AI and the Development of Architecture Design - White Paper

AI and the Development of Architecture Design

by RICARDO GUISSE

Introduction

Moore’s Law states that computer power doubles every eighteen months. (Kaku, 1997) And as technology continues to develop acceleratingly more powerful, it will also potentially become far more ubiquitous in the future. Sometimes it is better to ponder upon this realization with optimism rather than to imagine it as another Black Mirror episode.

Humans and machines have become increasingly codependent from each other. Machines, on one hand, can compute millions of calculations. (Broussard, Chapter 2: Hello, World, 2018) Humans, on the other hand, are vastly dependent on machines to perform said calculations, as well as an array of other commands and functions. Yet for a machine to be useful, it highly depends on its purpose to humans.

In architecture, starting with the release of PRONTO, the first prototype of CAD (Computer Assisted Drawing) software, to the present use of Autodesk’s Revit, a BIM (Building Information Modeling) software, machines have irrevocably become part of the development and production of architecture. (Chaillou, 2019) Machines are capable of rigorous control of geometry; boosting design’s reliability, feasibility and cost; facilitating and easing collaboration among designers; and enabling more design iterations than traditional hand-sketching. (Chaillou, 2019) And just like previous technologies, artificial intelligence, or AI, has found its way into the realm of architecture, which comes with benefits, repercussions, and a future which are worth exploring.

Patrick Hanratty, developer of PRONTO

Autodesk Revit

Image at cover: Compilation of StyleGAN images. Ricardo Guisse

Moore’s Law. Bilby.

Generative AI

One aspect of AI that can be applied to architecture is the use of Generative Adversarial Neural Networks or GANs, which could be interpreted as a type of Narrow AI, which uses mathematical methods for prediction. (Broussard, Chapter 3: Hello, AI, 2018) GANs are machine-learning models that generate images using a structure made up of two key models: a Generator and a Discriminator. The Discriminator is trained to recognize images from a dataset. The Generator, however, is trained to create images resembling the ones from the dataset. These images can be produced by the machine itself using a technique known as a Neural Style Transfer, where a “content” image or a base image gets modified by a “style” image in order to generate a new image. (del Campo, Manninger, & Carlson, 2019)

With this technique, a machine can grasp the complexities of images from a dataset and be “trained” to learn how to identify certain characteristics, such as shape, color, lines, etc. The larger the dataset, the most efficient the training model becomes.

One study using style transfers was performed at The Art and Artificial Intelligence Laboratory at Rutgers University, which conducted this machine-learning technique to generate “art.” In this scenario, the machine used large datasets of paintings and art pieces from different periods in history.

The machine later generated what can be arguably described as art. With continuous training, the art emulated by the machine was identified by the human subjects of the study, in which 3 out 4 thought the art was made by a human artist. (Elgammal, 2017) However, it should be understood that the machine was using a dataset containing art created by humans and merely replicating them. Therefore, it should be implied that the machine could not have come up with art of its own without human input.

Neural Style Transfer process. Pathmind AI Wiki.

Neural Style Transfer Sample. Ricardo Guisse.

Content Image

Examples of images generated by the system after trained on 80K paintings from the 15th century to 20th century and forced to deviate from established styles. Rutgers University.

Style Image

Generated Image

AI in Architecture Design

Just as AI has found its way in art, so too it has found itself in architecture. The use of AI in architecture is in its early stages, with the potential of reshaping the architectural discipline. (Chaillou, 2019) Involving both computer programming and design disciplines, AI is closely becoming a new tool in the development of architecture design. But what would its potential use be?

In architecture design, floor plans are extensively common and vital in communicating the dimensional qualities of a built environment. In its most basic meaning, the floor plan is an abstraction that allows to execute in a control manner the materialization of matter and space. (del Campo, Manninger, & Carlson, 2019). Due to the nature of this abstraction, a machine can be trained to identify plans, and perform style transfers to generate new ones. For example: GAN-models can progressively learn how to layout rooms and fenestrations (windows and doors) for housing units. In addition, the machine can learn how to identify characteristics of plans, such as kitchens, living rooms, bathrooms, bedrooms, and other areas within. (Chaillou, 2019)

As opposed to CAD and BIM, which are tools for drafting a design and input information about buildings respectively, generative AI can progressively develop into a basic design generator. It can achieve much more than just generating floor plans, as it also can be trained to adapt a design within constraints. (Chaillou, 2019) Architects and designers can potentially apply this technology in developing, not just plans, but building sections, building elevations, sketches, volumes, and spatial programs.

One example of applying generative AI in architecture comes from the 2021 Fall studio project led by Matias del Campo at the University of Michigan’s Taubman College of Architecture and Planning, where yours truly took part of. Two of my fellow colleagues and I developed a library design using neural style transfers and GAN techniques. The basis for our design was purely based on the architectural interpretation of the images generated through machine-learning. We used satellite imagery of our assigned location to generate our overall site design, and later used a large dataset of building sections to create the program within our building design. Through the interpretation of the generated images, we came up with an amoeba-shaped building.

Process of design using style transfer and StyleGAN models. Later 3D modeling was input to create the building design of a library. Ricardo Guisse, Jacob Brown, Yanlin Zhou.

Another example comes from Stanislas Chaillou’s thesis project at Harvard University’s Graduate School of Design from 2019, where he designed apartment units for a large-scale housing development. For this project, Chaillou pushed the boundaries of GANs in order to solve highly constrained problems with remarkable flexibility. The result: 380 one-of-a-kind apartment layouts. (Chaillou, 2019)

In both examples, having AI as part of the design process not only allowed for imaginative ways of addressing context and constraints, but also the resulting interpretations lead to new and unique design explorations. To this extent, Matias del Campo provides us with this insight from his paper Imaginary Plans:

“[The use of AI in architecture] interrogates the unique position of the human mind when it comes to creative processes and questions aspects of creativity in planning processes.” (del Campo, Manninger, & Carlson, 2019)

The Benefits

As an early tool in architecture, AI and machine-learning have great potential in helping explore unprecedented avenues for design composition and inspiration. Like many new technologies, this can boost challenging and interesting designs. If computer aided technologies like CAD and BIM already help, not just make better buildings, but also compute geometries and parametric shapes with ease, then AI can conceive unimaginable designs.

New tools provide new possibilities, and humans have always found creative ways of using new technologies. Thus, human creativity can undoubtably push the boundaries of AI technology much farther in attempting to achieve newer and bolder ideas.

In a pragmatic way, AI can also potentially reduce the time it takes for architects and designers to narrow down a design from a pool of generated options. (Chaillou, 2019) Coming up with a design schematic can sometimes require laborious hours of intense brain power. Add the time it takes to assess a workable design, and all that is left is just a few good options to further develop. Therefore, AI can stimulate the creative drive of a designer by generating multiple options, widening the design possibilities that would perhaps not be humanly possible.

Presently, the processes of AI in the architecture realm are still generated with human input and are not yet reaching a design autonomy for design solutions. (del Campo, Manninger, & Carlson, 2019) As a discipline dedicated to the built environment and its occupants, a sense of humanity in architecture design is still necessary.

Apartment Architectural Sequence. Stanislas Chaillou.

Model III output, furnishing of each individual unit. Stanislas Chaillou.

Model II output, program for each floor plate. Stanislas Chaillou.

The convenience we experience from technology can imperatively lead to dependency. Just as it was the case with computers and smartphones, we often rely heavily on their use as they have become so embedded within the functions of our society. Similarly, AI could potentially become another paradigm of this dependency. And in architecture, relying purely on the outcomes of machinelearning with no input from the designer can likely lead to unfitting designs. Especially ones that exclude any cultural practices, customs, accessibility, or context which are traits that constitute humanity.

Farther from the possible dependence on AI by designers is the rising issue of the unparalleled and rapid improvement of machine-learning. Remember Moore’s Law? Well, the quality of the output from machines is subject to continuous improvement, and they can become as good as the content of their datasets. (Chaillou, 2019) The better the data, the better the models, the better the designs. And just as AI continues to improve its outputs to be later used in architecture, the need for touching up AI-generated designs could involve less drafting. Consequently, this will only result in another probable outcome, e.g., losing design occupations such as drafters or rendering providers.

This begs the question: could AI then be capable of designing autonomously? As mentioned in the last section, it is unlikely but also not disregarded. Nevertheless, in the event AI reaches that capacity, it is also likely that designs could become less creative or worse, less human.

Worse yet, what about an outcome less human? What if AI is used inhumanely? This perhaps leads to an issue most related to the purposes of technology. One could argue that AI can aid in creating problematic building designs, such prisons, or accessories to war. AI can also aid in the design of oppressive institutions. It should be acknowledged that sexism and racism are still major problems in the application of AI technology in the present day.

(Klaus Neuburg, 2020) For technology itself is not the issue, rather its user. This moral disjunction has been expressed in the past by German philosopher Martin Heidegger in his book The Question Concerning Technology:

“Enframing blocks the shining-forth and holding-sway of truth. The designing that sends into ordering is consequently the extreme danger. What is dangerous is not technology. There is no demonry of technology, but rather is the mystery of its essence.”

(Heidegger, 1977)

In other words, it is the human behind the technology that provides it with the essence of its purpose. It would be ludicrous not to think that AI, just like any other technology, could find its way in the wrong hands and become another tool for potential misuse. Therefore, a bad designer can consequently threat the very positive and moral aspect of using AI in architecture design.

People using smartphones. iStock. New York Times

Just like any emerging technology, AI is still changing, and examining its positive and negative outcomes may help us see it with sense of reserved optimism. Whether AI has a place in architecture firms, small design practices, or academia, it is vital that architects and designers shape its development by refining it within contextual, cultural, and human sensitivities. It is entirely up to humans to make good use of machines, which can vastly expand the reach and positive outcome of architectural design. (Kilian, n.d.)

AI can reinstate simplicity and clarity as a driving principle to architecture. (Chaillou, 2019) The hope is that it can provide a better future for the built environment and for the humans that partake in the designs that involve using AI.

There is no question that machines can perform tasks better than most humans, like mathematical computations. But machines cannot compute humanity. Our dependency on technology should not lead to the renouncement of our most human traits, such as empathy, intelligence, and the capacity for imagination. (Broussard, Chapter 3: Hello, AI, 2018) The use of AI in the development of architecture design is first and foremost in service of the architect and designer, who successively are in the service of improving the built environment for its inhabitants. No matter how advantageous and wonderful the use of AI is for architecture, it ought not lose the human touch.

One may involve the other, but in the end, someone ought to push the button.

The defamiliarization of the plan. Matias del Campo.

StyleGAN of architecture facades. Ricardo Guisse

Row house style physical model. Stanislas Chaillou.

GAN-enabled building layouts. Stanislas Chaillou.

Broussard, M. (2018). Chapter 2: Hello, World. In Artificial Intelligence: how computers misunderstand the world. Cambridge, MA: The MIT Press.

Broussard, M. (2018). Chapter 3: Hello, AI. In Artificial Intelligence: how computers misunderstand the world. Cambridge, MA: The MIT Press.

Chaillou, S. (2019). I: The Advent of Architectural AI. In AI + Architecture: Towards a New Approach. Cambridge, MA: Harvard University.

del Campo, M., Manninger, S., & Carlson, A. (2019). Imaginary Plans. Proceedings of the 2019 ACADIA Conference - Ubiquity and Autonomy, p. 3.

Elgammal, A. (2017, June 26). Generating “art” by Learning About Styles and Deviating from Style Norms. Retrieved March 2022, from Medium: https://medium.com/@ ahmed_elgammal/generating-art-by-learning-about-styles-and-deviating-from-stylenorms-8037a13ae027

Heidegger, M. (1977). The question concerning technology. Bremen, Germany: Garland Publishing.

Kaku, M. (1997). Chapter 2: The Invisible Computer. In Visions: how science will revolutionize the 21st Century. (p. 28). New York, NY: Anchor Books.

Kilian, A. (n.d.). Autonomous Architectural Robots. Retrieved March 2022, from e-flux Architecture: https://www.e-flux.com/architecture/artificial-labor/140671/ autonomous-architectural-robots/

Klaus Neuburg, S. Q. (2020). Will artificial intelligence make designers obsolete? DAI Conference 2020, (p. 5). Berlin.