Abstract
The nature of design tools is related to the social relationships they serve. This paper speculates on the emergence of a new professional configuration - the synthesis of architect and engineer - and on the nature of new computational tools and methods that will be required to support such a reconfiguration. Extending previous work that established a framework for the application of Machine Learning (ML) to Generative Architectural Design (GAD), we present here an approach that employs two distinct evaluators: The first stands in for the engineer, and quantifies structural performance; The second stands in for the architect, and assesses candidate designs based on qualitative factors, an evaluation that is made possible by employing a neural net. This new framework is demonstrated through an investigation into tension nets and their structurally derived forms. Since such a tool allows for these evaluators to be employed in combination or in isolation, the resulting solution sets can illuminate both synthetic solutions and each of the two desires independently - a capacity that implies value not only as an optimization tool, but also as a tool for exploration and education.
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Notes
- 1.
A visual programming environment and parametric modeler popular in architectural design.
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For the purposes of this prototype, the number of samples remains small (approximately 300). We speculate that scaling up the number of samples would allow for a more robust critic and suggest this for a future scope of work.
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Acknowledgements
The authors would like to express appreciation to the Department of Architecture at the University of California, Berkeley, for the Chester Miller Fellowship supporting thesis research; and to acknowledge the SmartGeometry organization for its support of previous work on which this research was based.
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Turlock, M., Steinfeld, K. (2020). Necessary Tension. In: Gengnagel, C., Baverel, O., Burry, J., Ramsgaard Thomsen, M., Weinzierl, S. (eds) Impact: Design With All Senses. DMSB 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-29829-6_20
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