Abstract

This research project aims to develop a toolkit for ML-based architectural design. Traditionally, architectural design 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, 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.

Here, 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 the specified performance goals.

We will develop and validate our AI-Augmented Architectural Design (AAAD) toolkit with a generic approach through case studies that are based on two different design categories. The first category concerns 2.5D surfaces, which are evaluated based on their fabricability (for 3D contour printing) and on environmental performance such as acoustics or sunlight protection, targeting architectural applications such as acoustic panels and façade panels. The second category is discrete element assemblies, which comprise load-bearing structures made of columns and beams. This category is evaluated based on structural or environmental performance goals.

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 revolutionizing computational design methods in architecture.