CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

PDF papers
id ecaade2014_192
authors David Stasiuk and Mette Ramsgaard Thomsen
year 2014
title Learning to be a Vault - Implementing learning strategies for design exploration in inter-scalar systems
source Thompson, Emine Mine (ed.), Fusion - Proceedings of the 32nd eCAADe Conference - Volume 1, Department of Architecture and Built Environment, Faculty of Engineering and Environment, Newcastle upon Tyne, England, UK, 10-12 September 2014, pp. 381-390
wos WOS:000361384700038
summary Parametric design models enable the production of dynamic form, responsive material assemblies, and numerically and geometrically analytical feedback. The value potential for design produced through the procedural transformation of input parameters (or features) through algorithmic models has been repeatedly demonstrated and epistemically refined. However, despite their capacity to improve productivity and iteration, parametric models are nonetheless prone to inflexibility and reduction, both of which obscure processes of invention and discovery that are central to an effective design practice. This paper presents an experimental approach for the application of multiple, parallel computational design modelling strategies which are tested in the production of an inter-scalar model array that synthesises design intent, the simulation of material behaviours, performance-driven adaptation, and open-ended processes of discovery and categorical description. It is particularly focused on the computational potentials embedded in interdependent applications of simulation and machine learning algorithms as generative and descriptive drivers of form, performance, and architectural quality. It ultimately speculates towards an architectural design modelling method that privileges open model topologies and emergent feature production as critical operators in the generation of flexible and adaptive design solutions.
keywords Parametric design; computational modelling; machine learning; multi-objective optimisation; k-means clustering
series eCAADe
full text file.pdf (656,715 bytes)
references Content-type: text/plain
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