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...
Published in: | Journal of Offshore Mechanics and Arctic Engineering |
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American Society for Mechanical Engineers
2017
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Online Access: | https://doi.org/10.1115/1.4036832 http://ecite.utas.edu.au/117171 |
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ftunivtasecite:oai:ecite.utas.edu.au:117171 2023-05-15T14:25:29+02:00 Pitting degradation modelling of ocean steel structures using Bayesian network Bhandari, J Khan, F Abbassi, R Garaniya, V Ojeda, R 2017 https://doi.org/10.1115/1.4036832 http://ecite.utas.edu.au/117171 en eng American Society for Mechanical Engineers http://dx.doi.org/10.1115/1.4036832 Bhandari, J and Khan, F and Abbassi, R and Garaniya, V and Ojeda, R, Pitting degradation modelling of ocean steel structures using Bayesian network, Journal of Offshore Mechanics and Arctic Engineering, 139, (5) Article 051402. ISSN 0892-7219 (2017) [Refereed Article] http://ecite.utas.edu.au/117171 Engineering Environmental engineering Air pollution modelling and control Refereed Article PeerReviewed 2017 ftunivtasecite https://doi.org/10.1115/1.4036832 2022-11-07T23:17:14Z 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 eCite UTAS (University of Tasmania) Journal of Offshore Mechanics and Arctic Engineering 139 5 |
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Open Polar |
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eCite UTAS (University of Tasmania) |
op_collection_id |
ftunivtasecite |
language |
English |
topic |
Engineering Environmental engineering Air pollution modelling and control |
spellingShingle |
Engineering Environmental engineering Air pollution modelling and control Bhandari, J Khan, F Abbassi, R Garaniya, V Ojeda, R Pitting degradation modelling of ocean steel structures using Bayesian network |
topic_facet |
Engineering Environmental engineering Air pollution modelling and control |
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://doi.org/10.1115/1.4036832 http://ecite.utas.edu.au/117171 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
http://dx.doi.org/10.1115/1.4036832 Bhandari, J and Khan, F and Abbassi, R and Garaniya, V and Ojeda, R, Pitting degradation modelling of ocean steel structures using Bayesian network, Journal of Offshore Mechanics and Arctic Engineering, 139, (5) Article 051402. ISSN 0892-7219 (2017) [Refereed Article] http://ecite.utas.edu.au/117171 |
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_ |
1766297864372224000 |