Chapter Early Visualization Approach to the Generative Architectural Simulation Using Light Analysis Images
This paper presents the potential utility of generative artificial intelligence-based light analysis simulation visualization image in the early phase of architectural planning and design. Facilitating the simulation of a building's performance during the early stages of planning and design presents...
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| Príomhchruthaitheoirí: | , , , |
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| Formáid: | Online |
| Teanga: | Béarla |
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Firenze University Press
2024
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| Ábhair: | |
| Rochtain ar líne: | ONIX_20240402_9791221502893_5 |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| _version_ | 1865099992425824256 |
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| author | Chae, Sumin Kim, Bomin Yoo, Youngjin Lee, Jin-Kook |
| author_browse | Chae, Sumin Kim, Bomin Lee, Jin-Kook Yoo, Youngjin |
| author_facet | Chae, Sumin Kim, Bomin Yoo, Youngjin Lee, Jin-Kook |
| author_sort | Chae, Sumin |
| collection | Directory of Open Access Books |
| description | This paper presents the potential utility of generative artificial intelligence-based light analysis simulation visualization image in the early phase of architectural planning and design. Facilitating the simulation of a building's performance during the early stages of planning and design presents numerous advantages, such as cost savings and enhanced ease of communication among stakeholders. However, the assessment of design performance is typically conducted during the design development phase or post-design completion. Processing a substantial volume of data based on design alternatives demands considerable time and resources, thus constraining the immediate provision of simulation results. This paper aims to utilize generative AI to produce visualization results of simulations with a predefined level of accuracy, with a specific focus on the architectural aspect rather than the physical and engineering functionalities of the simulation. Consequently, the study employs the following approach: 1) Analyze prominent characteristics and elements within light analysis simulation. 2) Based on this analysis, generate high-quality visualization image data additionally through Building Information Modeling (BIM). 3) Construct a dataset by pairing the generated lighting analysis visualization image with prompts. 4) Utilize the established dataset to create an additional learning model for light analysis visualization images. This study is expected to provide immediate and efficient assistance in design decision-making during the early phases by generating visualization images with high accuracy, reflecting prominent qualitative aspects related to light analysis and processing within the simulation |
| format | Online |
| id | doab-20.500.12854ir-136791 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1367912024-05-08T19:59:16Z Chapter Early Visualization Approach to the Generative Architectural Simulation Using Light Analysis Images Chae, Sumin Kim, Bomin Yoo, Youngjin Lee, Jin-Kook Architectural Design Architectural Visualization Generative AI BIM (building information modeling) Fine Tuning Model thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization This paper presents the potential utility of generative artificial intelligence-based light analysis simulation visualization image in the early phase of architectural planning and design. Facilitating the simulation of a building's performance during the early stages of planning and design presents numerous advantages, such as cost savings and enhanced ease of communication among stakeholders. However, the assessment of design performance is typically conducted during the design development phase or post-design completion. Processing a substantial volume of data based on design alternatives demands considerable time and resources, thus constraining the immediate provision of simulation results. This paper aims to utilize generative AI to produce visualization results of simulations with a predefined level of accuracy, with a specific focus on the architectural aspect rather than the physical and engineering functionalities of the simulation. Consequently, the study employs the following approach: 1) Analyze prominent characteristics and elements within light analysis simulation. 2) Based on this analysis, generate high-quality visualization image data additionally through Building Information Modeling (BIM). 3) Construct a dataset by pairing the generated lighting analysis visualization image with prompts. 4) Utilize the established dataset to create an additional learning model for light analysis visualization images. This study is expected to provide immediate and efficient assistance in design decision-making during the early phases by generating visualization images with high accuracy, reflecting prominent qualitative aspects related to light analysis and processing within the simulation 2024-05-08T19:59:14Z 2024-05-08T19:59:14Z 2024-04-02T15:44:22Z 2023 chapter ONIX_20240402_9791221502893_5 2704-5846 https://library.oapen.org/handle/20.500.12657/89036 9791221502893 https://directory.doabooks.org/handle/20.500.12854/136791 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89036/1/9791221502893_96.pdf Firenze University Press 10.36253/979-12-215-0289-3.96 10.36253/979-12-215-0289-3.96 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 7 Florence open access |
| spellingShingle | Architectural Design Architectural Visualization Generative AI BIM (building information modeling) Fine Tuning Model thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization Chae, Sumin Kim, Bomin Yoo, Youngjin Lee, Jin-Kook Chapter Early Visualization Approach to the Generative Architectural Simulation Using Light Analysis Images |
| title | Chapter Early Visualization Approach to the Generative Architectural Simulation Using Light Analysis Images |
| title_full | Chapter Early Visualization Approach to the Generative Architectural Simulation Using Light Analysis Images |
| title_fullStr | Chapter Early Visualization Approach to the Generative Architectural Simulation Using Light Analysis Images |
| title_full_unstemmed | Chapter Early Visualization Approach to the Generative Architectural Simulation Using Light Analysis Images |
| title_short | Chapter Early Visualization Approach to the Generative Architectural Simulation Using Light Analysis Images |
| title_sort | chapter early visualization approach to the generative architectural simulation using light analysis images |
| topic | Architectural Design Architectural Visualization Generative AI BIM (building information modeling) Fine Tuning Model thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization |
| topic_facet | Architectural Design Architectural Visualization Generative AI BIM (building information modeling) Fine Tuning Model thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization |
| url | ONIX_20240402_9791221502893_5 |
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