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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Garaniya, Vikram
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (13/13 displayed)
- 2024Classification of pitting corrosion damage in process facilities using supervised machine learningcitations
- 2022Experimental analysis of pitting corrosion in offshore structurescitations
- 2020Pitting corrosion modelling of X80 steel utilized in offshore petroleum pipelinescitations
- 2018Condition monitoring of subsea pipelines considering stress observation and structural deteriorationcitations
- 2017Modelling the impacts of fire in a typical FLNG processing facility
- 2017Modelling the impacts of fire in a typical FLNG processing facility
- 2017Accelerated pitting corrosion test of 304 stainless steel using ASTM G48; Experimental investigation and concomitant challengescitations
- 2017Integrated probabilistic modelling of pitting and corrosion fatigue damage of subsea pipelines
- 2017Pitting degradation modelling of ocean steel structures using Bayesian networkcitations
- 2016Dynamic risk-based maintenance for offshore processing facilitycitations
- 2016Reliability assessment of offshore asset under pitting corrosion using Bayesian Network
- 2016Reliability assessment of offshore asset under pitting corrosion using Bayesian Network
- 2015Modelling of pitting corrosion in marine and offshore steel structures - A technical reviewcitations
Places of action
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article
Condition monitoring of subsea pipelines considering stress observation and structural deterioration
Abstract
The increasing demand by the world for energy has prompted the development of offshore oil and gas pipelines as the mode of transportation for hydrocarbons. The maintenance of these structures has also gained much attention for research and development with novel methodologies that can increase the efficiency of integrity management. This paper presents a probabilistic methodology for monitoring the condition of offshore pipelines and predicting the reliability when consideration is given to structure deterioration. Hydrodynamic simulations are carried out for an offshore pipeline to obtain the time history data from which the stress ranges are computed using a rainflow counting algorithm. To model the fatigue damage growth, a Bayesian Network (BN) is established based on a probabilistic solution of Paris? law. Corrosion effects are also incorporated into the network providing a more realistic prediction of the degradation process. To demonstrate the application of the proposed methodology, a case study of a Steel Catenary Riser (SCR) subjected to fatigue cracks and corrosion degradation is studied. This method provided the growth rate of a crack during its lifetime during which the safety of operation can be assessed and efficient maintenance plans can be scheduled by the asset managers. The proposed method can also be applied by the designer to optimize the design of pipelines for specific environments.