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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
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document
Integrated probabilistic modelling of pitting and corrosion fatigue damage of subsea pipelines
Abstract
Degradation of subsea pipelines in the presence of corrosive agents and cyclic loads may lead to the failure of these structures. In order to improve their reliability, the deterioration process through pitting and corrosion-fatigue phenomena should be considered simultaneously for prognosis. This process that starts with pitting nucleation, transits to fatigue damage and leads to fracture, is influenced by many factors such as material and process conditions, each incorporating a high level of uncertainty. This study proposes a novel probabilistic methodology for integrated modelling of pitting and corrosion-fatigue degradation processes of subsea pipelines. The entire process is modelled using a Dynamic Bayesian Network (DBN) methodology, representing its temporal nature and varying growth rates. The model also takes into account the factors influencing each stage of the processes. To demonstrate its application, the methodology is applied to estimate the remaining useful life of high strength steel pipelines. This information along with Bayesian updating based on monitoring results can be adopted for the development of effective maintenance strategies.