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oapen-20.500.12657-890362024-04-03T02:22:42Z 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 2024-04-02T15:44:22Z 2024-04-02T15:44:22Z 2023 chapter ONIX_20240402_9791221502893_5 2704-5846 9791221502893 https://library.oapen.org/handle/20.500.12657/89036 eng Proceedings e report application/pdf n/a 9791221502893_96.pdf https://books.fupress.com/doi/capitoli/979-12-215-0289-3_96 Firenze University Press 10.36253/979-12-215-0289-3.96 10.36253/979-12-215-0289-3.96 bf65d21a-78e5-4ba2-983a-dbfa90962870 9791221502893 137 7 Florence open access
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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
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