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Semantic matching model of potential safety hazards in hydroelectric project construction
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Shu CHEN1, 2, Dianxue WANG1, 2, Yingliu YANG3, **, Kunyu CAO1, 2, Benwu NIE2, 4
China Safety Science Journal | 2024, 34(12) : 40 - 47
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China Safety Science Journal | 2024, 34(12): 40-47
Safety engineering technology
Semantic matching model of potential safety hazards in hydroelectric project construction
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Shu CHEN1, 2, Dianxue WANG1, 2, Yingliu YANG3, **, Kunyu CAO1, 2, Benwu NIE2, 4
Affiliations
  • 1 Hubei Key Laboratory of Construction and Management in Hydropower Engineering,China Three Gorges University,Yichang Hubei 443002,China
  • 2 College of Hydraulic & Environmental Engineering,China Three Gorges University,Yichang Hubei 443002,China
  • 3 School of Management Science & Real Estate,Chongqing University,Chongqing 400044,China
  • 4 China Energy Investment Co.,Ltd.,Chengdu Sichuan 610095,China
Published: 2024-12-28 doi: 10.16265/j.cnki.issn1003-3033.2024.12.0795
Outline
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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.

hydropower project construction  /  safety hazard  /  semantic matching  /  management measures  /  intelligent recommendation  /  knowledge graph
Shu CHEN, Dianxue WANG, Yingliu YANG, Kunyu CAO, Benwu NIE. Semantic matching model of potential safety hazards in hydroelectric project construction[J]. China Safety Science Journal, 2024 , 34 (12) : 40 -47 . DOI: 10.16265/j.cnki.issn1003-3033.2024.12.0795
Year 2024 volume 34 Issue 12
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.12.0795
  • Receive Date:2024-07-11
  • Online Date:2025-07-09
  • Published:2024-12-28
Article Data
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History
  • Received:2024-07-11
  • Revised:2024-09-15
Funding
Affiliations
    1 Hubei Key Laboratory of Construction and Management in Hydropower Engineering,China Three Gorges University,Yichang Hubei 443002,China
    2 College of Hydraulic & Environmental Engineering,China Three Gorges University,Yichang Hubei 443002,China
    3 School of Management Science & Real Estate,Chongqing University,Chongqing 400044,China
    4 China Energy Investment Co.,Ltd.,Chengdu Sichuan 610095,China
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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