Resilience assessment of a subsea pipeline using dynamic Bayesian network

Mohammad Yazdi, Faisal Khan, Rouzbeh Abbassi, Noor Quddus

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

Microbiologically influenced corrosion (MIC) is a serious concern and plays a significant role in the marine and subsea industry’s infrastructure failure. A probabilistic methodology is introduced in the present study to assess the subsea system’s resilience under MIC. Conventionally, the risk-based models are constructed using the system’s characteristic features. This helps decision-makers understand how a system operates and how the failed system can be recovered. The subsea system needs to be designed with sufficient resilience to maintain the performance under the time-varying interdependent stochastic conditions. This paper presents the dynamic Bayesian network-based approach to model the subsea system’s resilience as a function of time. An industry-based application study of the subsea pipeline is studied to demonstrate the efficiency and effectiveness of the proposed methodology for the resilience assessment. The proposed methodology will assist decision-makers in considering the resilience in the system design and operation.
Original languageEnglish
Article number100053
JournalJournal of Pipeline Science and Engineering
Volume2
Issue number2
Early online date22 Mar 2022
DOIs
Publication statusPublished - 30 Jun 2022

Keywords

  • pipeline
  • offshore
  • Bayesian network
  • engineering resilience
  • MIC
  • subsea system

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