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Book Chapter

Extracting Information from Construction Safety Requirements Using Large Language Model

  • Si Tran
  • Nasrullah Khan
  • Emmanuel Charles Kimito
  • Akeem Pedro
  • Mehrtash Soltani
  • Rahat Hussain
  • Taehan Yoo
  • Chansik Park

The construction industry has long been recognized for its complex safety regulations, which are essential to ensure the well-being of on-site employees. However, navigating these regulations and ensuring compliance can be challenging due to the volume and complexity of the documents involved. This study proposes a novel approach to extracting information from construction safety documents utilizing Large Language Models (LLM), called CSQA, to provide real-time, precise answers to queries related to safety regulations. The approach comprises three modules: (1) the construction safety investigation module (CSI) collects safety regulations for building the information needed. By leveraging a collection of safety regulation PDFs, the system follows a process of text extraction, preprocessing, and global indexing for efficient search. (2) The safety condition identification module (SCI) retrieves the CSI database; after that, the LLM, with its extensive training, processes user queries, searches the indexed regulations, and retrieves pertinent information. (3) the safety information delivery (SID) would provide the answer to the user and incorporate a feedback mechanism to further refine system accuracy based on user responses. Preliminary evaluations reveal the system's superior performance over traditional search engines, owing to its ability to grasp query context and nuances. The CSQA presents a promising method for accessing safety regulations, with potential benefits including reduced non-compliance incidents, enhanced worker safety, and streamlined regulatory consultations in construction

  • Keywords:
  • Construction safety document,
  • extraction,
  • LLM,
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Si Tran

Chung Ang University, Korea (the Republic of) - ORCID: 0000-0003-0080-751X

Nasrullah Khan

Chung Ang University, Korea (the Republic of)

Emmanuel Charles Kimito

Chung Ang University, Korea (the Republic of)

Akeem Pedro

Chung Ang University, Korea (the Republic of) - ORCID: 0000-0002-7884-5316

Mehrtash Soltani

Chung Ang University, Korea (the Republic of) - ORCID: 0000-0002-5217-2010

Rahat Hussain

Chung Ang University, Korea (the Republic of) - ORCID: 0000-0002-6909-5189

Taehan Yoo

Chung Ang University, Korea (the Republic of) - ORCID: 0009-0008-9739-1867

Chansik Park

Chung Ang University, Korea (the Republic of) - ORCID: 0000-0003-2256-300X

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  • Publication Year: 2023
  • Pages: 761-767

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  • Publication Year: 2023

Chapter Information

Chapter Title

Extracting Information from Construction Safety Requirements Using Large Language Model

Authors

Si Tran, Nasrullah Khan, Emmanuel Charles Kimito, Akeem Pedro, Mehrtash Soltani, Rahat Hussain, Taehan Yoo, Chansik Park

DOI

10.36253/979-12-215-0289-3.76

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

78

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