- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- Edited by Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
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
- © 2023 Author(s) |
- CC BY-NC 4.0
- DOI: 10.36253/979-12-215-0289-3.76
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,
Chung Ang University, Korea (the Republic of) - ORCID: 0000-0003-0080-751X
Chung Ang University, Korea (the Republic of)
Chung Ang University, Korea (the Republic of)
Chung Ang University, Korea (the Republic of) - ORCID: 0000-0002-7884-5316
Chung Ang University, Korea (the Republic of) - ORCID: 0000-0002-5217-2010
Chung Ang University, Korea (the Republic of) - ORCID: 0000-0002-6909-5189
Chung Ang University, Korea (the Republic of) - ORCID: 0009-0008-9739-1867
Chung Ang University, Korea (the Republic of) - ORCID: 0000-0003-2256-300X
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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
Metadata License
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
Metadata License
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