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AIXD: AI-eXtended Design
AI-Augmented Architectural Design
Impact Printing
Human-Machine Collaboration
AR Timber Assemblies
Autonomous Dry Stone
Architectural Design with Conditional Autoencoders
Integrated 3D Printed Facade
Robotic Plaster Spraying
Additive Manufactured Facade
Timber Assembly with Distributed Architectural Robotics
Eggshell Benches
Eggshell
CantiBox
RIBB3D
Data Driven Acoustic Design
Mesh Mould Prefabrication
Data Science Enabled Acoustic Design
Thin Folded Concrete Structures
FrameForm
Adaptive Detailing
Deep Timber
Robotic Fabrication Simulation for Spatial Structures
Jammed Architectural Structures
RobotSculptor
Digital Ceramics
On-site Robotic Construction
Mesh Mould Metal
Smart Dynamic Casting and Prefabrication
Spatial Timber Assemblies
Robotic Lightweight Structures
Mesh Mould and In situ Fabricator
Complex Timber Structures
Spatial Wire Cutting
Robotic Integral Attachment
Mobile Robotic Tiling
YOUR Software Environment
Aerial Construction
Smart Dynamic Casting
Topology Optimization
Mesh Mould
Acoustic Bricks
TailorCrete
BrickDesign
Echord
FlexBrick
Additive processes
Room acoustics
AI-Augmented Architectural Design, Zurich, 2021-2024
Background
Traditionally, architectural design is an iterative process and involves combining and optimizing many criteria and constraints. For performance-driven design, architects and engineers create parametric design models to generate, simulate and evaluate many design instances, in order to gather performance feedback on design alterations. However, this is typically a hierarchical process, unable to deal with multiple concurrent objectives and only investigating a narrow spectrum of the design space. Instead of tuning input parameters until the result meets certain performance criteria, we envision that machine-learned models of the design problem will allow us to find and explore design instances in the proximity of specified performance goals. In this way, the designer can discover unexplored areas within the design space and solutions that were previously intangible.

Approach
This research project addresses these shortcomings through creating a toolkit for machine-learning-based architectural design. We develop AXID toolkit for AI-eXtended Design and validate it with a generic approach through case studies that are based on two different design categories: The first category concerns surfaces, which are evaluated based on their fabricability (for 3D contour printing), acoustics, as well as on environmental performance or sunlight protection while targeting architectural applications such as acoustic panels and façade panels. The second category concerns discrete element assemblies, which comprises load-bearing structures made of columns and beams. This category is evaluated based on structural or environmental performance goals. We will first divide each of the design problems into subproblems and incrementally increase the complexity by successively integrating more performance measures or testing different design data representations. Throughout the project, we will redefine and regenerate the synthetic datasets and adapt the problem definition based on the performance of the respective machine learning (ML) models and the discovery of new design spaces. This will be facilitated through design interfaces developed in parallel, that allow for fast interaction and a flexible selection of in- and outputs.

Impact
This approach will allow for generic, reusable interfaces and will form an open-source and extendable toolkit based on ML methods that can be integrated into CAD software. The ultimate goal of this project is to augment the designer’s creative and analytical capabilities in the decision-making process by creating interactive design environments and thus augment computational design methods in architecture.

Related projects:

AIXD: AI-eXtended Design

Architectural Design with Conditional Autoencoders: Semiramis Case Study

Credits:
Gramazio Kohler Research, ETH Zurich

In cooperation with: Swiss Data Science Center (Dr. Luis Salamanca Mino, Prof. Dr. Fernando Perez Cruz )
Research programme: Swiss Data Science Center (SDSC)
Collaborators: Dr. Aleksandra Apolinarska (project lead), Dr. Romana Rust, Gonzalo Casas, Prof. Matthias Kohler
Consultancy: Dr. Kurt Heutschi (Empa), Christian Frick (Rocket Science AG), Jürgen Strauss (Strauss Electroacoustic GmbH), Prof. Dr. Norman Sieroka (University of Bremen)

Copyright 2023, Gramazio Kohler Research, ETH Zurich, Switzerland
Gramazio Kohler Research
Chair of Architecture and Digital Fabrication
ETH Zürich HIB E 43
Stefano-Franscini Platz 1 / CH-8093 Zurich

+41 44 633 49 06
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