Abstract:

In order to assist in the development of safety hazard management measures for hydropower project construction, the safety hazard texts accumulated during the construction inspection of hydropower projects were collected. Entities and relationships from the semi-structured safety hazard texts were extracted using Python. A knowledge graph of safety hazards was constructed and imported into the neo4j graph database for storage. A Sentence-Bidirectional Encoder Representations from Transformer (BERT) model based on bidirectional coding was built for the semantic matching of construction hazards in hydropower projects. The deep semantic features of target hazards and historical hazards were learned, and the historical safety hazards most similar to target hazards were recommended. Using the Cypher query statement, the governance measures corresponding to the historical security risk were searched. The results show that the Sentence-BERT model has an accuracy of 96.48% in identifying architecturally and historically similar safety hazards, which is significantly better than BERT, Word2vec-Deep Semantic Similarity Model (Word2vec-DSSM), and BERT-DSSM models. Among 150 randomly selected target safety hazard data, the accuracy rate of testing historical similar safety hazard suggestions reaches 92%, and the retrieval effect of hazard management measures is demonstrated through the hazard knowledge graph, which verifies the applicability and effectiveness of the method.

Key words: hydropower project construction, safety hazard, semantic matching, management measures, intelligent recommendation, knowledge graph