| id |
sigradi2021_200 |
| authors |
Karabagli, Kaan, Koc, Mustafa, Basu, Prithwish and As, Imdat |
| year |
2021 |
| title |
A Machine Learning Approach to Translate Graph Representations into Conceptual Massing Models |
| source |
Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 191–202 |
| summary |
Machine learning (ML) has popular applications in domains involving image, video, text and voice. However, in architecture, image-based ML systems face challenges capturing the complexity of three-dimensional space. In this paper, we leverage a graph-based ML system that can capture the inherent topology of architectural conceptual designs and identify high-performing latent patterns within such designs. In particular, our goal is to translate architectural graph data into three-dimensional massing models. We are building on our prior ML work, where we, a. discovered latent topological features, b. composed building blocks into new designs, c. evaluated their feasibility, and d. explored Generative Adversarial (Neural) Networks (GAN)-generated design variations. We trained the ML system with architectural design data that we gathered from an online architectural design competition platform, translated them into machine-readable graph representations, and identified their essential subgraphs to develop novel compositions. In this paper, we explore how these novel designs (outputted in graph form), can be translated into three-dimensional architectural form. We present an ML approach to turn graph representations into functional volumetric massing models. The ultimate goal of the study is to develop an end-to-end pipeline to generate architectural design - from a graph representation to a fully developed conceptual proxy of a designed product. The research question is promising in automating conceptual design, and we believe the outcome can be relevant to other design disciplines as well. |
| keywords |
Architectural design, machine learning, conceptual design, deep learning, artificial intelligence |
| series |
SIGraDi |
| email |
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| full text |
 file.pdf (876,062 bytes) |
| references |
Content-type: text/plain
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BIBLIOGRAPHY As, l., Pal, S., & Basu, P. (2018)
 Artificial Intelligence in Architecture: Generating Conceptual Design via Deep Learning
, International Journal of Architectural Computing, 16(4), 306-327
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| last changed |
2022/05/23 12:10 |
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