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

by   Lukas Fuhrimann, et al.

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. In the third step, the solution space is interpreted with the help of a Uniform Manifold Approximation and Projection algorithm (UMAP). This allows gaining important insights in the structure of the solution space. A specific case study is used to illustrate how the infinite equilibrium states of a given topology can be defined and represented by clusters. Furthermore, three classes, related to the non-linear interaction between the input parameters and the form space, are verified and a statement about the entire manifold of the solution space of the case study is made. To conclude, this work presents an innovative approach on how the manifold of a solution space can be grasped with a minimum amount of data and how to operate within the manifold in order to increase the diversity of solutions.



There are no comments yet.


page 1

page 2

page 3

page 4


Data-driven Efficient Solvers and Predictions of Conformational Transitions for Langevin Dynamics on Manifold in High Dimensions

We work on dynamic problems with collected data {x_i} that distributed o...

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

This paper introduces a surrogate modeling scheme based on Grassmannian ...

Cellular morphogenesis of three-dimensional tensegrity structures

The topology and form finding of tensegrity structures have been studied...

A Combinatorial Algorithm for the Multi-commodity Flow Problem

This paper researches combinatorial algorithms for the multi-commodity f...

Prototype Discovery using Quality-Diversity

An iterative computer-aided ideation procedure is introduced, building o...

Learning by stochastic serializations

Complex structures are typical in machine learning. Tailoring learning a...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.