Interdependent Critical Infrastructure Systems Research Articles

Protection-interdiction-restoration for resilient multi-commodity networks

Interdependent critical infrastructure systems represent substantial financial investments and are vital to maintain a fundamental level of social and economic well-being, making them attractive targets for malevolent actors. Many of these infrastructure systems carry multiple products, each with unique needs and importance to different stakeholders. This work extends a tri-level protection-interdiction-restoration model from a single commodity to multiple commodities, solving the model with an integration of Benders’ decomposition and set covering decomposition. We propose a method to limit unmet weighted demand across commodities, considering unique interdependencies between network components and commodity-specific capacity requirements. An optimal solution is found iteratively by alternately fixing protection and interdiction variables. This work is illustrated with a case study of interdependent Swedish power and multi-commodity railway systems. Results demonstrate the convergent behavior of the master and subproblems, the value of network hardening, and the non-uniform network recovery trajectory. For the case study, the railway system was more vulnerable to disruption than the power system, making it a more attractive target for both protection and interdiction. The components that were commonly selected as part of a protection or interdiction plan were generally the ones with the greatest demand for iron ore, a highly critical commodity.

ResQ-IOS: An iterative optimization-based simulation framework for quantifying the resilience of interdependent critical infrastructure systems to natural hazards

Critical Infrastructure Systems are highly complex and interdependent. Growing complexity and interdependency between infrastructure systems and frequent exposure to extreme events have inevitably increased the probability of cascading failures and the prolonged lack of serviceability in urban communities, especially so for energy systems. The resilience analysis of interdependent infrastructure systems against natural hazards provides stakeholders with a comprehensive outlook on recovery strategies to minimize the damage costs and losses caused by extreme events. This paper introduces the ResQ-IOS, a Resilience Quantification Iterative Optimization-based Simulation (IOS) framework for quantifying the resilience of interdependent infrastructure systems to natural hazards with the capability of considering the real-world conditions for the status of infrastructure systems' components. The ResQ-IOS framework consists of five modules: risk assessment, simulation, optimization, database, and controller. To evaluate the capabilities of this framework, the seismic resilience of interdependent energy infrastructure networks (power, natural gas, and water) in Shelby County (TN), USA, was assessed. The results of the resilience analysis of the case study suggest that the water network is the best candidate for implementing pre-disaster Resilience Enhancement Measures (REMs), like increasing the supply capacity. Due to the controlling role of the power network in the community's recovery process, it is recommended that post-disaster REMs, such as increasing the number of Repair and Maintenance (R&M) teams, should be applied to the power network to speed up the restoration of failed components in that network and consequently, shorten the recovery duration of the community. The ResQ-IOS can be employed as a useful computational tool for planning the resilience-oriented sustainable development of urban communities by, for example, deploying Renewable Energy (RE)-based strategies to enhance their disaster resilience.

A simulation‐based generalized framework to model vulnerability of interdependent critical infrastructure systems under incomplete information

AbstractThis paper proposes a novel simulation‐based hybrid approach coupled with time‐dependent Bayesian network analysis to model multi‐infrastructure vulnerability over time under physical, spatial, and informational uncertainties while considering cascading failures within and across infrastructure networks. Unlike existing studies that unrealistically assume that infrastructure managers have full knowledge of all the infrastructure systems, the proposed approach considers a realistic scenario where complete information about the infrastructure network topology or the supply–demand flow characteristics is not available while estimating multi‐infrastructure vulnerability. A novel heuristic algorithm is proposed to construct a dynamic fault tree to abstract the network topology of any infrastructure. In addition, to account for the unavailability of exact supply–demand flow characteristics, the proposed approach constructs the interdependence links across infrastructure network systems using different simulated parameters considering the physical, logical, and geographical dependencies. Finally, using parameters for geographical proximity, infrastructure managers' risk perception, and the relative importance of one infrastructure on another, the multi‐infrastructure vulnerability over time is estimated. Results from the numerical experiment show that for an opportunistic risk perception, the interdependencies attribute to redundancies, and with an increase in redundancy, the vulnerability decreases. On the other hand, from a conservative risk perspective, the interdependencies attribute to deficiencies/liabilities, and the vulnerability increases with an increase in the number of such interdependencies.

Impacts of varying network parameters on the vulnerability and resilience of interdependent critical infrastructure systems

ABSTRACT Critical infrastructure systems are complex and subjected to evolving risks and hazards, which makes anticipating their behavior difficult. To prioritize among actions that increase system resilience, it is critical to understand their impacts on parameters defining a network and on anticipated network performance. In this paper, the authors investigate the impacts of variations in three parameters on network vulnerability: component vulnerabilities, service interdependency redundancies, and system link configuration. The advances of this work compared to prior studies include: 1) The impacts of parameters varied across a range of values at the component level are evaluated considering component functionality and connectivity; 2) quantitative analyses of component performance as parameters vary are investigated based on system redundancies; and 3) probabilistic system interdependencies are analyzed through a Bayesian network that considers component pathways. Results quantify effects of changes in component vulnerabilities and dependencies and are used to discuss impacts on system resilience.

High level architecture-based framework for modeling interdependent critical infrastructure systems

Interdependencies among urban critical infrastructure systems (CISs) significantly impact the reliability and performance of CISs and the resilience of modern societies. Although several approaches exist for modeling interdependent CISs and studying their behavior, models developed in previous studies often fail to incorporate CIS domain knowledge, capture systemic heterogeneities among the CISs, and accurately model CISs interdependencies. Consequently, existing models have a limited ability to simulate interdependent CISs with sufficient detail and accuracy. To address these limitations, this study proposes a high-level architecture (HLA)-based framework for modeling interdependent CISs that can leverage and integrate well-tested practices, knowledge, data and simulation tools accumulated over years of wide usage in various CIS domains. The framework provides a methodology for co-simulating heterogeneous fine-grained CIS domain-specific models and modeling complex interactions between them and with their external environments, hence reproducing with high fidelity the complex coupled systems. A case study of two interdependent power and water systems was conducted, which demonstrated the efficacy of the proposed framework. Simulation results revealed that the HLA-based CISs model could capture the heterogeneous behaviors of the CISs and reveal a variety of failure-induced system vulnerabilities and feedback loops which may not be observable when using other existing modeling approaches.

Resilience of Critical Infrastructure Systems to Floods: A Coupled Probabilistic Network Flow and LISFLOOD-FP Model

In this paper, a network-flow model was constructed to simulate the performance of interdependent critical infrastructure systems during flood hazards, when there is shortage of commodities such as electrical power and water. The model enabled us to control the distribution of commodities among different consumers whose demand cannot be fully met. Incorporating time-variance in the model allowed for evaluating the time evolution of the functional level of the infrastructure systems and quantifying their resilience. As a demonstration of the model’s capability, the network model was coupled with a raster-based hydraulic flooding model in the way of Monte Carlo simulations. It was then used to investigate the cascading effects of flood-related failures of individual infrastructure assets on the performance of the critical infrastructure systems of a coastal community under different flooding scenarios and future climate impacts. The coupled modelling framework is essential for correctly assessing the interdependences and cascading effects in the infrastructure systems in the case of flood hazards. While in the considered example, the extent of inundation becomes less severe with a changing climate, the risk to infrastructure does not recede because of the cascading effects. This behaviour could not be captured by the flood model alone.

Risk-Informed Decision-Making for Predisaster Risk Mitigation Planning of Interdependent Infrastructure Systems: Case Study of Jamaica

Hazards cause huge social disruptions and economic losses each year. Community disaster resilience relies on the capacity of critical infrastructure systems to maintain acceptable levels of functionality during a disruptive event and to recover promptly after the event. A proper predisaster risk mitigation planning framework can aid communities by working in advance to understand the preexisting local needs, identify the leaders, form partnerships, establish resources, and seize opportunities to increase community disaster resilience. Although numerous theoretical studies exist on developing models to support infrastructure predisaster risk mitigation, these models have not been properly incorporated into the decision-making process in existing studies. This lack of incorporation impedes the transfer of theoretical knowledge to practical policymaking. High-level predisaster infrastructure risk mitigation decisions for multiple infrastructure systems are especially in need of such an illustration due to the complexity introduced by the scale. This paper defines the interdependent infrastructure risk mitigation (IIRM) problem in a risk-informed decision-making framework and illustrates the entire decision-making process using a case study on predisaster risk mitigation planning of the interdependent critical infrastructure systems in Jamaica. This region is vulnerable to natural hazards but has received little attention in the literature.

Modeling cascading failure of interdependent critical infrastructure systems using HLA-based co-simulation

Cascading failure modeling in interdependent critical infrastructure systems (CISs) is an important but challenging work that lays the foundation for disaster impact assessment and recovery planning. However, despite the increasing volume of studies that examine cascading failure across interdependent CISs, the majority of modeling approaches proposed in prior studies do not capture the complex heterogeneous nature and behavior of the CISs under disaster, limiting the ability of these approaches to detailly model the cascading failure process. To address the above limitation, this study proposes a high level architecture (HLA)-based co-simulation approach to integrate heterogeneous domain-specific CIS models and model the cross-domain interdependencies and failure propagation processes. Moreover, the approach provides a novel solution for modeling the dynamic impact of disaster on CISs and analyze its impact on the cascading failure of interdependent CISs. A case study of two interdependent power and water systems was conducted, which demonstrated the efficacy of the proposed approach. The results showed that the model developed using the proposed approach could provide granular data of the state and heterogeneous behaviors of the systems, capture the dynamic evolution of disaster impact on system components, and reveal the paths and mechanisms of failure propagation within and across the systems. The proposed modeling approach can overcome the drawbacks in current interdependent CISs cascading failure models, and provide a foundation for resilience assessment of CISs.

A Functional All‐Hazard Approach to Critical Infrastructure Dependency Analysis

The critical infrastructures protection landscape is a vast and varied pattern of independent, but interconnected infrastructure systems that are essential to the function of our modern society. The U.S. policy on critical infrastructure protection has been continually evolving since the “President's Commission on Critical Infrastructure Protection” was published in 1997. In response to these policies, federal, state, and local governments, along with research institutions, have invested a substantial amount of time and effort into identifying and analyzing critical infrastructure, their functions, and dependencies/interdependencies to better understand their vulnerabilities. To date, the ability to assess vulnerabilities, resiliency, and priorities for protecting interdependent critical infrastructure systems from an all‐hazards perspective remains a difficult problem. In this paper we introduce the All‐Hazards Analysis (AHA) methodology, which provides an integrated functional basis across infrastructure systems, through the implementation of a common language and a scalable level of decomposition to effectively evaluate the resilience of interconnected infrastructure systems. AHA models infrastructure systems as directed multidimensional graphs, which enable the evaluation of cross‐sector interdependencies prior to, during, and after disruptive events. Finally, and by design, AHA enables the cross linking of data taxonomies to enable more effective data sharing, such as the National Critical Functions (NCF) and Infrastructure Data Taxonomy (IDT).

Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory

In this paper, we present a methodological framework for resilience analysis of interdependent critical infrastructure systems and use artificial interdependent power and gas network as an example. We use deep learning to identify network topology attributes and analyze the vulnerability process of interdependent infrastructure systems to different failure scenarios and coupling modes under structural perspective. Then, functional model of the interdependent network is constructed, and the vulnerability process based on functional characteristics is analyzed. At last, we propose different recovery strategies and use a resilience triangle to study the restoration process, and the optimal resilience improvement strategy is acquired from both structural and functional perspectives. The method proposed in this paper can help decision makers develop mitigation techniques and optimal protection strategies.

Assessing the impact of systemic heterogeneity on failure propagation across interdependent critical infrastructure systems

The systemic heterogeneity between different critical infrastructure systems (CISs) can significantly influence the failure propagation process across the systems. For instance, when power supply systems are affected by an earthquake, physical damages mainly occur at nodes and seldom occur at links, since the cables are flexible enough to withstand the impacts. Water supply systems on the other hand are prone to experience physical damages at both nodes and links. However, despite the increasing volume of literature that examines failure propagation risks across interdependent CISs, only few studies have accounted for various systemic heterogeneity factors (HFs) and their potential impact on cascading failures. The aim of this study is to reveal and quantify the significance of such impact, for which a four-step approach is introduced. A case study was conducted, which examined the impact of three HFs on failure propagation across two interdependent power and water supply systems in the event of a simulated earthquake. Seven improved models were developed and their respective simulation results were compared. The comparison of simulation results from the baseline model and improved models revealed that the impact of the simulated earthquake disaster on CISs would be significantly misestimated if the HFs were not considered, and that each HF impacted the failure propagation in a different way. The proposed approach and the findings in the case study are expected to uncover the drawbacks in current CISs failure propagation models, and provide a foundation for the development of more reliable failure propagation modeling approaches in future research.

A network-of-networks percolation analysis of cascading failures in spatially co-located road-sewer infrastructure networks

This paper presents a network-of-networks analysis framework of interdependent critical infrastructure systems, with a focus on the co-located road-sewer network. The constructed interdependency considers two types of node dynamics: co-located and multiple-to-one dependency, with different robustness metrics based on their function logic. The objectives of this paper are twofold: (1) to characterize the impact of the interdependency on networks’ robustness performance, and (2) to unveil the critical percolation transition threshold of the interdependent road-sewer network. The results show that (1) road and sewer networks are mutually interdependent and are vulnerable to the cascading failures initiated by sewer system disruption; (2) the network robustness decreases as the number of initial failure sources increases in the localized failure scenarios, but the rate declines as the number of failures increase; and (3) the sewer network contains two types of links: zero exposure and severe exposure to liquefaction, and therefore, it leads to a two-phase percolation transition subject to the probabilistic liquefaction-induced failures. This indicates that tiered vulnerability of the liquefaction-prone links will result in multiple percolation transitions. The proposed framework provides a holistic approach to analyze the network robustness under different failure scenarios and can be extended to a larger interdependent system.

Rethinking Resilience Analytics.

The concept of "resilience analytics" has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data-driven analytic techniques, as well as the prevalence of high-profile natural and man-made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber-physical-social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamentalsurprise.

An adaptive robust framework for the optimization of the resilience of interdependent infrastructures under natural hazards

This paper proposes a novel adaptive robust optimization (ARO)-based mathematical framework for resilience enhancement of interdependent critical infrastructure (CI) systems against natural hazards (NHs). In this framework, the potential impacts of a specific NH on an infrastructure are firstly evaluated, in terms of failure and recovery probabilities of system components; these are, then, fed into a two-stage ARO model to determine the optimal planning of resilience strategies under limited investment budget, anticipating the most-likely worst realization of the uncertainty of component failures under the NH. For its exact solution, a decomposition method based on simultaneous column-and-constraint generation is adopted. The approach is applied to a case study concerning the resilience of interdependent power and gas networks subject to (simulated) wind storms. The numerical results demonstrate the effectiveness of the proposed framework for the optimization of the resilience of interdependent CIs under hazardous events; this provides a valuable tool for making informed pre-hazard preparation decisions. The value of a coordinated pre-hazard planning that takes into account CI interdependencies is also highlighted.

Modeling the damage and recovery of interdependent critical infrastructure systems from natural hazards

Understanding the impact of disasters to civil infrastructure network will guide strategic pre-disaster hazard mitigation and post-disaster recovery planning of a community. The facilities in civil infrastructure network depend on each other to exchange product, information or services. When disaster happens, these dependencies would aggravate the initial damage and lead to cascading failures. Thus, understanding the dependencies among infrastructure facilities is essential in modeling the damage and recovery of a community under disruptive events. This study builds upon the Dynamic Inoperability Input–output Model to assess the recovery of the civil infrastructure facilities by considering the facility-level dependencies. The recovery of the integrated network was assessed utilizing the characteristic parameters from graph theory. This methodology was demonstrated with a hypothetical infrastructure network, consisting of power, water and telecommunication systems under a hurricane. The sensitivity of the network recovery to the dependency measures was investigated, which showed that the recovery time is sensitive to the level of dependency between different systems. The proposed methodology was compared with a conventional model with system-level interdependencies and showed that considering facility-level dependencies would yield more refined result. The model was then applied to simulate the power system restoration of Galveston City, Texas under Hurricane Ike.

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.

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.

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?

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.