[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81714-en":3,"doc-seo-81714-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81714,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Towards an automated AI-based framework for floor plan compliance checks for residential buildings","The framework targets improved apartment well-being in Australia through automated compliance checking of regulations such as SEPP65, BADS, and SPP7.3, which demand precise geometric and spatial analysis. Manual auditing is time-consuming and does not scale to thousands of units, while existing AI floor-plan methods are fragmented and often limited to single apartments. The approach combines an LLM rule generation pipeline, a computer-vision data extraction engine, and a compliance evaluation engine using a structured building graph for scalable, consistent, and explainable results across jurisdictions.","arXiv :2607 .00015v1 [ cs .CY] 26 May 2026  \nTowards an automated AI-based framework for floor plan compliance checks for  \nresidential buildings  \nSubash Gautama,b,∗, Debaditya Acharyaa , Alexandra Kleemanb , Sarah Fosterb  \na School of Global Urban and Social Studies, RMIT University, Melbourne, Australia b Department of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Australia  \nAbstract  \nTo 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.  \nKeywords: Building compliance check, Automation in construction, Building Policy, Large Language Models, Computer Vision  \n1. Introduction  \nAustralia’s housing landscape has undergone a significant transformation in recent years, shaped by rapid urban population growth, rising land values, and concerns about the environmental and economic sustainability of urban sprawl (National Housing Supply and Affordability Council, 2024) . To mitigate these impacts, governments have turned to urban consolidation and higher-density development as key strategies to accommodate growth within established areas. Apartments play an increasingly large role in the Australian property market, housing more than 2.6 million people (10% of Australians) (Australian Bureau of Statistics, 2022) . This trend is particularly pronounced in Australia’s most populated cities, Sydney and Melbourne, where apartments accounted for 31% and 16% of occupied dwellings, respectively, in 2021 .  \nAmid the shift toward apartment living, some have voiced concerns about the design quality of newly built developments (Easthope & Judd, 2010) . A growing body of research shows that apartment design influences not only residents’ comfort and satisfaction, but also health outcomes (Foster et al., 2022b; Giles-Corti et al., 2015; Hooper et al., 2023; Kleeman & Foster, 2023, 2024; Kleeman et al., 2022) . Key health-promoting apartment design features include adequate space (Evans et al., 1996), daylight access (Brown & Jacobs, 2011), natural ventilation (Wong & Huang, 2004), acoustic privacy (Andargie et al., 2021; Babisch et al., 2014), thermal comfort (Lloyd et al. , 2008), and access to green/natural outlooks (Kaplan, 2001) . Building-level features, such as communal open space and circulation areas, can also foster social interaction and a sense of community among ","cbCaii8Q7I4F5AeV","https://ap.wps.com/l/cbCaii8Q7I4F5AeV","pdf",35081473,4,1,24,"English","en",105,"# Introduction\n## Apartment design quality and health outcomes\n## Policy reforms for apartment design in Australia\n# Proposed automated compliance framework\n## Rule engine with LLM-generated, executable policies\n## Data extraction and building graph construction\n## Compliance check engine and evaluation\n# Challenges and implications for scalable adoption","[{\"question\":\"Why is automated compliance checking for apartment floor plans needed?\",\"answer\":\"Compliance requires precise geometric and spatial analysis tied to health-related design features, but manual checking is time-intensive and hard to scale across large numbers of apartments.\"},{\"question\":\"What multi-component pipeline does the proposed framework use?\",\"answer\":\"A rule engine converts textual building codes into executable, explainable rules using an LLM, while a data extraction engine segments floor plan images into elements and builds a structured graph for evaluation by a compliance check engine.\"},{\"question\":\"How does the framework improve scalability and transparency compared with prior approaches?\",\"answer\":\"It provides a unified framework for multi-apartment buildings and uses explainable, LLM-generated rules applied to a structured building representation, enabling consistent and transparent assessments across 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is automated compliance checking for apartment floor plans needed?","Question",{"text":75,"@type":76},"Compliance requires precise geometric and spatial analysis tied to health-related design features, but manual checking is time-intensive and hard to scale across large numbers of apartments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What multi-component pipeline does the proposed framework use?",{"text":80,"@type":76},"A rule engine converts textual building codes into executable, explainable rules using an LLM, while a data extraction engine segments floor plan images into elements and builds a structured graph for evaluation by a compliance check engine.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the framework improve scalability and transparency compared with prior approaches?",{"text":84,"@type":76},"It provides a unified framework for multi-apartment buildings and uses explainable, LLM-generated rules applied to a structured building representation, 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