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 language | English |
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Article number | 100053 |
Journal | Journal of Pipeline Science and Engineering |
Volume | 2 |
Issue number | 2 |
Early online date | 22 Mar 2022 |
DOIs | |
Publication status | Published - 30 Jun 2022 |
Keywords
- pipeline
- offshore
- Bayesian network
- engineering resilience
- MIC
- subsea system