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SYSTEMATIC CLASSIFICATION OF ADAPTIVE FAÇADES – PREPARING A DATABASE

Published online by Cambridge University Press:  19 June 2023

Michael P. Voigt ,
Daniel Roth  and
Matthias Kreimeyer
Michael P. Voigt*
Affiliation:
University of Stuttgart
Daniel Roth
Affiliation:
University of Stuttgart
Matthias Kreimeyer
Affiliation:
University of Stuttgart
*
Voigt, Michael P., University of Stuttgart, Germany, michael.voigt@iktd.uni-stuttgart.de

Abstract

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Adaptive façades (AF) present a promising approach to reduce environmental impacts in the Architecture, Engineering, and Construction sector. However, the automatization of the façade produces new challenges as the complexity of the system increases. To support the early phase of interdisciplinary development, solution collections such as databases are helpful. Previous research identified that existing solution collections of AF do not meet the requirements that such a method demands. In the effort to develop an optimized database, this paper investigates how the state of the art can be structured in terms of content in order to present it in the database. Here, a set of design parameters is developed based on identified requirements and on the main characteristics of AF that were previously elaborated. This set offers a comprehensive perspective on the previously realized functions and mechanisms of AF and can also contribute to finding creative solutions in the form of new concepts by combining the design parameters in new ways. Finally, 40 case studies of previously implemented adaptive façades are used to evaluate the set of design parameters.

Keywords

Adaptive façadesDesign methodsEarly design phasesDesign to XDatabase
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 3295 - 3304
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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