strategy must demonstrate rapid optimal restoration of a disrupted system performance while minimizing recovery costs. In summary, the systematic methodology described above, assesses and enhances the network resilience. The initial results rank the most vulnerable and robust components of the network. The algorithms developed throughout the study advance the weighted network analysis state of art. The topological enhancement strategy offered basis to justify capital improvement. Post failure recovery analysis and the cost model serves to inform decision makers in identifying best recover strategies with special attention not only to restoring performance of a system but also on reducing associated failure and recovery costs. The use of the methodology proposed in this dissertation may lead to significant societal benefits by reducing the risk of catastrophic failures, providing references for mitigation of disruption due to adverse events, and offering resilience- based strategies, and related pursuits.Item THE TROUBLE WITH VOLUNTARY CARBON TRADING FOR BUILDINGS EXPOSED TO HURRICANE RISK(2017) Liu, Xiaoyu; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Increased climate risks pose challenges of combining climate mitigation and adaptation goals into building designs. These two goals are often misaligned, as adaptation measures use additional materials and equipment that are sources of carbon emissions. This phenomenon causes building design to involve tradeoffs between enhancing structural resilience and reducing emissions. This dissertation addresses the need to identify the optimal investment mechanisms for the design of buildings in hurricane-prone regions. Dynamic decision-making models are developed for individual investors to characterize emission trading and risk mitigation behaviors over a building’s lifecycle. The models enable the following outcomes: (i) evaluation and selection of baseline rules for sectoral emission trading, (ii) ability to reflect resilience goals in the building design, construction and maintenance, and to balance between climate mitigation and adaptation goals for a wide range of building examples, and (iii) policy implications for improving emission trading efficiencies and achieving environmental and economic sustainability at community level. Modeling results indicate that the trouble of voluntary emission trading is mainly attributed to imperfect market information and future climate risks. The uncertainty in predicting emissions and potential baseline manipulation leads to the production of non-additional carbon offsets and an extension of sectoral emission caps. This situation is even bleaker when emission trading are implemented in the areas that exposure to significant risks of catastrophic events such as hurricanes. The results reveal a trend of a transition from long-advocated low-carbon investment to a risk-oriented portfolio for building retrofits in hurricane-prone regions. The risk mitigation efforts should be pursued with discretion on the accuracy of insurance premium discounts. Meanwhile, subsidies for emission abatements are recommended to accommodate existing emission trading schemes and building property values.Item RESILIENCE OF NETWORKED INFRASTRUCTURE WITH EVOLVING COMPONENT CONDITIONS: A PAVEMENT NETWORK APPLICATION(2016) Asadabadi, Ali; Miller-Hooks, Elise; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis deals with quantifying the resilience of a network of pavements. Calculations were carried out by modeling network performance under a set of possible damage-meteorological scenarios with known probability of occurrence. Resilience evaluation was performed a priori while accounting for optimal preparedness decisions and additional response actions that can be taken under each of the scenarios. Unlike the common assumption that the pre-event condition of all system components is uniform, fixed, and pristine, component condition evolution was incorporated herein. For this purpose, the health of the individual system components immediately prior to hazard event impact, under all considered scenarios, was associated with a serviceability rating. This rating was projected to reflect both natural deterioration and any intermittent improvements due to maintenance. The scheme was demonstrated for a hypothetical case study involving Laguardia Airport. Results show that resilience can be impacted by the condition of the infrastructure elements, their natural deterioration processes, and prevailing maintenance plans. The findings imply that, in general, upper bound values are reported in ordinary resilience work, and that including evolving component conditions is of value.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.Item RESILIENCE OF TRANSPORTATION INFRASTRUCTURE SYSTEMS: QUANTIFICATION AND OPTIMIZATION(2013) Faturechi, Reza; Miller-Hooks, Elise; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Transportation systems are critical lifelines for society, but are at risk from natural or human-caused hazards. To prevent significant loss from disaster events caused by such hazards, the transportation system must be resilient, and thus able to cope with disaster impact. It is impractical to reinforce or harden these systems to all types of events. However, options that support quick recovery of these systems and increase the system's resilience to such events may be helpful. To address these challenges, this dissertation provides a general mathematical framework to protect transportation infrastructure systems in the presence of uncertain events with the potential to reduce system capacity/performance. A single, general decision-support optimization model is formulated as a multi-stage stochastic program. The program seeks an optimal sequence of decisions over time based upon the realization of random events in each time stage. This dissertation addresses three problems to demonstrate the application of the proposed mathematical model in different transportation environments with emphasis on system-level resilience: Airport Resilience Problem (ARP), Building Evacuation Design Problem (BEDP), and Travel Time Resilience in Roadways (TTR). These problems aim to measure system performance given the system's topological and operational characteristics and support operational decision-making, mitigation and preparedness planning, and post-event immediate response. Mathematical optimization techniques including, bi-level programming, nonlinear programming, stochastic programming and robust optimization, are employed in the formulation of each problem. Exact (or approximate) solution methodologies based on concepts of primal and dual decomposition (integer L-shaped decomposition, Generalized Benders decomposition, and progressive hedging), disjunctive optimization, scenario simulation, and piecewise linearization methods are presented. Numerical experiments were conducted on network representations of a United States rail-based intermodal container network, the LaGuardia Airport taxiway and runway pavement network, a single-story office building, and a small roadway network.