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.

Item Type:	Refereed Article
Keywords:	offshore structures, pitting corrosion, pit depth, Bayesian network, phenomenological model
Research Division:	Engineering
Research Group:	Environmental engineering
Research Field:	Air pollution modelling and control
Objective Division:	Mineral Resources (Excl. Energy Resources)
Objective Group:	Environmentally sustainable mineral resource activities
Objective Field:	Environmentally sustainable mineral resource activities not elsewhere classified
UTAS Author:	Bhandari, J (Mr Jyoti Bhandari)
UTAS Author:	Khan, F (Professor Faisal Khan)
UTAS Author:	Abbassi, R (Dr Rouzbeh Abbassi)
UTAS Author:	Garaniya, V (Associate Professor Vikram Garaniya)
UTAS Author:	Ojeda, R (Dr Roberto Ojeda Rabanal)
ID Code:	117171
Year Published:	2017
Web of Science® Times Cited:	18
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2017-06-01
Last Modified:	2018-04-19
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