In digital design practice, the connection and feedback between physical and digital modelling is receiving increasing attention and is seen as a source of creativity and design innovation. The authors present a workflow that supports real-time design collaboration between human and machine intelligence through physical model building. The proposed framework is investigated through a case study, where we test the direct connectivity of physical and digital modelling environments with the integration of artificial neural networks. By combining 3D capturing tools and machine learning algorithms, the research creates an instant feedback loop between human and machine, introducing a hybrid immediacy that puts physical model building back at the centre of the digitally focused design process. By fusing physical models and digital workflows, the research aims to create interactivity between data, material and designer already at the early stage of the design.
- Computational immediacy
- Early design phase
- Physical model building
- Hybrid design tool
- 3D capturing
- Machine intelligence
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
A famous example of such a concept model is the digital model for the Moebius house by UN Studio. See image in (Hirschberg et al., 2020).
Bühlmann, V.: Die Empörung des Modells // models, outraged“ – Zhdk conference “David and Goliath – Models between art and architecture. https://monasandnomos.org/2013/11/19/die-emporung-des-modells-models-outraged-abstract-for-my-paper-at-the-zhdk-conference-david-and-goliath-models-between-art-and-architecture/ (2013)
Carpo, M.: The Second Digital Turn: Design Beyond Intelligence. The MIT Press, Cambridge (2017)
Frazer, J.: An Evolutionary Architecture. Architectural Association (1995)
Hirschberg, U., Hovestadt, L., Fritz, O. (eds.): Atlas of Digital Architecture: Terminology, Concepts, Methods, Tools, Examples, Phenomena. Birkhauser (2020)
Hsieh, C.T.: A new kinect-based scanning system and its application. Appl. Mech. Mater. 764–765, 1375–1379 (2015). https://doi.org/10.4028/www.scientific.net/AMM.764-765.1375
Jeffrey, L., Hakon, D., Hakon, F., Stanislas, C.: (2020)
Kanopoulos, N., Vasanthavada, N., Baker, R.L.: Design of an image edge detection filter using the Sobel operator. IEEE J. Solid-State Circuits 23(2), 358–367 (1988). https://doi.org/10.1109/4.996
Leach, N.: Architecture in the Age of Artificial Intelligence: An introduction to AI for architects. Bloomsbury Publishing Plc (2021). https://doi.org/10.5040/9781350165557
Oxman, R.: Digital architecture as a challenge for design pedagogy: theory, knowledge, models and medium. Des. Stud. 29, 99–120 (2008). https://doi.org/10.1016/j.destud.2007.12.003
Souza, L., Pathirana, I., Mcmeel, D., Amor, R.: Kinect to Architecture (2011)
Stachowiak, H.: Allgemeine Modelltheorie. Springer, Vienna (1973). https://doi.org/10.1007/978-3-7091-8327-4
Stavrić, M., Sid̄anin, P., Tepavc̆ević, B.: Architectural Scale Models in the Digital Age: Design, Representation and Manufacturing. Springer (2013)
Thomsen, M.R., Tamke, M.: The active model: a calibration of material intent. In: Persistent Modelling: Extending the Role of Architectural Representation, 1st edn. Routledge (2012)
This work was funded by the Austrian Science Fund (FWF) project F77 (SFB “Advanced Computational Design”).
Editors and Affiliations
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bank, M. et al. (2023). Hybrid Immediacy: Designing with Artificial Neural Networks Through Physical Concept Modelling. 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_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13248-3
Online ISBN: 978-3-031-13249-0