Meta-parametric design: Developing a computational approach for early stage collaborative practice

Harding, John

Abstract

Computational design is the study of how programmable computers can be integrated into the process of design. It is not simply the use of pre-compiled computer aided design software that aims to replicate the drawing board, but rather the development of computer algorithms as an integral part of the design process. Programmable machines have begun to challenge traditional modes of thinking in architecture and engineering, placing further emphasis on process ahead of the final result. Just as Darwin and Wallace had to think beyond form and inquire into the development of biological organisms to understand evolution, so computational methods enable us to rethink how we approach the design process itself. The subject is broad and multidisciplinary, with influences from design, computer science, mathematics, biology and engineering. This thesis begins similarly wide in its scope, addressing both the technological aspects of computational design and its application on several case study projects in professional practice. By learning through participant observation in combination with secondary research, it is found that design teams can be most effective at the early stage of projects by engaging with the additional complexity this entails. At this concept stage, computational tools such as parametric models are found to have insufficient flexibility for wide design exploration. In response, an approach called Meta-Parametric Design is proposed, inspired by developments in genetic programming (GP). By moving to a higher level of abstraction as computational designers, a Meta-Parametric approach is able to adapt to changing constraints and requirements whilst maintaining an explicit record of process for collaborative working.

Citation

Harding, J. (2014). Meta-parametric design: Developing a computational approach for early stage collaborative practice. (Thesis). University of the West of England. Retrieved from https://uwe-repository.worktribe.com/output/824568

• Thesis Type: Thesis
• Publicly Available Date: Jun 6, 2019
• Peer Reviewed: Not Peer Reviewed
• Public URL: https://uwe-repository.worktribe.com/output/824568