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Kakaie, A, Guedes Soares, C, Ariffin, AK and Punurai, W (2023) Fatigue Reliability Analysis of Submarine Pipelines Using the Bayesian Approach. Journal of Marine Science and Engineering, 11 (3). p. 580.
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Fatigue Reliability Analysis of Submarine Pipelines Using the Bayesian Approach.pdf - Published Version Available under License Creative Commons Attribution. Download (4MB) | Preview |
Abstract
A fracture mechanics-based fatigue reliability analysis of a submarine pipeline is investigated using the Bayesian approach. The proposed framework enables the estimation of the reliability level of submarine pipelines based on limited experimental data. Bayesian updating method and Markov Chain Monte Carlo simulation are used to estimate the posterior distribution of the parameters of a fracture mechanics-based fatigue model regarding different sources of uncertainties. Failure load cycle distribution and the reliability-based performance assessment of API 5L X56 submarine pipelines as a case study are estimated for three different cases. In addition, the impact of different parameters, including the stress ratio, maximum load, uncertainties of stress range and initial crack size, corrosion-enhanced factor, and also the correlation between material parameters on the reliability of the investigated submarine pipeline has been indicated through a sensitivity study. The applied approach in this study may be used for uncertainty modelling and fatigue reliability-based performance assessment of different types of submarine pipelines for maintenance and periodic inspection planning.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 0405 Oceanography; 0704 Fisheries Sciences; 0911 Maritime Engineering |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TC Hydraulic engineering. Ocean engineering |
| Divisions: | Engineering |
| Publisher: | MDPI AG |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 19 May 2023 12:18 |
| Last Modified: | 19 May 2023 12:18 |
| DOI or ID number: | 10.3390/jmse11030580 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/19539 |
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