Modeling and assessing interdependencies between critical infrastructures using Bayesian network: A case study of inland waterway port and surrounding supply chain network

Hossain, Niamat Ullah Ibne; Amrani, Safae El; Jaradat, Raed; Marufuzzaman, Mohammad; Buchanan, Randy K.; Rinaudo, Christina H.; Hamilton, Michael A.

doi:10.1016/j.ress.2020.106898

Cited by 95 publications

(30 citation statements)

References 61 publications

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“…After an extreme event, it is necessary to define an action plan to recover from its effects. For this, in case of an extreme event, researchers in [46] proposed a combination of agent-based modelling and reinforcement learning to optimize the repairs and crew location after an extreme event to mitigate some of its impacts. In [47], authors studied the interdependency between infrastructures and their surrounding supply chain to model and assess these interdependencies.…”

Section: Systematic Literature Surveymentioning

confidence: 99%

Disaster Management in Smart Cities

et al. 2021

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The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.

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Section: Systematic Literature Surveymentioning

confidence: 99%

Disaster Management in Smart Cities

et al. 2021

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“…The physical infrastructure consists of networks for transportation, electricity, water, and telecommunications [7]. The dependencies between infrastructures enhance the complexity of infrastructure resilience quantification, which can be modeled using different approaches [12][13][14].…”

Section: Socio-physical Infrastructurementioning

confidence: 99%

Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network

2021

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Resilience is the capability of a system to resist any hazard and revive to a desirable performance. The consequences of such hazards require the development of resilient infrastructure to ensure community safety and sustainability. However, resilience-based housing infrastructure design is a challenging task due to a lack of appropriate post-disaster datasets and the non-availability of resilience models for housing infrastructure. Hence, it is necessary to build a resilience model for housing infrastructure based on a realistic dataset. In this work, a Bayesian belief network (BBN) model was developed for housing infrastructure resilience. The proposed model was tested in a real community in Northeast India and the reliability, recovery, and resilience of housing infrastructure against flood hazards for that community were quantified. The required data for resilience quantification were collected by conducting a field survey and from public reports and documents. Lastly, a sensitivity analysis was performed to observe the critical parameters of the proposed BBN model, which can be used to inform designers, policymakers, and stakeholders in making resilience-based decisions.

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“…Modelling and simulation methods are highly suggested as proper tools to study CPSs. With the main goal of enhancing the resilience and security of complex systems, valuable researches have been conducted for modelling the dependencies of and in such systems; these include Complex Networks Theory/Graph Theory, Petri-Nets [ 8 ], Well-Formed Nets (SWN) [ 9 ], Input-Output Models [ 10 ], Bayesian Networks [ 11 ], Matrix representations, Boolean logic Driven Markov Processes (BDMP), Agent-Based Models and Multi-Agent Modelling [ 12 ]. Most of the aforementioned studies focus on qualitative or semi-qualitative analyses.…”

Section: Introductionmentioning

confidence: 99%

Identifying and Analyzing Dependencies in and among Complex Cyber Physical Systems

Akbarzadeh

Katsikas

2021

Sensors

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Contemporary Critical Infrastructures (CIs), such as the power grid, comprise cyber physical systems that are tightly coupled, to form a complex system of interconnected components with interacting dependencies. Modelling methodologies have been suggested as proper tools to provide better insight into the dependencies and behavioural characteristics of these complex systems. In order to facilitate the study of interconnections in and among critical infrastructures, and to provide a clear view of the interdependencies among their cyber and physical components, this paper proposes a novel method, based on a graphical model called Modified Dependency Structure Matrix (MDSM). The MDSM provides a compact perspective of both inter-dependency and intra-dependency between subsystems of one complex system or two distinct systems. Additionally, we propose four parameters that allow the quantitative assessment of the characteristics of dependencies, including multi-order dependencies in large scale CIs. We illustrate the workings of the proposed method by applying it to a micro-distribution network based on the G2ELAB 14-Bus model. The results provide valuable insight into the dependencies among the network components and substantiate the applicability of the proposed method for analyzing large scale cyber physical systems.

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Modeling and assessing interdependencies between critical infrastructures using Bayesian network: A case study of inland waterway port and surrounding supply chain network

Cited by 95 publications

References 61 publications

Disaster Management in Smart Cities

Disaster Management in Smart Cities

Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network

Identifying and Analyzing Dependencies in and among Complex Cyber Physical Systems

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