AI-Driven Design and Generative Architecture

Parametric design strategies for the generation of creative designs

JuHyun Lee, Ning Gu and Anthony P.Williams

Published 2014

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📄 Paper
As one of the emerging Computer-Aided Design (CAD) technologies for digital design and visualisation in the Architecture, Engineering and Construction (AEC) domain, parametric design potentially offers an innovative way of generating new design solutions. Despite this potential, design strategies associated with algorithmic scripting are not well understood.This paper provides a comprehensive understanding of individual design strategies supporting creative solutions in parametric design, using the combined application of protocol analysis and Consensual Assessment Technique (CAT).The article examines the generative and evolutionary aspects of parametric design that play an important role in the generation of creative designs.An in-depth analysis conceptualises designers’ parametric design strategies into problem-forwarding strategy and solution reflecting strategy.The solution-reflecting strategy focusing on the solution space of designing has potential to produce creative solutions by parametric design.A more in-depth understanding of parametric design strategies supports its effective adaptation to better serve the needs of digital design and visualisation in the AEC industry

Abstract

As one of the emerging Computer-Aided Design (CAD) technologies for digital design and visualisation in the Architecture, Engineering and Construction (AEC) domain, parametric design potentially offers an innovative way of generating new design solutions. Despite this potential, design strategies associated with algorithmic scripting are not well understood.This paper provides a comprehensive understanding of individual design strategies supporting creative solutions in parametric design, using the combined application of protocol analysis and Consensual Assessment Technique (CAT).The article examines the generative and evolutionary aspects of parametric design that play an important role in the generation of creative designs.An in-depth analysis conceptualises designers’ parametric design strategies into problem-forwarding strategy and solution reflecting strategy.The solution-reflecting strategy focusing on the solution space of designing has potential to produce creative solutions by parametric design.A more in-depth understanding of parametric design strategies supports its effective adaptation to better serve the needs of digital design and visualisation in the AEC industry

Topics

Generative Design and Design Space ExplorationParametric, Computational and Algorithmic Designdesign automationgeometry generation

Cite this paper

@article{8b67fb45, title={Parametric Design Strategies for the Generation of Creative Designs}, volume={12}, ISSN={1478-0771, 2048-3988}, url={https://journals.sagepub.com/doi/10.1260/1478-0771.12.3.263}, DOI={10.1260/1478-0771.12.3.263}, abstractNote={As one of the emerging Computer-Aided Design (CAD) technologies for digital design and visualisation in the Architecture, Engineering and Construction (AEC) domain, parametric design potentially offers an innovative way of generating new design solutions. Despite this potential, design strategies associated with algorithmic scripting are not well understood. This paper provides a comprehensive understanding of individual design strategies supporting creative solutions in parametric design, using the combined application of protocol analysis and Consensual Assessment Technique (CAT). The article examines the generative and evolutionary aspects of parametric design that play an important role in the generation of creative designs. An in-depth analysis conceptualises designers’ parametric design strategies into problem-forwarding strategy and solution-reflecting strategy. The solution-reflecting strategy focusing on the solution space of designing has potential to produce creative solutions by parametric design. A more in-depth understanding of parametric design strategies supports its effective adaptation to better serve the needs of digital design and visualisation in the AEC industry.}, number={3}, journal={International Journal of Architectural Computing}, author={Lee, JuHyun and Gu, Ning and Williams, Anthony P.}, year={2014}, month=sep, pages={263–282}, language={en} }



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