Building Information Modeling (BIM) is widely used in the Architecture, Engineering, and Construction (AEC) industry, but the complexity of Industry Foundation Classes (IFC) limits accessibility for non-expert users. To address this, we introduce IfcLLM, a hybrid framework for natural language interaction with IFC-based BIM models. It transforms IFC models into complementary representations: a relational representation for structured element properties and geometry, and a graph representation for topological relationships. These representations are integrated through iterative retry-and-refine LLM reasoning. We implement the framework using an open-weight LLM (GPT OSS 120B), supporting reproducible and deployment-oriented workflows. Evaluation on three IFC models with queries derived from 30 scenarios shows first-attempt accuracy of 93.3%-100%, with all failures recovered using a fallback LLM. The results show that combining complementary representations with iterative reasoning enables more accessible natural language querying of IFC data while supporting routine BIM analysis tasks.
面向IFC模型自然语言查询的关系与图表示混合框架 / A Hybrid Framework for Natural Language Querying of IFC Models with Relational and Graph Representations
本文提出一种名为IfcLLM的混合框架，通过将建筑信息模型（IFC）同时转化为关系型数据库（存储构件属性与几何信息）和图结构（存储构件之间的拓扑连接），并利用大语言模型（LLM）的迭代试错与优化推理机制，使得非专业用户也能用日常语言直接查询复杂的BIM模型，实验表明首次查询准确率高达93.3%以上。
源自 arXiv: 2605.13236
