Deep Learning–Based Named Entity Recognition and Resolution of Referential Ambiguities for Enhanced Information Extraction from Construction Safety Regulations
Publication: Journal of Computing in Civil Engineering
Volume 37, Issue 5
Abstract
Construction safety regulations and standards contain a massive number of fall protection requirements with respect to different equipment, facilities, and operations. Automated field compliance checking aims to detect field violations of construction safety regulations for improved compliance and safety. Recent research efforts focused on automated tracking of labor and equipment toward improved violation detection and safety compliance. However, extracting and modeling safety requirements for supporting automated violation detection or safety alert systems remains highly manual. Toward addressing this gap, information extraction provides an opportunity to automatically extract requirements from construction safety regulations for comparisons with field information to detect violations (or predict and prevent violations before they occur). However, existing information extraction methods are limited in terms of their scalability and/or accuracy. To address this need, this paper proposes a deep learning–based information extraction method for automatically extracting named entities describing fall protection requirements [e.g., scaffold, horizontal direction, or 1.82 m (6 ft)] from construction safety regulations and resolving referential ambiguities. The proposed information extraction method consists of three main submethods: (1) a deep learning–based method to recognize entities from the regulations, (2) a deep learning–based method to recognize referential ambiguities in the extracted entities, and (3) a named entity normalization method to resolve these ambiguities. The proposed method was implemented and tested on 20 selected Occupational Safety and Health Administration (OSHA) sections related to fall protection. An overall information extraction precision, recall, and measure of 93.2%, 89.6%, and 91.1% were obtained, which indicates good information extraction performance.
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Data Availability Statement
Some data, models, or code generated or used during the study (the labeled gold standard for evaluation) are available from the corresponding author by request.
Acknowledgments
The authors would like to thank the National Science Foundation (NSF). This paper is based on work supported by NSF under Grant No. 1827733. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.
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Published In
Journal of Computing in Civil Engineering
Volume 37 • Issue 5 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Mar 2, 2022
Accepted: Aug 18, 2022
Published online: Jun 30, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 30, 2023
ASCE Technical Topics:
- Automation and robotics
- Building codes
- Business management
- Construction engineering
- Construction equipment
- Construction management
- Construction methods
- Engineering fundamentals
- Equipment and machinery
- Laws and regulations
- Legal affairs
- Occupational safety
- Practice and Profession
- Public administration
- Public health and safety
- Safety
- Standards and codes
- Systems engineering
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Cited by
- Haoxi Wang, Sheng Xu, Dongdong Cui, Hong Xu, Hanbin Luo, Information Integration of Regulation Texts and Tables for Automated Construction Safety Knowledge Mapping, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-14436, 150, 5, (2024).