用大模型自动检查建筑模型是否符合规范，省时又准确
Large Language Model-Driven Code Compliance Checking in Building Information Modeling
- 用大模型解析建筑规范，自动生成Python脚本执行检查
- 案例显示检查时间大幅减少，违规项识别更精准
- 适合建筑信息模型从业者，尤其需频繁合规审查的团队
本研究针对建筑信息模型（BIM）中人工合规检查耗时且易出错的问题，提出一种基于大语言模型（LLM）的半自动化解决方案。系统集成GPT、Claude、Gemini和Llama等大模型与Revit软件，实现对建筑规范的语义理解、Python脚本生成及BIM环境内的合规性检查。在单户住宅与办公楼项目上的案例研究表明，该方法显著降低了检查所需时间和人力，提升了准确性。系统可自动识别非合规的房间尺寸、材料使用及构件位置等问题，通过分析对象间关系并生成可操作报告，有效避免重复劳动，简化复杂法规，并确保标准可靠遵循。该方案具备全面性、可扩展性和成本效益，为建筑工程中多样化法规文档的合规检查提供了有前景的解决方案。
原文摘要 · Abstract (English)
This research addresses the time-consuming and error-prone nature of manual code compliance checking in Building Information Modeling (BIM) by introducing a Large Language Model (LLM)-driven approach to semi-automate this critical process. The developed system integrates LLMs such as GPT, Claude, Gemini, and Llama, with Revit software to interpret building codes, generate Python scripts, and perform semi-automated compliance checks within the BIM environment. Case studies on a single-family residential project and an office building project demonstrated the system's ability to reduce the time and effort required for compliance checks while improving accuracy. It streamlined the identification of violations, such as non-compliant room dimensions, material usage, and object placements, by automatically assessing relationships and generating actionable reports. Compared to manual methods, the system eliminated repetitive tasks, simplified complex regulations, and ensured reliable adherence to standards. By offering a comprehensive, adaptable, and cost-effective solution, this proposed approach offers a promising advancement in BIM-based compliance checking, with potential applications across diverse regulatory documents in construction projects.
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