Research Papers: Materials Technology

Pitting Degradation Modeling of Ocean Steel Structures Using Bayesian Network

[+] Author and Article Information
Jyoti Bhandari, Rouzbeh Abbassi, Vikram Garaniya, Roberto Ojeda

Australian Maritime College,
University of Tasmania,
Launceston TAS 7250, Australia

Faisal Khan

Centre for Risk, Integrity and
Safety Engineering (C-RISE),
Faculty of Engineering and
Applied Science,
Memorial University of Newfoundland,
St. John’s, NF A1B 3X5, Canada
e-mail: fikhan@mun.ca

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received February 9, 2016; final manuscript received April 12, 2017; published online June 9, 2017. Assoc. Editor: Lance Manuel.

J. Offshore Mech. Arct. Eng 139(5), 051402 (Jun 09, 2017) (11 pages) Paper No: OMAE-16-1018; doi: 10.1115/1.4036832 History: Received February 09, 2016; Revised April 12, 2017

Modeling 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 (BN). The proposed BN 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.

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Grahic Jump Location
Fig. 1

Schematic diagram of the theoretical nonlinear corrosion model (Adapted from Ref. [24])

Grahic Jump Location
Fig. 2

Structure of BN model (the arrow in the network represents the relationship between the nodes through the probability distributions function) [28]

Grahic Jump Location
Fig. 3

Comparison of different available probability distributions

Grahic Jump Location
Fig. 4

A probability plot based on Anderson–Darling approach to identify the best fit distributions for the environmental parameters such as salinity

Grahic Jump Location
Fig. 5

Development of a methodology for predicting the long-term time-dependent pitting corrosion depth

Grahic Jump Location
Fig. 6

Prior probability distribution of a influencing factor “temperature”

Grahic Jump Location
Fig. 7

Developed BN model to predict pitting depth constants (A&B)

Grahic Jump Location
Fig. 8

Localized corrosion data for mild steel exposed to surface seawater conditions at four different sites (the solid lines represent power law model and dotted line represents actual data)

Grahic Jump Location
Fig. 9

Posterior probability distribution for constants (A and B)

Grahic Jump Location
Fig. 10

The pitting corrosion loss model developed from Ref.[59] data, which is applied to proposed methodology



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