IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v250y2024ics0951832024003260.html
   My bibliography  Save this article

A data-driven and knowledge graph-based analysis of the risk hazard coupling mechanism in subway construction accidents

Author

Listed:
  • Huo, Xiaosen
  • Yin, Yuan
  • Jiao, Liudan
  • Zhang, Yu

Abstract

With the development and expansion of subway construction, accidents usually cause economic losses and seriously threaten personnel safety. Exploring the experience and lessons from accident reports is beneficial for promoting subway construction safety management. Therefore, this study proposes a data-driven and knowledge graph-based approach to reveal the coupling mechanism of risk hazards in subway construction. A total of 231 subway construction accident reports in China from 2001 to 2023 are collected as the database of the knowledge graph. By developing a subway construction safety accident knowledge graph (SCSAKG), the potential paths for accidents can be revealed. To further investigate the internal features of the knowledge graph, topological indicators are proposed to explore the relationships between hazards, hazard types, hazard frequencies, hazard occurrence times, accidents, and accident consequences. Prevention strategies for key risk hazards in subway construction are developed based on the proposed coupling mechanism. The proposed methods that combine knowledge graphs with accident reports can provide decision-making references for subway construction safety management.

Suggested Citation

  • Huo, Xiaosen & Yin, Yuan & Jiao, Liudan & Zhang, Yu, 2024. "A data-driven and knowledge graph-based analysis of the risk hazard coupling mechanism in subway construction accidents," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:reensy:v:250:y:2024:i:c:s0951832024003260
    DOI: 10.1016/j.ress.2024.110254
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024003260
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110254?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.

    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:250:y:2024:i:c:s0951832024003260. 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: 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.