用自然语言生成可直接导入BIM的建筑平面图，减少设计重复劳动。
Text-to-Layout: A Generative Workflow for Drafting Architectural Floor Plans Using LLMs
- 通过提示词工程+算法优化，将文字描述转为带墙门窗家具的布局
- 案例显示能快速生成功能完整、空间合理的住宅平面方案
- 输出保留Revit全部参数属性，适合建筑师直接用于专业流程
本文提出一种基于大语言模型（LLMs）的AI辅助工作流，可根据自然语言提示自动生成包含墙体、门、窗及家具布置的初步建筑平面图。系统结合提示词工程、家具布局优化算法与Python脚本，生成的空间布局合理，且兼容Autodesk Revit等设计工具。以中等规模住宅为例的案例研究证明，该方法可在极少人工干预下产出功能完备、结构清晰的设计草案。整个流程设计透明，关键提示参数均已记录，便于其他研究者独立复现。生成的模型完整保留了Revit原生的参数化属性，可直接接入专业BIM工作流程。
原文摘要 · Abstract (English)
This paper presents the development of an AI-powered workflow that uses Large Language Models (LLMs) to assist in drafting schematic architectural floor plans from natural language prompts. The proposed system interprets textual input to automatically generate layout options including walls, doors, windows, and furniture arrangements. It combines prompt engineering, a furniture placement refinement algorithm, and Python scripting to produce spatially coherent draft plans compatible with design tools such as Autodesk Revit. A case study of a mid-sized residential layout demonstrates the approach's ability to generate functional and structured outputs with minimal manual effort. The workflow is designed for transparent replication, with all key prompt specifications documented to enable independent implementation by other researchers. In addition, the generated models preserve the full range of Revit-native parametric attributes required for direct integration into professional BIM processes.
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