| id |
ecaade2022_367 |
| authors |
Doumpioti, Christina and Huang, Jeffrey |
| year |
2022 |
| title |
Field Condition - Environmental sensibility of spatial configurations with the use of machine intelligence |
| doi |
https://doi.org/10.52842/conf.ecaade.2022.2.067
|
| source |
Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 67–74 |
| summary |
Within computational environmental design (CED), different Machine Learning (ML) models are gaining ground. They aim for time efficiency by automating simulation and speeding up environmental performance feedback. This study suggests an approach that enhances not the optimization but the generative aspect of environmentally driven ML processes in architectural design. We follow Stan Allen's (2009) idea of 'field conditions' as a bottom-up phenomenon according to which form and space emerge from local invisible and dynamic connections. By employing parametric modeling, environmental analysis data, and conditional Generative Adversarial Networks [cGAN] we introduce a generative approach in design that reverses the typical design process of going from formal interpretation to analysis and encourages the emergence of spatial configurations with embedded environmental intelligence. We call it Intensive-driven Environmental Design Computation [IEDC], and we employ it in a case study on a residential building typology encountered in the Mediterranean. The paper describes the process, emphasizing dataset preparation as the stage where the logic of field conditions is established. The proposed research differentiates from cGAN models that offer automatic environmental performance predictions to one that spatial predictions stem from dynamic fields. |
| keywords |
Field Architecture, Environmental Design, Generative Design, Machine Learning, Residential Typologies |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (929,892 bytes) |
| references |
Content-type: text/plain
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Allen S. (2009)
 From Object to field in Space Reader: Heterogeneous Space in Architecture
, John Wiley & Sons
|
|
|
|
|
Ayoub, M. (2020)
A review on machine learning algorithms to predict daylighting inside buildings
, Solar Energy, 202, 249-275. https://doi.org/10.1016/j.solener.2020.03.104Bitmap+ (2022). Available at: https://www.food4rhino.com/en/app/bitmap?lang=en (Accessed: 1 Aril 2022)
|
|
|
|
|
Doumpioti, C. & Huang, J. (2021)
Intensive Differences in Spatial Design
, 39th eCAADe ConferenceNovi Sad, Serbia (pp.9-16)
|
|
|
|
|
Emami, Hajar, Majid Moradi Aliabadi, Ming Dong, and Ratna Babu Chinnam. (2020)
SPA-GAN: Spatial Attention GAN for Image-to-Image Translation
, arXiv:1908.06616. arXiv. https://doi.org/10.48550/arXiv.1908.06616
|
|
|
|
|
Galanos, T, Chronis, A, Vesely, O, Aichinger, A, and Koenig, R. (2019)
Best of both worlds - using computational design and deep learning for real-time urban performance evaluation
, 1st International Conference on Optimization Driven Architectural Design
|
|
|
|
|
Huang, W., & Zheng, H. (2018)
Architectural Drawings Recognition and Generation through Machine Learning
, 38th Annual Conference of the Association for Computer Aided DesignArchitecture, Recalibration. On imprecisionand infidelity. ACADIA 2018, Mexico City (pp. 156-165)
|
|
|
|
|
Isola, P, Zhu, J., Zhou, T., & Efros, A., A. (2017)
Image-to-Image Translation with Conditional Adversarial Networks
, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5967-5976. https://github.com/phillipi/pix2pix
|
|
|
|
|
Mokhtar, S., Sojka, A., & Davila, C. C. (n.d.)
Conditional Generative Adversarial Networks for Pedestrian Wind Flow Approximation
, 11th annual Symposium on Simulation for Architecture and Urban Design (SimAUD)
|
|
|
|
|
Yousif, S., & Bolojan, D. (2021)
Deep-Performance: Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems
, 26th International Conference on Computer-Aided Architectural Design ResearchAsia: Projections, CAADRIA 2020 (pp. 151-160). The Association for Computer-Aided Architectural Design ResearchAsia (CAADRIA)
|
|
|
|
|
Zhu, Jun-Yan, Taesung Park, Phillip Isola, and Alexei A. Efros. (2020)
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
, arXiv:1703.10593. arXiv. https://doi.org/10.48550/arXiv.1703.10593
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| last changed |
2024/04/22 07:10 |
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