IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-981-19-5256-2_7.html
   My bibliography  Save this book chapter

Tunneling Risk Visualization Using BIM and Dynamic Bayesian Network

In: Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • Ting Deng

    (Shenzhen University)

  • DongDong Tang

    (Shenzhen University)

  • Shuaishuai Jin

    (Shenzhen University)

  • Yi Tan

    (Shenzhen University)

Abstract

Ground tunneling construction increases the uncertainty of project risk, which cannot be proactively managed. Therefore, dynamic risk prediction and management become extremely significant for tunneling projects. This study proposes to combine the probability analysis of Dynamic Bayesian Network (DBN) with building information modeling (BIM) to manage risk during tunneling. Firstly, the tunnel BIM model is created based on the actual survey data. Secondly, the parameter related to risk factors stored in BIM is extracted into the DBN for risk prediction, including accurate geological information and relevant data of tunnel segments. Then the DBN is used to calculate the risk levels of different factors and to update the risk probability. The calculated risk level can be visualized in the BIM model through Dynamo, and different pipe sections are colored according to their predicted risk levels. An illustrative example is used to illustrate this method. Through the risk prediction analysis of the case, the feasibility of the method proposed in this paper is verified.

Suggested Citation

  • Ting Deng & DongDong Tang & Shuaishuai Jin & Yi Tan, 2022. "Tunneling Risk Visualization Using BIM and Dynamic Bayesian Network," Lecture Notes in Operations Research, in: Hongling Guo & Dongping Fang & Weisheng Lu & Yi Peng (ed.), Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, pages 75-86, Springer.
    • Handle: RePEc:spr:lnopch:978-981-19-5256-2_7
    DOI: 10.1007/978-981-19-5256-2_7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:lnopch:978-981-19-5256-2_7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.