Automating compliance check for geometry-intensive regulations remains a significant technical bottleneck in Building Information Modeling (BIM), primarily due to the semantic disparity between high-level regulatory logic and structured IFC data. Existing methods, often reliant on static rule templates, struggle to traverse multi-hop reasoning chains or resolve latent spatial dependencies across multiple building entities. To address these challenges, a Spatial-Geometric Reasoning System for Building Information Modeling (SGR-BIM) is proposed as an integrative graph-driven reasoning framework. SGR-BIM dynamically constructs a cross-modal knowledge graph that aligns user intent, regulatory semantics, and BIM geometry, enabling interpretable reasoning without rigid hard-coding. Validated on 679 expert-verified queries from fire safety codes, the framework achieves 84.3% accuracy, representing an 8.6% improvement over enhanced-tool single-agent baselines. This research provides a graph-based semantic reasoning paradigm, enhancing the transparency and flexibility of automated geometric compliance check workflows in the Architecture, Engineering, and Construction (AEC) industry.

翻译：针对几何密集型规范的自动化合规检查仍是建筑信息模型（BIM）领域的重要技术瓶颈，其根本原因在于高层级法规逻辑与结构化IFC数据之间存在语义差异性。现有方法通常依赖静态规则模板，难以实现多跳推理链的遍历或解析跨多个建筑实体的潜在空间依赖关系。为应对这些挑战，本文提出面向建筑信息模型的空间几何推理系统（Spatial-Geometric Reasoning System for Building Information Modeling, SGR-BIM），该框架采用集成式图驱动推理结构。SGR-BIM可动态构建跨模态知识图谱，将用户意图、法规语义与BIM几何信息进行对齐，在无需刚性硬编码的前提下实现可解释推理。经679条来自防火规范且经专家验证的查询语句测试，该框架达到84.3%的准确率，相较于增强型工具单智能体基线模型提升8.6%。本研究提出的基于图的语义推理范式，增强了建筑、工程与施工（AEC）行业中自动化几何合规检查工作流的透明性与灵活性。