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Growth-Based Methodology for the Topology Optimisation of Trusses

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Towards Radical Regeneration (DMS 2022)

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Abstract

This paper presents a novel methodology for a growth-based topology optimization of trusses. While most methods of topology optimization are based on voxel grids that result in free-form volumes, the topology optimization of trusses exists as subtractive methods that start with a large number of initial beams. This method instead commences with a minimal amount of beams. The model is iteratively refined by node repositioning and node division according to structural forces to arrive at a complex truss. Case studies of a cantilever and a table show the results and reduction in mass achieved by the algorithm.

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Acknowledgements

This project was carried out as part of a doctorate under the supervision of Klaus Bollinger at the University of Applied Arts Vienna.

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Authors and Affiliations

  1. University of Cincinnati, Cincinnati, USA

    Christoph Klemmt

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  1. Christoph Klemmt
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Correspondence to Christoph Klemmt .

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Editors and Affiliations

  1. Institute of Architecture and Urban Planning, Department for Structural Design and Engineering, KET, Berlin University of the Arts, Berlin, Berlin, Germany

    Christoph Gengnagel

  2. Laboratoire Navier/GSA, School of Architecture of Grenoble and Ecole des Ponts Paristech, Grenoble, France

    Olivier Baverel

  3. Architectural Design for Building Performance, University of California, Berkeley, Berkeley, CA, USA

    Giovanni Betti

  4. Civil Engineering and Geosciences, Delft University of Technology, Delft, The Netherlands

    Mariana Popescu

  5. CITA, The Royal Danish Academy of Fine Arts, Copenhagen, Denmark

    Mette Ramsgaard Thomsen

  6. Faculty of Architecture, Regenerative Architecture and Biofabrication, KU Leuven, Leuven, Belgium

    Jan Wurm

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Klemmt, C. (2023). Growth-Based Methodology for the Topology Optimisation of Trusses. In: Gengnagel, C., Baverel, O., Betti, G., Popescu, M., Thomsen, M.R., Wurm, J. (eds) Towards Radical Regeneration. DMS 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-13249-0_37

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  • DOI: https://doi.org/10.1007/978-3-031-13249-0_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13248-3

  • Online ISBN: 978-3-031-13249-0

  • eBook Packages: EngineeringEngineering (R0)

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