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Early Visualization Approach to the Generative Architectural Simulation Using Light Analysis Images

  • Bomin Kim
  • Sumin Chae
  • Youngjin Yoo
  • Jin-Kook Lee

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

  • Keywords:
  • Architectural Design,
  • Architectural Visualization,
  • Generative AI,
  • BIM (building information modeling),
  • Fine Tuning Model,
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Bomin Kim

Yonsei University, Korea (the Republic of) - ORCID: 0009-0007-2500-3231

Sumin Chae

Yonsei University, Korea (the Republic of)

Youngjin Yoo

Yonsei University, Korea (the Republic of) - ORCID: 0009-0002-5362-328X

Jin-Kook Lee

Yonsei University, Korea (the Republic of) - ORCID: 0000-0002-5179-6550

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  • Publication Year: 2023
  • Pages: 958-964

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  • Publication Year: 2023

Chapter Information

Chapter Title

Early Visualization Approach to the Generative Architectural Simulation Using Light Analysis Images

Authors

Bomin Kim, Sumin Chae, Youngjin Yoo, Jin-Kook Lee

DOI

10.36253/979-12-215-0289-3.96

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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