RE S E AR CH G A N-I TECTURE
HASEY / ELLIOT 2020
1
“O ur cap a ci ty to go beyond the m achine r ests in our power to a ssi mi l a te th e m achine. Until we have absor bed the l esso n s o f o b j ectivity, im per sonality, neutr ality, the lessons o f th e mech a n i cal r ealm , we cannot go fur ther in our d e ve l op men t to war d the m or e r ichly or ganic, the m or e p rofo u n d l y hu man.�
- Lewis M um for d. Technics and Civilization ( 1934)
3
G AN - IT EC T U R E year: type: Collaborators:
April 2019 - Present Research Michael Hasey Sage Elliott
5
A I : A NE W TOOL FOR ARCHI TECTS Artificial Intelligence is currently being used across a wide range of industries to address complex problems with new
and effective solutions. From driverless cars that autonomously understand road signs to news articles written by computers using complex algorithms, the opportunities for AI are endless. Modeled after the human brain, in some ways the
cognitive abilities of AI have now far exceeded our own, revealing a new frontier of knowledge and understanding that will profoundly change our world. Within the architecture field however, the integration of AI has only recently begun.
Currently, the architectural design process is largely manual and completed by a single or group of architects over an extended period of time. Typically, designs may take months or even years to complete and properly prepare for construction. Though software does exist to reduce the most tedious tasks, the majority of the process remains intensively
hands-on and time consuming. As a result, there is a huge potential to harness AI’s ability to automate traditionally manual tasks to help reduce production time, increase efficiency, optimize designs, and open doors to new types of architectural creativity.
In this study, we have focused our efforts on the first step of the architectural design process called the “Conceptual
Design Stage”. Within this stage, architects create and explore a range of design options through drawing, and physical and digital models with the intent of synthesizing what will become the final design. In theory, this “final design” is the
result of all previous design explorations and represents all lessons learned and the best architectural solution to satisfy
the project’s goals (aesthetics, function, economy, sustainability and quality). Though conceptual design is the most cre-
ative stage of the design process, it is also the most essential, as it ultimately lays down the overall design scheme and drives the entire direction of the project throughout the remainder of it’s lifetime. In fact, any design changes made after
this stage become exponentially more difficult and more costly to implement, thereby seriously threatening budget and construction time line goals. Currently, the Conceptual Design Stage is an intensly manual process where architects are
severely restricted in the number of design iterations they can produce due to cost, time constraints enforced by deadlines
and the sheer limitations of human endurance. As a result, final designs may be based on just a handful of studies that may not capture the fullest functional, sustainability, economic, or aesthetic potential of the project. This issue identifies
a serious problem within the field; contemporary architects lack the proper toolset to optimally explore the full range of possibilities available to them, resulting in potentially sub-par final building designs that may lead to decades negative economic, functional, performance, or psychological effects. As a response to this problem, our work begins to explore
how AI may provide the future architect with a toolset to very efficiently discover an exponentially greater number of design options in order to make better decisions and build better architecture.
C U R R EN T ME T H OD
Manual and time consuming with few results
VS
A I M E T HOD
Autonomous and rapid with many results
“Bijou on Bloor� design options by Quadrangle architects (above), AI generated building design options (below)
7
G EN ERAT ING ZA HA HADI D I N S P I RE D A RCHIT E CTU R E WI TH AI The primary goal of our research is to explore AI’s capacity to generate new building designs in a specific and assigned architectural style. Instead of taking hours to create a handful of building designs via the traditional method, we intend use
the latest neural-network-based algorithms to create thousands within a fraction of the time. For this study, we have used
a powerful algorithm called W-GAN (Wasserstein Generative Adversarial Networks) to create new architectural designs. In this case, we wanted to generate new designs in the style of Zaha Hadid, one of the most renowned parametric-design
oriented architects of recent time. As shown here, her buildings are easily recognizable by her signature sweeping wavelike gestures, elegant curves, and lightweight and playful masses. On the following pages you’ll both see and learn how
WGANS have the ability to accurately learn then mimic her style. Once trained these algorithms are capable of generating thousands of new building designs within an extremely short span of time. In addition, we will demonstrate how our
algorithmic approach creates new and emergent styles of its own through the uncovering of previously hidden patterns and phenomena found within her original work. These poweful algorithms begin to challenge the status-quo of current
architectural design processes and enhance our own understanding of past architectural work. My research suggests that a closer and more rigorous scientific rationality between architect and artificial intelligent system can be pursued in or-
der to empower the architectural community and reveal the next wave of architectural creativity, analysis and discourse.
Examples of Zaha Hadid Projects
9
Z A HA HADI D O R I G I NALS
NE W AI G ENERATED D ESI G N S
11
ARTIFICIAL IN
Tr a i n i n g set of 5000+ original i m a ges of Zaha Hadi d buildings
N T E LLI GENCE
Hundreds of t housands of Zaha Hadid inspi red building designs
13
15
W H AT A RE GA NS ? Generative adversarial networks (GANs) are deep learning neural networks comprised of two algorithms that train each other to “learn” then “re-create” patterns of statistically significant phenomena found within pre-existing training sets
of original data. Original data may include images, music, text, and so on. What makes GANs special is their ability to recreate new data that is incredibly similar to the originals, yet unequivocally unique.
Introduced by Ian Goodfellow on December 5, 2016 at the NIPS conference in Barcelona, GANS are considered one of the
key turning points for generative AI technology. Due to their ability to mimic any type of data fed into them, the poten-
tial of leveraging GANS within creative industries is huge. They have become so effective that many of these artificial recreations are nearly indistinguishable from real ones made by humans. Above are some examples of incredibly realistic
“fake” faces created by Nvidia’s 2019 “Style-Based” GAN. Unbelievably, these are faces of people who have never existed before. On the opposite page, a GAN has generated a series of landscape expressionist paintings that could easily be
hung and applauded at any art gallery. Below these paintings is a diagram illustrating the various layers of feature and pattern recognition that deep learning neural networks can learn and then re-create. In this example, a GAN learns the key features of George Washington’s face (labelled as “input”) by breaking it down into its essential components of pixels, edges, and features in order to then recreate faces with similar features (labelled as “output”). The ultimate potential for these early stage algorithms after further refinement and reflection is immediately apparent.
An illustration of a Deep Learning Neural Network (Above)
17
H O W DO GA NS W ORK? The innovative nature of GANS is simple at its core. Their basic primary software architecture hinges on two algorithmic networks; the generator and the discriminator. In our study, the generator’s task is to attempt to create images of “fake”
Zaha Hadid buildings that look convincingly similar to her real designs. In this case, we have compiled more than 10,000 images of Zaha Hadid buildings into a training set which is fed into the generator portion of the GAN. The discriminator’s task is to then decide whether the images of fake Zaha Hadid buildings . Once trained our algorithms are capable
of generating appear convincingly real or not. If the images do not look real, for example, look more like a cat then a building, the discriminator assigns a low score to the generated image. This low score indicates to the generator that it
C R E ATING OUR TRA I N I NG SET
IDENTIFY GOAL Generate Zaha Hadid style buildings
SEARCH Find images of Zaha Hadid buildings
SCRAPE & SORT Download 10k+ original images and sort into image categories
must improve its effectiveness and produce more convincing images of “fake” Zaha Hadid styled buildings in hopes of fooling the discriminator next time. As a result, the generator will correct its mistakes and will generate more and more convincing building design images over time. As the generator gets better at creating convincing images, the discriminator
must then improve its ability to identify fake images in order to outperform the generator. Through this back and forth,
cat and mouse competition of trying to outdo one another, both the discriminator and the generator improve at their craft
over thousands of iterations or what are called “epochs”. In this way, GANs can, over time, “learn” to generate unique and extremely convincing images of Zaha Hadid styled buildings that are nearly indistinguishable from the originals.
R U N N ING OUR GA N
APPLY AI
(real images) PASS
NOISE DISCRIMINATOR GENERATOR
FAIL
(fake image) 1 epoch
19
OUR WGAN SOURCE CODE Our WGAN source code is written in Python and runs on a deep learning Tensorflow backend and utilizes a Keras computer-vision and Leaky_Rel neural network Libraries.
Source code available on GitHub here:
www.github.com/michaelhasey/GAN-ITECTURE
21
I M AG E OP TIMIZ ATION O VER TI ME “After many epochs, the discriminator and generator will have found an equilibrium, that allows the generator to learn meaningful information from the discriminator and the quality of the images will start to improve. By observing images
produced by the generator at specific epochs during training, it is clear that the generator is becoming increasingly adept
at producing images that could have been drawn from the training set.” - David Foster, “Generative Deep Learning Teaching Machines to Paint, Write, Compose and Play”, 2019.
1 3 2 4 Loss Funct ion
5
6
I M P R O V ING IMA GE QUALI TY O VER TI ME
1
1 epoch
2
25 epochs
3
50 epochs
4
1000 epochs
Below is a graph illustrating how GAN performance increased in our own hands over time. The values below represent
the decreasing loss function as the GAN improves after thousands of epochs (individual feedback loop cycles of the GAN algorithm). The loss function is used by neural networks to compare its predicted output (generated Zaha designs) to the ground truth (real Zaha designs). The lower the number, the better the network is performing. As you can see, the decreasing loss function corresponds with increasingly accuracy and quality of generated Zaha Hadid building designs.
7
8
Ep oc h s
5
2,200 epochs
6
4,500 epochs
7
10,000 epochs
8
19,000 epochs
23
R ES U LTS
Generated images after 20,000 - 25,000 epochs
LAYE R S T Y LE After running our GAN beyond 15,000 epochs, we observed how the algorithms could “learn” and recreate particular architectural styles, phenomena, and forms that were present within the original training dataset of architectural imagery.
In the examples shown here, our GAN was able to “learn” the rules and patterns that governed the layered, curved, and banded architectural styles found within the “Innovation Tower” and “Wangjing SOHO” projects. After learning these rules, the GAN then applied them when generating new architectural forms in a similar style. This process was accomplished so accurately that at a first glance, many of these generated designs are indistinguishable from the originals themselves.
Original Innovation Tower images used in training set
Original Wangjing SOHO images used in training set
Generated images after 16,000 - 25,000 epochs
25
R ES U LTS
Generated images after 20,000 - 25,000 epochs
TO W ER S TY LE During the span of her career, Zaha Hadid designed a number of towers such as the “Torre Hadid” in Milan, “Mariners Cove” in Australia, and the “Rublyovo-Arkhangelskoye” in Moscow. As a result, we included thousands of images of these
and other of her tower designs in our training set with the intent of creating similar designs. After thousands of epochs,
our GAN algorithm was able to “learn” the stylistic rules and parameters that drove the signature elegant forms, balanced compositions, and sleek vertical elements expressed in these and many of her other tower designs. Like “Layer Style”, our GAN then applied these rules to new generated architectural designs in this Zaha Hadid influenced “Tower Style”.
Original Zaha Hadid tower designs included in training set
Generated images after 16,000 - 25,000 epochs
27
R ES U LTS
Generated images after 20,000 - 25,000 epochs
WAVE S TY LE The Heydar Aliyewv Center in Azerbaijan is one Zaha Hadid’s most iconic buildings. Designed and built at the height of
her career, the center embodies many of her most signature design elements; sweeping wave like gestures, elegant curves, and lightweight and playful masses. By feeding hundreds of images of this building into our training dataset, we were able to “teach” the GAN to embody many of these stylistic elements within new generated architectural designs.
Original Zaha Hadid designs included in training set
Generated images after 16,000 - 25,000 epochs
29
R ES U LTS
Generated images of new emergent styles after 20,000 - 25,000 epochs
N EW E ME RGE NT S TYLES On the previous pages, we’ve demonstrated how GAN based computation can mimic Zaha’s signature architectural styles within generated building designs. However, even more compelling are its abilities to create completely new styles that emerge from its deeper understanding of hidden patterns, relationships, and phenomena inherent within her work.
Though many of the new emergent styles shown here are starkly independent, they still remain eerily similar to and
undoubtedly influenced by her original designs. These abilities to recognize and draw inspiration from patterns beyond
human cognition highlight one of the most exciting realms of AI’s potential within architectural design and discourse and call for further investigation and testing.
New emergent styles after 16,000 - 25,000 epochs
31
R ES U LTS
Generated images after 12,000 - 15,000 epochs
I N T E RIORS In addition to generating exterior views of Zaha Hadid-like architecture, we also generated images of interior views. Similar to the exterior views, all interior views are “fake” images based off of a training set of “real” Zaha Hadid interior
images. In this case, our training set consisted of 6,355 images downloaded from online image and social media platforms such as Google images, Flickr, and Instagram.
Original Zaha Hadid interior designs included in training set
Generated images after 12,000 - 15,000 epochs
33
F U T U RE RE S E A RCH The adoption of AI within the architecture field is simply just the latest step within a continuing evolution of increasingly
sophisticated techniques, tools and approaches being applied to the field. As the latest step within a 4-part evolution
(modular design - 1930’s onward, computer aided design - 1960’s onward, and parametric design - 2000’s onward) [1], AI based tools and systems provide the next frontier of architectural exploration, understanding, and creativity.
The work described in this report, i.e. convincingly replicating the designs of a well known architect, is intended primarily as a proof of concept. In the future, similar algorithmic tools or approaches may identify, quantify and combine the de-
fining elements of many architects, architectural styles and even elements from the natural world such as landscape and flora to create highly original architectural designs. In this study, we took an “outside - in” approach, by exploring AI’s
ability to independently create external expressions of architectural form. Other researchers such as Stanislas Chaillou [1], Nathan Peters [2], and Zheng & Huang [3], took an alternate “inside - out” approach by starting at the level of the
floor plan. Within their studies, they took rigorous steps to layout a process for AI to accurately recreate functional and
rational spaces. In contrast, our “outside - in” approach allowed us to jump directly into an environment of augmented creativity whereby emerging AI driven architectural languages massively amplify the creative potential of the human architect in an almost entirely unexplored manner where human and machine complement each other, something which
may have never been attempted before. These AI-driven methods allowed us to identify and draw inspiration from the
deepest layers of pattern and phenomena that underly successful pre-existing architectural designs. Moving forward, a deeper and more thorough understanding of AI’s application in both the internal & external realm may lead to greater efficiency, accelerated creative development and perhaps even the emergence of an entirely new design process.
Pragmatic process of generating AI based floor plans - inside - out approach [1]
+
New emergent exterior AI design languages - “outside - in” approach
=
?
[1] Stanislas Chillour. 2018. “AI + Architecture: Towards a New Approach”. Harvard, Cambridge, MA. [2] Nathan Peters. 2017. “Enabling Alternative Architectures: Collaborative Frameworks for Participatory Design”. Harvard, Cambridge, MA. [3] Hao Zheng, Weixin Huang. 2018. “Architectural Drawings Recognition and Generation through Machine Learning”. Cambridge, MA, ACADIA.
35
vi si t www.m ichael hasey. com f or addit ional wo r k
37