Generative AI Application for Building Industry
This paper investigates the transformative potential of generative AI technologies, particularly large language models (LLMs), within the building industry. By leveraging these advanced AI tools, the study explores their application across key areas such as energy code compliance, building design op...
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| creator | Wan, Hanlong Zhang, Jian Chen, Yan Xu, Weili Feng, Fan |
| description | This paper investigates the transformative potential of generative AI
technologies, particularly large language models (LLMs), within the building
industry. By leveraging these advanced AI tools, the study explores their
application across key areas such as energy code compliance, building design
optimization, and workforce training. The research highlights how LLMs can
automate labor-intensive processes, significantly improving efficiency,
accuracy, and safety in building practices. The paper also addresses the
challenges associated with interpreting complex visual and textual data in
architectural plans and regulatory codes, proposing innovative solutions to
enhance AI-driven compliance checking and design processes. Additionally, the
study considers the broader implications of AI integration, including the
development of AI-powered tools for comprehensive code compliance across
various regulatory domains and the potential for AI to revolutionize workforce
training through realistic simulations. This paper provides a comprehensive
analysis of the current capabilities of generative AI in the building industry
while outlining future directions for research and development, aiming to pave
the way for smarter, more sustainable, and responsive construction practices. |
| doi_str_mv | 10.48550/arxiv.2410.01098 |
| format | Article |
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technologies, particularly large language models (LLMs), within the building
industry. By leveraging these advanced AI tools, the study explores their
application across key areas such as energy code compliance, building design
optimization, and workforce training. The research highlights how LLMs can
automate labor-intensive processes, significantly improving efficiency,
accuracy, and safety in building practices. The paper also addresses the
challenges associated with interpreting complex visual and textual data in
architectural plans and regulatory codes, proposing innovative solutions to
enhance AI-driven compliance checking and design processes. Additionally, the
study considers the broader implications of AI integration, including the
development of AI-powered tools for comprehensive code compliance across
various regulatory domains and the potential for AI to revolutionize workforce
training through realistic simulations. This paper provides a comprehensive
analysis of the current capabilities of generative AI in the building industry
while outlining future directions for research and development, aiming to pave
the way for smarter, more sustainable, and responsive construction practices.</description><identifier>DOI: 10.48550/arxiv.2410.01098</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Systems and Control</subject><creationdate>2024-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2410.01098$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2410.01098$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Wan, Hanlong</creatorcontrib><creatorcontrib>Zhang, Jian</creatorcontrib><creatorcontrib>Chen, Yan</creatorcontrib><creatorcontrib>Xu, Weili</creatorcontrib><creatorcontrib>Feng, Fan</creatorcontrib><title>Generative AI Application for Building Industry</title><description>This paper investigates the transformative potential of generative AI
technologies, particularly large language models (LLMs), within the building
industry. By leveraging these advanced AI tools, the study explores their
application across key areas such as energy code compliance, building design
optimization, and workforce training. The research highlights how LLMs can
automate labor-intensive processes, significantly improving efficiency,
accuracy, and safety in building practices. The paper also addresses the
challenges associated with interpreting complex visual and textual data in
architectural plans and regulatory codes, proposing innovative solutions to
enhance AI-driven compliance checking and design processes. Additionally, the
study considers the broader implications of AI integration, including the
development of AI-powered tools for comprehensive code compliance across
various regulatory domains and the potential for AI to revolutionize workforce
training through realistic simulations. This paper provides a comprehensive
analysis of the current capabilities of generative AI in the building industry
while outlining future directions for research and development, aiming to pave
the way for smarter, more sustainable, and responsive construction practices.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Systems and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMgEKGBgaWFpwMui7p-alFiWWZJalKjh6KjgWFORkJgO5-XkKaflFCk6lmTkpmXnpCp55KaXFJUWVPAysaYk5xam8UJqbQd7NNcTZQxdsdHxBUWZuYlFlPMiKeLAVxoRVAAD0gTAt</recordid><startdate>20241001</startdate><enddate>20241001</enddate><creator>Wan, Hanlong</creator><creator>Zhang, Jian</creator><creator>Chen, Yan</creator><creator>Xu, Weili</creator><creator>Feng, Fan</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20241001</creationdate><title>Generative AI Application for Building Industry</title><author>Wan, Hanlong ; Zhang, Jian ; Chen, Yan ; Xu, Weili ; Feng, Fan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2410_010983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Systems and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Wan, Hanlong</creatorcontrib><creatorcontrib>Zhang, Jian</creatorcontrib><creatorcontrib>Chen, Yan</creatorcontrib><creatorcontrib>Xu, Weili</creatorcontrib><creatorcontrib>Feng, Fan</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wan, Hanlong</au><au>Zhang, Jian</au><au>Chen, Yan</au><au>Xu, Weili</au><au>Feng, Fan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generative AI Application for Building Industry</atitle><date>2024-10-01</date><risdate>2024</risdate><abstract>This paper investigates the transformative potential of generative AI
technologies, particularly large language models (LLMs), within the building
industry. By leveraging these advanced AI tools, the study explores their
application across key areas such as energy code compliance, building design
optimization, and workforce training. The research highlights how LLMs can
automate labor-intensive processes, significantly improving efficiency,
accuracy, and safety in building practices. The paper also addresses the
challenges associated with interpreting complex visual and textual data in
architectural plans and regulatory codes, proposing innovative solutions to
enhance AI-driven compliance checking and design processes. Additionally, the
study considers the broader implications of AI integration, including the
development of AI-powered tools for comprehensive code compliance across
various regulatory domains and the potential for AI to revolutionize workforce
training through realistic simulations. This paper provides a comprehensive
analysis of the current capabilities of generative AI in the building industry
while outlining future directions for research and development, aiming to pave
the way for smarter, more sustainable, and responsive construction practices.</abstract><doi>10.48550/arxiv.2410.01098</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Systems and Control |
| title | Generative AI Application for Building Industry |
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