Challenges in the vulnerability and risk analysis of critical infrastructures

Zio, Enrico

doi:10.1016/j.ress.2016.02.009

Cited by 315 publications

(207 citation statements)

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“…Woodward et al [19] designed the flood risk strategies to allow for flexible adaptive measures by capturing the concepts of real options and multiobjective optimization to evaluate potential flood risk management opportunities. Zio [20] examined a framework of vulnerability and risk analysis in relation to its application for the protection and resilience of critical infrastructure. Liu et al [21] presented a risk analysis of derailment rates on Class I railroad mainlines based on data from the US Federal Railroad Administration and the major freight railroads.…”

Section: Literature Reviewmentioning

confidence: 99%

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Yang

Gao

et al. 2017

Journal of Advanced Transportation

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Traditional models of timetable generation for last trains do not account for the fact that decision-maker (DM) often incorporates transfer demand variability within his/her decision-making process. This study aims to develop such a model with particular consideration of the decision-makers' risk preferences in subway systems under uncertainty. First, we formulate an optimization model for last-train timetabling based on mean-variance (MV) theory that explicitly considers two significant factors including the number of successful transfer passengers and the running time of last trains. Then, we add the mean-variance risk measure into the model to generate timetables by adjusting the last trains' departure times and running times for each line. Furthermore, we normalize two heterogeneous terms of the risk measure to provide assistance in getting reasonable results. Due to the complexity of MV model, we design a tabu search (TS) algorithm with specifically designed operators to solve the proposed timetabling problem. Through computational experiments involving the Beijing subway system, we demonstrate the computational efficiency of the proposed MV model and the heuristic approach.

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Section: Literature Reviewmentioning

confidence: 99%

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Yang

Gao

et al. 2017

Journal of Advanced Transportation

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“…These are organised in networks of assets which facilitate the flow of resources and the delivery 21 of services (Zio 2016). To study the emerging behaviour of infrastructure systems, the graph-theoretic 22 techniques, originally developed in the field of statistical mechanics (Barabasi & Albert 2002), are being used 23 increasingly.…”

Section: Introduction 19mentioning

confidence: 99%

Community Detection in Action: Identification of Critical Elements in Infrastructure Networks

Galvan

Agarwal

2018

J. Infrastruct. Syst.

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“…For example, unexpected events occurring in critical infrastructures, such as power distribution networks, can propagate their disrupting consequences to other connected systems and lead to cascading effects with huge economical and safety impacts (Kröger & Zio, 2011;Vaiman et al, 2012;Zio, 2016a). However, such consequences can be strongly mitigated if those events are -to some extent -known in advance (PateCornell, 2002;Paté-Cornell, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…(Santner et al, 2003;Simpson et al, 2001). Particular interest is in the identification of those factors, parameters and variables that can lead the system (actually the model that represents it) to critical conditions (Bier et al, 1999;Zio, 2016a).…”

Section: Introductionmentioning

confidence: 99%

Simulation-based exploration of high-dimensional system models for identifying unexpected events

Turati

Pedroni

Zio

2017

Reliability Engineering & System Safety

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. Simulation-based exploration of highdimensional system models for identifying unexpected events. Reliability Engineering and System Safety.http://dx.doi.org/10.1016/j.ress.2017.04.004Simulation-based exploration of high-dimensional system models for identifying unexpected events AbstractMathematical numerical models are increasingly employed to simulate system behavior and identify sequences of events or configurations of the system's design and operational parameters that can lead the system to extreme conditions (Critical Region, CR). However, when a numerical model is: i) computationally expensive, ii) high-dimensional, and iii) complex, these tasks become challenging.In this paper, we propose an adaptive framework for efficiently tackling this problem: i) a dimensionality reduction technique is employed for identifying the factors and variables that most affect the system behavior; ii) a meta-model is sequentially trained to replace the computationally expensive model with a computationally cheap one; iii) an adaptive exploration algorithm based on Markov Chain Monte Carlo is introduced for exploring the system state space using the meta-model; iv) clustering and other techniques for the visualization of high dimensional data (e.g., parallel coordinates plot) are employed to summarize the retrieved information.The method is employed to explore a power network model involving 20 inputs. The CRs are properly identified with a limited computational cost, compared to another exploration technique of literature (i.e., Latin Hypercube Sampling).

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Challenges in the vulnerability and risk analysis of critical infrastructures

Cited by 315 publications

References 100 publications

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Community Detection in Action: Identification of Critical Elements in Infrastructure Networks

Simulation-based exploration of high-dimensional system models for identifying unexpected events

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