2026-09-19T00:21:50Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1298552026-06-06T00:47:30Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Takehiko Nagakura.
Wu, Chaoyun,M. ArchMassachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Architecture.
Massachusetts Institute of Technology. Department of Architecture
2021-02-19T20:22:16Z
2021-02-19T20:22:16Z
2020
2020
https://hdl.handle.net/1721.1/129855
1237108247
Thesis: M. Arch., Massachusetts Institute of Technology, Department of Architecture, February, 2020
Cataloged from student-submitted thesis.
Includes bibliographical references (pages 99-100).
Technology has always been an important factor that shapes the way we think about Architecture. In recent years, Machine Learning technology has been gaining more and more attention. Different from traditional types of programming that rely on explicit instructions, Machine Learning allows computers to learn to execute certain tasks "by themselves". This new technology has revolutionized many industries and showed much potential. Examples like AlphaGo and OpenAI Five had shown Machine Learning's capability in solving complex problems. The Architectural design industry is not an exception. Early-stage explorations of this technology are emerging and have shown potential in solving certain design problems. However, basic problems regarding the nature of Machine Learning and its role in Architecture design remain to be answered. What does Machine Learning mean to Architecture? What will be its role in Architectural design? Will it replace human architects? Will it merely be a design tool? Or is it relevant to Architecture at all? To answer these questions, this thesis explored with a specific type of Machine Learning algorithm called Pix2Pix to investigate what can and cannot be learned by a computer through Machine Learning, and to evaluate what Machine Learning means for architects. It concluded that Machine Learning cannot be a creative design agent, but can be a powerful tool in solving conventional design problems. On this basis, this thesis proposed a prototype pipeline of integrating the technology into the design process, which is a combination of Generative Adversarial Network (Pix2Pix), Bayesian Network and Evolutionary Algorithm.
by Chaoyun Wu.
M.Arch.
M.Arch. Massachusetts Institute of Technology, Department of Architecture
101 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Architecture.
Machine learning in housing design : exploration of generative adversarial network in site plan / floorplan generation
Exploration of generative adversarial network in site plan / floorplan generation
Thesis
application/pdf
2021-02-19T20:21:46Z
Publication
Master
Arch
unknown
unknown
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