A hybrid deep semantic mining method considering fuzzy expressions for the automatic recognition of construction safety hazard information

Abstract

Safety hazards are a key consideration in construction management. The efficient recognition of safety hazard information can help managers formulate safety hazard management measures and improve the efficiency of construction safety management. However, construction site safety hazard data are stored in semistructured and unstructured text formats, which cannot be directly converted into understandable and usable information. Moreover, safety hazard text contains many fuzzy expressions, thereby increasing the difficulty of text semantic analysis; thus, how to accurately mine safety hazard information from complex and diverse text data is an urgent problem that must be solved. In consideration of this problem, we propose a bidirectional long short-term memory (BiLSTM) method with a fuzzy word vector and self-attention mechanism (FSABiLSTM) to automatically recognize safety hazard information. This method adopts TextRank and Word2vec to calculate the fuzzy word vector and process fuzzy expressions in safety hazard text. The safety hazard text semantic features are deeply extracted based on BiLSTM and a fuzzy word vector, and the extracted semantic features are analyzed via a self-attention mechanism. Actual construction safety hazard text is used to verify the reliability and applicability of the method, and the results indicate that the accuracy of this method, which outperforms existing machine learning methods, is 91.70%. In addition, the FSABiLSTM method can be used to automatically evaluate the risk degree of safety hazards; this use is beneficial to managing and controlling safety hazards. Concerning safety hazard text data, this study provides a new deep mining approach that can enhance safety management efficiency.