Dynamic reliability model for subsea pipeline risk assessment due to third-party interference

Reza Aulia, Henry Tan, Srinivas Sriramula* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
7 Downloads (Pure)

Abstract

The accidents of subsea pipelines due to third-party interference often result in catastrophic impacts, therefore, risk assessment has progressively become substantial to ensure the safety and reliability of the systems. However, the current risk analysis approaches are unable to minimize the uncertainties in the analysis due to the high demands of the qualitative inputs. The Bayesian network approach is believed to be able to provide answers to such a problem. The main advantage of this technique is that it allows the inference model and predictive analysis for constructing the current and future performance of the system based on the observed evidence. These can be achieved by introducing the subsea pipeline’s accident history and operational data in the model for developing the conditional probability distribution of each variable in the analysis. This paper proposes a dynamic reliability model for subsea pipeline risk assessment due to third-party interference based on the Bayesian approach. This technique is combined with fault tree and the finite element models for producing a reliable risk assessment framework for subsea pipelines. It is expected that the proposed model will be able to minimize the number of qualitative inputs in the analysis and also provides dynamic results for estimating the risk level of the subsea pipeline throughout its service life.
Original languageEnglish
Pages (from-to)277-289
Number of pages13
JournalJournal of Pipeline Science and Engineering
Volume1
Issue number3
Early online date6 Dec 2021
DOIs
Publication statusPublished - 2021

Bibliographical note

This research was sponsored by the Ministry of Finance of the Republic of Indonesia through the Indonesia Endowment Fund for Education (LPDP RI) (grant number: PRJ-4202 /LPDP.3/2016).

Keywords

  • Risk analysis
  • Subsea pipelines
  • Bayesian networks
  • Third-party interference
  • Dynamic performance

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