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Chapter 5: Evolution of Generative Design Understanding
CHAPTER 5:
Evolution of Generative Design Understanding
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CHAPTER 5: EVOLUTION OF GENERATIVE DESIGN UNDERSTANDING
5.1 Contribution of Parametricism and BIM
The development of parametricism has incorporated algorithms for exploring complex spatial configurations in buildings. TheAEC industry has evolved past 2D drafting implements. Design forms are being dealt with softwares from initiation till execution on site. The buildings are not limited to linear forms
anymore.
5.1.1. Major contributions of parametricism
Since parametricism defines spatial relationships through algorithms, design forms are greatly flexible. Any changes in the root algorithm or the progressive algorithms are reflected directly on the design form.
Designs achieved/ formulated through parametric softwares are capable of
• accommodating modifications seamlessly,
• Integrating large number of influencing factors/parameters
• Producing faster, iterative results
• Optimizing designs to yield the desired output which proves to be environmentally friendly, cost effective and innovative.
Parametric geometries achieve “complexity from ordered simplicity”. A basic geometry develops into a complex pattern with simple transformationsoverlapping, rotation, repetition (LS, n.d.)
Looking back on Soddu’s principles, the above-mentioned principles clearly outline the prerequisites mentioned by him in his research paper- Endless Interpretations- Infinite in the mirror and in 20 Years of Argenia.
Parametric Softwares are capable of defining a starting point in 3D space, formulating progressive algorithms functioning within constraints, can transform geometries and most importantly, they are able to analyze and optimize designs according to environmental constraints. All of these were foundational principles for Celestino Soddu’s original requirements which led him to develop the first generative design software-Argenia.
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5.1.2. Major contributions of BIM
Building Information Modelling or BIM serves as a communication tool between different design formulation and execution stages. Primarily, this has been instrumental in demystifying/clarifying the architect’s vision to contractors, engineers, project managers and the client.
BIM involves 3D representation of buildings with detailed information about material specifications, quantities and estimated time for completion of project. It takes into account factors like- daylight exposure, natural ventilation through air currents and other factors influencing microclimate of site. The context is incorporated from the beginning into the design process. The software optimizes the building’s carbon footprint, minimizes heat gains, optimizes and estimates the material quantities and project cost. Execution of non-linear forms and determination of their structural integrity has been improved by the use of BIM in 3D modelling of structures.
5.1.3. Conclusion
The developments in BIM led to the formulation of a digital library/ database which could be accessed for developing the computer’s understanding of architecture. The context and other intangibles could be expressed in terms of algorithms which could define the position and function of geometries in a three-dimensional space.
Moreover, parametricism enabled progressive development of geometries from simple concepts. It allows seamless integration of modifications in real time. Incorporating generative algorithms and introducing complexity and subjectivity in the array of parametric softwares is comparatively easier.
Generative design is being developed after the development of BIM and Parametricism, carrying advantages of both. This coupled with algorithm advancements and development in simulations, have set a higher probability of Generative design being successful in architecture.
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In 2016Autodesk University lectures, Bill Allendescribes that thedevelopment ofBuilding Information Modellingcanbedivided intothreemajortimeperiods.
Until 2015, architecture was in a ‘Data Gathering Stage’ . This involved static modelling of buildings and input of information manually. Any iterations introduced had to be remodeled manually and requirements adjusted accordingly in spreadsheets. This was the beginning of 3D representation of buildings. (Allen , 2016)
We have been in the ‘Data Manipulation Stage’. This majorly includes development of algorithmic modelling and parametricism. Iterations and changes made in the model do not require manual remodeling but require change in numerical values in base algorithms. Grasshopper and dynamo are plugins in Rhino and Revit respectively that integrate BIM and algorithmic modeling (Allen , 2016)
The industry is looking towards ‘Data Otimisation Stage’. Modelling softwares are now developing to generate design options on the basis of desired parameters and constraints. The aim is to look for the most efficient solution for a design problem. Autodesk has introduced Dreamcatcher Project which incorporates generative design in architecture. The application of generative design has improved our understanding of structural integrity of structures, helped us improved sustainability measures and improved habitability of buildings (Allen , 2016)
These understanding of time periods help us map the step-by-step development of softwares.
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Figure 18: Data gathering, manipulation and optimization stages in time (Souza, 2020)
In an article defining the role of generative design and explaining its progress and its benefits in architectural softwares, (Autodesk, n.d.) has outlined the basic steps in the process of formulation of a design form through generative design. The workflow includes the following stages:
1. Generate
Algorithms are defined and constraints are set to make the program ready to generate viable options.
2. Analyze
Design forms andplans generated arecrossreferenced with theirdesired objectives.
3. Rank
Options are narrowed down and ranked on the basis of user preference.
4. Evolve
Algorithms are refined to achieve more optimization in the most preferred alternatives.
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5. Explore
Stakeholders and designers are open to adopt unconventional or innovative solutions due to the comparative analysis presented to them. Thus, exploring innovative solutions to challenging problems.
6. Integrate
After the selection of most appropriate option, the model can be interpolated and coordinated in different softwares for execution of the project (Autodesk, n.d.)
Figure 19:Simultaneous work-sharing between softwares. Live changes in Rhino(L) and Dynamo (R) (Erzetic, 2017)
“GD is constituted by three main components:
1) a generative model that can describe a wide design space of possible solutions;
2) an evaluative component that comprises the specified design goals;
3) a metaheuristic search algorithm, in this case a GA, that can navigate the design space and generate better and better design solution” (Nagy & Villaggi, Generative Design for Architectural Space Planning, 2018)
For simplifying workflow, the above mentioned six processes are classified as Pre-GD, GD and Post GD. Pre GD consist of acquiring data and setting 33
constraints for the project. Every piece of information gathered fordevelopment ofalgorithms is accountedforin this stage. ThenGDconstitutes theformulation of options by the software by processing the input parameters. Post GD deals with evaluation ofoptions and refining algorithms toarriveat themost optimum solution. These steps will be well understood through case studies discussed in the next chapter.
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