Resilience engineering: theory and practice in interdependent infrastructure systems

The power transmission grid, as well as many other complex critical infrastructure systems, display characteristics of a critical or near critical complex system with the risk of large cascading failures. Understanding this risk and its relation to the system state as it evolves, could allow for a more realistic risk assessment over time and even for mitigation or at least preparation if in a high risk state. In order to facilitate this type of analysis in the context of the power grid as a complex system, we develop a new measure of the complex system state, the generalized autonomous generational average Lambda-gaga, which correlates with the risk. The Lambda-gaga measure is an extension of the standard cascading propagation measure lambda but avoids the contamination of that measure by small events.

This paper will explore the concept of resilience from its roots in ecology to the application of this ecological concept of resilience to social and community resilience in the context of climate change. In this context, resilience is seen as a property of complex adaptive communities rather than of individuals. This paper will explore how this ecological concept of resilience has been taken up both by climate adaptation research and by the Transition Town movement. This ecological concept of resilience is at odds with the individualism of both psychological and economic approaches to resilience in relation to climate change.

Critical infrastructures (CIs) ranging from energy, finance, telecom, healthcare, water, and transport infrastructures form the basis of societal stability and economic consistency for various countries. These CIs themselves are progressing at an unprecedented pace into these new paradigm shifts of cloud computing and virtualization technologies, and Internet of Things (IoT) integration and 5G networks; simultaneously experiencing an ever-expanding set of more complex threats to their cybersecurity. The paper would examine these new trends and outlooks related to critical infrastructure security with knowledge gained from research studies offered by Maglaras et al. (2022) related to this topic and with novel perspectives introduced hereinafter. These trends and outlooks would cover IT/OT integration, vulnerabilities found within traditional infrastructural systems with age factors, unshared visibility with distributed systems infrastructure, and threats posed to these infrastructures with ever-growing supply chains, among others. Cloud computing, Edge intelligence, artificial intelligence, blockchain, or 5G/6G technologies represent new areas of promising security and exciting areas of potential risks. By providing an overview of case studies reported in recent years related to the energy sector, financial industry, telecommunication industry, and healthcare sector, this paper draws attention to real-life consequences of CI compromise and risks associated with interdependent infrastructure systems. Recommendations provided at the end of this study could focus on developing technical specifications at national or International Governments to adapt and overcome these risks related to critical infrastructure security. It’s an interdisciplinary and international challenge to ensure essential infrastructure security.

We consider the problem faced by managers of critical civil interdependent infrastructure systems of restoring essential public services after a non-routine event causes disruptions to these services. In order to restore the services, we must determine the set of components (or tasks) that will be temporarily installed or repaired, assign these tasks to work groups, and then determine the schedule of each work group to complete the tasks assigned to it. These restoration planning and scheduling decisions are often undertaken in an independent, sequential manner. We provide mathematical models and optimization algorithms that integrate the restoration and planning decisions and specifically account for the interdependencies between the infrastructure systems. The objective function of this problem provides a measure of how well the services are being restored over the horizon of the restoration plan, rather than just focusing on the performance of the systems after all restoration efforts are complete. We test our methods on realistic data representing infrastructure systems in New York City. Our computational results demonstrate that we can provide integrated restoration and scheduling plans of high quality with limited computational resources. We also discuss the benefits of integrating the restoration and scheduling decisions.

Extreme risks associated with natural and man-made disasters involve disruptions to the production of goods or provision of services in interdependent systems. The reduced supply of goods and services will degrade “as-planned” production and create ripple effects. Hence, maintaining above-minimum levels of inventory is a resilience strategy that could effectively reduce the onset of disruption. This research integrates the uncertainty in inventory levels to assess the economic impacts of moderate and extreme disastrous events on interdependent systems. The unique contribution of this research is the formulation of a stochastic inventory-based risk assessment model using a multi-objective optimization framework for minimizing (1) extreme economic losses and (2) sector inoperability. Empirical distributions are derived from inventory-to-sales ratio (ISR) of the manufacturing and trade sectors from the Bureau of Economic Analysis database. Simulations of inventory enable the initialization of inoperability functions of a dynamic inoperability input–output model (DIIM). The stochastic inventory-based DIIM-computed values of economic loss and inoperability are simultaneously minimized to identify inventory-enhancement opportunities for critically disrupted systems. A lean production case for a moderate-intensity hurricane in Virginia reveals an overestimation of regional economic loss relative to the expected inventory levels from the ISR data. The conditional expected value of regional economic loss for an extreme event is found to be $12 M higher than a moderate-intensity case. Identification of resilience-enhancement opportunities using the proposed multi-objective optimization framework could reduce expected economic loss by $24 M for an extreme-event and $17 M for a moderate-intensity case.

Technical sectors compose an inseparable and elementary part of a complex critical infrastructure (CI) system. Their provided services are essential to the functioning of all of the dependent sectors of CI on which society and states depend, especially in areas experiencing high levels of urbanisation. The initial point for effective CI elements’ protection is the permanent assessment and strengthening of their capacity for resilience to the negative effects of internal and external threats. The current perceptions of resilience focus primarily on repressive components responsive to incidents (i.e., robustness, recoverability, and adaptability), while minimal attention is paid to the preventative components. The article’s contribution to this literature gap is its definition of resistance, which can be considered as a CI element’s ability to prevent the occurrence of incidents. To this goal, the current study defines (1) the individual factors (variables and parameters) determining CI resistance and (2) the methodological procedure for infrastructure element resistance assessment in order to identify the weak points throughout a complex CI system and subsequently strengthen them. Moreover, a practical example of resistance assessment for a selected critical energy infrastructure element is presented. The main outcome of this article is the definition of the primary steps for the expansion of the CIERA method, via the enhancement of CI components’ resilience capacity in the prevention phase.

The ability to swiftly restore functionality following an extreme event is an essential characteristic of a disaster resilient infrastructure system. However, the restoration of functionality of a single infrastructure system often depends on the functionality of other systems that provide resources the considered system needs to operate and recover. Furthermore, infrastructure systems are crucial for the post-disaster functional recovery of the building stock of a community. Thus, community resilience assessment and improvement require a system-of-systems perspective, considering the post-disaster performance of several interdependent infrastructure systems and the building stock at the same time. One of the principal issues in resilience assessment and improvement is that such system-of-systems consideration may require detailed information on the vulnerability and recoverability of numerous components. While such information might be available for certain systems (e.g., housing), for others, the information might be unavailable due to privacy and security concerns (e.g., electric power supply systems or buildings housing important functions). In this paper, we propose a supply/demand interface between the system-of-systems simulator, defined within the interdependent Resilience - Compositional Demand/Supply (iRe-CoDeS) framework, and the individual infrastructure system simulators. Such an interface can be used for regional recovery simulation and resilience assessment of interdependent infrastructure systems, while allowing infrastructure system operators to maintain system’s privacy and/or security. We define a tiered supply/demand interface, where the amount of information provided by individual systems can range from system-level to component-level post-disaster evolution of resource supply and demand, assessed using expert opinion or confidential in-house models. The proposed supply/demand interfaces are illustrated in a semi-virtual case study, assessing the seismic resilience of North-East San Francisco, focusing on the effect of interdependent infrastructure on the functional recovery of residential buildings.

Understanding and managing modern systems is becoming more challenging as they grow in complexity and become more integrated, as emphasized in the INCOSE Systems Engineering Vision for 2035. These new challenges increase the risk of system failure. Resilience Engineering is a subfield within Systems Engineering that studies system performance in the face of disruptions. Due to the term resilience having multiple connotations, many methods measure resilience in systems differently. Moreover, traditional methods to measure resilience, such as linear recovery or probability‐based models, can become insufficient as systems grow in size, complexity, and nonlinearity. Bifurcation Analysis (BA) is a mathematical approach to understanding system behavior under changing conditions. BA is commonly used to study nonlinear systems and transition states known as bifurcations. While it has been extensively used in many fields, BA is not widely used in Resilience Engineering. This paper suggests the BARD—Bifurcation Analysis for Resilience Diagnostics—framework to aid Resilience Engineering practitioners in addressing system resilience in complex systems. While drawing from control theory, the framework is used solely as a diagnostic instrument to help system engineers identify risks and performance regimes; the framework does not attempt to design or tune control systems. The BARD framework is applied to two cases to demonstrate its uses and potential. Contributions are an overview of the current state of Resilience Engineering and Bifurcation Analysis, the introduction of BARD as a general‐use framework to assess resilience via Bifurcation Analysis, and two applications of the framework.

In this chapter, we will explore the challenges and opportunities of grid integration of electric and hybrid electric vehicles in cyber-physical-social systems (CPSS). Transportation systems that move people, goods, and services in societies worldwide pose unprecedented environmental, economic, and social challenges, particularly with the growing urgency to conserve energy, cut back on carbon emissions and pollution, avoid crashes, and relieve congestion. Advances in intelligent transportation systems and Smart Grid offer great promise to address these challenges and have the potential to revolutionize future transportation systems. In the last decade, the government around the world has spurred efforts to boost the utilization of transportation electrification technologies because of their low-pollution emissions, energy independence, and high fuel economy. An ever-increasing number of electric and hybrid electric vehicles will radically change the traditional view of the power industry, transportation industry, social environment, and business world. Research on grid integration of electric and hybrid electric vehicles typically addresses topics at the vehicle-grid boundary, such as peak load impacts and agent-based charging control. While researchers around the world are making significant advances in these areas, there is very little work addressing the coupled cyber-physical-social effects of electric and hybrid electric vehicle charging with the mobility-focused, transportation ecosystem to meet the dynamic needs of a changing society. It is important to recognize that today's critical infrastructure is an interdependent network of networks. A single network consists of millions of subnetworks and individual agents. The "tie point" is critical to its reliability, cost-effectiveness, and resiliency. As such "tie points," the emerging deployment of electric and hybrid electric vehicle charging facilities would complicate the understanding and design of interdependent critical infrastructure systems. For example, as a transportation tool and electricity carrier, electric and hybrid electric vehicle can be charged at any charging facility and at any time, which brings more spatial and temporal uncertainty to the power grid's load forecast. The retail electricity price and parking fee may also have an impact on customer behavior, eventually leading to a change in traffic flow. Besides engineering considerations, the placement of electric and hybrid electric vehicle charging stations are constrained by applicable local policy and regulation, financial incentives, and public interests. We will discuss the state of the art of grid integration of electric and hybrid electric vehicles in a CPSS environment. Moreover, we will present a future perspective to enable the dramatic increase of electrified vehicles, and ultimately lead to (1) reduced fossil fuel consumption; (2) reduced carbon emissions and pollution; (3) increased customer satisfaction; (4) increased reliability and efficiency for moving people and goods; (5) improved efficiency of intelligent transportation systems; (6) accelerated adoption of Smart Grid technologies; and (7) increased use of infrastructure capacity.

As a consequence of the U.S. effort to increase infrastructure security and resilience, the Department of Homeland Security (DHS) and other U.S. federal agencies have identified 16 critical infrastructure sectors that are considered vital to the nation’s well-being in terms of economic security, public health, and safety. However, there remains no articulated set of values that justify this particular list of infrastructure systems or how decision-makers might prioritize investments towards one critical sector over another during a crisis. To offer a more integrated and holistic approach to critical infrastructure resilience, this research employs the capabilities approach to human development, which offers an alternative view of critical infrastructure that focuses on the services that infrastructure provides rather than its physical condition or vulnerability to threats. This service-based perspective of infrastructure emphasizes the role of infrastructure in enabling and supporting central human capabilities that build adaptive capacity and improve human well-being. We argue that the most critical infrastructure systems are those that are essential for providing and/or supporting central human capabilities. This paper examines the DHS designation of criticality from a capabilities perspective and argues for a capabilities basis for making distinctions between those systems that should be considered most critical and those that might be temporarily sacrificed. A key implication of this work is that an across sector approach is required to reorganize existing critical infrastructure efforts around the most valuable infrastructure end-services.

The notion of transportation refers to the movement of people and goods across non-trivial geographical distances. Its history is as old as our civilization and its importance and scope are enormous, ranging from people walking on the earth's surface to carts and chariots driven by animals, automobiles, trains, airplanes and ships. To date, the discipline of transportation has experienced two major revolutions. Prior to the discovery of electromagnetic communication, information on the transport of goods and people was propagated along with the transported material and, in essence, their transit speeds were identical. The use of electromagnetic communication, the first major revolution in the discipline of transportation, made the propagation of information about the movement of goods and people significantly faster than the actual transport of the material at limited speeds. This resulted in superior planning as well as tracking of the movement of material through the transportation network. The availability of computing engines fuelled the second major revolution in transportation systems wherein fast and precise computers were exploited efficiently to control and coordinate the transport of goods and people across the system. For a number of reasons, including simplicity and the desire to maintain consistent control, the information and guidance providers of the transportation systems evolved as centralized units. A central control collected information about every entity within a specific transportation system and provided guidance and information to them as necessary. Today, the transportation discipline is on the brink of experiencing the third major revolution and possibly the most complex-the transformation from the centralized paradigm to the asynchronous, distributed paradigm which will integrate fast computers and high-performance computer networks through novel computer algorithms. This paper presents fundamental issues and principles underlying today's and tomorrow's intelligent transportation systems, including railways, highways, air travel, cargo networks and personalized rapid transportation systems. These principles have been utilized successfully in developing systems for railways and vehicles that have been reported in the literature. The paper also presents motivations for further research into the key areas of modelling and simulation and visualization of intelligent transportation systems.

This research utilizes Bayesian network to address a range of possible risks to the electrical power system and its interdependent networks (EIN) and offers possible options to mitigate the consequences of a disruption. The interdependent electrical infrastructure system in Washington, D.C. is used as a case study to quantify the resilience using the Bayesian network. Quantification of resilience is further analyzed based on different types of analysis such as forward propagation, backward propagation, sensitivity analysis, and information theory. The general insight drawn from these analyses indicate that reliability, backup power source, and resource restoration are the prime factors contributed towards enhancing the resilience of an interdependent electrical infrastructure system.

By 2030, forecasts suggest that urban areas will house 60 percent of the world’s population and one in every three people will live in cities with at least half a million inhabitants. Within the same time frame, the number of global megacities is expected to jump from 33 today to 43 in 2030 [1]. Underpinning these large urban areas will be an interconnected network of critical physical infrastructures reliant on Internet-connected Industrial Control Systems and susceptible to increasingly sophisticated, e.g., AI-enabled, cyber threats. In hand, the cyber threat landscape is shifting rapidly. We are seeing a sharp rise in the number of cyberattacks on critical infrastructure [2] with significant impacts cascading across multiple sectors and causing disruption to the provisioning of essential goods and services. Security scholars suggest that these impacts are not always equitable and that disruption to critical infrastructure can affect vulnerable groups differently [3], which further emphasizes the need to improve cybersecurity between critical infrastructure sectors [4]. Through structured analysis of city statistics, demographic information, cyber incidents, and current cyber policy, our presentation will articulate potential social implications of megacity growth through the lens of cyber-physical infrastructure disruption. We investigate the largest 15 megacities in the world and find that megacities continue to grow in population but not in cyber policy. We highlight recent examples of cyber-physical disruption in Mumbai and New York City with focus on implications for vulnerable populations. Our work suggests the need for future research on social responsibility regarding security of these critical infrastructure sectors and on the need for technology-focused law, policy, and regulation guidelines.

This introductory chapter provides an overview and a brief history of complexity science, which is the study of complex systems. All living systems and all intelligent systems are complex systems. Complexity science is relatively new but already indispensable. Many of the most important problems in engineering, medicine, and public policy are now addressed with the ideas and methods of complexity science. However, there is no agreement about the definition of 'complexity' or 'complex system', nor even about whether a definition is possible or needed. The conceptual foundations of complexity science are disputed, and there are many and diverging views among scientists about what complexity and complex systems are. Even the status of complexity as a discipline can be questioned given that it potentially covers almost everything. The origins of complexity science lie in cybernetics and systems theory, both of which began in the 1950s. Complexity science is related to dynamical systems theory, which matured in the 1970s, and to the study of cellular automata, which were invented at the end of the 1940s. By then computer science had become established as a new scientific discipline.

Offshore wind farms and naval vessels are examples of complex systems. A number of differences exist, e.g. the first is an exemplar of a developing technology, the second a technology having been developed and enhanced over centuries. Never the less a number of similarities exist, e.g. the development of responsive systems in physically demanding environments. Each of the technologies adheres to a prescribed product lifecycle, e.g. “ISO 15288, Systems and software engineering – System life cycle processes”, whereby each phase has distinct information and knowledge requirements. Furthermore, the adoption of a structured lifecycle ensures each technology considers the complete lifecycle and its integration within a potential system of systems. This cross sectoral study will review in-service knowledge management in two different fields of engineering, firstly Offshore Wind Generation which is a complex infrastructure system and secondly Royal Navy vessels which are complex marine engineering systems.