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Structural Form-Finding Enhanced by Graph Neural Networks

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Towards Radical Regeneration (DMS 2022)

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Abstract

Computational form-finding methods hold great potential for enabling resource-efficient structural design. In this context, the Combinatorial Equilibrium Modelling (CEM) allows the design of cross-typological tension-compression structures starting from an input topology diagram in the form of a graph. This paper presents an AI-assisted design workflow in which the graph modelling process required by the CEM is simplified through the application of a Graph Neural Network (GNN). To this end, a GNN model is used for the automatic labelling of edges of unlabelled topology diagrams. A synthetic topology diagram data generator is developed to produce training data for the GNN model. The trained GNN is tested on a dataset of typical bridge topologies based on real structures. The experiments show that the trained GNN generalises well to unseen synthetic data and data from real structures similar to the synthetic data. Hence, further developments of the GNN model have the potential to make the proposed design workflow a valuable tool for the conceptual design of structures.

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Correspondence to Lazlo Bleker .

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Bleker, L., Pastrana, R., Ohlbrock, P.O., D’Acunto, P. (2023). Structural Form-Finding Enhanced by Graph Neural Networks. 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_3

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  • DOI: https://doi.org/10.1007/978-3-031-13249-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13248-3

  • Online ISBN: 978-3-031-13249-0

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