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Generative design

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"Algorithmic design" redirects here. For design of algorithms, see Algorithm § Design.
Schema of generative design as an iterative process
Samba, a piece of furniture created by Guto Requena with generative design

Generative design is an iterative design process that uses software to generate outputs that fulfill a set of constraints iteratively adjusted by a designer. Whether a human, test program, or artificial intelligence, the designer algorithmically or manually refines the feasible region of the program's inputs and outputs with each iteration to fulfill evolving design requirements.[1] By employing computing power to evaluate more design permutations than a human alone is capable of, the process is capable of producing an optimal design that mimics nature's evolutionary approach to design through genetic variation and selection.[citation needed] The output can be images, sounds, architectural models, animation, and much more. It is therefore a fast method of exploring design possibilities that is used in various design fields such as art, architecture, communication design, and product design.[2]

Generative design has become more important, largely due to new programming environments or scripting capabilities that have made it relatively easy, even for designers with little programming experience, to implement their ideas.[3] Additionally, this process can create solutions to substantially complex problems that would otherwise be resource-exhaustive with an alternative approach making it a more attractive option for problems with a large or unknown solution set.[4] It is also facilitated with tools in commercially available CAD packages.[5] Not only are implementation tools more accessible, but also tools leveraging generative design as a foundation.[6]

Generative design in architecture

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Generative design in architecture is an iterative design process that enables architects to explore a wider solution space with more possibility and creativity.[7] Architectural design has long been regarded as a wicked problem.[8] Compared with traditional top-down design approach, generative design can address design problems efficiently, by using a bottom-up paradigm that uses parametric defined rules to generate complex solutions. The solution itself then evolves to a good, if not optimal, solution.[9] The advantage of using generative design as a design tool is that it does not construct fixed geometries, but take a set of design rules that can generate an infinite set of possible design solutions. The generated design solutions can be more sensitive, responsive, and adaptive to the problem.

Generative design involves rule definition and result analysis which are integrated with the design process.[10] By defining parameters and rules, the generative approach is able to provide optimized solution for both structural stability and aesthetics. Possible design algorithms include cellular automata, shape grammar, genetic algorithm, space syntax, and most recently, artificial neural network. Due to the high complexity of the solution generated, rule-based computational tools, such as finite element method and topology optimisation, are more preferable to evaluate and optimise the generated solution.[11] The iterative process provided by computer software enables the trial-and-error approach in design, and involves architects interfering with the optimisation process.

Historical precedent work includes Antoni Gaudí's Sagrada Família, which used rule based geometrical forms for structures,[12] and Buckminster Fuller's Montreal Biosphere where the rules to generate individual components is designed, rather than the final product.[13]

More recent generative design cases include Foster and Partners' Queen Elizabeth II Great Court, where the tessellated glass roof was designed using a geometric schema to define hierarchical relationships, and then the generated solution was optimized based on geometrical and structural requirement.[14]

See also

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References

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  1. ^ Meintjes, Keith. ""Generative Design" – What's That? - CIMdata". Retrieved 2018-06-15.
  2. ^ ENGINEERING.com. "Generative Design: The Road to Production". www.engineering.com. Retrieved 2019-12-05.
  3. ^ Schwab, Katharine (16 April 2019). "This is the first commercial chair made using generative design". Fast Company. Retrieved 13 August 2019.
  4. ^ Prasanta, Rajamoney, Shankar A. Rosenbloom, Paul S.; Wagner, Chris Bose (2014-09-04). Compositional model-based design: A generative approach to the conceptual design of physical systems. University of Southern California. OCLC 1003551283.{{cite book}}: CS1 maint: multiple names: authors list (link)
  5. ^ Barbieri, Loris; Muzzupappa, Maurizio (2022). "Performance-Driven Engineering Design Approaches Based on Generative Design and Topology Optimization Tools: A Comparative Study". Applied Sciences. 12 (4): 2106. doi:10.3390/app12042106.
  6. ^ Anderson, Fraser; Grossman, Tovi; Fitzmaurice, George (2017-10-20). Trigger-Action-Circuits: Leveraging Generative Design to Enable Novices to Design and Build Circuitry. ACM. pp. 331–342. doi:10.1145/3126594.3126637. ISBN 9781450349819. S2CID 10091635.
  7. ^ Krish, Sivam (2011). "A practical generative design method". Computer-Aided Design. 43 (1): 88–100. doi:10.1016/j.cad.2010.09.009.
  8. ^ Rittel, Horst W. J.; Webber, Melvin M. (1973). "Dilemmas in a General Theory of Planning" (PDF). Policy Sciences. 4 (2): 155–169. doi:10.1007/bf01405730. S2CID 18634229. Archived from the original (PDF) on 30 September 2007.
  9. ^ Mitchell, Melanie; Taylor, Charles E (1999). "Evolutionary computation: an overview". Annual Review of Ecology and Systematics. 30 (1): 593–616. doi:10.1146/annurev.ecolsys.30.1.593.
  10. ^ Shea, Kristina; Aish, Robert; Gourtovaia, Marina (2005). "Towards integrated performance-driven generative design tools". Automation in Construction. 14 (2): 253–264. doi:10.1016/j.autcon.2004.07.002.
  11. ^ Dapogny, Charles; Faure, Alexis; Michailidis, Georgios; Allaire, Grégoire; Couvelas, Agnes; Estevez, Rafael (2017). "Geometric constraints for shape and topology optimization in architectural design" (PDF). Computational Mechanics. 59 (6): 933–965. Bibcode:2017CompM..59..933D. doi:10.1007/s00466-017-1383-6. S2CID 41570887.
  12. ^ Hernandez, Carlos Roberto Barrios (2006). "Thinking parametric design: introducing parametric Gaudi". Design Studies. 27 (3): 309–324. doi:10.1016/j.destud.2005.11.006.
  13. ^ Edmondson, Amy C (2012). "Structure and pattern integrity". A Fuller explanation: The synergetic geometry of R. Buckminster Fuller (PDF). Springer Science & Business Media. pp. 54–60. doi:10.1007/978-1-4684-7485-5. ISBN 978-0-8176-3338-7.
  14. ^ Williams, Chris JK (2001). Burry, Mark; Datta, Sambit; Dawson, Anthony; Rollo, John (eds.). The analytic and numerical definition of the geometry of the British Museum Great Court Roof (PDF). Proceedings of mathematics & design 2001: the third international conference. Vol. 200. Geelong Vic Australia: Deakin University. pp. 434–440. ISBN 0-7300-2526-8.

Further reading

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