Resilience assessment of a subsea pipeline using dynamic Bayesian network

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.

Automated summary

The study introduces a probabilistic methodology to assess the resilience of subsea systems under microbiologically influenced corrosion (MIC), using a dynamic Bayesian network approach to model system resilience as a function of time. An industry-based application study on subsea pipelines demonstrates the efficiency and effectiveness of the proposed methodology in resilience assessment, helping decision-makers consider resilience in system design and operation.