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Cutting-Edge Practical Research on Generative Design, IoT and Digital Twins

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Abstract

This chapter explores two areas of research undertaken by Autodesk over the last decade and how they both have the potential to impact the construction industry. The first area relates to the Internet of Things and the possibilities around integrating sensor data from smart buildings into a 3D context for exploration and visualization. This has the potential to drive interesting workflows related to understanding and optimizing the performance of the built environment, but more broadly will have the potential to impact design and fabrication processes. The other area of research described in this chapter relates to generative design, particularly in the context of the AEC industry. Autodesk’s work in this area started in 2010 with a project named Dreamcatcher. It was further accelerated in 2014 with the acquisition of The Living, an architectural studio which had—under the guidance of its principal David Benjamin—started exploring the application of multi-objective optimization through the use of genetic algorithms for architecture, engineering and construction industry (AEC) workflows. Looking to the future, real-world performance data captured via IoT—and hosted in Digital Twins—will increasingly influence the Generative Design process, as Autodesk and other software providers start to complement algorithmic exploration of the design space with machine learning systems trained with data from prior projects and captured using physical sensors. This has the potential to drive “closed-loop” processes where performance increases with each design iteration or system re-configuration.

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Correspondence to Kean Walmsley .

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Walmsley, K. (2022). Cutting-Edge Practical Research on Generative Design, IoT and Digital Twins. In: Ghaffar, S.H., Mullett, P., Pei, E., Roberts, J. (eds) Innovation in Construction. Springer, Cham. https://doi.org/10.1007/978-3-030-95798-8_7

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  • DOI: https://doi.org/10.1007/978-3-030-95798-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95797-1

  • Online ISBN: 978-3-030-95798-8

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