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Deep Sight: A Toolkit for Design-Focused Analysis of Volumetric Datasets
University of Arts, Crafts and Design, Department of Design, Interior Architecture and Visual Communication (DIV), Industrial Design.ORCID iD: 0000-0001-5068-9350
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2022 (English)In: Towards Radical Regeneration / [ed] Christoph Gengnagel, Olivier Baverel, Giovanni Betti, Mariana Popescu, Mette Ramsgaard Thomsen, Jan Wurm, 2022, p. 543-555Conference paper, Published paper (Refereed)
Abstract [en]

In response to global challenges of resource scarcity, increasing attention is being paid to bio-based materials - a domain that covers familiar materials such as timber and emerging materials such as bio-plastics and mycelium composites. The ability to observe, analyse, simulate, and design with their interior heterogeneity and behaviour over time is a necessity for a bio-based and cyclical material practice and opens a deep reservoir of creative and technical innovation potentials within architecture and aligned design practices.This paper describes a research inquiry which seeks to integrate volumetric material data acquired through non-intrusive methods into materially-led digital design workflows. The inquiry is developed as a set of computational tools and approaches to architectural modelling, and demonstrated through three main material tracks: structural glue-laminated timber assemblies, mycelium composites, and bio-luminescent bacteria substrates. Each addresses the acquisition, analysis, and simulation of deep volumetric material data at different scales and in different deployment contexts. In doing so, we demonstrate a novel shift in the digital modelling of bio-based architectural materials and set out its implications for new design practices that deeply embed the individuality and temporality of materials.We contribute a perspective on the possibilities afforded by a volumetric modelling approach to bio-architecture and a computational framework for operating with volume data of heterogeneous materials.

Place, publisher, year, edition, pages
2022. p. 543-555
National Category
Architecture
Identifiers
URN: urn:nbn:se:konstfack:diva-8728DOI: 10.1007/978-3-031-13249-0_43ISBN: 978-3-031-13249-0 (print)OAI: oai:DiVA.org:konstfack-8728DiVA, id: diva2:1708162
Conference
Design Modelling Symposium Berlin 2022
Available from: 2022-11-02 Created: 2022-11-02 Last updated: 2022-11-08Bibliographically approved

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Gatz, Sebastian

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