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A novel critical infrastructure resilience assessment approach using dynamic Bayesian networks

  • Baoping Cai*
  • , Min Xie
  • , Yonghong Liu
  • , Yiliu Liu
  • , Renjie Ji
  • , Qiang Feng
  • *Corresponding author for this work
  • China University of Petroleum (East China)
  • City University of Hong Kong
  • Norwegian University of Science and Technology
  • Hong Kong Polytechnic University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The word resilience originally originates from the Latin word "resiliere", which means to "bounce back". The concept has been used in various fields, such as ecology, economics, psychology, and society, with different definitions. In the field of critical infrastructure, although some resilience metrics are proposed, they are totally different from each other, which are determined by the performances of the objects of evaluation. Here we bridge the gap by developing a universal critical infrastructure resilience metric from the perspective of reliability engineering. A dynamic Bayesian networks-based assessment approach is proposed to calculate the resilience value. A series, parallel and voting system is used to demonstrate the application of the developed resilience metric and assessment approach.

Original languageEnglish
Title of host publication2nd International Conference on Materials Science, Resource and Environmental Engineering, MSREE 2017
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735415683
DOIs
StatePublished - 5 Oct 2017
Event2nd International Conference on Materials Science, Resource and Environmental Engineering, MSREE 2017 - Hubei Province, China
Duration: 27 Oct 2017 → 29 Oct 2017

Publication series

NameAIP Conference Proceedings
Volume1890
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2nd International Conference on Materials Science, Resource and Environmental Engineering, MSREE 2017
Country/TerritoryChina
CityHubei Province
Period27/10/17 → 29/10/17

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