Proceedings of the 2024 10th International Conference on Architectural, Civil and Hydraulic Engineering (ICACHE 2024)

<Previous Article In Volume
Next Article In Volume>

Intelligent Extraction Method of Engineering Construction Safety Accident Cause Based on Semantic Information Understanding

Authors
Binghua Shen1, *
1College of Intelligent Manufacturing and Non Destructive Testing, Wuhan College of Arts and Science, Wuhan, 430345, China
*Corresponding author. Email: shenbinghua@163.com
Corresponding Author
Binghua Shen
Available Online 3 March 2025.
DOI
10.2991/978-94-6463-658-1_30How to use a DOI?
Keywords
Construction safety accident report; Accident cause extraction; Semantic information understanding; KeyBERT
Abstract

Aiming at the frequent occurrence of safety accidents in engineering construction and the high time consuming and error-prone problem of accident report text analysis, this paper proposes an intelligent extraction method based on semantic understanding for the cause of safety accidents in engineering construction. The core of this method is to use KeyBERT model to mine the cause factors efficiently and accurately. The model can deeply understand the text content, capture the key information in the text, and generate a list of keywords representing the gist of the text. Compared to traditional manual analysis or simple word frequency statistics methods, KeyBERT shows significant advantages in understanding text semantics and capturing contextual information. The experimental results show that this method has excellent performance in accident causation mining in the text of engineering construction safety accident report. This study also designed a comparative experiment to prove the superiority of this method, so as to improve the accuracy and comprehensiveness of accident causation mining and provide strong support for accident handling and prevention measures.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2024 10th International Conference on Architectural, Civil and Hydraulic Engineering (ICACHE 2024)
Series
Advances in Engineering Research
Publication Date
3 March 2025
ISBN
978-94-6463-658-1
ISSN
2352-5401
DOI
10.2991/978-94-6463-658-1_30How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Binghua Shen
PY  - 2025
DA  - 2025/03/03
TI  - Intelligent Extraction Method of Engineering Construction Safety Accident Cause Based on Semantic Information Understanding
BT  - Proceedings of the 2024 10th International Conference on Architectural, Civil and Hydraulic Engineering (ICACHE 2024)
PB  - Atlantis Press
SP  - 290
EP  - 300
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-658-1_30
DO  - 10.2991/978-94-6463-658-1_30
ID  - Shen2025
ER  -