CE-ISMLLM:用于知识检索与事故预防的土木工程智能安全管理大语言模型框架OACHSSCD

With the rapid development of generative artificial intelligence in the era of Industry 4.0,large language model(LLM)has been widely applied to various vertical domains.To build a proprie-tary RAG-based LLM for intelligent safety management in civil engineering,the primary priority is to segment and embed expert knowledge text of civil engineering management into the basic LLM's vec-tor database without pre-training or fine-tuning.By combining prompt enhancement and expert evalua-tion mechanisms,the output of the LLM undergoes iterative refinement through generative adversarial iterations,ultimately yielding an ideal response.The performance of the LLM is tested by specific pro-fessional questions and real accident cases.Various types of questions and accident details are used as query inputs.The responses generated by the LLM are quantitatively evaluated through multiple indi-cators.Test results indicate that the proposed civil engineering intelligent safety management LLM(CE-ISMLLM)framework has more advantages in knowledge retrieval and accident prevention than general LLM.