Peculiar Perspective

Introduction

Just imagine an architect who no longer needs the dusty old drafting table but now commands a computer, and bam! He ends up with thousands of drawing ideas! And listen up, this isn’t a myth!

Indeed, in this article, what I am talking about is the evolution of architectural design, starting with modular design and traveling all the way to AI!

For your information, the way AI found its feet on the grounds of architecture wasn’t something that happened overnight! But rather, it came along as a natural progression, gathering knowledge from the related technologies that have been used in the past.

These developments, in a sense, caused the roof to collapse. However, have you ever thought about what happens if the roof collapses? Yes, the stars show themselves clearly! However, I suppose that’s a story for another time. Let’s continue exploring the world of architectural evolution for the time being. A world where computer screens grow to heights!

A Historical Overview: From Ancient Methods to Modern Technology

The chronological evolution of using AI in architecture can be divided into four major parts. Modularity, computational design, parametric design, and, of course, AI.

Modularity: The First Leap Toward Standardization

So, modularity in the world of architecture is about using prefabricated units or other repeating things to build stuff that’s flexible and can change. This whole idea really got people talking during that modernist thing because of architects like Le Corbusier! He totally thought that standardized, modular units were the way forward for making functional living spaces.

Now, don’t get me wrong, this modularity thing isn’t a new-fangled idea; it’s been around since, like, the olden days in Greek, Roman, and those ancient Egyptian times. They’d use things such as columns, which made their designs look pretty but kept things practical too. Gave a kind of sense of unity. However, modularity was always a big deal in architecture. Whether through prefab parts or repeated things!

Then there was that guy, Walter Gropius, who gave modularity a real boost with his Bauhaus in 1920. He was all about keeping things simple and cheap in architecture, and then Le Corbusier took this and ran with it. He made up his “Modulor” system. Adapting modularity to match human proportions. It’s all kind of evolved into building systems inside of modules. as seen in Buckminster Fuller’s house, the Dymaxion one? City planning also had modular ideas like “Plugin City.”. This was a thing by Archigram. Their goal? To make cities that could change and adapt.

But, let’s face it, some of these designs had a few problems. Lacked some creativity, and there were construction issues. But! Modularity is still super important in architecture right now! Despite all.

The Transition to Computer-Aided Design (CAD)

Nicholas Negroponte was a pioneer of computational design in architecture. He is indeed the founder and leader of the Architecture Machine Group (AMG) at MIT. Negroponte and his supportive team were deeply invested in finding out the potential of machines! To enhance the creative process in architectural production.

The group’s project, which includes Urban II and Urban V, showed the value of computation in architecture and prepared the path for the wide adoption of 3D design software, in the architecture field!

Furthermore, Frank Gehry made a very loud noise advocating for the use of computation in architecture. He found Gehry Technologies, where he is using early CAD-CAM software to handle complex, like very complex, geometric problems, therefore setting a precedent for using computational design in architecture.

Frei Otto, another architect and engineer, was also known for his usage of computational design—you know, to create innovative, unique architectural forms. He was especially known for his work with tensile structures. like the roof of the Munich Olympic Stadium. Architects grasped the new system on the basis of a crystal-clear rationale: computational design!

With computational design, it allows stringent control of geometry, thus improving the design’s reliability, feasibility, and cost. It also makes collaboration among designers more effortless.

Finally, more design iterations are now possible than traditional hand-sketching could allow, Imagine more tests & more options for better resulting designs.

However, along the way, designers were engaging with computational design, and a few shortcomings came along the way. Notably, task repetitiveness and the lack of control over complex geometric shapes became a massive headache. These act as steppingstones towards a new movement that was about to sprout within computation design, parametricism.”

Parametric Design

Parametricism is an approach to architectural design that uses computational algorithms to create dynamic and responsive forms. It was introduced by architect Patrick Schumacher, who believes it represents the next step in the evolution of architecture.

Famous architects who have used parametricism in their work include Zaha Hadid, Daniel Libeskind, and Rem Koolhaas. Examples of parametrism in architecture include Zaha Hadid’s design for the Guangzhou Opera House in China, which features interlocking panels that move in response to changes in the environment, and Rem Koolhaas’s design for the CCTV Headquarters in Beijing, which has an innovative and highly functional form created using computational design techniques.

Over the past decade, parametric design has hit a snag, both technically and conceptually. Parametric modeling couldn’t handle it.

(1)  multiple variables at once,

(2)  the need for style and space organization over strict efficiency,

(3)  various scenarios, and

(4) the computational cost of simulations.

But that’s not all. Parametric design’s theoretical premise is flawed: it suggests that architecture can be boiled down to a fixed set of parameters, ignoring its context, environment, and history. In reality, architecture is much more complex, with countless parameters and cultural influences shaping urban equilibrium. Now, with the rise of artificial intelligence, we can finally address these challenges head-on.

Architectural Ai

Artificial intelligence is fundamentally a statistical approach to architecture. The premise of AI, which blends statistical principles with computation, is a new approach that can improve on the drawbacks of parametric architecture.

“Learning,” as understood by machines, corresponds to the ability of a computer, when faced with a complicated issue, first to grasp the complexity of the options shown to him and second to build an “intuition” to solve the problem at stake. In fact, when coining the concept of AI, John McCarthy, back in 1956, defined it as “using the human brain as a model for machine logic.”. Instead of designing a deterministic model built for a set number of variables and rules, AI lets the computer create intermediary parameters from information either collected from the data or transmitted by the user. Once the “learning phase” is achieved, the machine can generate solutions, not simply answering a set of predefined parameters, but creating results emulating the statistical distribution of the information shown to him during the learning phase. This concept is at the core of the paradigm shift brought about by AI.

The penetration of artificial intelligence in the architectural field was already forecast early on by a few theorists, who, before us, saw AI’s potential for architectural design. Far from crafting intelligent algorithms, these precursors designed and speculated on the potential of such systems. As Urban II was released by Negroponte and his group, the idea of a “machine assistant” was already well underway.

Although the potential AI represents for architecture is quite promising, it still remains contingent on designers’ ability to communicate their intent to the machine. And as the machine has to be trained to become a reliable “assistant,” architects are faced with two main challenges: 

(1)  they must pick up an adequate taxonomy, i.e. the right set of adjectives that can translate into quantifiable metrics for the machine and

(2) They must select, in the vast field of AI, the proper tools and train them. Those two preconditions will eventually determine the success or failure of AI-enabled architectures.

Conclusion

From the humble beginnings of modularity to the sophisticated dance of machine learning, the journey of architectural AI has been nothing short of miraculous. As we stand on this precipice, looking towards a future where AI not only shapes our buildings but our cities at large, it’s clear that the possibilities are as infinite as our imagination.

Let’s embrace this journey together, exploring how AI can continue to reshape architectural practices, making our world a more efficient, sustainable, and beautiful place to live.

Any Question? Just leave a comment.

FAQs

What is architectural AI?

Architectural AI refers to the application of artificial intelligence technologies to enhance the field of architecture in design, planning, construction, and operation.

How does AI improve the architectural design process?

AI improves the design process by enhancing efficiency, precision, and creativity. It enables architects to analyze more data, generate innovative design solutions, and optimize building performance.

Are there any risks associated with the use of AI in architecture?

Yes, there are risks, including concerns about data privacy, ethical use of AI, reliance on technology, and the potential for increased unemployment in certain sectors. Addressing these issues thoughtfully is essential.

Can AI replace human architects?

While AI can augment and enhance the work of architects, it cannot replace the creativity, intuition, and emotional understanding that human architects bring to the design process.

What is the future of AI in architecture?

The future of AI in architecture is bright, with potential developments in smart cities, sustainable design, and creative processes powered by advanced AI technologies.