- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
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- Chinese Science Citation Database
| Citation: | ZHU Qing, WANG Yanjun, DING Yulin, WU Tingchen, CHEN Shichu, HUANG Qiyu, WANG Xiaoyong, HAN Weifeng. A Knowledge Graph Construction Method for Tunnel Boring Machine Jamming Risk Assessment Based on Multi-Factor Semantic Associations[J]. Journal of Southwest Jiaotong University, 2026, 61(3): 1009-1020. doi: 10.3969/j.issn.0258-2724.20250430
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In complex mountainous areas, tunnel boring machine (TBM) construction often faces adverse geological conditions, such as fault fracture zones and large deformation of surrounding rock, which can easily cause jamming accidents. TBM jamming risk assessment involves multi-source heterogeneous information such as monitoring data and empirical rules. Existing studies mostly focus on the local modeling of assessment data or models and lack a systematic characterization of key assessment factors and their semantic associations, leading to a fragmented risk representation structure, which makes it difficult to support dynamic assessment and analysis under complex conditions. To address the above issues, a construction method of a knowledge graph for TBM jamming risk assessment based on multi-factor semantic associations was proposed. This method constructed an ontology structure by integrating five types of core factors: "assessment task, assessment data, assessment parameter, assessment model, and assessment indicator", achieving semantic association representation of the risk assessment process. Combined with a stepwise prompting strategy of a large language model, explicit and implicit knowledge was extracted from risk assessment literature, construction standards, and engineering cases, and the effectiveness of the prompting strategy was verified through ablation experiments. Furthermore, the Neo4j graph database was utilized to realize structured storage and management of the knowledge graph. The experimental results indicate that on the constructed annotated dataset, the precision, recall, and F1 score of knowledge extraction are 88.54%, 83.42%, and 85.67%, respectively, showing a significant improvement compared with using a single prompting strategy. The analysis of typical engineering cases shows that the proposed method can effectively organize factors related to jamming risk, exhibits a good performance in the completeness of risk information representation and semantic organization, and provides effective support for the structured representation and assessment of jamming risk.
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