IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-981-97-1949-5_23.html
   My bibliography  Save this book chapter

Investigating the Potential of ChatGPT in Construction Management: A Study of Interpreting Construction Crane-Related Accident Reports

In: Proceedings of the 28th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • Yifan Wang

    (Hong Kong Polytechnic University
    Southeast University)

  • Junyu Chen

    (Hong Kong Polytechnic University)

  • Bo Xiao

    (Michigan Technological University)

  • Yuxuan Zhang

    (Nanjing University of Aeronautics and Astronautics)

  • Yuan Chen

    (Tianjin University)

  • Qiming Li

    (Southeast University)

Abstract

Large language models, such as Chat Generative Pre-training Transformer (ChatGPT), have shown great potential to revolutionize the construction industry by automating repetitive and time-consuming tasks. This study presents a framework to explore the feasibility of ChatGPT to automatically analyze causal factors using narrative accident reports in construction. Firstly, the ChatGPT was used to extract primary causal factors from accident reports from the perspectives of human-related, crane-related, environment-related, and management-related. Then, outputs from ChatGPT were evaluated by a group of participants in terms of clarity, specificity, reliability, and inspiration. Finally, the outputs from ChatGPT were further corrected by the same participants through careful addition, deletion, or re-judgment of the factors. The findings of this study include: (1) ChatGPT was generally considered a satisfying assisting tool for causal factor extraction and analysis; (2) the results also implied that this technology still needs further development before it can be widely applied in professional areas, such as construction crane safety. Overall, this study highlights the potential and the required further efforts of applying large language models in research.

Suggested Citation

  • Yifan Wang & Junyu Chen & Bo Xiao & Yuxuan Zhang & Yuan Chen & Qiming Li, 2024. "Investigating the Potential of ChatGPT in Construction Management: A Study of Interpreting Construction Crane-Related Accident Reports," Lecture Notes in Operations Research, in: Dezhi Li & Patrick X. W. Zou & Jingfeng Yuan & Qian Wang & Yi Peng (ed.), Proceedings of the 28th International Symposium on Advancement of Construction Management and Real Estate, chapter 0, pages 327-340, Springer.
    • Handle: RePEc:spr:lnopch:978-981-97-1949-5_23
    DOI: 10.1007/978-981-97-1949-5_23
    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:978-981-97-1949-5_23. 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.