Pitting degradation modelling of ocean steel structures using Bayesian network
Modelling depth of long-term pitting corrosion is of interest for engineers in predicting the structural longevity of ocean infrastructures. Conventional models demonstrate poor quality in predicting the long-term pitting corrosion depth. Recently developed phenomenological models provide a strong u...
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American Society for Mechanical Engineers
2017
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Online Access: | https://eprints.utas.edu.au/44647/ https://doi.org/10.1115/1.4036832 |
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ftunivtasmania:oai:eprints.utas.edu.au:44647 2023-05-15T14:26:40+02:00 Pitting degradation modelling of ocean steel structures using Bayesian network Bhandari, J Khan, F Abbassi, R Garaniya, V Ojeda, R 2017 https://eprints.utas.edu.au/44647/ https://doi.org/10.1115/1.4036832 unknown American Society for Mechanical Engineers Bhandari, J, Khan, F orcid:0000-0002-5638-4299 , Abbassi, R orcid:0000-0002-9230-6175 , Garaniya, V orcid:0000-0002-0090-147X and Ojeda, R orcid:0000-0002-2421-0032 2017 , 'Pitting degradation modelling of ocean steel structures using Bayesian network' , Journal of Offshore Mechanics and Arctic Engineering, vol. 139, no. 5 , 051402-051402-11 , doi:10.1115/1.4036832 <http://dx.doi.org/10.1115/1.4036832>. offshore structures pitting corrosion pit depth Bayesian network phenomenological model Article PeerReviewed 2017 ftunivtasmania https://doi.org/10.1115/1.4036832 2022-02-28T23:17:24Z Modelling depth of long-term pitting corrosion is of interest for engineers in predicting the structural longevity of ocean infrastructures. Conventional models demonstrate poor quality in predicting the long-term pitting corrosion depth. Recently developed phenomenological models provide a strong understanding of the pitting process however they have limited engineering applications. In this study, a novel probabilistic model is developed for predicting the long-term pitting corrosion depth of steel structures in marine environment using Bayesian Network. The proposed Bayesian Network model combines an understanding of corrosion phenomenological model and empirical model calibrated using real-world data. A case study, which exemplifies the application of methodology to predict the pit depth of structural steel in long-term marine environment, is presented. The result shows that the proposed methodology succeeds in predicting the time dependent, long-term anaerobic pitting corrosion depth of structural steel in different environmental and operational conditions. Article in Journal/Newspaper Arctic University of Tasmania: UTas ePrints Journal of Offshore Mechanics and Arctic Engineering 139 5 |
institution |
Open Polar |
collection |
University of Tasmania: UTas ePrints |
op_collection_id |
ftunivtasmania |
language |
unknown |
topic |
offshore structures pitting corrosion pit depth Bayesian network phenomenological model |
spellingShingle |
offshore structures pitting corrosion pit depth Bayesian network phenomenological model Bhandari, J Khan, F Abbassi, R Garaniya, V Ojeda, R Pitting degradation modelling of ocean steel structures using Bayesian network |
topic_facet |
offshore structures pitting corrosion pit depth Bayesian network phenomenological model |
description |
Modelling depth of long-term pitting corrosion is of interest for engineers in predicting the structural longevity of ocean infrastructures. Conventional models demonstrate poor quality in predicting the long-term pitting corrosion depth. Recently developed phenomenological models provide a strong understanding of the pitting process however they have limited engineering applications. In this study, a novel probabilistic model is developed for predicting the long-term pitting corrosion depth of steel structures in marine environment using Bayesian Network. The proposed Bayesian Network model combines an understanding of corrosion phenomenological model and empirical model calibrated using real-world data. A case study, which exemplifies the application of methodology to predict the pit depth of structural steel in long-term marine environment, is presented. The result shows that the proposed methodology succeeds in predicting the time dependent, long-term anaerobic pitting corrosion depth of structural steel in different environmental and operational conditions. |
format |
Article in Journal/Newspaper |
author |
Bhandari, J Khan, F Abbassi, R Garaniya, V Ojeda, R |
author_facet |
Bhandari, J Khan, F Abbassi, R Garaniya, V Ojeda, R |
author_sort |
Bhandari, J |
title |
Pitting degradation modelling of ocean steel structures using Bayesian network |
title_short |
Pitting degradation modelling of ocean steel structures using Bayesian network |
title_full |
Pitting degradation modelling of ocean steel structures using Bayesian network |
title_fullStr |
Pitting degradation modelling of ocean steel structures using Bayesian network |
title_full_unstemmed |
Pitting degradation modelling of ocean steel structures using Bayesian network |
title_sort |
pitting degradation modelling of ocean steel structures using bayesian network |
publisher |
American Society for Mechanical Engineers |
publishDate |
2017 |
url |
https://eprints.utas.edu.au/44647/ https://doi.org/10.1115/1.4036832 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
Bhandari, J, Khan, F orcid:0000-0002-5638-4299 , Abbassi, R orcid:0000-0002-9230-6175 , Garaniya, V orcid:0000-0002-0090-147X and Ojeda, R orcid:0000-0002-2421-0032 2017 , 'Pitting degradation modelling of ocean steel structures using Bayesian network' , Journal of Offshore Mechanics and Arctic Engineering, vol. 139, no. 5 , 051402-051402-11 , doi:10.1115/1.4036832 <http://dx.doi.org/10.1115/1.4036832>. |
op_doi |
https://doi.org/10.1115/1.4036832 |
container_title |
Journal of Offshore Mechanics and Arctic Engineering |
container_volume |
139 |
container_issue |
5 |
_version_ |
1766299946076602368 |