Data-Driven Design: Exploring new Structural Forms using Machine Learning and Graphic Statics

Understand

The aim of this research is to introduce a novel structural design process that allows architects and engineers to extend their typical design space horizon and thereby promoting the idea of creativity in structural design.

• The theoretical base of this work builds on the combination of structural form-finding and state-of-the-art machine learning algorithms.
• In the first step of the process, Combinatorial Equilibrium Modelling (CEM) is used to generate a large variety of spatial networks in equilibrium for given input parameters.
• In the second step, these networks are clustered and represented in a form-map through the implementation of a Self Organizing Map (SOM) algorithm.