AI & Typology



Introduction
For thousands of years, people always have a perceptual recognition of churches although the configuration of churches varies within history. The essence of religious spirit does exist in the space of the churches but it is never detected and summarized as a design method. And nowadays, ritual space is gradually diversified in the modern church due to the flexible modernity and technological development.

Inspired by Aldo Rossi’s architectural typology theory, we conclude “church” is a "type" that we intend to train AI to learn the basic logic within it. AI can generate new styles of church by using neural networks. Church is always the place of spiritual aggregation of humans, as Rossi mentioned the best way to understand a city is finding the collective memory. That's also the main concept that narrates why we tend to research churches and their ritual space.

Therefore, we wonder could AI learn this historical typology of churches and develop their own method of the "type and style" in the new digital era. We seek silence, quietude, rituals, and symbolism, not only in form but in sentiment. Our project aims at creating new church spaces by training the AI to learn hundreds of church models, and by the way of 3D GAN.

Theory
Reading through Aldo Rossi's theory of Typology, we realize the central concept of "type and style." “Type” is a permanent logic that is prior to form; “style” is the changing morphology within the history. We are also inspired by Professor Matias del Campo’s article about “type and style”. “Type” is the starting point, and “style” is an instrument that identifies the changing characteristics and the passage of time within historical architectural production.

Over decades, many architects, researchers, or photographers worked a lot in architectural typology research. German photographers Bernhard Becher and Hilla Becher exhibit typological documentation photography of industrial facades, which represents the human way to clearly build a database and could be used by AI to learn its inherent type. Aldo Rossi also proposed an analogical city presenting the historical geometry learning and the new city design. Thus, we conclude the architectural typology as three steps: 1. Concrete (documenting the cases of a specific type); 2. Abstract (comparing, classifying, summarizing); 3. Concrete (use logical principle of “type” and characteristics of “style” to generate new cases). Here, AI Deep Learning serves as a typological design method. Database, deep learning, and generating is equal to architectural typology of documentation, learning “type,” and transferring “style.”

Working Process
AI & Typology picks “church” as a specific architectural type to study the application of artificial intelligence in the architectural typology research. Church is a building type used for Christian worship services and other Christian religious activities. The historical church styles are driven by cultural sympathy, functions, creativities and distinctive materiality. This style defines artistic achievements in various terms, and consists of great flexibility to show the continuous transformation in history. Because of the scope of this semester, we limited our research objects to two styles. Gothic churches obtain uniform and decorated styles, while modern churches are more flexible and geometry formed. We think these two styles represent two distinct directions in the development of history to represent the basic type of church architecture.

By collecting 2000 church models (1000 Gothic churches and 1000 modern churches), we start building a church database, forming a workflow, and experimenting AI training results. A 3D PointCloud GAN called TreeGAN is what we use for generating a new church exterior prototype without labeling. We also use 2D to 3D Neural Style Transfer to attach the contextual texture of Manhattan onto the exterior prototype surface to get our final church exterior. But TreeGAN can only be used for exterior generation. For church interior, we decide to use 2D to 2D Neural Style transfer to generate desired church images and use 2D to 3D Neural Style transfer to apply them onto interior architectural components, like vaults or columns, to get the final church interior. Then, we will combine them to get our final church.

Context
AI & Typology investigates the existing churches circumstances in Manhattan. The congregations have started to notice that one of the best ways to make their churches stay active and open is to sell the property for demolition. Although the Gothic architecture has long been admired, the interior infrastructure is crumbling. Because of that, the old churches’ leadership started to strike a deal with the developers to demolish the building and use the land to construct some other types of architecture, like residential buildings. However, the number of the Christans in Manhattan is increasing every year, and the demand for churches is very huge.

Our target church is the Norfolk Street Baptist Church, which was built in 1848. It still has all the wonderful Gothic Revival touches of a mid-19th century church. But in 2012, the congregation asked the landmarks commission for permission to tear down the synagogue and sell the land to developers. Till now, the request is still on hold. Also citizens are unsatisfied with the reconstruction projects because they may destroy the historic urban texture of New York. So we are trying to use AI to generate a new church so that it could substitute the old structures in the same place. The new church will not only meet the demand of Christains, but also it will respond to the local history background and architectural style.
Faculty Advisor:
Matias del Campo