Intelligent mining of safety hazard information from construction documents using semantic similarity and information entropy

Project construction on-site is known to be very dangerous workplace environments due to large numbers of safety hazards. Analysis of construction safety hazards is essential to formulate rational safety management plans and prevent accidents. Construction documents contain large volumes of safety hazard information available for analysis. However, such analyses are challenging because the safety hazard information in the construction documents is presented in an unstructured or semi-structured format. This study proposes a method for intelligent mining of safety hazard information, which comprises safety hazard technical term recognition and safety hazard information analysis. The safety hazard technical term recognition model is developed based on semantic similarity and information correlation to build a safety hazard technical term library. The safety hazard information based on the technical term library is mined and analyzed using the term frequency-inverse document frequency method (TF-IDF). Finally, the proposed method is applied to build the safety hazard technical term library, which contains 2697 technical terms, and develop a hydraulic project construction safety hazard analysis system, which can realize the intelligent recognition and application of technical terms. Meanwhile, this system can automatically extract safety hazard information and provide a visualization interface to intuitively show the safety hazard analysis results, which improves the extraction efficiency of safety hazard information. The study provides a new approach for recognizing technical terms and mining safety hazard information, which can lead to enhancing management efficiency and practical knowledge discovery for safety management.