BIM合规检查的自动化是建筑行业的长期痛点，SGR-BIM用图推理解决了多跳空间依赖问题。做BIM开发或建筑规范自动化的团队，可以直接参考其84.3%准确率的验证结果。
建筑信息模型（BIM）中几何密集型规范的合规检查自动化长期受限于高层法规逻辑与结构化IFC数据之间的语义鸿沟。现有方法依赖静态规则模板，难以处理多跳推理链或跨实体的空间依赖。为此，研究者提出SGR-BIM框架，通过动态构建跨模态知识图谱，将用户意图、法规语义与BIM几何对齐，实现可解释的推理。在679个消防规范专家验证查询上，该框架达到84.3%的准确率，比增强工具的单智能体基线提升8.6%。该研究为AEC行业提供了更透明、灵活的几何合规检查自动化范式。
Automating Geometry-Intensive Compliance Checking in BIM: Graph-Based Semantic Reasoning Framework
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.