Corpus ID: 52811139

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

  title={Data-Driven Design: Exploring new Structural Forms using Machine Learning and Graphic Statics},
  author={Lukas Fuhrimann and V. Moosavi and Patrick Ole Ohlbrock and P. Dacunto},
  • Lukas Fuhrimann, V. Moosavi, +1 author P. Dacunto
  • Published 2018
  • Computer Science, Mathematics
  • ArXiv
  • 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… CONTINUE READING
    5 Citations


    Dimensionality reduction for parametric design exploration
    • 11
    Constraint-Driven Design with Combinatorial Equilibrium Modelling
    • 4
    • PDF
    Computing With Contextual Numbers
    • 12
    • PDF
    Self-organized formation of topologically correct feature maps
    • T. Kohonen
    • Mathematics, Computer Science
    • Biological Cybernetics
    • 2004
    • 6,588
    • PDF
    Designing With Data : Moving Beyond The Design Space Catalog
    • 2017
    • 9
    • PDF
    Adaptive filtering with the self-organizing map: A performance comparison
    • 30
    Visualizing Data using t-SNE
    • 15,824
    • PDF
    An Integrated Computational Approach for Creative Conceptual Structural Design
    • 13
    • PDF
    Combinatorial equilibrium modeling
    • 9