Third-party damage is an important factor leading to subsea pipelines failure, and risk analysis an efficient approach to mitigate and control such events. However, available crisp probabilities for input events are usually limited, missing or unknown, which introduces data uncertainty. Furthermore, conventional risk analysis methods are known to have a static structure, which introduces model uncertainty. This paper presents a dynamic model for risk analysis under uncertainty and illustrates it by a case of third-party damage on subsea pipelines. Proposed model makes use of fuzzy set theory and evidence theory to handle data uncertainty, and utilizes Bayesian network (BN) to address model uncertainty. Primary accident scenario is developed by the FT-ESD approach, and it is transformed into BN to circumvent model uncertainty by relaxing the limitations of conventional methods. Expert elicitation is integrated into fuzzy set theory and evidence theory to obtain the crisp probabilities of input events in BN. Based on the model, a robust probability reasoning is conducted, through which the most probable factors contributing to the occurrence of unexpected consequence are identified. As new observations become available, potential accident probabilities are updated over time to produce a dynamic risk profile. The case study demonstrates the applicability and effectiveness of the model, which indicates that it is an alternative approach for risk analysis in the process industries under uncertainty.
Journal of Loss Prevention in the Process Industries – Elsevier
Published: Jul 1, 2018
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