A Novel and Intelligent Safety-Hazard Classification Method with Syntactic and Semantic Features for Large-Scale Construction Projects

Dan Tian, Mingchao Li, Shuai Han, Yang Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Article number04022109
JournalJournal of Construction Engineering and Management
Issue number10
Publication statusPublished - 1 Oct 2022


  • Bidirectional Encoder Representations from Transformers (BERT)
  • Bidirectional long short-term memory (BiLSTM)
  • Construction safety hazard
  • Graph convolutional network (GCN)
  • Graph structure
  • Large-scale project
  • Text classification

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Industrial relations
  • Strategy and Management


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