INTELLIGENT IDENTIFICATION AND DECISION SUPPORT SYSTEM FOR TBM CONSTRUCTION RISK SYNERGISTICALLY DRIVEN BY KNOWLEDGE GRAPHS AND LARGE LANGUAGE MODELS

Tunnel Boring Machines (TBMs) are widely used in subway and tunnel projects due to their efficiency and safety. However, the complexity and high uncertainty of the construction environment lead to lag and limitations in traditional risk identification methods. Addressing the urgent need for intelligent management, this study combines the advantages of Knowledge Graphs (KG) in structured knowledge representation and Large Language Models (LLMs) in natural language understanding to construct a synergistically driven intelligent risk identification and decision support system for TBM construction.First, the study employs ontology modeling to build a risk knowledge graph covering risk factors, risk-causing mechanisms, and consequences, achieving entity-relation extraction through multi-source heterogeneous data mining. Second, the Large Language Model is adapted and fine-tuned by constructing an engineering corpus and injecting domain knowledge. Finally, a synergistic mechanism is designed to realize intelligent risk identification and decision-making.The innovations of this study include: (1) Proposing a "Knowledge Retrieval - Semantic Understanding - Logical Reasoning" three-layer architecture, effectively fusing explicit knowledge with reasoning capabilities; (2) Developing a multi-modal risk feature fusion identification model, improving identification accuracy and real-time performance; and (3) Building an intelligent recommendation engine for risk response plans, supporting multi-scenario simulation. Experimental and engineering application results indicate that the system significantly improves the precision and recall of risk identification, effectively enhancing the risk management level of TBM construction.