用多模型协作提升钢构拆除方案生成的智能化与精准度
Research on intelligent generation of structural demolition suggestions based on multi-model collaboration
- 融合检索增强与低秩微调，提升大模型在拆除领域的文本生成能力
- 相比CivilGPT，更聚焦结构关键信息，建议更具针对性
- 可基于工程实况自动推理，实现类人思考的智能建议生成
钢构拆除方案需依据具体工程特征及有限元模型更新结果编制，设计人员须参考标准规范和工程案例，但信息检索与语言组织耗时长，自动化与智能化程度低。本文提出一种基于多模型协作的结构拆除建议智能生成方法，通过检索增强生成（Retrieval-Augmented Generation）与低秩微调（Low-Rank Adaptation Fine-Tuning）技术，提升大语言模型在结构拆除领域的文本生成性能。所提多模型协同框架可从具体工程情况出发，驱动大模型以类人思维推理，生成与结构特征高度一致的拆除建议。相较于CivilGPT，本方法更聚焦结构关键信息，建议更具针对性。
原文摘要 · Abstract (English)
The steel structure demolition scheme needs to be compiled according to the specific engineering characteristics and the update results of the finite element model. The designers need to refer to the relevant engineering cases according to the standard requirements when compiling. It takes a lot of time to retrieve information and organize language, and the degree of automation and intelligence is low. This paper proposes an intelligent generation method of structural demolition suggestions based on multi-model collaboration, and improves the text generation performance of large language models in the field of structural demolition by Retrieval-Augmented Generation and Low-Rank Adaptation Fine-Tuning technology. The intelligent generation framework of multi-model collaborative structural demolition suggestions can start from the specific engineering situation, drive the large language model to answer with anthropomorphic thinking, and propose demolition suggestions that are highly consistent with the characteristics of the structure. Compared with CivilGPT, the multi-model collaboration framework proposed in this paper can focus more on the key information of the structure, and the suggestions are more targeted.
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