An approach for modelling interdependent infrastructures in the context of vulnerability analysis

Johansson, Jonas; Hassel, Henrik

doi:10.1016/j.ress.2010.06.010

Cited by 332 publications

(180 citation statements)

References 33 publications

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“…In recent decades, research was carried out in applied science on cataloguing, analysing and modelling the interdependences in critical infrastructure as well as modelling cascading failures in coupled critical infrastructure networks 40,[42][43][44][45][46][47][48] . However, no systematic mathematical framework, such as percolation theory, is currently available for adequately addressing the consequences of disruptions and failures occurring simultaneously in interdependent critical infrastructures.…”

Section: Figure 1 | Schematic Demonstration Of First-and Second-ordermentioning

confidence: 99%

Networks formed from interdependent networks

et al. 2011

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Complex networks appear in almost every aspect of science and technology. Although most results in the field have been obtained by analysing isolated networks, many real-world networks do in fact interact with and depend on other networks. The set of extensive results for the limiting case of non-interacting networks holds only to the extent that ignoring the presence of other networks can be justified. Recently, an analytical framework for studying the percolation properties of interacting networks has been developed. Here we review this framework and the results obtained so far for connectivity properties of 'networks of networks' formed by interdependent random networks.T he interdisciplinary field of network science has attracted a great deal of attention in recent years . This development is based on the enormous number of data that are now routinely being collected, modelled and analysed, concerning social 31-39 , economic 14,36,40,41 , technological 40,42-48 and biological 9,13,49,50 systems. The investigation and growing understanding of this extraordinary volume of data will enable us to make the infrastructures we use in everyday life more efficient and more robust.The original model of networks, random graph theory, was developed in the 1960s by ErdAEs and Rényi, and is based on the assumption that every pair of nodes is randomly connected with the same probability, leading to a Poisson degree distribution. In parallel, in physics, lattice networks, where each node has exactly the same number of links, have been studied to model physical systems. Although graph theory is a well-established tool in the mathematics and computer science literature, it cannot describe well modern, real-life networks. Indeed, the pioneering 1999 observation by Barabasi 2 , that many real networks do not follow the ErdAEs-Rényi model but that organizational principles naturally arise in most systems, led to an overwhelming accumulation of supporting data, new models and computational and analytical results, and to the emergence of a new science, that of complex networks.Complex networks are usually non-homogeneous structures that in many cases obey a power-law form in their degree (that is, number of links per node) distribution. These systems are called scale-free networks. Real networks that can be approximated as scale-free networks include the Internet 3 , the World Wide Web 4 , social networks 31-39 representing the relations between individuals, infrastructure networks such as those of airlines 51 , networks in biology 9,13,49,50 , in particular networks of proteinprotein interactions 10 , gene regulation and biochemical pathways, and networks in physics, such as polymer networks or the potentialenergy-landscape network. The discovery of scale-free networks led to a re-evaluation of the basic properties of networks, such as their robustness, which exhibit a drastically different character than those of ErdAEs-Rényi networks. For example, whereas homogeneous ErdAEs-Rényi networks are extremely vulnerable to random fa...

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Section: Figure 1 | Schematic Demonstration Of First-and Second-ordermentioning

confidence: 99%

Networks formed from interdependent networks

et al. 2011

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“…In this paper, although Lafourche Parish is determined to be one of the less vulnerable areas because of its limited density of critical infrastructure, it may still indirectly affect other areas that are dependent on facilities located in that parish. Several different models were studied in past research dealing with interdependent infrastructure [24][25][26]; however, most studies were based on a single non-interacting infrastructure type. A much more complex network framework is needed to understand the interdependencies in the critical energy infrastructure locations, and any further discussion of this framework is outside the scope of this paper.…”

Section: Resultsmentioning

confidence: 99%

Identifying the Vulnerabilities of Working Coasts Supporting Critical Energy Infrastructure

Dismukes

Narra

2015

Water

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The U.S. Gulf of Mexico (GOM) is an excellent example of a working coast that supports a considerable degree of critical energy infrastructure across several sectors (crude oil, natural gas, electric power, petrochemicals) and functionalities (production, processing/refining, transmission, distribution). The coastal communities of the GOM form a highly productive and complicated human, physical, and natural environment that interacts in ways that are unlike anywhere else around the globe. This paper formulates a Coastal Infrastructure Vulnerability Index (CIVI) that characterizes interactions between energy assets and the physical and human aspects of GOM communities to identify and prioritize, using a multi-dimensional index, coastal vulnerability. The CIVI leads to results that are significantly different than traditional methods and serves as an alternative, and potentially more useful tool for coastal planning and policy, particularly in those areas characterized by very high infrastructure concentrations.

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“…In the first, vulnerability means susceptibility to injury or attack [16]. In this sense, vulnerability is the manifestation of the inherent states of the system that express the extent of adverse effects caused by disruptive events that originate both within and outside the system boundary [17][18][19][20][21]. The second definition focuses on the fact that a system component or an aspect of a system can weaken or limit the system's ability to withstand a threat or to resume a new stable condition [22][23][24].…”

Section: Vulnerability Factors Of Rail Transit Systemmentioning

confidence: 99%

“…To a certain extent, this interpretation of vulnerability is similar concept of risk, which is seen as encompassing the estimation of probabilities of risk scenarios and associated negative consequences [19]. However, rather than assessing probabilities and consequences, the focus of analyzing vulnerability is to systematically explore the effects of failures for the purpose of identifying system weakness that may be vulnerable to exploitation by unknown or unimagined threats [20,25]. From this point of view, addressing the vulnerability factors of urban rail transit system involves answering the following question: what factors have the propensity to turn into the weaknesses that resulted in the failure of the system?…”

Section: Vulnerability Factors Of Rail Transit Systemmentioning

confidence: 99%

Using an AHP-ISM Based Method to Study the Vulnerability Factors of Urban Rail Transit System

et al. 2017

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Abstract:As a sustainable means of public transportation, urban rail transit system undergoes rapid expansion in China. How to provide a safe and reliable service has been the subject of growing attention in this context. However, such work is challenging because rail transit systems are quite vulnerable and influenced by a set of interacting factors. Studying these vulnerability factors will contribute significantly to the operation of rail transit system. From this perspective, this paper made an exploration of the vulnerability factors based on an integrated method consisting of AHP (Analytical Hierarchy Process) and ISM (Interpretative Structural Modeling). Based on literature review, 21 vulnerability factors were identified. Subsequently, expert elicitation was employed to ascertain the importance of each factor and the interrelations among them. The results suggest that management and individual factors have the highest importance weights and the interrelations among vulnerability factors could be expressed as a five-layer structure, in which management factors were inclined to be at the lower level. The research provides valuable information for decision makers to take proactive strategies and reinforcement policies to guarantee safety operation of urban rail transit system and ensure urban public safety, which could promote the sustainable development of cities.

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An approach for modelling interdependent infrastructures in the context of vulnerability analysis

Cited by 332 publications

References 33 publications

Networks formed from interdependent networks

Networks formed from interdependent networks

Identifying the Vulnerabilities of Working Coasts Supporting Critical Energy Infrastructure

Using an AHP-ISM Based Method to Study the Vulnerability Factors of Urban Rail Transit System

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