基于多模型协作的结构拆除建议智能生成研究
Research on intelligent generation of structural demolition suggestions based on multi-model collaboration
AI总结：
本文针对钢结构拆除方案编制自动化程度低的问题，提出基于多模型协作的智能生成方法，结合检索增强生成与低秩适配微调技术，使拆除建议更聚焦结构关键信息且更具针对性。
摘要：
钢结构拆除方案需要根据具体工程特点和有限元模型的更新结果进行编制。设计人员在编制时需要按照规范要求参考相关工程案例。检索信息和组织语言需要耗费大量时间，自动化和智能化程度较低。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，并通过检索增强生成和低秩适配微调技术提升大语言模型在结构拆除领域的文本生成性能。多模型协作的结构拆除建议智能生成框架能够从具体工程情况出发，驱动大语言模型以拟人化思维进行回答，提出与结构特征高度一致的拆除建议。与CivilGPT相比，本文提出的多模型协作框架能够更聚焦于结构的关键信息，建议更具针对性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
