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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.2
- 수록면
- 67 - 76 (10page)
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초록· 키워드
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This study delves into the use of generative AI, specifically ChatGPT and DALL.E, in architectural design, focusing on their abilities to comprehend architectural blueprints, interpret sketches, and generate 3D images. It represents pioneering research in integrating ChatGPT into architecture, offering fresh insights. The methodology consists of two phases: the first phase assessed ChatGPT"s capacity to interpret and provide feedback on architectural sketches, revealing its potential for creative input but limitations in understanding complex designs; the second phase examined DALL.E"s effectiveness in generating 3D images from textual descriptions, where it showed promise in producing contextually relevant images, albeit with some challenges in detailed architectural understanding. This study finds that while these AI tools can enhance initial design stages with innovative suggestions and preliminary analyses, they cannot fully replace the nuanced judgment and expertise of professional architects. However, this research highlights ChatGPT"s relevance for specific architectural tasks, enhancing their efficiency. One limitation is the reliance on a single building design, potentially limiting the scope of architectural challenges examined. Future studies should incorporate diverse designs for broader validation. In summary, integrating generative AI into architectural design shows promise for boosting creativity and efficiency, although these tools are not yet equipped for complex architectural planning. They lay a valuable foundation for future advancements in this field.
목차
Abstract
1. 서론
2. 연구 방법
3. ChatGPT의 건축적 활용
4. 토의
5. 결론
REFERENCES
참고문헌 (17)
참고문헌 신청
Barr, J., & Shaw, P. (2018). Ai application to data analysis, automatic file processing. In 2018 First International Conference on Artificial Intelligence for Industries, AI4I, 100-105. IEEE.
Grace, K., Salvatier, J., Dafoe, A., ... & Evans, O. (2018). When will AI exceed human performance? Evidence from AI experts. Journal of Artificial Intelligence Research, 62, 729-754.
Haymaker, J. R. (2011). Opportunities for AI to improve sustainable building design processes. In2011 AAAI Spring Symposium Series.
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., ... & Sifre, L. (2022). Training compute-optimal large language models, 2203.15556.
Joshi, G., Walambe, R., & Kotecha, K. (2021). A review on explainability in multimodal deep neural nets. IEEE Access, 9, 59800-59821.
Grace, K., Salvatier, J., Dafoe, A., ... & Evans, O. (2018). When will AI exceed human performance? Evidence from AI experts. Journal of Artificial Intelligence Research, 62, 729-754.
Haymaker, J. R. (2011). Opportunities for AI to improve sustainable building design processes. In2011 AAAI Spring Symposium Series.
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., ... & Sifre, L. (2022). Training compute-optimal large language models, 2203.15556.
Joshi, G., Walambe, R., & Kotecha, K. (2021). A review on explainability in multimodal deep neural nets. IEEE Access, 9, 59800-59821.
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