Artificial intelligence has garnered the attention of architects, who fear losing jobs for their architectural practice (Leach, 2023). Other architects see it as an opportunity (Hsueh, 2018), while some view it with neutrality (Brennecke, 2023). However, there needs to be more focus on some crucial aspect of the Architectural in all these distractions: computational design. Its definition is “the convergence power and design techniques through a sequence of logical processes” (Hnin, 2022). It has many types (Hnin, 2022) to show to select from: its full definition and influence, Parametric Design, Generative Design, & Algorithmic Design (Hnin, 2022).
Computational design is simple, with complex branches of the tree and many benefits (Hnin, 2022 & Ramage, 2022). There are ways to identify what architects need to frame the concept of computational design: the definition, the types, other design methods, and sectors (Hnin, 2022 & Ramage, 2022). At first, the definition interprets them as solving design problems with computers with “algorithms and parameters” and logical sequences to replace traditional design, which takes time and resources to increase efficiency (Hnin, 2022 & Ramage, 2022). There are many types of computational design: parametric, algorithmic, generative, performative, and form-finding (Hnin, 2022). Other design methods closely resemble the architectural computational design: biometric, digital fabrication, topology optimization, machine Learning and AI, & Material computation(ibid.). The realm of architecture is also relevant to architecture and interdisciplinary sectors for architects to know: Environmental Design, Engineering, Construction, Automation & Robotics, Fashion, Furniture & Product Design, Gaming Environment & Metaverse, and Automobile Design (ibid.).
The benefit and the future of using computational design for architects are great for some reasons: Design Better Solutions, Automate Repetitive Tasks, Mitigate Design Risks, Reduce Project Costs, Creative Freedom and a Demand With a Demand (ibid.). Applying most visual programming rather than codes, the computational tools to solve each set of problems, though some codes are necessary (ibid.). The list of computational design tools is Grasshopper, Dynamo, Param-O, & Marionette. Architects can find ways to become familiar with future artificial intelligence to ensure they diversify their skills and knowledge. Overall, there are many ways architects can optimize the computational design for the sample of three types to showcase what they can do.
Ramage (2022) describes Parametric design as “an interactive design process that uses a set of rules and inputted parameters to control a design model.” That includes identifying each design element in detail with a complex design that makes them easily accessible and modifiable for each architect (ibid.). Using Grasshopper on Rhinoceros 6 and plugins like Teklas Structures helps architects modify the parametric design (ibid.). Image 1 shows that the manual hand compared to the parametric design cannot compete with computer power to design complex architecture (ibid.). Overall, the computing design for parametric design helps the design process more quickly in addition to another type called generative design.
Ramage (2022) defines Generative design as “an iterative process that uses user-defined inputs to produce multiple design concepts that meet specific goals.” The evaluation in assistance with AI can help the process with predictable outcomes and successes in trial and error, even finding “happy accidents” with multiple design solutions in the process (ibid.). The goal of the generative design is to have an iterative process to optimize the analysis and minimise resources used for the architecture, as shown in Image 2. There will be a comparison with the algorithmic design to add to the computational design of the framework as needed in this process.
Lastly, the Algorithmic design “is a design method guided by algorithms” (Ramage, 2022, Image 3). Rather, it is a system to carry instructions sets to define the rules to resolve the problems rather than just each element (ibid.). Algorithmic design is the opposite in choosing fewer results to find (ibid.). They are modified to connect like a connected node for each building element or even a separate line of code. Overall, Algorithmic design is selective of the complex computational design.
Conclusion about the Computational Design for Architects
Generally, the three samples of design and the summary of the computational design only scratch the surface of the computer world of architects. The summary guides through the lists of necessary components, software, and the need for architects to apply more efficiently to their architectural design. The parametric design helps designers to utilize complex shapes following the rules and programs to move more efficiently. The generative design analyses ensure many options for architects to work more sustainably with the pieces of equipment to choose with better judgement. The last one, the algorithm, oversees the systems to select a few options. Those designs and the computational design itself will help future architects to explore better the equipment for architects to formulate better management of all architectures. If there is anything architects do not know, they will be better equipped to be curious about computational design’s strengths to learn more about them.
Autodesk (no date) MaRS Generative Design: description of specification of geometric model, world-architects.com. Available at: https://www.world-architects.com/en/architecture-news/insight/the-promise-of-generative-design (Accessed: May 2, 2023).
Brennecke, T. (2023) Does ai allow architects to return to the heart of their profession?, Parametric Architecture. Available at: https://parametric-architecture.com/does-ai-allow-architects-to-return-to-the-heart-of-their-profession/ (Accessed: May 2, 2023).
Hnin, T. (2022) Understanding computational design (the ultimate guide) – 2023, Online Professional Courses for Designers, Architects & Engineers. Available at: https://www.oneistox.com/blog/computational-design-guide#:~:text=Computational%20Design%20enables%20architects%20to,design%20principles%20found%20in%20nature. (Accessed: May 2, 2023).
Hsueh, D. (2018) Artificial Intelligence: Should designers and architects fear it or love it?, Arcadis IBI Group. Available at: https://www.ibigroup.com/ibi-insights/artificial-intelligence-fear-it-or-love-it/ (Accessed: May 2, 2023).
Leach, N. (2023) “ai is putting our jobs as architects unquestionably at risk”, Dezeen. Available at: https://www.dezeen.com/2023/02/13/ai-architecture-jobs-risk-neil-leach-opinion/ (Accessed: May 2, 2023).
N509FZ (2022) Beijing National Stadium, Wikipedia. Wikimedia Foundation. Available at: https://en.wikipedia.org/wiki/Beijing_National_Stadium#/media/File:Beijing_National_Stadium_from_the_Central_Axis_(20220905140702)_(cropped).jpg (Accessed: May 2, 2023).
Ramage , M. (2022) What is computational design?, Constructible. Available at: https://constructible.trimble.com/construction-industry/what-is-computational-design (Accessed: May 2, 2023).
Simon, J. (no date) Mapping Process of Floor Plan with Algorithm Design, Joel Simon. Available at: https://www.joelsimon.net/evo_floorplans.html?utm_medium=website&utm_source=archdaily.com (Accessed: 10 May 2023).