| id |
caadria2021_000 |
| authors |
A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.) |
| year |
2021 |
| title |
CAADRIA 2021: Projections, Volume 1 |
| doi |
https://doi.org/10.52842/conf.caadria.2021.1
|
| source |
PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, 768 p. |
| summary |
Rapidly evolving technologies are increasingly shaping our societies as well as our understanding of the discipline of architecture. Computational developments in fields such as machine learning and data mining enable the creation of learning networks that involve architects alongside algorithms in developing new understanding. Such networks are increasingly able to observe current social conditions, plan, decide, act on changing scenarios, learn from the consequences of their actions, and recognize patterns out of complex activity networks. While digital technologies have already enabled architecture to transcend static physical boxes, new challenges of the present and visions for the future continue to call for both innovative responses integrating emerging technologies into experimental architectural practice and their critical reflection. In this process, the capability of adapting to complex social and environmental challenges through learning, prototyping and verifying solution proposals in the context of rapidly shifting realities has become a core challenge to the architecture discipline.
Supported by advancing technologies, architects and researchers are creating new frameworks for digital workflows that engage with new challenges in a variety of ways. Learning networks that recognize patterns from massive data, rapid prototyping systems that flexibly iterate innovative physical solutions, and adaptive design methods all contribute to a flexible and networked digital architecture that is able to learn from both past and present to evolve towards a promising vision of the future. |
| series |
CAADRIA |
| full text |
 file.pdf (597,578,917 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:49 |
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