<div><div><div><p>The resilience of critical infrastructure, such as water distribution systems and power systems, is critical for both the economy and public safety and health. However, methods and tools for evaluating and improving the resilience of these systems must be able to address the large network sizes, nonlinear physics, discrete decisions, and uncertainty. This dissertation focuses on the development of modeling and optimization techniques that address these difficulties, enabling the evaluation and improvement of power and water distribution system resilience.</p><p>In Part I, we present novel stochastic optimization models to improve power systems resilience to extreme weather events. We consider proactive redispatch, transmission line hardening, and transmission line capacity increases as alternatives for mitigating the effects of extreme weather. Our model is based on linearized or "DC" optimal power flow, similar to models in widespread use by independent system operators (ISOs) and regional transmission operators (RTOs). Our computational experiments indicate that each of these strategies can play a major role in power systems resilience.</p><p>We then extend the resilience formulations to investigate the role chemical process facilities, as industrial energy consumers, can play in improving electric grid resilience through demand response (DR). For process facilities to effectively negotiate demand response (DR) contracts and make investment decisions regarding flexibility, they need to quantify their additional value to the grid. We also reformulate the DR problems using the more accurate nonlinear alternating current power flow model to investigate the effect of the linear DC approximation. Our numerical results demonstrate that the linearized model often underestimates the amount of DR needed, motivating scalable solution algorithms for Mixed-Integer Nonlinear Programming (MINLP) problems in power systems.</p><div><div><div><p>An important step in many MINLP algorithms is the global solution of a Nonlinear Programming (NLP) subproblem. For power systems applications, this involves global solution of NLP’s containing the alternating current (AC) power flow model. This thesis presents several advances to aid in global optimization of AC power flow equations. We show that a strong upper bound on the objective of the alternating current optimal power flow (ACOPF) problem can significantly improve the effectiveness of optimization-based bounds tightening (OBBT) on a number of relaxations. Furthermore, we investigate the effect of the reference bus on OBBT. We find that, if reference bus constraints are included, relaxations of the rectangular form significantly strengthen existing relaxations and that the effectiveness of OBBT at a given iteration is directly related to the distance of the corresponding bus from the reference bus.</p><p>Ultimately, with OBBT alone, we are able to reduce the optimality gap to less than 0.1% on all but 5 NESTA test cases with up to 300 buses. However, the computational expense required for OBBT grows rapidly with the size of the network. We present a decomposition algorithm based on graph partitioning to drastically improve this performance. Our numerical results demonstrate that our decomposed bounds tightening (DBT) algorithm results in variable bounds nearly as tight as those obtained with traditional, full-space OBBT. Furthermore, the computational expense of the DBT algorithm scales far more favorably with problem size, resulting in drastically reduced wallclock times, especially for large networks.</p><p>In Part II, we describe the Water Network Tool for Resilience (WNTR), an new open source Python package designed to help water utilities investigate resilience of water distribution systems to hazards and evaluate resilience-enhancing actions. The WNTR modeling framework is presented and a case study is described that uses WNTR to simulate the effects of an earthquake on a water distribution system. The case study illustrates that the severity of damage is not only a function of system integrity and earthquake magnitude, but also of the available resources and repair strategies used to return the system to normal operating conditions. While earthquakes are particularly concerning since buried water distribution pipelines are highly susceptible to damage, the software framework can be applied to other types of hazards, including power outages and contamination incidents.</p></div></div></div></div></div></div>

In October 2012, Hurricane Sandy caused massive disruption and destruction to the Mid-Atlantic region of the United States. The intensity of the storm forced the Port of New York and New Jersey to close, forcing cargo diversion to the Port of Norfolk in Virginia. Because of the Jones Act restriction on foreign vessels moving between U.S. ports, the restriction on short sea shipping was viewed as a barrier to recovery. Much of the critical infrastructure resilience and security literature focuses on the "hardening" of physical infrastructure, but not the relationship between law, policy, and critical infrastructure. Traditional views of transportation systems do not adequately address questions of governance and behaviors that contribute to resilience. In contrast, recent development of a System of Systems framework provides a conceptual framework to study the relationship of law and policy systems to the transportation systems they govern. Applying a System of Systems framework, this research analyzed the effect of relaxing the Jones Act on freight transportation networks experiencing a disruptive event. Using WebTRAGIS (Transportation Routing Analysis GIS), the results of the research demonstrate that relaxing the Jones Act had a marginal reduction on highway truck traffic and no change in rail traffic volume in the aftermath of a disruption. The research also analyzed the Jones Act waiver process and the barriers posed by the legal process involved in administration and review for Jones Act waivers. Recommendations on improving the waiver process include greater agency coordination and formal rulemaking to ensure certainty with the waiver process. This research is the first in studying the impact of the Jones Act on a multimodal freight transportation network. Likewise, the use of the System of Systems framework to conceptualize the law and a critical infrastructure system such as transportation provides future opportunities for studying different sets of laws and policies on infrastructure. This research externalizes law and policy systems from the transportation systems they govern. This can provide policymakers and planners with an opportunity to understand the impact of law and policy on the infrastructure systems they govern. / PHD

Cyber-physical systems (CPSs) are systems that integrate physical elements with a cyber layer that enables sensing, monitoring, and processing the data from the physical components. Examples of CPSs include autonomous vehicles, unmanned aerial vehicles (UAVs), smart grids, and the Internet of Things (IoT). In particular, many critical infrastructure (CI) that are vital to our modern day cities and communities, are CPSs. This wide range of CPSs domains represents a cornerstone of smart cities in which various CPSs are connected to provide efficient services. However, this level of connectivity has brought forward new security challenges and has left CPSs vulnerable to many cyber-physical attacks and disruptive events that can utilize the cyber layer to cause damage to both cyber and physical components. Addressing these security and operation challenges requires developing new security solutions to prevent and mitigate the effects of cyber and physical attacks as well as improving the CPSs response in face of disruptive events, which is known as the CPS resilience. To this end, the primary goal of this dissertation is to develop novel analytical tools that can be used to study, analyze, and optimize the resilience and security of critical CPSs. In particular, this dissertation presents a number of key contributions that pertain to the security and the resilience of multiple CPSs that include power systems, the Internet of Things (IoT), UAVs, and transportation networks. First, a mathematical framework is proposed to analyze and mitigate the effects of GPS spoofing attacks against UAVs. The proposed framework uses system dynamics to model the optimal routes which UAVs can follow in normal operations and under GPS spoofing attacks. A countermeasure mechanism, built on the premise of cooperative localization, is then developed to mitigate the effects of these GPS spoofing attacks. To practically deploy the proposed defense mechanism, a dynamic Stackelberg game is formulated to model the interactions between a GPS spoofer and a drone operator. The equilibrium strategies of the game are analytically characterized and studied through a novel, computationally efficient algorithm. Simulation results show that, when combined with the Stackelberg strategies, the proposed defense mechanism will outperform baseline strategy selection techniques in terms of reducing the possibility of UAV capture. Next, a game-theoretic framework is developed to model a novel moving target defense (MTD) mechanism that enables CPSs to randomize their configurations to proactive deter impending attacks. By adopting an MTD approach, a CPS can enhance its security against potential attacks by increasing the uncertainty on the attacker. The equilibrium of the developed single-controller, stochastic MTD game is then analyzed. Simulation results show that the proposed framework can significantly improve the overall utility of the defender. Third, the concept of MTD is coupled with new cryptographic algorithms for enhancing the security of an mHealth Internet of Things (IoT) system. In particular, using a combination of theory and implementation, a framework is introduced to enable the IoT devices to update their cryptographic keys locally to eliminate the risk of being revealed while they are shared. Considering the resilience of CPSs, a novel framework for analyzing the component- and system-level resilience of CIs is proposed. This framework brings together new ideas from Bayesian networks and contract theory – a Nobel prize winning theory – to define a concrete system-level resilience index for CIs and to optimize the allocation of resources, such as redundant components, monitoring devices, or UAVs to help those CIs improve their resilience. In particular, the developed resilience index is able to account for the effect of CI components on the its probability of failure. Meanwhile, using contract theory, a comprehensive resource allocation framework is proposed enabling the system operator to optimally allocate resources to each individual CI based on its economic contribution to the entire system. Simulation results show that the system operator can economically benefit from allocating the resources while dams can have a significant improvement in their resilience indices. Subsequently, the developed contract-theoretic framework is extended to account for cases of asymmetric information in which the system operator has only partial information about the CIs being in some vulnerability and criticality levels. Under such asymmetry, it is shown that the proposed approach maximizes the system operator's utility while ensuring that no CI has an incentive to ask for another contract. Next, a proof-of-concept framework is introduced to analyze and improve the resilience of transportation networks against flooding. The effect of flooding on road capacities and on the free-flow travel time, is considered for different rain intensities and roads preparedness. Meanwhile, the total system's travel time before and after flooding is evaluated using the concept of a Wardrop equilibrium. To this end, a proactive mechanism is developed to reduce the system's travel time, after flooding, by shifting capacities (available lanes) between same road sides. In a nutshell, this dissertation provides a suite of analytical techniques that allow the optimization of security and resilience across multiple CPSs. / Doctor of Philosophy / Cyber-physical systems (CPSs) have recently been used in many application domains because of their ability to integrate physical elements with a cyber layer allowing for sensing, monitoring, and remote controlling. This pervasive use of CPSs in different applications has brought forward new security challenges and threats. Malicious attacks can now leverage the connectivity of the cyber layer to launch remote attacks and cause damage to the physical components. Taking these threats into consideration, it became imperative to ensure the security of CPSs. Given that many CPSs provide critical services, for instance many critical infrastructure (CI) are CPSs such as smart girds and nuclear reactors; it is then inevitable to ensure that these critical CPSs can maintain proper operation. One key measure of the CPS’s functionality, is resilience which evaluates the ability of a CPS to deliver its designated service under potentially disruptive situations. In general, resilience measures a CPS’s ability to adapt or rapidly recover from disruptive events. Therefore, it is crucial for CPSs to be resilient in face of potential failures. To this end, the central goal of this dissertation is to develop novel analytical frameworks that can evaluate and improve security and resilience of CPSs. In these frameworks, cross-disciplinary tools are used from game theory, contract theory, and optimization to develop robust analytical solutions for security and resilience problems. In particular, these frameworks led to the following key contributions in cyber security: developing an analytical framework to mitigate the effects of GPS spoofing attacks against UAVs, introducing a game-theoretic moving target defense (MTD) framework to improve the cyber security, and securing data privacy in m-health Internet of Things (IoT) networks using a MTD cryptographic framework. In addition, the dissertation led to the following contributions in CI resilience: developing a general framework using Bayesian Networks to evaluate and improve the resilience of CIs against their components failure, introducing a contract-theoretic model to allocate resources to multiple connected CIs under complete and asymmetric information scenarios, providing a proactive plan to improve the resilience of transportation networks against flooding, and, finally, developing an environment-aware framework to deploy UAVs in disaster-areas.

Public infrastructure systems are crucial components of modern urban communities as they play major roles in elevating countries’ socio-economics. However, the inherent complexity and systemic interdependence of infrastructure construction/renewal projects have left sites hindered with multiple forms of performance disruptions (e.g., schedule delays, cost overruns, workplace injuries) that result in long-term consequences such as claims, disputes, and stakeholder dissatisfactions. The evolution of advanced data-driven tools (e.g., machine learning and complex network analytics) can play a pivotal role in driving improvements in the management strategies of complex projects due to such tools’ usefulness in applications related to interdependent systems. In this respect, the research presented in this dissertation is aimed at developing data-driven strategies geared towards a resilience-based approach to managing complex infrastructure projects. Such strategies can support project managers and stakeholders with data-informed decision-making to mitigate the impacts of systemic interdependence-induced risks at different levels of their projects. Specifically, the developed data-driven resilience-based strategies can empower decision-makers with the ability to: i) predict potential performance disruptions based on real-time and dynamic project conditions such that proactive response/mitigation strategies and/or contingencies can be deployed ahead of time; and ii) develop adaptive solutions against potential interdependence-induced cascade project disruptions such that rapid restoration of the most important set of performance targets can be restored. It is important to note that data-driven strategies and other analytics-based approaches are not proposed herein to replace but rather to complement the expertise and sensible judgment of project managers and the capabilities of available analysis tools. Specifically, the enriched predictive and analytical insights together with the proactive and rapid adaptation capabilities facilitated by the developed strategies can empower the new paradigm of resilience-guided management of complex dynamic infrastructure projects. / Thesis / Doctor of Philosophy (PhD)

<p dir="ltr">Restoration of civil infrastructure is <b>not</b> equivalent to the full recovery of a community from natural hazards. Considering the recovery of only civil infrastructure in quantifying the disaster recovery of a community does not allow for capturing the long-term socio-economic impacts of natural hazards (e.g., stress, anxiety, unemployment, etc.). The role of having a robust social infrastructure in facilitating disaster recovery and addressing both short-term and long-term impacts of natural hazards needs to be explored. Social infrastructure is defined as formal entities (e.g., governmental organizations, community centers, NGOs, religious centers, etc.) as well as informal social ties such as individuals and households that assist in post-disaster recovery and alleviate the distress caused by natural hazards. Social infrastructure not only addresses post-disaster tangible needs such as shelter, food, and water but also helps alleviate disaster-induced socio-economic distress in communities.</p><p dir="ltr">This research focuses on identifying the capacity needs of the social infrastructure to facilitate disaster recovery (measured using community well-being as the recovery metric), while integrating the cascading impacts from other affected inter-dependent infrastructure systems (i.e., civil, civic, cyber, financial, environmental, and educational). Using community well-being, which is defined as the state in which the needs of a community are fulfilled, allows for incorporating both short-term and long-term impacts of natural hazards.</p><p dir="ltr">The research starts with modeling post-disaster community well-being using the indicators selected from existing community well-being models. After the selection of indicators, several data sources such as phone call, survey, and FEMA support programs data were used to 1) verify the structure of the community well-being model, and 2) quantify post-disaster community well-being. Chapter 3 elaborates on this process and its outcome, which is a framework for quantifying post-disaster community well-being based on disaster helpline and survey data.</p><p dir="ltr">Chapter 4 introduces a Bayesian Network<b> </b>modeling framework for quantifying the role of social infrastructure services in the form tangible, emotional, and informational support in enhancing post-disaster community well-being. The Bayesian model was then used to propose capacity building strategies for increasing the robustness of social infrastructure and its supporting infrastructure to foster post-disaster community well-being in the face of future hurricanes.</p><p dir="ltr"><b>Intellectual Merit</b>: the proposed research is unique in its kind as it leverages social and psychological well-being models and theories to characterize the role of social infrastructure in the recovery of communities from natural disasters. The research contributes to infrastructure and urban resilience models by considering the role of social infrastructure services using community well-being as the recovery metric. It also contributes to social sciences by introducing 2-1-1 disaster helpline data as an inexpensive and timely replacement for multiple rounds of survey questionnaires for quantifying community well-being.</p><p dir="ltr"><b>Broader Impacts</b>: the proposed model and the obtained results can serve as an Ex-Ante Capacity building tool for decision-makers to predict the status of communities in the face of future natural hazards and propose capacity building strategies to have higher post-disaster support, and thereby, community well-being.<br></p><p dir="ltr"><br></p>

<p>Is North America vulnerable to widespread electrical blackout from natural or man-made disasters? Yes. Are electric utilities and critical infrastructure (CI) operators prepared to maintain CI operations such as, hospitals, sewage lift stations, food, water, police stations etc., after electrical blackout to maintain National security and sustainability? No. Why? Requirements to prioritize electrical restoration to CI do not exist as a requirement or regulation for electrical distribution operators. Thus, the CI operators cannot maintain services to the public without electricity that provides power for the critical services to function. The problem is that electric utilities are not required to develop or deploy a prioritized systematic plan or procedure to decrease the duration of electrical outage, commonly referred to as blackout. The consequence of local blackout to CI can be multi-billion-dollar financial losses and loss of life for a single outage event attributed to the duration of blackout. This study utilized the review of authoritative literature to answer the question: “Can a plan be developed to decrease the duration of electrical outage to critical infrastructure”. The literature revealed that electric utilities are not required to prioritize electrical restoration efforts and do not have plans available to deploy minimizing the duration of blackout to CI. Thus, this study developed a plan and subsequent model using Model Based System Engineering (MBSE) to decrease the duration of blackout by providing incremental electrical service to CI.</p>