Resilience assessment of a subsea pipeline using dynamic Bayesian network

• Published in: Journal of Pipeline Science and Engineering Vol. 2; no. 2; p. 100053
• Main Authors: Yazdi, Mohammad, Khan, Faisal, Abbassi, Rouzbeh, Quddus, Noor
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2022; KeAi Communications Co. Ltd
• Subjects: Bayesian network; Engineering resilience; MIC; Offshore; Pipeline; Subsea system; Offshore; Subsea system; Engineering resilience; Bayesian network; MIC; Pipeline
• ISSN: 2667-1433; 2667-1433
• DOI: 10.1016/j.jpse.2022.100053

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.