IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/7518284.html
   My bibliography  Save this article

Integration of Interpretive Structural Modeling with Fuzzy Bayesian Network for Risk Assessment of Tunnel Collapse

Author

Listed:
  • Leping He
  • Tao Tang
  • Qijun Hu
  • Qijie Cai
  • Zhijun Li
  • Shaowu Tang
  • Yichun Wang

Abstract

Frequent collapse accidents in tunnels are associated with many construction risk factors, and the interrelationship among these risk factors is complex and ambiguous. This study’s aim is to clarify the relationship among risk factors to reduce the tunnel collapse risk. A multicriteria decision-making method is proposed by combining interpretive structural modeling (ISM) and fuzzy Bayesian network (FBN). ISM is used to determine the hierarchical relationships among risk factors. FBN quantitatively analyzes the strength of the interaction among risk factors and conducts risk analysis. The ISM-FBN method contains three steps: (1) drawing the ISM-directed graph; (2) obtaining the probability of the FBN nodes; and (3) using GeNle to implement risk analysis. The proposed method is also used to assess the collapse risk and detect the critical factors in the Canglongxia Tunnel, China. This method’s tunnel collapse risk model can provide managers with clear risk information and better realize project management.

Suggested Citation

  • Leping He & Tao Tang & Qijun Hu & Qijie Cai & Zhijun Li & Shaowu Tang & Yichun Wang, 2021. "Integration of Interpretive Structural Modeling with Fuzzy Bayesian Network for Risk Assessment of Tunnel Collapse," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, December.
    • Handle: RePEc:hin:jnlmpe:7518284
    DOI: 10.1155/2021/7518284
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/7518284.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/7518284.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/7518284?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Longlong He & Ruiyu Pan & Yafei Wang & Jiani Gao & Tianze Xu & Naqi Zhang & Yue Wu & Xuhui Zhang, 2024. "A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process," Mathematics, MDPI, vol. 12(7), pages 1-31, April.

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:7518284. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.