Computational Design: A Brief History of the Evolution
- PrimaVersity
- Mar 24
- 6 min read
The method of solving design problems with sophisticated computer processing through the use of parameters and algorithms is known as computational design. It uses algorithms and both artificial and human intelligence to generate several design iterations in order to identify effective functional and aesthetic design solutions.
Architects may increase productivity, do more tasks with fewer resources, and design more quickly and iteratively via computational design. It gives architects the ability to realize amazing designs that defy gravity.
Computational design, however, is not the result of an epiphany. It is the result of decades of technical advancement, design and creativity progression, and the desire for more effective design solutions. Design processes and equipment have changed significantly from the days of 2D drafting to the present decade, when digital has surpassed analog.
The Evolution of Computational Design in Architecture
The evolution of computational design can be mapped out in 5 stages –
Stage 1: 2D Drafting
The groundbreaking Sketchpad was first made available to the architecture and design community in 1963 by Ivan Sutherland, the inventor of computer graphics. Drawing and designing on a computer screen in real time was made possible by the highly interactive program Sketchpad, the first Computer-Aided Design (CAD) system. For the first time in history, a computer could be used to edit, store, and use drawings later.
Sketchpad was the first example of computational design in architecture, while being a constraint-based methodology. AutoCAD was developed twenty years later to visualize designs in two dimensions. Because AutoCAD was easier to use and less expensive than Sketchpad, it gained a devoted following among architects and other professionals in the AEC sector.
Stage 2: 3D Modelling
The introduction of the 3D modeling stage of computational design, which involves creating 3D models, in the 1990s marked the beginning of the first digital revolution in architecture. Blob architecture, a post-modern architectural style that emphasized creating structures that resembled amoebas and flowed freely, took the stage at the beginning of this phase.
Although architect Greg Lynn initially adopted the phrase "blob architecture" in 1995 after using it in his design experiments with "Metaball Graphical Software," Czech-British architect Jan Kaplický originally used the term for the "Blob Office Building" in London in 1986. It quickly became well-liked by a large number of architects and product designers who produced distinctive and out-of-the-ordinary blob-like shapes.
In the digitisation era, tools like Rhinoceros, Cobalt, 3D Studio Max, and Maya entered the market and empowered architects to create 3D visualizations of designs and solve complex geometry to give architecture a new dimension.
Stage 3: Building Information Modelling (BIM)
Although the word "building model" was mentioned in several works as early as the mid-1980s, the phrase "building information modeling" was first used in a 1992 study by G.A. van Nederveen and F. P. Tolman. After Autodesk published a whitepaper on the subject in 2002, its abbreviation, "BIM," became well-known.
By making visualization possible in more than simply 2D and 3D, this stage advanced design technology. With Building Information Modelling (BIM) technology at the forefront, it made it possible to integrate physical attributes to the 3D model of any planned constructed environment.
All project stakeholders may document and share important information thanks to BIM tools such as Revit, Navisworks, Dynamo, BIM 360, and others. This facilitates the creation of accurate plans while allowing clients, designers, architects, MEP engineers, HVAC industry professionals, etc. to work together without any obstacles.
Stage 4: Algorithm-based Computational Design
At this point, computational design knowledge developed into a tool that helps with both documentation and design, even if the prior three phases provided computer-based technology that transformed and expedited project planning and execution. It transcends the limitations of traditional geometry and aids in the rapid and iterative rationalization, creation, and analysis of numerous design choices, enabling architects to construct settings that were previously unthinkable.
With computational design, architects can digitally encode a set of principles as a series of parametric equations that, when executed, produce the model (or several iterations of it) instead of having to sketch forms and blueprints.
Even though the earliest records of explorations into design computation date back to the pioneering works of Nicholas Negroponte and Chuck Eastman among others (1960s and 1970s), it was only with the release of Generative Components (2003) and Grasshopper (2007), that design computation took off in mainstream architectural practices.
Parametric Design, Geometric Rationalization, Generative Design, Form Finding, Network Analysis, Programmatic Analysis, GIS, Solar Access, and Daylight analysis are the products of this development stage of computational design.
Stage 5: Machine Learning
Machine Learning (ML) goes beyond the algorithmic computational design stage, which involved designers and architects encoding a set of principles each time to produce design output. Machine learning enables computers to produce designs based on historical inputs and statistical models created on the inputs through the three processes of training, analysis, and application.
In order to transform the way architects construct and even conceptualize designs, it integrates a variety of algorithms, pattern recognition, neural networks, generative design, artificial intelligence, and distributed computation.
How Computational Design is Being Used Today
Whether it is a commercial or a residential project, healthcare, hospitality or urban design redevelopment, computational design is being used widely across every vertical in Architecture, Engineering and Construction (AEC) to create efficient designs and find optimum design solutions otherwise unfathomable.
The International Terminal at Waterloo Station in London (1993) by Grimshaw; the International Migratory Bird Town Convention & Exhibition Center in China by OI Architects; the Museum of The Future in Dubai by Killa Design architects; The Line by US studio Morphosis; and the One Thousand Museum by Zaha Hadid in Miami are just a few of the well-known projects worldwide that have used computational design to create mind-bending built environments.
The Al Janoub Stadium, which was redesigned by Zaha Hadid Architects (ZHA) for the 2022 FIFA World Cup, is one of the more recent instances.
The stadium's curved, retractable top, which mimics a boat's sails, was inspired by the classic Dhow boats. For the best player and spectator comfort in the stadium enclosure, computer modeling, wind tunnel testing, and passive design concepts were employed. Sustainability and building performance optimization were prioritized.
The Al Janoub Stadium is a perfect example of how computational design is being used today to create surreal experiences that create a perfect confluence of design, culture, nature, and sustainability – a visual, structural, and functional masterpiece.
Conclusion - The Way Forward
The aforementioned stages of evolution demonstrate that computational design is not the result of a single epiphany. Its gradual development is the result of more than 50 years of technological advancement and the desire to produce superior designs. The technologies and construction equipment we use today were eventually created by building on the successes and milestones of one stage.
The ability of computational design to efficiently decrease effort and boost production distinguishes it from other tools and techniques. Multiple designs of the same building or project with different contexts and levels of complexity can be created with a few clicks.
Engineers can concentrate on creating spaces that are perfectly in harmony with the built environment, culture, and nature, while simultaneously challenging traditional geometry and the laws of gravity.
Even today, the AEC industry has not realized the full potential of computational design. With accelerated adoption, algorithms and AI will become sophisticated and display more intelligence in every output.
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