A Novel and Intelligent Safety-Hazard Classification Method with Syntactic and Semantic Features for Large-Scale Construction Projects
Publication: Journal of Construction Engineering and Management
Volume 148, Issue 10
Abstract
To improve the efficiency of safety management, it is important to classify massive and complex construction site safety hazard texts in large-scale projects. High-precision safety hazard text classification is a lengthy and challenging process. Most existing safety hazard text classification methods capture semantic information using machine learning or deep learning, ignoring the syntactic dependency between words. However, syntactic dependency contains rich structural information that is useful to alleviate information loss and enrich text features. To address these issues, this study proposes a graph structure–based hybrid deep learning method to achieve the automatic classification of large-scale project safety hazard texts. The method uses syntactic dependency and Bidirectional Encoder Representation from Transformers to express the syntactic structure and semantic information of text, and a graph structure fusing the syntactic structure and semantic information is constructed to quantify text information. Further, an encoding-decoding mechanism is built using a graph convolutional neural network and bidirectional long short-term memory to address graph structure data and classify safety hazard texts. Our proposed method is used to classify hydraulic engineering construction safety hazard texts, and the classification accuracy reaches 86.56%. Meanwhile, the experimental results demonstrate that our model achieves superior performance compared to existing methods. This proves the ability of our model to capture and analyze text information and verifies the reliability and effectiveness of this method in large-scale project safety hazard management.
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Data Availability Statement
Data generated and analyzed during this study are available from the corresponding author by request.
Acknowledgments
This research was supported by the National Natural Science Foundation of China (Grant 52179139) and the Open Fund of Hubei Key Laboratory of Construction and Management in Hydropower Engineering (Grant 2020KSD05).
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Published In
Journal of Construction Engineering and Management
Volume 148 • Issue 10 • October 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Feb 22, 2022
Accepted: May 31, 2022
Published online: Aug 1, 2022
Published in print: Oct 1, 2022
Discussion open until: Jan 1, 2023
ASCE Technical Topics:
- Business management
- Construction engineering
- Construction management
- Construction methods
- Construction sites
- Design (by type)
- Disaster risk management
- Engineering fundamentals
- Management methods
- Occupational safety
- Practice and Profession
- Public administration
- Public health and safety
- Risk management
- Safety
- Structural design
- Structural safety
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