0
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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Chaves, I. , and Melchers, R. , 2013, “ Long Term Localised Corrosion of Marine Steel Piling Welds,” Corros. Eng., Sci. Technol., 48(6), pp. 469–474. [CrossRef]
Saville, G. , Richardson, S. , and Barker, P. , 2004, “ Leakage in Ethylene Pipelines,” Process Saf. Environ. Prot., 82(1), pp. 61–68. [CrossRef]
Bhandari, J. , Lau, S. , Abbassi, R. , Garaniya, V. , Ojeda, R. , Lisson, D. , and Khan, F. , 2017, “ Accelerated Pitting Corrosion Test of 304 Stainless Steel Using ASTM G48; Experimental Investigation and Concomitant Challenges,” J. Loss Prev. Process Ind., 47, pp. 10–21. [CrossRef]
Stewart, M. G. , and Al-Harthy, A. , 2008, “ Pitting Corrosion and Structural Reliability of Corroding RC Structures: Experimental Data and Probabilistic Analysis,” Reliab. Eng. Syst. Saf., 93(3), pp. 373–382. [CrossRef]
Melchers, R. E. , 2014, “ Microbiological and Abiotic Processes in Modelling Longer-Term Marine Corrosion of Steel,” Bioelectrochemistry, 97, pp. 89–96. [CrossRef] [PubMed]
Abood, T. H. , 2008, “ The Influence of Various Parameters on Pitting Corrosion of 316L and 202 Stainless Steel,” Master thesis, University of Technology (UOT), Baghdad, Iraq.
Melchers, R. , 2004, “ Pitting Corrosion of Mild Steel in Marine Immersion Environment—Part 2: Variability of Maximum Pit Depth,” Corrosion, 60(10), pp. 937–944. [CrossRef]
Davydov, A. , 2008, “ Analysis of Pitting Corrosion Rate,” Russ. J. Electrochem., 44(7), pp. 835–839. [CrossRef]
Yevtushenko, O. , Bettge, D. , Bohraus, S. , Bäßler, R. , Pfennig, A. , and Kranzmann, A. , 2014, “ Corrosion Behavior of Steels for CO2 Injection,” Process Saf. Environ. Prot., 92(1), pp. 108–118. [CrossRef]
Bhandari, J. , Khan, F. , Abbassi, R. , Garaniya, V. , and Ojeda, R. , 2015, “ Modelling of Pitting Corrosion in Marine and Offshore Steel Structures: A Technical Review,” J. Loss Prev. Process Ind., 37, pp. 39–62. [CrossRef]
Bhandari, J. , Khan, F. , Abbassi, R. , Garaniya, V. , and Ojeda, R. , 2016, “ Reliability Assessment of Offshore Asset Under Pitting Corrosion Using Bayesian Network,” CORROSION, Vancouver, BC, Canada, Mar. 6–10, SPE Paper No. NACE-2016-7070.
Melchers, R. E. , and Jeffrey, R. , 2011, “ Bacteria Have Transient Influences on Marine Corrosion of Steel,” The University of Newcastle, NSW, Australia.
Guedes Soares, C. , Garbatov, Y. , and Zayed, A. , 2011, “ Effect of Environmental Factors on Steel Plate Corrosion Under Marine Immersion Conditions,” Corros. Eng., Sci. Technol., 46(4), pp. 524–541. [CrossRef]
Ha, H.-Y. , and Kwon, H.-S. , 2012, “ Effects of pH Levels on the Surface Charge and Pitting Corrosion Resistance of Fe,” J. Electrochem. Soc., 159(9), pp. C416–C421. [CrossRef]
Nešić, S. , 2007, “ Key Issues Related to Modelling of Internal Corrosion of Oil and Gas Pipelines–A Review,” Corros. Sci., 49(12), pp. 4308–4338. [CrossRef]
Melchers, R. , 2014, “ Modelling Long Term Corrosion of Steel Infrastructure in Natural Marine Environments,” Understanding Biocorrosion: Fundamentals and Applications, Vol. 66, The University of Newcastle, Callaghan, Australia, p. 213.
Szklarska-Smialowska, Z. , 1986, “ Pitting Corrosion of Metals,” National Association of Corrosion Engineers (NACE), Houston, TX.
Ryan, M. P. , Williams, D. E. , Chater, R. J. , Hutton, B. M. , and McPhail, D. S. , 2002, “ Why Stainless Steel Corrodes,” Nature, 415(6873), pp. 770–774. [CrossRef] [PubMed]
Melchers, R. E. , and Wells, T. , 2006, “ Models for the Anaerobic Phases of Marine Immersion Corrosion,” Corros. Sci., 48(7), pp. 1791–1811. [CrossRef]
Guedes Soares, C. , Garbatov, Y. , Zayed, A. , Wang, G. , Melchers, R. , and Paik, J. , 2005, “ Non-Linear Corrosion Model for Immersed Steel Plates Accounting for Environmental Factors. Discussion,” Soc. Nav. Archit. Mar. Eng., Trans., 113, pp. 306–329.
Katano, Y. , Miyata, K. , Shimizu, H. , and Isogai, T. , 2003, “ Predictive Model for Pit Growth on Underground Pipes,” Corrosion, 59(2), pp. 155–161. [CrossRef]
Aziz, P. , 1956, “ Application of the Statistical Theory of Extreme Values to the Analysis of Maximum Pit Depth Data for Aluminum,” Corrosion, 12(10), pp. 35–46. [CrossRef]
Melchers, R. E. , 2008, “ Extreme Value Statistics and Long-Term Marine Pitting Corrosion of Steel,” Probab. Eng. Mech., 23(4), pp. 482–488. [CrossRef]
Melchers, R. , and Jeffrey, R. , 2008, “ The Critical Involvement of Anaerobic Bacterial Activity in Modelling the Corrosion Behaviour of Mild Steel in Marine Environments,” Electrochim. Acta, 54(1), pp. 80–85. [CrossRef]
Melchers, R. E. , 2015, “ Using Models to Interpret Data for Monitoring and Life Prediction of Deteriorating Infrastructure Systems,” Struct. Infrastruct. Eng., 11(1), pp. 63–72. [CrossRef]
Hou, W. , and Liang, C. , 2004, “ Atmospheric Corrosion Prediction of Steels,” Corrosion, 60(3), pp. 313–322. [CrossRef]
Chaves, I. A. , and Melchers, R. E. , 2014, “ Extreme Value Analysis for Assessing Structural Reliability of Welded Offshore Steel Structures,” Struct. Saf., 50, pp. 9–15. [CrossRef]
Jain, S. , Beavers, J. A. , Ayello, F. , and Sridhar, N. , 2013, “ Probabilistic Model for Stress Corrosion Cracking of Underground Pipelines Using Bayesian Networks,” CORROSION, Orlando, FL, Mar. 17–21, SPE Paper No. NACE-2013-2616.
Melchers, R. , 2005, “ Statistical Characterization of Pitting Corrosion—Part 1: Data Analysis,” Corrosion, 61(7), pp. 655–664. [CrossRef]
Melchers, R. E. , 2012, “ Modeling and Prediction of Long-Term Corrosion of Steel in Marine Environments,” Int. J. Offshore Polar Eng., 22(4), pp. 257–263.
Melchers, R. , 2003, “ Modeling of Marine Immersion Corrosion for Mild and Low-Alloy Steels—Part 1: Phenomenological Model,” Corrosion, 59(4), pp. 319–334. [CrossRef]
Melchers, R. E. , 2006, “ Advances in Mathematical Probabilistic Modelling of the Atmospheric Corrosion of Structural Steels in Ocean Environments,” Third International ASRANet Colloquium, Glasgow, UK, June 10–12, pp. 1–12.
Caines, S. , Khan, F. , and Shirokoff, J. , 2013, “ Analysis of Pitting Corrosion on Steel Under Insulation in Marine Environments,” J. Loss Prev. Process Ind., 26(6), pp. 1466–1483. [CrossRef]
Melchers, R. , 2006, “ Examples of Mathematical Modelling of Long Term General Corrosion of Structural Steels in Sea Water,” Corros. Eng., Sci. Technol., 41(1), pp. 38–44. [CrossRef]
Melchers, R. E. , and Jeffrey, R. , 2008, “ Probabilistic Models for Steel Corrosion Loss and Pitting of Marine Infrastructure,” Reliab. Eng. Syst. Saf., 93(3), pp. 423–432. [CrossRef]
Phull, B. S. , Pikul, S. J. , and Kain, R. M. , 1997, Seawater Corrosivity Around the World: Results From Five Years of Testing, Vol. 1300, ASTM International, West Conshohocken, PA, pp. 34–73.
Nielsen, T. D. , and Jensen, F. V. , 2009, Bayesian Networks and Decision Graphs, Springer Science & Business Media, Aalborg, Denmark.
Neapolitan, R. E. , 2004, Learning Bayesian Networks, Pearson Prentice Hall, Upper Saddle River, NJ.
Pearl, J. , and Russell, S. , 1998, “ Bayesian Networks,” University of California, Los Angeles, CA.
Yang, M. , Khan, F. , and Amyotte, P. , 2015, “ Operational Risk Assessment: A Case of the Bhopal Disaster,” Process Saf. Environ. Prot., 97, pp. 70–79. [CrossRef]
Abimbola, M. , Khan, F. , Khakzad, N. , and Butt, S. , 2015, “ Safety and Risk Analysis of Managed Pressure Drilling Operation Using Bayesian Network,” Saf. Sci., 76, pp. 133–144. [CrossRef]
Bhandari, J. , Abbassi, R. , Garaniya, V. , and Khan, F. , 2015, “ Risk Analysis of Deepwater Drilling Operations Using Bayesian Network,” J. Loss Prev. Process Ind., 38, pp. 11–23. [CrossRef]
Khakzad, N. , Khan, F. , and Amyotte, P. , 2013, “ Dynamic Safety Analysis of Process Systems by Mapping Bow-Tie Into Bayesian Network,” Process Saf. Environ. Prot., 91(1), pp. 46–53. [CrossRef]
Abimbola, M. , Khan, F. , and Khakzad, N. , 2014, “ Dynamic Safety Risk Analysis of Offshore Drilling,” J. Loss Prev. Process Ind., 30, pp. 74–85. [CrossRef]
Bhandari, J. , Arzaghi, E. , Abbassi, R. , Garaniya, V. , and Khan, F. , 2016, “ Dynamic Risk-Based Maintenance for Offshore Processing Facility,” Process Saf. Prog., 35(4), pp. 399–406. [CrossRef]
Silverman, B. W. , 1986, Density Estimation for Statistics and Data Analysis, CRC Press, Boca Raton, FL.
Botev, Z. I. , Grotowski, J. F. , and Kroese, D. P. , 2010, “ Kernel Density Estimation Via Diffusion,” Ann. Stat., 38(5), pp. 2916–2957. [CrossRef]
Lehmann, E. L. , 1990, “ Model Specification: The Views of Fisher and Neyman, and Later Developments,” Stat. Sci., 5(2), pp. 160–168. [CrossRef]
Frankel, G. , and Sridhar, N. , 2008, “ Understanding Localized Corrosion,” Mater. Today, 11(10), pp. 38–44. [CrossRef]
Abdel-Ghany, R. , Saad-Eldeen, S. , and Leheta, H. , 2008, “ The Effect of Pitting Corrosion on the Strength Capacity of Steel Offshore Structures,” ASME Paper No. OMAE2008-57844.
Al-Fozan, S. A. , and Malik, A. U. , 2008, “ Effect of Seawater Level on Corrosion Behavior of Different Alloys,” Desalination, 228(1), pp. 61–67. [CrossRef]
Melchers, R. , 2002, “ Effect of Temperature on the Marine Immersion Corrosion of Carbon Steels,” Corrosion, 58(9), pp. 768–782. [CrossRef]
Melchers, R. , and Ahammed, M. , 1994, Nonlinear Modelling of Corrosion of Steel in Marine Environments, University of Newcastle, NSW, Australia.
Khan, F. I. , Haddara, M. M. , and Bhattacharya, S. K. , 2006, “ Risk-Based Integrity and Inspection Modeling (RBIIM) of Process Components/System,” Risk Anal., 26(1), pp. 203–221. [CrossRef] [PubMed]
Zhu, W. , 2008, “ An Investigation Into Reliability Based Methods to Include Risk of Failure in Life Cycle Cost Analysis of Reinforced Concrete Bridge Rehabilitation,” Master thesis, RMIT University, Victoria, Australia.
Pardo, A. , Otero, E. , Merino, M. , López, M. , Utrilla, M. , and Moreno, F. , 2000, “ Influence of pH and Chloride Concentration on the Pitting and Crevice Corrosion Behavior of High-Alloy Stainless Steels,” Corrosion, 56(4), pp. 411–418. [CrossRef]
Melchers, R. , and Jeffrey, R. , 2008, “ Modeling of Long-Term Corrosion Loss and Pitting for Chromium-Bearing and Stainless Steels in Seawater,” Corrosion, 64(2), pp. 143–154. [CrossRef]
Melchers, R. E. , 2004, “ Effect of Small Compositional Changes on Marine Immersion Corrosion of Low Alloy Steels,” Corros. Sci., 46(7), pp. 1669–1691. [CrossRef]
Southwell, C. , Forgeson, B. , and Alexander, A. , 1958, “ Corrosion of Metals in Tropical Environments—Part 2: Atmospheric Corrosion of Ten Structural Steels,” Corrosion, 14(9), pp. 55–59. [CrossRef]
Sowinski, G. , and Sprowls, D. O. , 1982, Atmospheric Corrosion, Wiley, New York, pp. 297–328.
Melchers, R. E. , 2008, “ Development of New Applied Models for Steel Corrosion in Marine Applications Including Shipping,” Ships Offshore Struct., 3(2), pp. 135–144. [CrossRef]
Aziz, P. , and Godard, H. P. , 1952, “ Pitting Corrosion Characteristics of Aluminum-Influence of Magnesium and Manganese,” Ind. Eng. Chem., 44(8), pp. 1791–1795. [CrossRef]
Moayed, M. H. , Laycock, N. , and Newman, R. , 2003, “ Dependence of the Critical Pitting Temperature on Surface Roughness,” Corros. Sci., 45(6), pp. 1203–1216. [CrossRef]

Figures

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

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In