Research Papers: Structures and Safety Reliability

Safety of Pipelines Subjected to Deterioration Processes Modeled Through Dynamic Bayesian Networks

[+] Author and Article Information
O. G. Palencia

Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais, No. 1,
Lisboa 1049-001, Portugal
e-mail: oscar.palencia@centec.tecnico.ulisboa.pt

A. P. Teixeira

Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa
Av. Rovisco Pais, No. 1,
Lisboa 1049-001, Portugal
e-mail: teixeira@centec.tecnico.ulisboa.pt

C. Guedes Soares

Fellow ASME
Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais, No. 1,
Lisboa 1049-001, Portugal;
Subsea Technology Laboratory,
Ocean Engineering Department,
Federal University of Rio de Janeiro,
Rio de Janeiro 21941-972, Brazil
e-mail: c.guedes.soares@centec.tecnico.ulisboa.pt

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 July 22, 2017; final manuscript received June 16, 2018; published online August 13, 2018. Assoc. Editor: Celso P. Pesce.

J. Offshore Mech. Arct. Eng 141(1), 011602 (Aug 13, 2018) (11 pages) Paper No: OMAE-17-1124; doi: 10.1115/1.4040573 History: Received July 22, 2017; Revised June 16, 2018

This paper studies the application of Dynamic Bayesian Networks (DBNs) for modeling degradation processes in oil and gas pipelines. A DBN tool consisting of a matlab code has been developed for performing inference on models. The tool is then applied for probabilistic modeling of the burst pressure of a pipe subjected to corrosion degradation and for safety assessment. The burst pressure is evaluated using the ASME B31G design method and other empirical formulas. A model for corrosion prediction in pipelines and its governing parameters are explicitly included into the probabilistic framework. Different sets of simulated corrosion measurements are used to increase the accuracy of the model predictions. Several parametric studies are conducted to investigate how changes in the observed corrosion (depth and length) and in the frequency of inspections affect the pipe reliability.

Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.


Sulaiman, N. S. , and Tan, H. , 2014, “Third Party Damages of Offshore Pipeline,” J. Energy Challenges Mech., 1(1), pp. 14–19. https://www.nscj.co.uk/JECM/PDF/1-1-3-Nurul%20Sulaiman-Henry%20Tan.pdf
Teixeira, A. P. , Guedes Soares, C. , Netto, T. A. , and Estefen, S. F. , 2008, “Reliability of Pipelines With Corrosion Defects,” Int. J. Pressure Vessels Piping, 85(4), pp. 228–237. [CrossRef]
Leira, B. J. , Næss, A. , and Brandrud Næss, O. E. , 2016, “Reliability Analysis of Corroding Pipelines by Enhanced Monte Carlo Simulation,” Int. J. Pressure Vessels Piping, 144, pp. 11–17. [CrossRef]
Teixeira, A. P. , Zayed, A. , and Guedes Soares, C. , 2010, “Reliability of Pipelines With Non-Uniform Corrosion,” J. Ocean Ship Technol., 1(1), pp. 12–30.
Bisaggio, H. D. C. , and Netto, T. A. , 2015, “Predictive Analyses of the Integrity of Corroded Pipelines Based on Concepts of Structural Reliability and Bayesian Inference,” Mar. Struct., 41, pp. 180–199. [CrossRef]
Aljaroudi, A. , Khan, F. , Akinturk, A. , Haddara, M. , and Thodi, P. , 2015, “Risk Assessment of Offshore Crude Oil Pipeline Failure,” J. Loss Prev. Process Ind., 37, pp. 101–109. [CrossRef]
Hasan, S. , Khan, F. , and Kenny, S. , 2012, “Probability Assessment of Burst Limit State Due to Internal Corrosion,” Int. J. Pressure Vessels Piping, 89, pp. 48–58. [CrossRef]
Gerginov, E. , Group, W. , Pty, K. , Sullivan, C. , Rathbone, A. , and Griffiths, T. , 2014, “Insights in the Application of Structural Reliability Analysis (SRA) for Challenging Pipeline Lateral Buckling Design,” ASME Paper No. OMAE2014-24450.
Oliveira, N. , Bisaggio, H. , and Netto, T. , 2016, “Probabilistic Analysis of the Collapse Pressure of Corroded Pipelines,” ASME Paper No. OMAE2016-54299.
Teixeira, A. P. , Palencia, O. G. , and Guedes Soares, C. , 2017, “Reliability Analysis of Corroded Pipelines Under External Pressure,” ASME Paper No. OMAE2017-61964.
Zhou, W. , 2010, “System Reliability of Corroding Pipelines,” Int. J. Pressure Vessels Piping, 87(10), pp. 587–595. [CrossRef]
Zhang, S. , and Zhou, W. , 2013, “System Reliability of Corroding Pipelines Considering Stochastic Process-Based Models for Defect Growth and Internal Pressure,” Int. J. Pressure Vessels Piping, 111–112, pp. 120–130. [CrossRef]
Ossai, C. I. , Boswell, B. , and Davies, I. J. , 2016, “Application of Markov Modelling and Monte Carlo Simulation Technique in Failure Probability Estimation—A Consideration of Corrosion Defects of Internally Corroded Pipelines,” Eng. Fail. Anal., 68, pp. 159–171. [CrossRef]
Shekari, E. , Khan, F. , and Ahmed, S. , 2015, “A Predictive Approach to Fitness-for-Service Assessment of Pitting Corrosion,” Int. J. Pressure Vessels Piping, 137, pp. 13–21. [CrossRef]
ASME, 2012, “Manual for Determining the Remaining Strength of Corroded Pipelines,” American Society of Mechanical Engineers, New York, Standard No. ASME B31G-2012.
Netto, T. A. , Ferraz, U. S. , and Estefen, S. F. , 2005, “The Effect of Corrosion Defects on the Burst Pressure of Pipelines,” J. Constr. Steel Res., 61(8), pp. 1185–1204. [CrossRef]
DNV, 2015, “Recommended Practice—Corroded Pipelines,” Det Norske Veritas, Oslo, Norway, Standard No. DNV-RP-F101.
Zhang, G. , Luo, J. , Zhao, X. , Zhang, H. , Zhang, L. , and Zhang, Y. , 2012, “Research on Probabilistic Assessment Method Based on the Corroded Pipeline Assessment Criteria,” Int. J. Pressure Vessels Piping, 95, pp. 1–6. [CrossRef]
Nahal, M. , and Khelif, R. , 2014, “Failure Probability Assessment for Pipelines Under the Corrosion Effect,” Am. J. Mech. Eng., 2(1), pp. 15–20. [CrossRef]
Seo, J. K. , Cui, Y. , Mohd, M. H. , Ha, Y. C. , Kim, B. J. , and Paik, J. K. , 2015, “A Risk-Based Inspection Planning Method for Corroded Subsea Pipelines,” Ocean Eng., 109, pp. 1–32. [CrossRef]
Melchers, R. E. , 1999, Structural Reliability and Analysis Prediction, John Wiley & Sons, Chichester, England.
Weber, P. , Medina-Oliva, G. , Simon, C. , and Iung, B. , 2012, “Overview on Bayesian Networks Applications for Dependability, Risk Analysis and Maintenance Areas,” Eng. Appl. Artif. Intell., 25(4), pp. 671–682. [CrossRef]
Straub, D. , and Kiureghian, A. D. , 2010, “Bayesian Network Enhanced With Structural Reliability Methods: Methodology,” J. Eng. Mech., 136(10), pp. 1248–1258. [CrossRef]
Bensi, M. T. , Der Kiureghian, A. , and Straub, D. , 2011, “A Bayesian Network Methodology for Infrastructure Seismic Risk Assessment and Decision Support,” Ph.D. thesis, Pacific Earthquake Engineering Research Center, College of Engineering, University of California, Berkeley, CA.
Iung, B. , Véron, M. , Suhner, M. C. , and Muller, A. , 2005, “Integration of Maintenance Strategies Into Prognosis Process to Decision-Making Aid on System Operation,” CIRP Ann. Manuf. Technol., 54(1), pp. 5–8. [CrossRef]
Palencia, O. G. , Teixeira, A. P. , and Guedes Soares, C. , 2017, “Modelling of Deterioration Processes in Ship Structures Through Dynamic Bayesian Networks,” Safety, Reliability, Risk, Resilience and Sustainability of Structures and Infrastructure, C. Bucher , B. R. Ellingwood , and D. M. Frangopol , eds., TU-Verlag, Vienna, Austria, pp. 1936–1946.
Straub, D. , 2009, “Stochastic Modeling of Deterioration Processes Through Dynamic Bayesian Networks,” J. Eng. Mech., 135(10), pp. 1089–1099. [CrossRef]
Straub, D. , 2010, “An Efficient Computational Framework for Probabilistic Deterioration Modeling and Reliability Updating,” Safety, Reliability and Risk of Structures, Infrastructures and Engineering Systems, H. Furuta , D. Frangopol , and M. Shinozuka , eds., Taylor & Francis Group, London, pp. 3255–3262.
Pots, B. F. M. , 2005, “Prediction of Corrosion Rates of the Main Corrosion Mechanisms in Upstream Applications,” Corrosion 2005, NACE International, Denver, CO, Document No. NACE-05550.
Nordsveen, M. , Nesic, S. , Nyborg, R. , and Stangeland, A. , 2003, “A Mechanistic Model for CO2 Corrosion With Protective Iron Carbonate Films—Part 1: Theory and Verification,” Corrosion, 59(5), pp. 443–456. [CrossRef]
de Waard, C. , Lotz, U. , and Milliams, D. E. , 1991, “Predictive Model for CO2 Corrosion Engineering in Wet Natural Gas Pipelines,” Corrosion, 47(12), pp. 976–985. [CrossRef]
Nyborg, R. , 2002, “Overview of CO2 Corrosion Models for Wells and Pipelines,” Corrosion/2002, NACE International, Denver, CO, Document No. NACE-02233.
Nyborg, R. , 2010, “CO2 Corrosion Models for Oil and Gas Production Systems,” Corrosion 2010, NACE International, San Antonio, Texas, Document No. NACE-10371.
Barbosa, A. A. , Teixeira, A. P. , and Guedes Soares, C. , 2017, “Strength Analysis of Corroded Pipelines Subjected to Internal Pressure and Bending Moment,” Progress in the Analysis and Design of Marine Structures, C. Guedes Soares , and Y. Garbatov , eds., Taylor & Francis Group, London, pp. 803–811.
de Waard, C. , and Milliams, D. E. , 1975, “Carbonic Acid Corrosion of Steel,” Corrosion, 31(5), pp. 177–181. [CrossRef]
de Waard, C. , Lotz, U. , and Dugstad, A. , 1995, “Influence of Liquid Flow Velocity on CO2 Corrosion: A Semi-Empirical Model,” Corrosion 95, NACE International, Baltimore, MD, Document No. NACE-128.
Russell, S. J. , and Norwig, P. , 2009, Artificial Intelligence: A Modern Approach, Pearson, New York.
Langseth, H. , Nielsen, T. D. , Rumí, R. , and Salmerón, A. , 2009, “Inference in Hybrid Bayesian Networks,” Reliab. Eng. Syst. Saf., 94(10), pp. 1499–1509. [CrossRef]
Zwirglmaier, K. , and Straub, D. , 2016, “A Discretization Procedure for Rare Events in Bayesian Networks,” Reliab. Eng. Syst. Saf., 153, pp. 96–109. [CrossRef]


Grahic Jump Location
Fig. 1

Pipeline with a corrosion defect

Grahic Jump Location
Fig. 2

Example of a DBN model

Grahic Jump Location
Fig. 3

Unrolling of a DBN

Grahic Jump Location
Fig. 4

Dynamic Bayesian network model for time-dependent reliability analysis of corroded pipes

Grahic Jump Location
Fig. 5

Simulated corrosion depth values

Grahic Jump Location
Fig. 6

Dynamic Bayesian Network tool convergence analysis and computational time (in seconds)

Grahic Jump Location
Fig. 7

Reliability index, comparison with Teixeira et al. [2] (DBN model with random variables discretized in 250 bins)

Grahic Jump Location
Fig. 8

Reliability index with and without evidences

Grahic Jump Location
Fig. 9

Limit state function mean values

Grahic Jump Location
Fig. 10

Limit state function and standard deviation values

Grahic Jump Location
Fig. 11

Reliability index and remaining thickness of the pipe

Grahic Jump Location
Fig. 12

Reliability index using different sets of simulated evidences

Grahic Jump Location
Fig. 13

Corrosion defect depth

Grahic Jump Location
Fig. 14

Reliability index using different burst pressure prediction methods

Grahic Jump Location
Fig. 15

Reliability index using different intervals between inspections

Grahic Jump Location
Fig. 16

Reliability index with decreasing pressure

Grahic Jump Location
Fig. 17

Corrosion depth rate with decreasing pressure

Grahic Jump Location
Fig. 18

Corrosion depth with decreasing pressure



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