0
Research Papers

Spatial Probabilistic Modeling of Corrosion in Ship Structures

[+] Author and Article Information
Jesus Luque

Engineering Risk Analysis Group,
Technische Universität München,
Theresienstr. 90,
Munich 80333, Germany
e-mail: jesus.luque@tum.de

Rainer Hamann

DNV GL,
Brooktorkai 18,
Hamburg 20457, Germany
e-mail: rainer.hamann@dnvgl.com

Daniel Straub

Engineering Risk Analysis Group,
Technische Universität München,
Theresienstr. 90,
Munich 80333, Germany
e-mail: straub@tum.de

Manuscript received August 4, 2015; final manuscript received October 12, 2016; published online June 12, 2017. Assoc. Editor: Michael Beer.

ASME J. Risk Uncertainty Part B 3(3), 031001 (Jun 12, 2017) (12 pages) Paper No: RISK-15-1088; doi: 10.1115/1.4035399 History: Received August 04, 2015; Revised October 12, 2016

Corrosion in ship structures is influenced by a variety of factors that are varying in time and space. Existing corrosion models used in practice only partially address the spatial variability of the corrosion process. Typical estimations of corrosion model parameters are based on averaging measurements for one ship type over structural elements from different ships and operational conditions. Most models do not explicitly predict the variability and correlation of the corrosion process among multiple locations in the structure. This correlation is of relevance when determining the necessary inspection coverage, and it can influence the reliability of the ship structure. In this paper, we develop a probabilistic spatiotemporal corrosion model based on a hierarchical approach, which represents the spatial variability and correlation of the corrosion process. The model includes as hierarchical levels vessel–compartment–frame–structural element–plate element. At all levels, variables representing common influencing factors (e.g., coating life) are introduced. Moreover, at the lowest level, which is the one of the plate element, the corrosion process can be modeled as a spatial random field. For illustrative purposes, the model is trained through Bayesian analysis with measurement data from a group of tankers. In this application, the spatial dependence among corrosion processes in different parts of the ships is identified and quantified using the proposed hierarchical model. Finally, how this spatial dependence can be exploited when making inference on the future condition of the ships is demonstrated.

FIGURES IN THIS ARTICLE
<>
Copyright © 2017 by ASME
Topics: Corrosion , Vessels , Ships
Your Session has timed out. Please sign back in to continue.

References

Gardiner, C. P. , and Melchers, R. E. , 2003, “ Corrosion Analysis of Bulk Carriers—Part I: Operational Parameters Influencing Corrosion Rates,” Mar. Struct., 16(8), pp. 547–566. [CrossRef]
Herzberg, E. , Chan, T. , Chang, P. , Kelly, A. , Kumaran, M. , and O'Meara, N. , 2010, “ The Annual Cost of Corrosion for Navy Ships,” Report No. MEC81T3.
Guedes Soares, C. , 1988, “ Reliability of Marine Structures,” Reliability Engineering, Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 513–559.
Shi, W. , 1993, “ In-Service Assessment of Ship Structures: Effects of General Corrosion on Ultimate Strength,” Trans. R. Inst. Naval Archit., 135, pp. 77–91.
Melchers, R. E. , 1998, “ Probabilistic Modelling of Immersion Marine Corrosion,” Structural Safety and Reliability, N. Shiraishi , M. Shinozuka , and Y. K. Wen , eds., Vol. 3, Balkema, Rotterdam, The Netherlands, pp. 1143–9.
Shreir, F. F. , 1976, Corrosion, Vol. 3, Newnes-Butterworths, London, UK.
Guedes Soares, C. , and Garbatov, Y. , 1999, “ Reliability of Maintained, Corrosion Protected Plates Subjected to Non-Linear Corrosion and Compressive Loads,” Mar. Struct., 12(6), pp. 425–445. [CrossRef]
Qin, S. , and Cui, W. , 2003, “ Effect of Corrosion Models on the Time-Dependent Reliability of Steel Plated Elements,” Mar. Struct., 16(1), pp. 15–34. [CrossRef]
Melchers, R. E. , 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. , and Jeffrey, R. , 2007, “ Probabilistic Models for Steel Corrosion Loss and Pitting of Marine Infrastructure,” Reliab. Eng. Syst. Saf., 93(3), pp. 423–432. [CrossRef]
Paik, J. K. , Thayamballi, A. K. , Park, Y. I. , and Hwang, J. S. , 2004, “ A Time-Dependent Corrosion Wastage Model for Seawater Ballast Tank Structures of Ships,” Corros. Sci., 46(2), pp. 471–486. [CrossRef]
Ayyub, B. M. , Stambaugh, K. A. , McAllister, T. A. , de Souza, G. F. , and Webb, D. , 2015, “ Structural Life Expectancy of Marine Vessels: Ultimate Strength, Corrosion, Fatigue, Fracture, and Systems,” ASME J. Risk Uncertainty Eng. Syst. Part B: Mech. Eng., 1(1), p. 011001.
Melchers, R. E. , 1999, “ Corrosion Uncertainty Modeling for Steel Structures,” Constr. Steel Res., 52(1), pp. 3–19. [CrossRef]
Southwell, C. R. , Bultman, J. D. , and Hummer, Jr., C. W. , 1979, “ Estimating of Service Life of Steel in Seawater,” Seawater Corrosion Handbook, Schumacher, M. , ed., Noyes Data Corporation, Park Ridge, NJ, pp. 374–87.
Straub, D. , and Faber, M. H. , 2007, “ Temporal Variability in Corrosion Modeling and Reliability Updating,” ASME J. Offshore Mech. Arct. Eng., 129(4), pp. 265–272. [CrossRef]
Sone, H. , Magaino, A. , Yamamoto, N. , and Harada, S. , 2003, “ Evaluation of Thickness Diminution in Steel Plates for the Assessment of Structural Condition of Ships in Service,” Nippon Kaiji Kyokai (ClassNK) Technical Bulletin, pp. 55–71.
Wang, G. , Spencer, J. , and Elsayed, T. , 2003, “ Estimation of Corrosion Rates of Structural Members in oil Tankers,” ASME Paper No. OMAE2003-37361.
Wang, G. , Spencer, J. , and Sun, H. , 2003, “ Assessment of Corrosion Risks to Aging Ships Using an Experience Database,” ASME Paper No. OMAE2003-37299.
Guedes Soares, C. , and Garbatov, Y. , 1997, “ Reliability Assessment of Maintained Ship Hulls With Correlated Corroded Elements,” Mar. Struct., 10(8–10), pp. 629–653. [CrossRef]
Andrade, A. R. , and Teixeira, P. F. , 2015, “ Statistical Modelling of Railway Track Geometry Degradation Using Hierarchical Bayesian Models,” Reliab. Eng. Syst. Saf., 142, pp. 169–183. [CrossRef]
Maes, M. , 2002, “ Updating Performance and Reliability of Concrete Structures Using Discrete Empirical Bayes Methods,” ASME J. Offshore Mech. Arct. Eng., 124(4), pp. 239–244. [CrossRef]
Qin, J. , and Faber, M. H. , 2012, “ Risk Management of Large RC Structures Within Spatial Information System,” Comput.-Aided Civil Infrastruct. Eng., 27(6), pp. 385–405. [CrossRef]
Schneider, R. , Fischer, J. , Buügler, M. , Nowak, M. , Thöns, S. , Borrmann, A. , and Straub, D. , 2015, “ Assessing and Updating the Reliability of Concrete Bridges Subjected to Spatial Deterioration–Principles and Software Implementation,” Struct. Concr., 16(3), pp. 356–365. [CrossRef]
Straub, D. , Malioka, V. , and Faber, M. H. , 2009, “ A Framework for the Asset Integrity Management of Large Deteriorating Concrete Structures,” Struct. Infrastruct. Eng., 5(3), pp. 199–213. [CrossRef]
Luque, J. , Hamann, R. , and Straub, D. , 2014, “ Spatial Model for Corrosion in Ships and FPSOs,” ASME Paper No. OMAE2014-23062.
Qin, H. , Zhou, W. , and Zhang, S. , 2015, “ Bayesian Inferences of Generation and Growth of Corrosion Defects on Energy Pipelines Based on Imperfect Inspection Data,” Reliab. Eng. Syst. Saf., 144, pp. 334–342. [CrossRef]
Zhang, S. , Zhou, W. , Al-Amin, M. , Kariyawasam, S. , and Wang, H. , 2014, “ Time-Dependent Corrosion Growth Modeling Using Multiple ILI Data,” ASME J. Pressure Vessel Technol., 136(4), p. 041202.
Raudenbush, S. W. , and Bryk, A. S. , 2008, Hierarchical Linear Models, Applications and Data Analysis Methods, 2nd ed., SAGE Publications, Thousand Oaks, CA.
Gelman, A. , Carlin, J. B. , Stern, H. S. , and Rubin, D. B. , 2004, Bayesian Data Analysis, 2nd ed., Chapman and Hall/CRC, Boca Raton, FL.
Maes, M. , Dann, M. , Breitung, K. , and Brehm, E. , 2008, “ Hierarchical Modeling of Stochastic Deterioration,” Graubner, Schmidt and Proske: Proceedings of the 6th International Probabilistic Workshop, Darmstadt, Germany, pp. 101–116.
Ntzoufras, I. , 2009, Bayesian Modeling Using WinBUGS, Wiley, Hoboken, NJ. [CrossRef]
Jensen, F. , and Nielsen, T. , 2007, Bayesian Networks and Decision Graphs, 2nd ed., Springer, New York. [CrossRef]
Straub, D. , 2009, “ Stochastic Modeling of Deterioration Processes Through Dynamic Bayesian Networks,” ASCE J. Eng. Mech., 135(10), pp. 1089–1099. [CrossRef]
Gamerman, D. , and Lopes, H. F. , 2006, Markov Chain Monte Carlo, Stochastic Simulation for Bayesian Inference, 2nd ed., Chapman and Hall/CRC, Boca Raton, FL.
Germanischer Lloyd Group, 2009, Rules for Classification and Construction: II Materials and Welding, Germanischer Lloyd Aktiengesellschaft, Hamburg, Germany.
Vanmarcke, E. H. , 2010, Random Fields, Analysis and Synthesis, World Scientific Publishing, Singapore. [CrossRef]
Gilks, W. R. , Richardson, S. , and Spiegelhalter, D. J. , 1996, Markov Chain Monte Carlo in Practice, Chapman and Hall/CRC, London.
Lunn, D. , Spiegelhalter, D. , Thomas, A. , and Best, N. , 2009, “ The BUGS Project: Evolution, Critique and Future Directions (With Discussion),” Stat. Med., 28(25), pp. 3049–3082. [CrossRef] [PubMed]

Figures

Grahic Jump Location
Fig. 1

Example of a hierarchical Bayesian deterioration model (D: deterioration, R: corrosion rate, μ: mean, σ: standard deviation) using (a) explicit and (b) hierarchical representations

Grahic Jump Location
Fig. 2

Hierarchical structure of the spatial corrosion model

Grahic Jump Location
Fig. 3

Hierarchical representation of the thickness margin

Grahic Jump Location
Fig. 4

Hierarchical representation of the coating life

Grahic Jump Location
Fig. 5

Hierarchical representation of the corrosion model parameters

Grahic Jump Location
Fig. 6

Hierarchical representation of the full corrosion model, including measurements

Grahic Jump Location
Fig. 7

Hierarchical representation of the simulated example

Grahic Jump Location
Fig. 8

Exemplary estimated marginal PDFs of model parameters

Grahic Jump Location
Fig. 9

(a) Location of the structural element floor in tankers and (b) locations of thickness measurements in one of the measurement campaigns

Grahic Jump Location
Fig. 10

Exemplary posterior distribution of parameters of the corrosion model for the tankers example

Grahic Jump Location
Fig. 11

Measurement campaign in tanker 5 at year 17 used for model updating (the diameter of the circle is proportional to the measured thickness diminution)

Grahic Jump Location
Fig. 12

Comparison between the original (prior) and the updated (posterior) distribution

Grahic Jump Location
Fig. 13

Spatial distribution of the expected margin, coating life, and corrosion rate per measured location. The grayscale of a circle represents the mean estimate and the size of a circle reflects the deviation of the value at this location from the total mean value (i.e., from all measured points). The larger circles are values in the tails of the distribution.

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
Articles from Part A: Civil Engineering
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