Interdependent Infrastructure Systems Research Articles

Resilience capacities assessment for critical infrastructures disruption: READ pilot applications (part 2)

The study aims at supporting the stakeholders involved in the emergency management (EM) activities to tackle the challenges related to scenarios involving interdependent critical infrastructure (CI) systems, by building resilience. The primary objective of implementing the capability-based approach is to enable and foster collaborative EM in the context of public-private collaborations for CI resilience. The READ framework and related tool have been tested to support stakeholders' resilience capacities assessment with respect to cross-border disruptions and thus identify the main areas where progress is needed. Two pilot cases were used to validate the approach and demonstrate its applicability in the context of regional public-private collaborations for critical infrastructure protection and Resilience with different degree of development and level of maturity, namely Basque Country (Spain) and Lombardy Region (Italy). The practitioners' feedback from both application cases confirmed the usefulness of such approach and helped to identify areas for future research and improvement.

Infrastructure as a Complex Adaptive System

National infrastructure systems spanning energy, transport, digital, waste, and water are well recognised as complex and interdependent. While some policy makers have been keen to adopt the narrative of complexity, the application of complexity‐based methods in public policy decision‐making has been restricted by the lack of innovation in associated methodologies and tools. In this paper we firstly evaluate the application of complex adaptive systems theory to infrastructure systems, comparing and contrasting this approach with traditional systems theory. We secondly identify five key theoretical properties of complex adaptive systems including adaptive agents, diverse agents, dynamics, irreversibility, and emergence, which are exhibited across three hierarchical levels ranging from agents, to networks, to systems. With these properties in mind, we then present a case study on the development of a system‐of‐systems modelling approach based on complex adaptive systems theory capable of modelling an emergent national infrastructure system, driven by agent‐level decisions with explicitly modelled interdependencies between energy, transport, digital, waste, and water. Indeed, the novel contribution of the paper is the articulation of the case study describing a decade of research which applies complex adaptive systems properties to the development of a national infrastructure system‐of‐systems model. This approach has been used by the UK National Infrastructure Commission to produce a National Infrastructure Assessment which is capable of coordinating infrastructure policy across a historically fragmented governance landscape spanning eight government departments. The application will continue to be pertinent moving forward due to the continuing complexity of interdependent infrastructure systems, particularly the challenges of increased electrification and the proliferation of the Internet of Things.

Restoration resource allocation model for enhancing resilience of interdependent infrastructure systems

Enhancing the resilience of infrastructure systems is critical to the sustainability of the society against multiple disruptive events. This paper develops an approach for allocating restoration resources to enhance resilience of interdependent infrastructure systems. According to Inoperability Input–Output Model, a resilience metric for infrastructure systems is developed, in which the performance loss of infrastructure systems resulting from a disruptive event is measured in economic loss and inoperability. Model for determining the optimal infrastructure restoration resources allocation is proposed with the objective of maximizing resilience. Infrastructure interdependence is modeled by the Dynamic Inoperability Input-Output Model (DIIM), which is an accepted economic model for describing the interconnected relationship of industry sectors. To investigate the utility of the restoration resource allocation model, numerical analysis is conducted with an example derived from the data provided by the US Bureau of Economic Analysis. The results show that: (1) the optimal restoration resource allocation varies with the resource budget; (2) for a specific disruptive event, there exists an optimal resource budget which can minimize the sum of restoration cost and the performance loss of infrastructure system; and (3) the significance of factors such as initial inoperability of infrastructure systems on the optimal allocation. The proposed model can assist the decision makers in (i) better understand the effects of resource allocation, and (ii) deciding which allocation strategies should be used following a disruptive event.

Interdependent Network Recovery Games.

Recovery of interdependent infrastructure networks in the presence of catastrophic failure is crucial to the economy and welfare of society. Recently, centralized methods have been developed to address optimal resource allocation in postdisaster recovery scenarios of interdependent infrastructure systems that minimize total cost. In real-world systems, however, multiple independent, possibly noncooperative, utility network controllers are responsible for making recovery decisions, resulting in suboptimal decentralized processes. With the goal of minimizing recovery cost, a best-case decentralized model allows controllers to develop a full recovery plan and negotiate until all parties are satisfied (an equilibrium is reached). Such a model is computationally intensive for planning and negotiating, and time is a crucial resource in postdisaster recovery scenarios. Furthermore, in this work, we prove this best-case decentralized negotiation process could continue indefinitely under certain conditions. Accounting for network controllers' urgency in repairing their system, we propose an ad hoc sequential game-theoretic model of interdependent infrastructure network recovery represented as a discrete time noncooperative game between network controllers that is guaranteed to converge to an equilibrium. We further reduce the computation time needed to find a solution by applying a best-response heuristic and prove bounds on ε-Nash equilibrium, where ε depends on problem inputs. We compare best-case and ad hoc models on an empirical interdependent infrastructure network in the presence of simulated earthquakes to demonstrate the extent of the tradeoff between optimality and computational efficiency. Our method provides a foundation for modeling sociotechnical systems in a way that mirrors restoration processes in practice.

Probabilistic multi-scale modeling of interdependencies between critical infrastructure systems for resilience

The prevalence of aging infrastructure and an increase in cascading failures have highlighted the need to focus on building strong, interdependent infrastructure systems to increase resilience. To understand the ways infrastructure systems depend on one another, we define three comprehensive interdependency types – service provision, geographic, and access for repair. We propose a methodology to model interdependencies probabilistically using a novel Bayesian network approach. By understanding how these interdependencies affect the fragility of overall systems, infrastructure owners can work towards creating more resilient infrastructure systems that sustain less damage from natural hazards and targeted attacks, and restore services to communities rapidly. Generalized expressions to create the multi-scale Bayesian network model accounting for each interdependency type are presented and applied to a real interdependent water, power, and gas network to demonstrate their use. These models enable us to probabilistically infer which interdependencies have the most critical effects and prioritize components for repair or reinforcement to increase resilience.

A mathematical framework to optimize resilience of interdependent critical infrastructure systems under spatially localized attacks

This paper defines the hazards that can cause direct localized damage or interruption as Spatially Localized Attacks (SLAs). The SLAs-induced impact on a critical infrastructure system (CIS) is modeled as the failure of system components that exist within some localized area while those outside this area remain operating. Instead of identifying and analyzing each type of the SLAs-related hazards, this paper makes a worst-case analysis and proposes a mathematical framework to support resilience optimization of interdependent CISs under the worst SLA. For illustrative purposes, this paper mainly considers two types of strategies to enhance CIS resilience, including protecting weak components, and building new components to increase redundancy. The problem is mathematically formulated as a tri-level defender–attacker–defender model, which is exactly solved by a proposed decomposition algorithm. The case study on interdependent power and water systems demonstrates how the proposed approach can not only identify the optimum resilience enhancement strategy as well as the worst-case SLA, but also analyze the importance of considering interdependencies from both the attacker's and the defender's perspectives.

Strategic analysis of the future of national infrastructure

There have been many calls for a more strategic, long-term approach to national infrastructure in the UK and elsewhere around the world. While appealing in principle, developing a national infrastructure strategy in practice poses major challenges of complexity and uncertainty. The UK Infrastructure Transitions Research Consortium has set out a systematic methodology for long-term analysis of the performance of national infrastructure systems. It deals with each infrastructure sector – energy, transport, digital communications, water supply, waste water, flood protection and solid waste – in a consistent framework and assesses the interdependencies between sectors. The method is supported with the world’s first infrastructure ‘system-of-systems’ model, which has been developed for long-term decision analysis in interdependent infrastructure systems. This paper presents the Nismod model’s analysis in the National Needs Assessment report launched at the Institution of Civil Engineers in October 2016.

Bridge life-cycle performance and cost: analysis, prediction, optimisation and decision-making

The development of a generalised framework for assessing bridge life-cycle performance and cost, with emphasis on analysis, prediction, optimisation and decision-making under uncertainty, is briefly addressed. The central issue underlying the importance of the life-cycle approach to bridge engineering is the need for a rational basis for making informed decisions regarding design, construction, inspection, monitoring, maintenance, repair, rehabilitation, replacement and management of bridges under uncertainty which is carried out by using multi-objective optimisation procedures that balance conflicting criteria such as performance and cost. A number of significant developments are summarised, including time-variant reliability, risk, resilience, and sustainability of bridges, bridge transportation networks and interdependent infrastructure systems. Furthermore, the effects of climate change on the probabilistic life-cycle performance assessment of highway bridges are addressed. Moreover, integration of SHM and updating in bridge management and probabilistic life-cycle optimisation considering multi-attribute utility and risk attitudes are presented.

A Perfect Storm? The collapse of Lancaster's critical infrastructure networks following intense rainfall on 4/5 December 2015

On 4/5 December 2015 a slow moving frontal system associated with the extratropical cyclone Storm Desmond brought record‐breaking levels of rainfall to Lancaster. Both high ground and low‐lying areas were already saturated following the wettest November on record, and on the evening of 5 December the River Lune flooded into the city. Critical road and rail transport networks ground to a halt, and the city and the surrounding areas were left without mains power for 2 days after a key substation flooded, consequently leaving the majority of communication services inoperable. Was this a ‘Perfect Storm’, or a glimpse of what the future may hold for our cities as they face more frequent extreme weather events, set against the backdrop of urban population growth, and increasingly interdependent critical urban infrastructure systems?

Probabilistic assessment of an interdependent healthcare–bridge network system under seismic hazard

Strong earthquakes can destroy infrastructure systems and cause injuries and/or fatalities. Therefore, it is important to investigate seismic performance of interdependent infrastructure systems and guarantee their abilities to cope with earthquakes. This paper presents an integrated probabilistic framework for the healthcare–bridge network system performance analysis considering spatial seismic hazard, vulnerability of bridges and links in the network, and damage condition of a hospital at component and system levels. The system level performance is evaluated considering travel and waiting time based on the damage conditions of the components. The effects of correlation among the seismic intensities at different locations are investigated. Additionally, the correlations associated with damage of the investigated structures are also incorporated within the probabilistic assessment process. The conditional seismic performance of the hospital given the damage conditions of the bridge network and the effect of bridge retrofit actions are also investigated. The approach is illustrated on a healthcare system located near a bridge network in Alameda, California.

Critical location identification and vulnerability analysis of interdependent infrastructure systems under spatially localized attacks

Infrastructure systems are usually spatially distributed in a wide area and are subject to many types of hazards. For each type of hazards, modeling their direct impact on infrastructure components and analyzing their induced system-level vulnerability are important for identifying mitigation strategies. This paper mainly studies spatially localized attacks that a set of infrastructure components located within or crossing a circle shaped spatially localized area is subject to damage while other components do not directly fail. For this type of attacks, taking interdependent power and gas systems in Harris County, Texas, USA as an example, this paper proposes an approach to exactly identify critical locations in interdependent infrastructure systems and make pertinent vulnerability analysis. Results show that (a) infrastructure interdependencies and attack radius largely affect the position of critical locations; (b) spatially localized attacks cause less vulnerability than equivalent random failures; (c) in most values of attack radius critical locations identified by considering only node failures do not change when considering both node and edge failures in the attack area; (d) for many values of attack radius critical locations identified by topology-based model are also critical from the flow-based perspective.

Post-Disaster Supply Chain Interdependent Critical Infrastructure System Restoration: A Review of Data Necessary and Available for Modeling

<p class="p1">The majority of restoration strategies in the wake of large-scale disasters have focused on short-term emergency response solutions. Few consider medium- to long-term restoration strategies to reconnect urban areas to national <em>supply chain interdependent critical infrastructure systems</em> (SCICI). These SCICI promote the effective flow of goods, services, and information vital to the economic vitality of an urban environment. To re-establish the connectivity that has been broken during a disaster between the different SCICI, relationships between these systems must be identified, formulated, and added to a common framework to form a system-level restoration plan. To accomplish this goal, a considerable collection of SCICI data is necessary. The aim of this paper is to review what data are required for model construction, the accessibility of these data, and their integration with each other. While a review of publically available data reveals a dearth of real-time data to assist modeling long-term recovery following an extreme event, a significant amount of static data does exist and these data can be used to model the complex interdependencies needed. For the sake of illustration, a particular SCICI (transportation) is used to highlight the challenges of determining the interdependencies and creating models capable of describing the complexity of an urban environment with the data publically available. Integration of such data as is derived from public domain sources is readily achieved in a geospatial environment, after all geospatial infrastructure data are the most abundant data source and while significant quantities of data can be acquired through public sources, a significant effort is still required to gather, develop, and integrate these data from multiple sources to build a complete model. Therefore, while continued availability of high quality, public information is essential for modeling efforts in academic as well as government communities, a more streamlined approach to a real-time acquisition and integration of these data is essential.

Post-Disaster Supply Chain Interdependent Critical Infrastructure System Restoration: A Review of Data Necessary and Available for Modeling

The majority of restoration strategies in the wake of large-scale disasters have focused on short-term emergency response solutions. Few consider medium- to long-term restoration strategies to reconnect urban areas to national supply chain interdependent critical infrastructure systems (SCICI). These SCICI promote the effective flow of goods, services, and information vital to the economic vitality of an urban environment. To re-establish the connectivity that has been broken during a disaster between the different SCICI, relationships between these systems must be identified, formulated, and added to a common framework to form a system-level restoration plan. To accomplish this goal, a considerable collection of SCICI data is necessary. The aim of this paper is to review what data are required for model construction, the accessibility of these data, and their integration with each other. While a review of publically available data reveals a dearth of real-time data to assist modeling long-term recovery following an extreme event, a significant amount of static data does exist and these data can be used to model the complex interdependencies needed. For the sake of illustration, a particular SCICI (transportation) is used to highlight the challenges of determining the interdependencies and creating models capable of describing the complexity of an urban environment with the data publically available. Integration of such data as is derived from public domain sources is readily achieved in a geospatial environment, after all geospatial infrastructure data are the most abundant data source and while significant quantities of data can be acquired through public sources, a significant effort is still required to gather, develop, and integrate these data from multiple sources to build a complete model. Therefore, while continued availability of high quality, public information is essential for modeling efforts in academic as well as government communities, a more streamlined approach to a real-time acquisition and integration of these data is essential.

New Perspectives on Building Resilience into Infrastructure Systems

Government and industry discussion of the Triple 3 Resilience Target was the focus of the 2014 Cascadia Earthquake Readiness Workshop in Washington, United States. Workshop findings are presented from breakout sessions on critical energy infrastructure, ports and waterways, and emergency management. These prompted the examination of new perspectives on building resilience into lifeline infrastructure systems (lifelines which are critical infrastructure), including a call for new Pacific Northwest regional, collaborative, cross-sector public-private leadership groups to develop coordinated restoration priorities, and enacting policies to promote, enforce, and track the building of resilience in complex, interdependent infrastructure systems. Finally, the resilience prism is introduced, which displays the link between critical infrastructure resilience and the Triple 3 Resilience Target to address postdisaster needs for both individuals and communities.

Synthetic Environments for Investigating Collaborative Information Seeking: An Application in Emergency Restoration of Critical Infrastructures

AbstractLarge-scale disasters can produce profound disruptions in the fabric of interdependent critical infrastructure systems such as water, telecommunications and electric power. The work of post-disaster infrastructure restoration typically requires information sharing and close collaboration across these sectors; yet – due to a number of factors – the means to investigate decision making phenomena associated with these activities are limited. This paper motivates and describes the design and implementation of a computer-based synthetic environment for investigating collaborative information seeking in the performance of a (simulated) infrastructure restoration task. The main contributions of this work are twofold. First, it develops a set of theoretically grounded measures of collaborative information seeking processes and embeds them within a computer-based system. Second, it suggests how these data may be organized and modeled to yield insights into information seeking processes in the performance of a complex, collaborative task. The paper concludes with a discussion of implications of this work for practice and for future research.

Rapid Assessment of Hurricane Damage and Disruption to Interdependent Civil Infrastructure Systems

AbstractCivil infrastructure systems such as power, water, and communications are critical for the well-being and functioning of citizens, industries, and organizations both under normal and disaster conditions. Infrastructure systems cannot be seen as independent entities because they rely a great deal on each other for services needed for operation. Damage to one system can have cascading effects throughout that system, and throughout all of the systems that rely on it. To obtain a meaningful prediction of disruption, these interdependencies among the systems must be considered. This paper presents two models to predict and analyze the effect that a hurricane can have on the performance of interdependent infrastructure systems. The first model uses a Monte Carlo simulation and statistical methods to predict the damage caused by a hurricane on the systems being considered. The second model applies optimization techniques to determine the cascading effects that the damage has throughout the infrastructure...

Building the Right Tool for the Job: Value of Stakeholder Involvement When Developing Decision-Support Technologies for Emergency Management

AbstractComputer-aided simulation and decision-support tools have a recognized role in planning for, and responding to, extreme hazard events. Multi-network interdependent critical infrastructure program for the analysis of lifelines (MUNICIPAL) is a multipurpose computer-simulation and decision-support tool that was developed to simulate the damage to interdependent critical infrastructure systems during extreme weather events and to guide their restoration following such events. This paper describes the interactive stakeholder-involvement process that was used during the development of MUNICIPAL to increase the likelihood that it would become a useful aid to planners and policymakers, emergency managers, and infrastructure service providers. It demonstrates that a structured process for obtaining input from the user community throughout the process can increase the likelihood that tools such as MUNICIPAL will be adopted for training, awareness raising, and team building among the diverse public and priv...

Resilience assessment of interdependent infrastructure systems: With a focus on joint restoration modeling and analysis

As infrastructure systems are highly interconnected, it is crucial to analyze their resilience with the consideration of their interdependencies. This paper adapts an existing resilience assessment framework for single systems to interdependent systems and mainly focuses on modeling and resilience contribution analysis of multi-systems’ joint restoration processes, which are seldom addressed in the literature. Taking interdependent power and gas systems in Houston, Texas, USA under hurricane hazards as an illustrative exmaple, five types of joint restoration stategies are proposed, including random restoration strategy RS1, independent restoration strategy RS2, power first and gas second restoration strategy RS3, gas aimed restoration strategy RS4, and power and gas compromised restoration strategy RS5. Results show that under limited restoration resources, RS1 produces the least resilience for both systems, RS2 and RS3 both generates the largest power system resilience while RS4 is the best for the gas system; and if quantifying the total resilience as the evenly weighted sum of two systems’ individual resilience, RS5 produces the largest total resilience. The proposed method can help decision makers search optimum joint restoration strategy, which can significantly enhance both systems’ resilience.

Infrastructure Decay Modeling With the Input-Output Inoperability Model

Asset management and infrastructure interdependency concepts are found to be useful in the study of infrastructure decay. As such, infrastructure decay is modeled with the input-output inoperability model (IIM), which is a method of analysis that captures cascading effects of a disturbance in interdependent infrastructure systems. This paper presents an extension to the IIM that simplifies the construction of the interdependency matrix central to the model and integrates the use of component decay curves for each component in the system. The revised model results in the ability of infrastructure asset managers to recognize the effect of decay across an entire infrastructure network or multiple networks.

Milk or Wine: Are Critical Infrastructure Protection Architectures Improving with Age?

This paper contextualizes the nature of threats to critical infrastructure, especially vulnerabilities within electric grid systems, and analyzes key considerations for the protection architecture of such systems. By exploring historical case studies, we demonstrate the potential for blind spots in infrastructure protection policy, which can leave electric grids vulnerable to a variety of threats, including improvisational malignant devices. These devices in turn have the potential to catalyze cascading failure scenarios within interdependent critical infrastructure systems, constituting “wicked problems” of complexity that bear relevance to a variety of public and private institutions responsible for the provision of essential services.