Critical Infrastructure Systems (CISs) are key for providing essential services and managing critical resources. The failure of one CIS can result in severe consequences on national security, health & safety, the environment, social well-being, and the economy. However, CISs are inherently complex, operating as systems-of-systems with dynamic, non-linear, and uncertain operation conditions, all geared towards fulfilling complex operational objectives. The complexity of both system architecture and operational objectives contributes to challenges in comprehending system-level behavior under normal and disruptive conditions. CISs are also highly exposed to multi-hazard environments characterized by probabilistic behaviors that can impact one or more system components—leading to diverse system failure modes. Understanding the dynamic interaction between hazards and the system response in such environments adds another layer of complexity to CISs safety. Addressing such complexity is crucial and it necessitates thorough investigations to ensure the continuous and reliable operation of CISs. Accordingly, the main objective of this thesis is to develop dynamic resilience quantification approaches for CISs in multi-hazard environments, considering the probabilistic behavior of both the hazard and the system. Given that dam infrastructure is one of the most significant CISs, this thesis employs an actual dam system as a demonstration application for the developed models. Nonetheless, it should be emphasized that the thesis focuses on the generalizability of the developed model to the CISs rather than the specificities related to dam systems, which are adopted herein merely to show the utility of the developed models to complex CISs. Specifically, this thesis first employs a meta-research approach (Chapter 2), using text analytics, to conduct a quantitative and qualitative review of extensive prior research focused on CISs operational safety, considering dam and reservoir systems as one of the key CISs. Such meta-research aims to unveil latent topics in the field and identify key opportunities for future research, particularly in addressing limitations associated with existing risk-based and resilience-based safety assessment approaches for CISs. To overcome such limitations, this thesis (Chapter 3) subsequently developed a coupled Continuous-Time Markov Chain and Bayesian network, facilitating the dynamic quantification of CISs failure risk (propagation of the system's probability of failure with time), considering the temporal variation of uncertainties in system components during operations. Starting from where the risk-based assessment ends (the immediate response of the system at the hazard realizations), resilience-based assessment focuses more on the dynamic system functionality gain/reduction and, subsequently, the system deterioration and recovery rates following hazard realizations. Accordingly, this thesis (Chapter 4) presents a resilience-centric System Dynamics simulation modeling approach capable of representing CISs components, estimating their dynamic system performance, and subsequent dynamic resilience (propagation of the system resilience with time). Such a modeling approach proposes a combinatorial procedure for generating multi-hazard scenarios, encompassing both natural and anthropogenic hazards, where one primary hazard can trigger one or more subsequent hazards. As a result, the developed models can investigate system operations under both single and multi-hazard environments. Furthermore, the coupling between System Dynamics and Monte Carlo simulations (Chapter 5) enables the model to seamlessly incorporate the probabilistic behaviors of both multi-hazard and system responses. The developed approaches can provide the decision-makers with a more detailed system representation that includes probabilistic dynamic system components with multi-operational objectives under probabilistic multi-hazard environments (Chapter 6). Moreover, the developed models can introduce more realistic evaluations for risk-adaptive and mitigation plans in real-time, contributing to more efficient safety assessment plans for the CISs. / Thesis / Doctor of Philosophy (PhD) / Critical infrastructure systems (CISs) play pivotal roles in delivering and supporting the essential needs of our daily lives. However, ensuring the safety of CISs poses layered challenges due to the complexity of their systems and operations, compounded by their susceptibility to multi-hazard environments, all with probabilistic behaviors. Recognizing the criticality and safety obstacles associated with CISs, this thesis introduces dynamics resilience quantification approaches for CISs safety based on a holistic system dynamics representation. The developed models are designed to enhance understanding of the system's performance under multi-hazard disruption conditions, considering the probabilistic behavior of both hazards and system response. Moreover, these models yield resilience-based metrics, allowing for the evaluation of the effectiveness of various risk mitigation plans, which would subsequently lead to more reliable safety assessment plans for CISs. Considering that dam infrastructure is a key CISs, this thesis focuses on the former as a demonstration application to show the developed models’ utility and their efficiency in devising resilience-guided assessment plans for CISs.

Sociotechnical systems (STSs) rely on the collaboration between humans and autonomous decision-making units to fulfill their objectives. Highly intertwined social and technical contextual factors influence the collaboration between these human and engineered elements, and consequently the performance characteristics of the STS. In the next two decades, the role allocated to STSs in our society will drastically increase. Thus, the effective design of STSs requires an improved understanding of the human-autonomy interdependency. This dissertation brings together management science along with systems thinking and uses a mixed-methods approach to investigate the interdependencies between people and the autonomous systems they collaborate within complex socio-technical enterprises. The dissertation is organized in three mutually exclusive essays, each investigating a distinct facet of STSs: safe management, collaboration, and efficiency measurement. The first essay investigates the amount of work allocated to safety-critical decision makers and quantifies Rasmussen's workload boundary that represents the limit of attainable workload. The major contribution of this study is to quantify the qualitative theoretical construct of the workload boundary through a Pareto-Koopmans frontier. This frontier allows one to capture the aggregate impact of the social and technical factors that originate from operational conditions on workload. The second essay studies how teams of humans and their autonomous partners share work, given their subjective preferences and contextual operational conditions. This study presents a novel integration of machine learning algorithms in an efficiency measurement framework to understand the influence of contextual factors. The results demonstrate that autonomous units successfully handle relatively simple operational conditions, while complex operational conditions require both workers and their autonomous counterparts to collaborate towards common objectives. The third essay explores the complementary and contrasting roles of efficiency measurement approaches that deal with the influence of contextual factors and their sensitivity to sample size. The results are organized in a structured taxonomy of their fundamental assumptions, limitations, mathematical structure, sensitivity to sample size, and their practical usefulness. To summarize, this dissertation provides an interdisciplinary and pragmatic research approach that benefits from the strengths of both theoretical and data-driven empirical approaches. Broader impacts of this dissertation are disseminated among the literatures of systems engineering, operations research, management science, and mechanical design. / Doctor of Philosophy / A system is an integrated set of elements that achieve a purpose or goal. An autonomous system (ADS) is an engineered element that often substitutes for a human decision-maker, such as in the case of an autonomous vehicle. Sociotechnical systems (STSs) are systems that involve the collaboration of a human decision-maker with an ADS to fulfill their objectives. Historically, STSs have been used primarily for handling safety critical tasks, such as management of nuclear power plants. By design, STSs rely heavily on a collaboration between humans and ADS decision-makers. Therefore, the overall characteristics of a STS, such as system safety, performance, or reliability; is fully dependent on human decisions. The problem with that is that people are independent entities, who can be influenced by operational conditions. Unlike their engineered counterparts, people can be cognitively challenged, tired, or distracted, and consequently make mistakes. The current dependency on human decisions, incentivize business owners and engineers alike to increase the level of automation in engineered systems. This allows them to reduce operational costs, increase performance, and minimize human errors. However, the recent commercial aircraft accidents (e.g., Boeing 737-MAX) have indicated that increasing the level of automation is not always the best strategy. Given that increasing technological capabilities will spread the adoption of STSs, vast majority of existing jobs will either be fully replaced by an ADS or will change from a manual set-up into a STS. Therefore, we need a better understanding of the relationships between social (human) and engineered elements. This dissertation, brings together management science with systems thinking to investigate the dependencies between people and the autonomous systems they collaborate within complex socio-technical enterprises. The dissertation is organized in three mutually exclusive essays, each investigating a distinct facet of STSs: safe management, collaboration, and efficiency measurement. The first essay investigates the amount of work handled by safety-critical decision makers in STSs. Primary contribution of this study is to use an analytic method to quantify the amount of work a person could safely handle within a STSs. This method also allows to capture the aggregate impact of the social and technical factors that originate from operational conditions on workload. The second essay studies how teams of humans and their autonomous partners share work, given their preferences and operational conditions. This study presents a novel integration of machine learning algorithms to understand operational influences that propel a human-decision maker to handle the work manually or delegate it to ADSs. The results demonstrate that autonomous units successfully handle simple operational conditions. More complex conditions require both workers and their autonomous counterparts to collaborate towards common objectives. The third essay explores the complementary and contrasting roles of data-driven analytical management approaches that deal with the operational factors and investigates their sensitivity to sample size. The results are organized based on their fundamental assumptions, limitations, mathematical structure, sensitivity to sample size, and their practical usefulness. To summarize, this dissertation provides an interdisciplinary and pragmatic research approach that benefits from the strengths of both theoretical and data-driven empirical approaches. Broader impacts of this dissertation are disseminated among the literatures of systems engineering, operations research, management science, and mechanical design.

<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>

Water distribution systems, electrical power transmission systems, and other civil infrastructure systems are essential to the smooth and stable operation of regional economies. Since the functions of such infrastructure systems often are inter-dependent, the systems sometimes suffer unforeseen functional disruptions. For example, the widespread power outage due to the malfunction of an electric power substation, which occurred in the northeastern United States and parts of Canada in August 2003, interrupted the supply of water to several communities, leading to inconvenience and economic losses. The sequence of such failures leading to widespread outages is referred to as a cascading failure. Assessing the vulnerability of communities to natural and man-made hazards should take the possibility of such failures into account. In seismic risk assessment, the risk to a facility or a building is generally specified by one of two basic approaches: through a probabilistic seismic hazard analysis (PSHA) and a stipulated scenario earthquake (SE). A PSHA has been widely accepted as a basis for design and evaluation of individual buildings, bridges and other facilities. However, the vulnerability assessment of distributed infrastructure facilities requires a model of spatial intensity of earthquake ground motion. Since the ground motions from a PSHA represent an aggregation of earthquakes, they cannot model the spatial variation in intensity. On the other hand, when a SE-based analysis is used, the spatial correlation of seismic intensities must be properly evaluated. This study presents a new methodology for evaluating the functionality of an infrastructure system situated in a region of moderate seismicity considering functional interactions among the systems in the network, cascading failure, and spatial correlation of ground motion. The functional interactions among facilities in the systems are modeled by fault trees, and the impact of cascading failures on serviceability of a networked system is computed by a procedure from the field of operations research known as a shortest path algorithm. The upper and lower bound solutions to spatial correlation of seismic intensities over a region are obtained.