Stochastic modelling of the effects of interdependencies between critical infrastructure

An approach to Quantitative Interdependency Analysis, in the context of Large Complex Critical Infrastructures, is presented in this paper. A Discrete state–space, Continuous–time, Stochastic Process models the operation of critical infrastructure, taking interdependencies into account. Of primary interest are the implications of both model detail (that is, level of model abstraction) and model parameterisation for the study of dependencies. Both of these factors are observed to affect the distribution of cascade–sizes within and across infrastructure

Integrated platform to assess seismic resilience at the community level

Due to the increasing frequency of disastrous events, the challenge of creating large-scale simulation models has become of major significance. Indeed, several simulation strategies and methodologies have been recently developed to explore the response of communities to natural disasters. Such models can support decision-makers during emergency operations allowing to create a global view of the emergency identifying consequences. An integrated platform that implements a community hybrid model with real-time simulation capabilities is presented in this paper. The platform's goal is to assess seismic resilience and vulnerability of critical infrastructures (e.g., built environment, power grid, socio-technical network) at the urban level, taking into account their interdependencies. Finally, different seismic scenarios have been applied to a large-scale virtual city model. The platform proved to be effective to analyze the emergency and could be used to implement countermeasures that improve community response and overall resilience

Preliminary Interdependency Analysis: An Approach to Support Critical Infrastructure Risk Assessment

We present a methodology, Preliminary Interdependency Analysis (PIA), for analysing interdependencies between critical infrastructure (CI). Consisting of two phases – qualitative analysis followed by quantitative analysis – an application of PIA progresses from a relatively quick elicitation of CI-interdependencies to the building of representative CI models, and the subsequent estimation of any resilience, risk or criticality measures an assessor might be interested in. By design, stages in the methodology are both flexible and iterative, resulting in interacting CI models that are scalable and may vary significantly in complexity and fidelity, depending on the needs and requirements of an assessor. For model parameterisation, one relies on a combination of field data, sensitivity analysis and expert judgement. Facilitated by dedicated software tool support, we illustrate PIA by applying it to a complex case-study of interacting Power (distribution and transmission) and Telecommunications networks in the Rome area. A number of studies are carried out, including: 1) an investigation of how “strength of dependence” between the CIs’ components affects various measures of risk and uncertainty, 2) for resource allocation, an exploration of different, but related, notions of CI component importance, and 3) highlighting the impact of model fidelity on the estimated risk of cascades

Disaster management in smart cities

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.info:eu-repo/semantics/publishedVersio

Reliability of Critical Infrastructure Networks: Challenges

Critical infrastructures form a technological skeleton of our world by providing us with water, food, electricity, gas, transportation, communication, banking, and finance. Moreover, as urban population increases, the role of infrastructures become more vital. In this paper, we adopt a network perspective and discuss the ever growing need for fundamental interdisciplinary study of critical infrastructure networks, efficient methods for estimating their reliability, and cost-effective strategies for enhancing their resiliency. We also highlight some of the main challenges arising on this way, including cascading failures, feedback loops, and cross-sector interdependencies.Comment: 12 pages, 3 figures, submitted for publication in the ASCE (American Society of Civil Engineers) technical repor

Foundations of Infrastructure CPS

Infrastructures have been around as long as urban centers, supporting a society’s needs for its planning, operation, and safety. As we move deeper into the 21st century, these infrastructures are becoming smart – they monitor themselves, communicate, and most importantly self-govern, which we denote as Infrastructure CPS. Cyber-physical systems are now becoming increasingly prevalent and possibly even mainstream. With the basics of CPS in place, such as stability, robustness, and reliability properties at a systems level, and hybrid, switched, and eventtriggered properties at a network level, we believe that the time is right to go to the next step, Infrastructure CPS, which forms the focus of the proposed tutorial. We discuss three different foundations, (i) Human Empowerment, (ii) Transactive Control, and (iii) Resilience. This will be followed by two examples, one on the nexus between power and communication infrastructure, and the other between natural gas and electricity, both of which have been investigated extensively of late, and are emerging to be apt illustrations of Infrastructure CPS