面向住宅建筑平面图合规检查的自动化AI框架
Towards an automated AI-based framework for floor plan compliance checks for residential buildings
AI总结：
提出一种基于大语言模型和规则引擎的自动化框架，用于多单元住宅平面图的合规检查，解决手动检查效率低、可扩展性差的问题。
摘要：
为了改善澳大利亚城市居民的福祉，政府引入了SEPP65、BADS和SPP7.3等政策改革，以提高公寓设计质量。这些法规要求精确的几何和空间分析，以评估与健康相关的特征，包括日照、自然通风、隐私和空间效率。然而，合规检查因其手动、耗时的特性而仍然具有挑战性。此外，不断变化的政策限制了大规模评估数千套公寓的可扩展性。现有的自动化平面图分析方法较为分散，通常只关注单个公寓，缺乏多单元合规检查的统一框架。本文探讨了自动化平面图分析的当前进展，特别是AI驱动的方法，并强调了其实际应用中的关键挑战。为了解决这些不足，提出了一个用于多公寓建筑自动化合规检查的概念框架。在规则引擎中使用大语言模型（LLM）将文本建筑规范转换为可执行、可解释的规则。数据提取引擎将平面图图像分割为墙、房间、固定装置、文本和符号等元素，并将其转换为具有拓扑关系的结构化建筑图。然后，合规检查引擎利用LLM生成的规则对该结构化表示进行评估。所提出的框架提供了一种可扩展、一致且透明的自动化合规检查方法，适用于不同司法管辖区，支持高效执行公寓设计标准，并促进更健康、更高密度的城市发展。
英文摘要：
To improve residents' well-being in Australia's urban areas, governments have introduced policy reforms such as SEPP65, BADS, and SPP7.3 to enhance apartment design quality. These regulations require precise geometric and spatial analysis to evaluate health-related features, including daylight access, natural ventilation, privacy, and space efficiency. However, compliance checking remains challenging due to its manual, time-intensive nature. Additionally, evolving policies limit scalability for large-scale assessments across thousands of apartments. Existing automated floor plan analysis methods are fragmented and typically focus on single apartments, lacking a unified framework for multi-unit compliance checking. This article explores current advancements in automated floor plan analysis, particularly AI-driven approaches, and highlights key challenges in their practical adoption. To address these gaps, a conceptual framework is proposed for automated compliance checking in multi-apartment buildings. A Large Language Model (LLM) is used within a Rule Engine to convert textual building codes into executable, explainable rules. A Data Extraction Engine segments floor plan images into elements such as walls, rooms, fixtures, text, and symbols, and transforms them into a structured building graph with topological relationships. This structured representation is then evaluated by a Compliance Check Engine, which leverages LLM-generated rules for assessment. The proposed framework offers a scalable, consistent, and transparent approach to automated compliance checking across jurisdictions, supporting efficient enforcement of apartment design standards and promoting healthier, higher-density urban development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
