A Virtual Tour at the Arsenal Pavilion

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AI & Architecture Virtual Tour | Source: Author

In this article, we present the content of “Artificial Intelligence & Architecture”, an ongoing exhibit on display at the Arsenal Pavilion Museum, in Paris. Due to the recent events, the museum has closed its doors, but reopens online, and offers today a virtual tour of the exhibition, freely available. We unveil here part of the exhibit’s content and invite you to visit it, using the Arsenal Pavilion’s Virtual Tour.

You can now access the Virtual Tour at this address.

Exhibit Virtual Tour Walk-through

I. Introduction

Artificial Intelligence (AI) has already made its way into the industry, providing it with the means to meet new challenges. Its use in the field of architecture is still in its infancy, but the expected results already obtained are promising. This technology is much more than a mere opportunity, it is without a doubt a decisive step forward, quite capable of transforming the architectural practice. This exhibition explores this engagement and its application to the built environment. Defining AI, explaining what it encompasses, both as techniques and as paradigms, is central to understanding its advent in architecture. …


A Computational Approach

Jeffrey Landes, Data Scientist,

Håkon Dissen, Software Engineer,

Håkon Fure, Data Scientist

Stanislas Chaillou, Architect & Data Scientist

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In this article, we unveil some of our recent results and methodologies implemented at Spacemaker AI over the past quarter. This project aimed at supporting Spacemaker’s long term vision, as one of our many ongoing research initiatives.

The design of floorplans can leverage machine intuition to generate and qualify potential design options. In this article, we address a specific abstraction of space: adjacency. Any floorplan carries its own embedded logic; in clear, the relative placement of rooms and their connections is driven by a certain logic of interdependence, and yields varying qualities across space. For instance, the presence of a room will condition the existence of other rooms, as well as the position of openings between them. First, we attempt here to qualify adjacencies of existing floorplans, to assess the relevance of adjacencies among rooms. We later turn to Bayesian modeling to generate adjacency graphs, either freely or under set constraints. …


GAN-enabled Floor Plan Generation

By Stanislas Chaillou, Architect and Data Scientist

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Figure 1: Apartment Building Dataset | Source: Author

In this article, we unveil some of our recent results and methodologies implemented at Spacemaker AI’s R&D department over the past quarter. This project is one of many ongoing research initiatives, aiming at supporting Spacemaker’s long term vision.

Apartment layout is a challenging yet fundamental task for any architect. Knowing how to place rooms, decide their size, find the relevant adjacencies among them, while defining relevant typologies are key concerns that any drafter takes into account while designing floor plans. In this article, we propose showcasing possibilities offered by Generative Adversarial Neural Networks models (GANs), and their ability to generate relevant floor plan designs. …


A Computational Approach

Jeffrey Landes, Data Scientist @ Spacemaker AI

Håkon Dissen, Software Engineer @ Spacemaker AI

Håkon Fure, Data Scientist @ Spacemaker AI

Stanislas Chaillou, Architect & Data Scientist @ Spacemaker AI

Image for post
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In this article, we unveil some of our recent results and methodologies implemented at Spacemaker AI over the past quarter. This project aimed at supporting Spacemaker’s long term vision, as one of our many ongoing research initiatives.

The design of floorplans can leverage machine intuition to generate and qualify potential design options. In this article, we address a specific abstraction of space: adjacency. Any floorplan carries its own embedded logic; in clear, the relative placement of rooms and their connections is driven by a certain logic of interdependence, and yields varying qualities across space. For instance, the presence of a room will condition the existence of other rooms, as well as the position of openings between them. First, we attempt here to qualify adjacencies of existing floorplans, to assess the relevance of adjacencies among rooms. We later turn to Bayesian modeling to generate adjacency graphs, either freely or under set constraints. …


GAN-enabled Floor Plan Generation

Stanislas Chaillou, Architect & Data Scientist @ Spacemaker AI

Image for post
Image for post

In this article, we unveil some of our recent results and methodologies implemented at Spacemaker AI’s R&D department over the past quarter. This project is one of many ongoing research initiatives, aiming at supporting Spacemaker’s long term vision.

Apartment layout is a challenging yet fundamental task for any architect. Knowing how to place rooms, decide their size, find the relevant adjacencies among them, while defining relevant typologies are key concerns that any drafter takes into account while designing floor plans. In this article, we propose showcasing possibilities offered by Generative Adversarial Neural Networks models (GANs), and their ability to generate relevant floor plan designs. …


GAN-enabled Floor Plan Generation

Stanislas Chaillou, Architect & Data Scientist @ Spacemaker AI

Image for post
Image for post
Figure 1: Apartment Building Dataset | Source: Author

In this article, we unveil some of our recent results and methodologies implemented at Spacemaker AI’s R&D department over the past quarter. This project is one of many ongoing research initiatives, aiming at supporting Spacemaker’s long term vision.

Apartment layout is a challenging yet fundamental task for any architect. Knowing how to place rooms, decide their size, find the relevant adjacencies among them, while defining relevant typologies are key concerns that any drafter takes into account while designing floor plans. In this article, we propose showcasing possibilities offered by Generative Adversarial Neural Networks models (GANs), and their ability to generate relevant floor plan designs. …


Séquence d’une Alliance

Stanislas Chaillou, Architecte & Data Scientist à Spacemaker AI

English Version

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La pratique de l’Architecture, ses méthodes, ses traditions et ses savoir-faire sont aujourd’hui au centre de débats passionnés. Ce métier vit sans nul doute une révolution sans précédent qui l’enjoint à redéfinir à nouveau son, ou ses, modèle(s). …


Une Perspective Expérimentale

Stanislas Chaillou, Harvard Graduate School of Design| 26 février 2019

English Version

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Plan Urbain, Généré par un Modèle GAN | Source: Auteur

L’intelligence artificielle (IA) est une discipline qui a déjà pénétré nombre d’industries, en leur apportant les moyens de relever des défis inédits. Son adoption en Architecture, comme évoqué dans un précédent article, en est encore à ses débuts, mais les résultats escomptés sont prometteurs. Cette technologie est pour nous plus qu’une simple opportunité : elle est un pas en avant sans doute décisif, à même de faire évoluer notre discipline.

Notre travail entend valider cette promesse, et son application au monde du bâti. En fait, nous souhaitons très spécifiquement appliquer l’IA à l’analyse et à la génération de plans. Notre objectif ultime est triple: (1) générer des plans d’étages (2) qualifier et classifier ses plans, afin de (3) faciliter la consultation des options et designs générés.


Assisting Design Through Machine Learning

Stanislas Chaillou | Harvard Graduate School of Design | Spring 2018

In collaboration with Thomas Trinelle

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The utilization of machine-based recommendation has been leveraged in countless industries, from suggestive search on the web, to photo stock image recommendation. At its core, a recommendation engine can query relevant information -text, images, etc- among vast databases and surface it to the user, as he/she interacts with a given interface. As large 3D data warehouses are being aggregated today, Architecture & Design could benefit from similar practices.

In fact, the design process in our discipline happens mostly through the medium of 3D software (Rhinoceros 3D, Maya, 3DSmax, AutoCAD). Might it be through CAD software(Computer-Aided Design), or today BIM engines (Building Information Modeling), Architects constantly translate their intention into lines and surfaces in 3D space. Suggesting relevant 3D objects, taken from exterior data sources, could be a way to enhance their design process. …


Assisting Design Through Machine Learning

Stanislas Chaillou | Harvard Graduate School of Design | Spring 2018

In collaboration with Thomas Trinelle

Image for post
Image for post

The utilization of machine-based recommendation has been leveraged in countless industries, from suggestive search on the web, to photo stock image recommendation. At its core, a recommendation engine can query relevant information -text, images, etc- among vast databases and surface it to the user, as he/she interacts with a given interface. As large 3D data warehouses are being aggregated today, Architecture & Design could benefit from similar practices.

In fact, the design process in our discipline happens mostly through the medium of 3D software (Rhinoceros 3D, Maya, 3DSmax, AutoCAD). Might it be through CAD software(Computer-Aided Design), or today BIM engines (Building Information Modeling), Architects constantly translate their intention into lines and surfaces in 3D space. Suggesting relevant 3D objects, taken from exterior data sources, could be a way to enhance their design process. …

About

Stanislas Chaillou

Architect & Data Scientist at Spacemaker AI | Harvard ’19 | http://stanislaschaillou.com

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