A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Generative workplace and space planning in architectural practice
https://orcid.org/0000-0003-3802-5599
https://orcid.org/0000-0002-6309-7394
https://orcid.org/0000-0002-8406-0427
https://orcid.org/0000-0002-7330-7551
https://orcid.org/0000-0001-8359-1181
Generative design is emerging as an important approach for design exploration and design analysis in architectural practice. At the interior design scale, although many approaches exist, they do not meet many requirements for implementing generative design in practice. These requirements include the need for end-user accessible tools and skills, rapid execution, the use of standard inputs and outputs, and being scalable and reusable. In this paper, we describe a hybrid process that uses both space allocation and shape grammar algorithms to solve workplace and space planning interior design problems. Space allocation algorithms partition spaces according to program requirements while shape grammar automates the placement of inventory and the production of high-resolution drawings. We evaluate using three real world example projects how this hybrid approach meets the identified requirements of generative space planning in architectural practice.
Generative workplace and space planning in architectural practice
https://orcid.org/0000-0003-3802-5599
https://orcid.org/0000-0002-6309-7394
https://orcid.org/0000-0002-8406-0427
https://orcid.org/0000-0002-7330-7551
https://orcid.org/0000-0001-8359-1181
Generative design is emerging as an important approach for design exploration and design analysis in architectural practice. At the interior design scale, although many approaches exist, they do not meet many requirements for implementing generative design in practice. These requirements include the need for end-user accessible tools and skills, rapid execution, the use of standard inputs and outputs, and being scalable and reusable. In this paper, we describe a hybrid process that uses both space allocation and shape grammar algorithms to solve workplace and space planning interior design problems. Space allocation algorithms partition spaces according to program requirements while shape grammar automates the placement of inventory and the production of high-resolution drawings. We evaluate using three real world example projects how this hybrid approach meets the identified requirements of generative space planning in architectural practice.
Generative workplace and space planning in architectural practice
https://orcid.org/0000-0003-3802-5599
https://orcid.org/0000-0002-6309-7394
https://orcid.org/0000-0002-8406-0427
https://orcid.org/0000-0002-7330-7551
https://orcid.org/0000-0001-8359-1181
Generative design is emerging as an important approach for design exploration and design analysis in architectural practice. At the interior design scale, although many approaches exist, they do not meet many requirements for implementing generative design in practice. These requirements include the need for end-user accessible tools and skills, rapid execution, the use of standard inputs and outputs, and being scalable and reusable. In this paper, we describe a hybrid process that uses both space allocation and shape grammar algorithms to solve workplace and space planning interior design problems. Space allocation algorithms partition spaces according to program requirements while shape grammar automates the placement of inventory and the production of high-resolution drawings. We evaluate using three real world example projects how this hybrid approach meets the identified requirements of generative space planning in architectural practice.
Generative workplace and space planning in architectural practice
Okhoya, Victor W (author) / Bernal, Marcelo (author) / Economou, Athanassios (author) / Saha, Nirvik (author) / Vaivodiss, Robert (author) / Hong, Tzu-Chieh K (author) / Haymaker, John (author)
International Journal of Architectural Computing ; 20 ; 645-672
2022-09-01
28 pages
Article (Journal)
Electronic Resource
English
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