Supporting Exploration of Design Alternatives using Multivariate Analysis Algorithms

Conference paper (2016)

Authors

R. Sileryte Design Informatics - Architecture and the Built Environment
A. D'Aquilio Design Informatics - Architecture and the Built Environment
D. Di Stefano ESTECO SpA
D. Yang South China University of Technology, Design Informatics - Architecture and the Built Environment
M. Turrin Design Informatics - Architecture and the Built Environment , South China University of Technology
Research Group
Design Informatics (Architecture and the Built Environment) (TU Delft)
Copyright: © 2016 R. Sileryte, A. D'Aquilio, D. Di Stefano, D. Yang, M. Turrin
Design Exploration Multivariate analysis Parametric Modelling Visual Analytics Design Computation Design & Optimization Multiobjective Optimization
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Published Date

2016

Language

English

Reuse Rights

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Faculty

Architecture and the Built Environment

Department

Architectural Engineering +Technology

Research Group

Design Informatics

Abstract

Parametric modelling allows quick generation of a large number of design alternatives. Ultimately, it can be combined with optimization algorithms for obtaining optimal performance-driven design. However, setup of design space for optimization is a very complex task requiring designer’s a priori knowledge and experience. Therefore, this paper focuses on the process that happens before the optimization. It proposes to use multivariate analysis algorithms for exploring and understanding the relations between various design parameters, after sampling the design space. Additionally, portrayal of geometry is
introduced as an extension of conventional visualization methods, which accounts for evaluation of ill-defined design criteria by using designer’s expertise. The proposed method is computationally efficient and integrated into an environment familiar to architects. It relies on multivariate analysis algorithms together with database querying capabilities and an interactive dashboard developed for geometry portrayal.

Files

SimAUD2016_.pdf
(.pdf | 1.25 Mb)