A Dynamic-Bayesian-Networks-Based Resilience Assessment Approach of Structure Systems: Subsea Oil and Gas Pipelines as A Case Study
doi: 10.1007/s13344-020-0054-0
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Abstract: Under unanticipated natural disasters, any failure of structure components may cause the crash of an entire structure system. Resilience is an important metric for the structure system. Although many resilience metrics and assessment approaches are proposed for engineering system, they are not suitable for complex structure systems, since the failure mechanisms of them are different under the influences of natural disasters. This paper proposes a novel resilience assessment metric for structure system from a macroscopic perspective, named structure resilience, and develops a corresponding assessment approach based on remaining useful life of key components. Dynamic Bayesian networks (DBNs) and Markov are applied to establish the resilience assessment model. In the degradation process, natural degradation and accelerated degradation are modelled by using Bayesian networks, and then coupled by using DBNs. In the recovery process, the model is established by combining Markov and DBNs. Subsea oil and gas pipelines are adopted to demonstrate the application of the proposed structure metric and assessment approach.
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Figure 2. System state transition laws: (a) two-state transition; (b) three-state transition.
Figure 20. Parameters sensitivity analysis results, (a) 1st hour (b) 20th hour (c) 40th hour.
Table 1. Parameters of the earthquake model
Parameter Distribution Mean Standard deviation D0 (mm) Exponential 0.15 2 M Normal 2.5 0.2 K Lognormal 2.5 0.36 $ {\textit{λ}}$ Deterministic 0.8 − 
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Table 2. Parameters of the corrosion model
Parameter Value Probability
(%)Value Probability
(%)Value Probability
(%)t (°C) 20 30 22.5 40 25 30 U (m/s) 1 20 2 50 3 30 d (mm) 40 23.3 50.8 56.2 65 20.5
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Table 3. Parameters of the sand erosion model
Parameter Value Probability
(%)Value Probability
(%)Value Probability
(%)Vp (m/s) 1 20 2 50 3 30 $ {\textit{θ}}$ (rad) 0.5 30 0.5235 40 0.55 30
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