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Early-Phase Performance-Driven Design Using Generative Models

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Computer-Aided Architectural Design. Design Imperatives: The Future is Now (CAAD Futures 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1465))

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Abstract

Current performance-driven building design methods are not widely adopted outside the research field for several reasons that make them difficult to integrate into a typical design process. In the early design phase, in particular, the time intensity and the cognitive load associated with optimization and form parametrization are incompatible with design exploration, which requires quick iteration. This research introduces a novel method for performance-driven geometry generation that can afford interaction directly in the 3d modeling environment, eliminating the need for explicit parametrization, and is multiple orders faster than the equivalent form optimization. The method uses Machine Learning techniques to train a generative model offline. The generative model learns a distribution of optimal performing geometries and their simulation contexts based on a dataset that addresses the performance(s) of interest. By navigating the generative model’s latent space, geometries with the desired characteristics can be quickly generated. A case study is presented, demonstrating the generation of a synthetic dataset and the use of a Variational Autoencoder (VAE) as a generative model for geometries with optimal solar gain. The results show that the VAE-generated geometries perform on average at least as well as the optimized ones, suggesting that the introduced method shows a feasible path towards more intuitive and interactive early-phase performance-driven design assistance.

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Notes

  1. 1.

    Paulson and MacLeamy have both elaborated on the impact of changes along the different phases of design [1, 2]. Similarly, Morbitzer argues that simulation should be used throughout the design process [3].

  2. 2.

    See for example the Progressive GAN model [39] trained on the CelebA dataset [40].

  3. 3.

    The reparametrization is an essential component of the VAE, but only mentioned here for reasons of completeness. For details we direct the interested reader to [41].

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Acknowledgements

The first author is partially funded by an Onassis Scholarship (Scholarship ID: F ZO 002/1 – 2018/2019).

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Authors and Affiliations

  1. Harvard Graduate School of Design, Cambridge, MA, 02138, USA

    Spyridon Ampanavos & Ali Malkawi

  2. Harvard Center for Green Buildings and Cities, Cambridge, MA, 02138, USA

    Spyridon Ampanavos & Ali Malkawi

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  1. Spyridon Ampanavos
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  2. Ali Malkawi
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Correspondence to Spyridon Ampanavos .

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Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    David Gerber

  2. University of Southern California, Los Angeles, CA, USA

    Evangelos Pantazis

  3. Florida International University, Miami, FL, USA

    Biayna Bogosian

  4. University of Calgary, Calgary, Canada

    Alicia Nahmad

  5. Aalto University, Espoo, Finland

    Constantinos Miltiadis

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Ampanavos, S., Malkawi, A. (2022). Early-Phase Performance-Driven Design Using Generative Models. In: Gerber, D., Pantazis, E., Bogosian, B., Nahmad, A., Miltiadis, C. (eds) Computer-Aided Architectural Design. Design Imperatives: The Future is Now. CAAD Futures 2021. Communications in Computer and Information Science, vol 1465. Springer, Singapore. https://doi.org/10.1007/978-981-19-1280-1_6

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