Interdependent Critical Infrastructures Research Articles

Resilience enhancement for interdependent power systems by AI-assisted disaster response

The failure of urban Electrical Distribution Systems (EDSs) can lead to direct or indirect consequences for their interdependent critical infrastructures, for example, the hospitals, the fire department, etc., negatively affecting the well-being of citizens in the city. To improve the resilience of the coupled power network, the Operational Interdependence Simulator (OIS) is proposed to help prioritize the queue of failure recovery and Minimum Spanning Tree and Shortest Path Tree topology reconfiguration to minimize critical load downtime. Additionally, AI agents are introduced to assist decision-making processes. By utilizing machine learning and training in thousands of offline scenarios, the system can be used for real-time calculation of the optimal recovery paths when given the number and location of electrical faults. The IEEE 70-node distribution system case validated the high prediction accuracy and post-disaster topology reconfiguration while considering the interdependencies.

Coordinated restoration of interdependent critical infrastructures: A novel distributed decision-making mechanism integrating optimization and reinforcement learning

The proper functioning of any society heavily depends on its critical infrastructures (CIs), such as power grids, road networks, and water and waste-water systems. These infrastructures consist of facilities spread across a community to provide essential services to its residents. Their spatial expansion and functional interdependencies make them highly vulnerable against natural/manmade disasters. Functional interdependencies mean that the functionality of components in one CI relies on the services provided by others. These features, combined with decentralized decision-making structure of CIs and the stochastic nature of post-disaster environments, highly complicate the optimization process for restoring CIs damaged in disasters. Optimizing CI restorations is critical to maximizing the post-disaster resilience of communities.In this paper, we integrate and leverage Reinforcement Learning (RL) and optimization strengths to design a novel distributed modeling and solution approach for advancing the restoration process for interdependent CIs after disasters. The proposed approach (1) bridges the gap between integrative and distinct decision-making, enabling coordinated restoration planning for CIs within a decentralized decision-making context; (2) handles post-disaster uncertainties (e.g., uncertainty in recovery times of disrupted components); (3) generates adaptive solutions that cope with post-disaster dynamics (e.g., varying numbers of recovery teams); and (4) is flexible enough to handle several restoration decisions (e.g., restoration scheduling and resource allocation) simultaneously.To evaluate its performance, we focus on restoring the road and power CIs in Sioux Falls, South Dakota, disrupted by several tornado scenarios. The numerical results show that coordinated policies in the restoration process of interdependent CIs consistently yield higher service for the community. The overperformance of the coordinated restoration policies can be as high as 27.9 %. The impact of coordination is more significant in severe disasters with higher disruptions and in the absence of efficient recovery resources.

Post-Disaster Performance and Restoration Sequences of Interdependent Critical Infrastructure Systems Considering Various Socioeconomic Impacts

Post-Disaster Performance and Restoration Sequences of Interdependent Critical Infrastructure Systems Considering Various Socioeconomic Impacts

Logical interdependencies in infrastructure: What are they, how to identify them, and what do they mean for infrastructure risk analysis?

A useful theoretical lens that has emerged for understanding urban resilience is the four basic types of interdependencies in critical infrastructures: the physical, geographic, cyber, and logical types. This paper is motivated by a conceptual and methodological limitation-although logical interdependencies (where two infrastructures affect the state of each other via human decisions) are regarded as one of the basic types of interdependencies, the question of how to apply the notion and how to quantify logical relations remains under-explored. To overcome this limitation, this study focuses on institutions (rules), for example, rules and planned tasks guiding human interactions with one another and infrastructure. Such rule-mediated interactions, when linguistically expressed, have a syntactic form that can be translated into a network form. We provide a foundation to delineate these two forms to detect logical interdependence. Specifically, we propose an approach to quantify logical interdependence based on the idea that (1) there are certain network motifs indicating logical relations, (2) such network motifs can be discerned from the network form of rules, and that (3) the higher the frequency of these motifs between two infrastructures, the greater the extent of logical interdependency. We develop a set of such motifs and illustrate their usage using an example. We conclude by suggesting a revision to the original definition of logical interdependence. This rule-focused approach is relevant to understanding human error in risk analysis of socio-technical systems, as human error can be seen as deviations from constraints that lead to accidents.

Flood risk assessments—Exploring maturity and challenges in Sweden

AbstractFloods are destructive to society. Hence, to assess and manage flood risk is imperative. Flood risk assessments require several layers of analysis, from flood hazard to societal impact. This paper explores the maturity level and challenges related to three components of a comprehensive risk assessment: flood hazard, direct consequences, and indirect consequences. This is achieved by introducing and applying a maturity framework on flood hazard reports and flood risk management plans in Sweden, as required by the EU Floods Directive (FD). A longitudinal analysis is conducted over two FD cycles (2009–2015 and 2016–2021). Complementary interviews with county administrative boards and responsible authorities provide deeper insights into processes and practical challenges. The results reveal that the maturity level of flood hazard assessment is high, while direct and, in particular, indirect consequences assessments need increased attention and substantial improvements. Further, there is no significant increase in maturity between the FD cycles, indicating fundamental challenges towards achieving this. Critical steps forward include developing applicable methods for analysing both direct and indirect consequences of floods, improving data availability on functionality and interdependency of critical infrastructure and society at large, and creating incentives for a broader range of societal actors to participate in flood risk management.

Agent-based modelling of high-speed railway interdependent critical infrastructures facing physical and cyber threats

Globally, high-speed rail systems serve nearly 2 billion passenger-km daily. By virtue, they are a critical infrastructure like telecommunication and power networks. Accordingly, they become a catalyst for societal and economic growth stemming from the mobility business. The highspeed rail operations are very complex and interdependent, owing to the escalated demands for long-distance interconnected transportation. In recent years, there have been unreasonable delays for passengers as a new norm due to unfortunate train cancellations and relaxation of mobility performance requirements. Therefore, accurate measurements, monitoring and prediction of disruptive impacts and service performance metrices are indispensable. Within the scope of high-speed rail services, this paper examines how agent-based and multi-agent-based models are utilized to address such the challenges. Our findings reveal that the current use of agents or multi-agent models has some limitations for practical applications. Previous studies showed that mathematical methods to assess the resilience of critical infrastructures, railway scheduling, and vehicle dispatching can yield more satisfactory outcomes, although the approaches can be relatively time-consuming. In contrast, agent-based and multi-agent-based models can shorten processing time and uncover disruptive events more promptly. The paper thus showcases several emerging concepts, including i) the utilization of big data for crisis management, ii) interconnectivity analysis of high-speed rail infrastructures, and iii) enhancement of transport resilience. In addition, our findings identify the most influential agents and their possible applications to enhance systems resilience of highspeed rail networks when dealing with unforeseen physical and cyber threats.

Approaching the challenge of multi-phase, multi-hazard volcanic impact assessment through the lens of systemic risk: application to Taranaki Mounga

Effective volcanic impact and risk assessment underpins effective volcanic disaster risk management. Yet contemporary volcanic risk assessments face a number of challenges, including delineating hazard and impact sequences, and identifying and quantifying systemic risks. A more holistic approach to impact assessment is required, which incorporates the complex, multi-hazard nature of volcanic eruptions and the dynamic nature of vulnerability before, during and after a volcanic event. Addressing this need requires a multidisciplinary, integrated approach, involving scientists and stakeholders to co-develop decision-support tools that are scientifically credible and operationally relevant to provide a foundation for robust, evidence-based risk reduction decisions. This study presents a dynamic, longitudinal impact assessment framework for multi-phase, multi-hazard volcanic events and applies the framework to interdependent critical infrastructure networks in the Taranaki region of Aotearoa New Zealand, where Taranaki Mounga volcano has a high likelihood of producing a multi-phase explosive eruption within the next 50 years. In the framework, multi-phase scenarios temporally alternate multi-hazard footprints with risk reduction opportunities. Thus, direct and cascading impacts and any risk management actions carry through to the next phase of activity. The framework forms a testbed for more targeted mitigation and response planning and allows the investigation of optimal intervention timing for mitigation strategies during an evolving eruption. Using ‘risk management’ scenarios, we find the timing of mitigation intervention to be crucial in reducing disaster losses associated with volcanic activity. This is particularly apparent in indirect, systemic losses that cascade from direct damage to infrastructure assets. This novel, dynamic impact assessment approach addresses the increasing end-user need for impact-based decision-support tools that inform robust response and resilience planning.

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.

Cyber–Physical–Social Model of Community Resilience by Considering Critical Infrastructure Interdependencies

Each year, several disasters occur, resulting in enormous human, infrastructural, and economic losses. To minimize losses and ensure an adequate emergency response, it is vital to prepare the community for greater shock absorption and recovery after an occurrence. This raises the concept of community resilience and also demands appropriate metrics and prediction models for improved preparedness and adaptability. While a community is impacted in three main ways during a disaster-namely social, physical, and cyber-there are currently no tools to model their interrelationship. Thus, this paper presents a multi-agent cyber-physical-social model of community resilience, taking into account the interconnection of power systems, emergency services, social communities, and cyberspace. To validate the model, we used data on two hurricanes (Irma and Harvey) collected from Twitter, GoogleTrends, FEMA, power utilities, CNN, and Snopes (a fact-checking organization). We also describe methods for quantifying social metrics such as the level of anxiety, risk perception, and cooperation using social sensing, natural language processing, and text mining tools. We examine the suggested paradigm through three different case studies: 1) hurricanes Irma and Harvey; 2) a group of nine agents; and 3) a society comprised of six distinct communities. According to the results, cooperation can positively change individual behavior. Relationships within a community are so crucial that a smaller population with greater empathy may be more resilient. Similar dynamic changes in social characteristics occur when two empathetic communities share resources after a disaster.

Evaluation of network expansion decisions for resilient interdependent critical infrastructures with different topologies

Resilient interdependent critical infrastructures (CIs) can better withstand cascading failures in disruptive events. This study proposes network expansion as a resilience improvement strategy for interdependent CIs and evaluates the influence of topology in interdependent network design for resilience optimization under disruption uncertainty. A resilience score consisting of network complexity and unmet demand metrics is introduced to quantify the resilience of expanded networks. Five synthetic interdependent network instances with random and hub-and-spoke (i.e., cluster) topologies are generated to represent CIs with heterogeneous node functions. Different network expansion opportunities are considered and critical node disruption scenarios are used to evaluate the impact of uncertain disruptions. We apply a two-stage stochastic multi-objective resilience optimization model to determine strategic investment decisions using the expected total cost and expected resilience score as competing objectives. Compromise solutions of expanded network designs are identified from Pareto optimal solutions and they are characterized according to their graph properties. The results show that expanded networks have improved resilience and the extent of improvement is affected by the network topology and type of disruption. Under critical node disruptions, a random network is more resilient than a hub-and-spoke structure due to its better connectivity. Characteristics of highly connected interdependent networks are high average node degree, high clustering coefficient, and low average shortest path length. Resilience improvement is more limited in expanded networks with a hub-and-spoke structure due to the negative impact of hub failures.

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.

Empirical patterns of interdependencies among critical infrastructures in cascading disasters: Evidence from a comprehensive multi-case analysis

Critical infrastructures (CIs), such as electric power, transportation, and energy systems, offer essential goods and services to maintain societal health and well-being. Any interruptions in their functioning will result in catastrophic consequences. Besides, due to their growing interdependencies, a local failure can spread across CI networks, exacerbating the vulnerability of the whole system. This study aims to investigate the interdependency patterns of CIs from a comprehensive and holistic perspective. The interdependency natures, including cascading paths, interdependency types, and degrees were analyzed through processing data from 12 typical cascading disaster cases covering various disaster categories. Furthermore, the criticality of the recovery time for CIs was also discussed. A comparative analysis with an existing database was conducted to validate the applicability of the data used in this research. The results reveal that first, almost all cascading effects across CIs propagate within the first and second-order ranges, accounting for 80.4% and 17.9%, respectively. In a few cases, positive feedback loop events with a self-reinforcing effect may also occur. Second, physical interdependency is the most common type among CIs, followed by geographic, and more than 79% of interaction behaviors among CIs are highly interdependent. The same CI pairs can have different interdependency types and degrees depending on specific disruption scenarios and temporal or spatial locations of the CI components being affected. Third, CI failure durations are the key factor in determining interdependency impacts. Maintaining moderate redundancy in CIs and improving their recovery performances can significantly mitigate these impacts. This study extends existing research and enriches the knowledge system of CI interdependencies, its findings can help inform decision-makers on improving the preparedness level of CIs and mitigating interdependency impacts.

Measuring Resilience in Smart Infrastructures: A Comprehensive Review of Metrics and Methods

In today’s world, the safety, economic prosperity, and social well-being of nations depend heavily on highly interconnected critical infrastructures. These infrastructures encompass power networks, natural gas systems, communication networks, water treatment facilities, and transportation systems. Gaining insight into the behavior of these infrastructures, particularly during stress or attacks, has become crucial for both the private and public sectors. Ensuring an adequate level of functionality during emergencies, such as disasters, is also a priority, which can be attained by enhancing infrastructure resilience. Resilience metrics and models play a significant role in understanding the complex interplay between the behaviors and operational characteristics of interdependent critical infrastructures. Additionally, these models and metrics must demonstrate the interdependencies among infrastructures to provide a more comprehensive representation of infrastructure resilience. This paper reviews, categorizes, and presents resilience metrics and models for Smart Interdependent Critical Infrastructures (Smart ICIs). This paper provides a comprehensive evaluation of various resilience models and measurements tailored specifically for interdependent critical smart infrastructures. It includes the essential terminology and definitions related to the resilience of Smart ICIs, investigates the universally recognized phases and capabilities of resilience, and examines the various types of failures that could potentially affect Smart ICIs.

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.

A two-stage stochastic multi-objective resilience optimization model for network expansion of interdependent power–water networks under disruption

Network expansion of interdependent critical infrastructures under disruption uncertainty is modeled as a mixed-integer two-stage stochastic multi-objective optimization program. In this model, expected total cost and expected post-disaster resilience are considered competing objectives. Network resilience is quantified through network complexity and unmet demand. Functional relationships between critical infrastructures are modeled using a network-based approach with the physical interdependency enforced through demand constraints. Uncertainty is introduced as a set of random parameters corresponding to disruption scenarios. The proposed model is demonstrated in a case study of coupled power–water networks with the power flow in the grid modeled using linear DC power flow approximation equations. The deterministic-equivalent multi-objective model is solved using the augmented ϵ-constraint method. Solutions from stochastic and deterministic models are compared and the value of stochastic optimization is discussed.

The Research On Statistical Identification of Critical Infrastructure Chain Failure Paths

Using literature studies, case studies, empirical induction and statistical identification, we proposed an analytical framework based on empirical induction to identify and analyze chain failure problems among interdependent critical infrastructures. A case database of critical infrastructure chain failures was established, and as the research emphasis the identification method of critical infrastructure chain failure paths was constructed and designed. The chain failure paths of the critical infrastructure and its sub-networks in China and other countries and regions were discussed. Logical relationships of critical infrastructure chain failures were extracted from the established database covering 229 critical infrastructure chain failure cases. Statistics, identification, and analysis of data were conducted to form a quantitative assessment and understanding of the vulnerability of interdependent critical infrastructure networks. Finally, identification methods and probabilistic statistical methods for chain failure paths of critical infrastructure were designed and applied to the case base of this paper. Using this approach, fatal and severe failure paths of critical infrastructure can be identified and the efficiency of the relevant authorities' inspection work will be improved under cost constraints.

Full-time scale resilience enhancement strategies for an integrated electricity-gas-transportation coupled system

The frequent occurrence of extreme disaster events with low probability and high risk poses a serious threat to the safe and stable operation of power grids. With the development of energy internet technology, the deep integration of power distribution, natural gas and transportation networks has opened a promising way for resilient distribution networks. This paper proposes a full-time scale resilience enhancement framework for an integrated electricity-gas-transportation coupled system oriented to energy internet. In this framework, a minimax regret robust optimization model considering the uncertainty of extreme disaster attacks is developed to make the line hardening strategy in the pre-disaster stage, which is solved by the column-and-constraint generation algorithm. Furthermore, the synthetic restoration model considering the co-optimization of dynamic dispatch of gas-fired units, network reconfiguration and damaged component repair is established, in which the coupling influence of the gas and transportation networks on the restoration strategy during the disaster and post disaster stages is taken into account. And the sequential cone programming method and generalized Benders decomposition algorithm is implemented to solve the above model. Finally, the proposed model and solution method are validated via a modified 33-bus power distribution network coupled with a 12-node transportation network and a 7-node gas network. The simulation results demonstrate that the proposed minmax regret robust optimization model can achieve a pre-disaster defensive strategy with less conservativeness in contrast to the conventional robust optimization model. Specifically, the cost under the worst-case attacks by the former has reduced by 11.52–22.29% compared to the latter. Moreover, the influence of the interdependent critical infrastructure networks on the synthetic restoration strategy in the disaster and post-disaster stages has been investigated. In particular, the power output by gas-fired units has reduced by 2699 kW when considering the operation constraints of the gas network. Additionally, due to the time-varying characteristics of traffic flow and commuting time in the transportation network, the repair time for the damaged components is shortened by 1.09 h while the preparation time of crews changes from 2.5 to 1.5 h.

A framework to evaluate systemic risks of inland waterway infrastructure

PurposeThis paper aims to enable the evaluation of systemic risks resulting from missing or misallocated repair measures of inland waterways infrastructure. In this context, cascading effects and risks arising from interdependent Critical Infrastructures (CIs) are of particular interest. The systemic risk assessment is implemented as a GIS-based tool to support decision makers in a risk-based maintenance strategy. MethodologyA framework based on a chain of interdependent risks of different levels of the system represents the base model. The interlinkages of industries are quantified by Input-Output-Modeling and the spatial dimension is implemented as a GIS-based decision tool. FindingsFrom an analytical perspective, the close interconnection of the systems' levels (subsystems) under consideration can be traced. The results highlight critical buildings leading to potentially serious impacts on industry and population if the infrastructure elements are not maintained. Research limitationsThis research is focused on the framework and impacts on interdependent CIs, while work on the vulnerability of constructions and population protection, which complements our approach, is explored in more depth elsewhere. Practical implicationsMaintenance of infrastructure elements should be more risk-based than time-oriented to avoid potential damage and reduce impacts. OriginalityWe examine the interconnected subsystems construction, industry and population in an aggregated risk framework to quantify risks stemming from complex infrastructure interdependencies with waterways as rarely explored infrastructure in this context. The implementation of a decision support tool for infrastructure operators as risk dashboard enables the integration of the approach into everyday infrastructure risk management.

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.

A resilience systemic model for assessing critical supply chain disruptions

AbstractAssessing the impact of natural disasters and manmade incidents against critical infrastructure systems is important and challenging. As critical infrastructure sectors become more interdependent, a method is needed to assess how disruptions to one sector affect other sectors. This need is particularly significant in the case of critical national supply chains which have not received the same attention as other critical infrastructure systems. This research develops a methodology for performing supply chain impact assessments by integrating model‐based systems engineering (MBSE) with discrete event simulation (DES). SysML models capture the supply chain structure and critical infrastructure interdependencies. Simulation of the supply chain model subjected to external constraints identifies the impacts from critical infrastructure disruptions. This approach enables researchers and public policy planners to assess critical supply chain risks associated with current infrastructure implementations, and to conduct what‐if analyses on alternative solutions. Application of this methodology to assess the natural disaster impacts on an urban food supply system demonstrates the effectiveness of modeling and simulation for evaluating the impacts of critical infrastructure disruptions on a wide range of critical supply chains. Future research should apply this methodology to different national supply chains and investigate additional critical infrastructure interdependency linkage mechanisms.