IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v203y2020ics0951832020305901.html
   My bibliography  Save this article

Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference

Author

Listed:
  • Zhang, Y.
  • Weng, W.G.

Abstract

The unintentional release of urban buried gas pipeline may cause crucial consequences to the economy, society and environment. Corrosion and external interference are primary causes of pipeline failure incidents. Due to the complexity and unpredictability of outside influence on the buried gas pipeline, this paper presents an approach to analyze pipeline failure frequency and leakage size caused by corrosion and external interference based on pipeline characteristics. Bayesian network method is used to construct a knowledge model. Pipeline characteristics statistics and failure data are collected to build the relationships among variables in the model and verify the applicability of the model. Results show that the proposed model can estimate buried gas pipeline failure frequency and leakage size caused by corrosion and external interference. It is also capable of highlighting the critical parameters to pipeline failure. Practical application of the model is demonstrated on the underground gas pipeline in the City of H, China. Results indicate that proposed model can explicitly quantify uncertainties and then put forward practical measures for buried gas pipeline parameter design, laying plan and operating maintenance.

Suggested Citation

  • Zhang, Y. & Weng, W.G., 2020. "Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
  • Handle: RePEc:eee:reensy:v:203:y:2020:i:c:s0951832020305901
    DOI: 10.1016/j.ress.2020.107089
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832020305901
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2020.107089?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Francis, Royce A. & Guikema, Seth D. & Henneman, Lucas, 2014. "Bayesian Belief Networks for predicting drinking water distribution system pipe breaks," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 1-11.
    2. Dundulis, Gintautas & ŽutautaitÄ—, Inga & Janulionis, Remigijus & UÅ¡puras, Eugenijus & RimkeviÄ ius, Sigitas & Eid, Mohamed, 2016. "Integrated failure probability estimation based on structural integrity analysis and failure data: Natural gas pipeline case," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 195-202.
    3. Kabir, Golam & Balek, Ngandu Balekelayi Celestin & Tesfamariam, Solomon, 2018. "Consequence-based framework for buried infrastructure systems: A Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 290-301.
    4. Brito, A.J. & de Almeida, A.T., 2009. "Multi-attribute risk assessment for risk ranking of natural gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 187-198.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yin, Yuanbo & Yang, Hao & Duan, Pengfei & Li, Luling & Zio, Enrico & Liu, Cuiwei & Li, Yuxing, 2022. "Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Å arÅ«nienÄ—, Inga & MartiÅ¡auskas, Linas & KrikÅ¡tolaitis, RiÄ ardas & Augutis, Juozas & Setola, Roberto, 2024. "Risk assessment of critical infrastructures: A methodology based on criticality of infrastructure elements," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Chen, Yinuo & Xie, Shuyi & Tian, Zhigang, 2022. "Risk assessment of buried gas pipelines based on improved cloud-variable weight theory," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Medeiros, Cristina Pereira & da Silva, Lucas Borges Leal & Alencar, Marcelo Hazin & de Almeida, Adiel Teixeira, 2021. "A new method for managing multidimensional risks in Natural Gas Pipelines based on non-Expected Utility," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    5. Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    6. Zheng Tang & Yijia Li & Xiaofeng Hu & Huanggang Wu, 2019. "Risk Analysis of Urban Dirty Bomb Attacking Based on Bayesian Network," Sustainability, MDPI, vol. 11(2), pages 1-12, January.
    7. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    8. Rogerson, Ellen C. & Lambert, James H., 2012. "Prioritizing risks via several expert perspectives with application to runway safety," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 22-34.
    9. Marlow, David R. & Beale, David J. & Mashford, John S., 2012. "Risk-based prioritization and its application to inspection of valves in the water sector," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 67-74.
    10. Shengli, Liu & Yongtu, Liang, 2019. "Exploring the temporal structure of time series data for hazardous liquid pipeline incidents based on complex network theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 26(C).
    11. Peng Hou & Xiaojian Yi & Haiping Dong, 2020. "A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network," Energies, MDPI, vol. 13(3), pages 1-16, February.
    12. Seth Guikema, 2020. "Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1117-1123, June.
    13. Robles-Velasco, Alicia & Cortés, Pablo & Muñuzuri, Jesús & Onieva, Luis, 2020. "Prediction of pipe failures in water supply networks using logistic regression and support vector classification," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    14. Kabir, Golam & Tesfamariam, Solomon & Sadiq, Rehan, 2015. "Predicting water main failures using Bayesian model averaging and survival modelling approach," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 498-514.
    15. Chen, Thomas Ying-Jeh & Guikema, Seth David & Daly, Craig Michael, 2019. "Optimal pipe inspection paths considering inspection tool limitations," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 156-166.
    16. Gangadharan, Lata & Harrison, Glenn W. & Leroux, Anke D., 2019. "Are risks over multiple attributes traded off? A case study of aid," Journal of Economic Behavior & Organization, Elsevier, vol. 164(C), pages 166-198.
    17. Casado, Ramon Swell Gomes Rodrigues & Alencar, Marcelo Hazin & de Almeida, Adiel Teixeira, 2022. "Combining a multidimensional risk evaluation with an implicit enumeration algorithm to tackle the portfolio selection problem of a natural gas pipeline," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    18. Md. Rabbi & Syed Mithun Ali & Golam Kabir & Zuhayer Mahtab & Sanjoy Kumar Paul, 2020. "Green Supply Chain Performance Prediction Using a Bayesian Belief Network," Sustainability, MDPI, vol. 12(3), pages 1-19, February.
    19. Jiang, Tao & Liu, Yu, 2017. "Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 3-15.
    20. Alessandro Pagano & Irene Pluchinotta & Raffaele Giordano & Anna Bruna Petrangeli & Umberto Fratino & Michele Vurro, 2018. "Dealing with Uncertainty in Decision-Making for Drinking Water Supply Systems Exposed to Extreme Events," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(6), pages 2131-2145, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:203:y:2020:i:c:s0951832020305901. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.