Intelligent decision support for tunnel fire incidents: integrating dynamic knowledge graph with large language models

• Proposed a knowledge extraction strategy for human-LLM collaboration. • Established a concept-fact-norm knowledge graph framework for tunnel fires. • Fused KG and LLM enabling second-level decision-making responses. • Integrated RAG with prompt, enabling over 70% reliability on professional QA. • Validated the reduced workload in a real tunnel design case of the optimized system. Tunnel fires pose severe threats due to the confined nature of tunnel environments, resulting in significant casualties, property damage, and transportation network disruptions. Effective fire prevention, emergency response, and post-disaster recovery rely heavily on comprehensive and well-structured knowledge. However, existing…

• NN
  National Natural Science Foundation of China
• NU
  National University's Basic Research Foundation of China