用图模型让建筑合规检查自动理解空间逻辑，准确率提升8.6%。
Automating Geometry-Intensive Compliance Checking in BIM: Graph-Based Semantic Reasoning Framework
- 构建跨模态知识图谱，关联法规语义与建筑几何数据。
- 在679个消防规范测试中达到84.3%准确率，优于基线8.6%。
- 适合建筑信息模型（BIM）领域需自动化合规审查的工程团队。
建筑信息模型（BIM）中自动化几何密集型合规检查仍面临重大技术瓶颈，主要源于高层法规逻辑与结构化IFC数据之间的语义差异。现有方法多依赖静态规则模板，难以处理多跳推理或跨越多个建筑实体的潜在空间依赖关系。为此，提出一种面向BIM的时空几何推理系统（SGR-BIM），作为集成式图驱动推理框架。SGR-BIM动态构建跨模态知识图谱，对齐用户意图、法规语义与BIM几何，实现无需硬编码的可解释推理。在679个经专家验证的消防规范查询上验证，该框架达84.3%准确率，较增强版单智能体基线提升8.6%。本研究提供了一种基于图的语义推理范式，显著提升了建筑、工程与施工（AEC）行业自动化几何合规检查流程的透明性与灵活性。
原文摘要 · Abstract (English)
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.
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