Holistic Design Explorations of Building Envelopes Supported by Machine Learning


  • Federico Bertagna ETH Zurich, Institute of Technology in Architecture, Chair of Structural Design
  • Pierluigi D'Acunto Technical University of Munich, Department of Architecture, Munich (Germany)
  • Patrick Ole Ohlbrock ETH Zurich, Institute of Technology in Architecture, Chair of Structural Design, Zurich
  • Vahid Moosavi ETH Zurich, Institute of Technology in Architecture, Chair of Digital Architectonics, Zurich




holistic design approach, building envelopes, graphic statics, conceptual structural design, machine learning, simplicity and performance


The design of building envelopes requires a negotiation between qualitative and quantitative aspects
belonging to different disciplines, such as architecture, structural design, and building physics.
In contrast to hierarchical linear approaches in which various design aspects are considered and
conceived sequentially, holistic frameworks allow such aspects to be taken into consideration
simultaneously. However, these multi-disciplinary approaches often lead to the formulation of
complex high-dimensional design spaces of solutions that are generally not easy to handle manually.
Computational optimisation techniques may offer a solution to this problem; however, they mainly
focus on quantitative aspects, not always guaranteeing the flexibility and interactive responsiveness
designers need in the early design stage. The use of intuitive geometry-based generative tools, in
combination with machine learning algorithms, is a way to overcome the issues that arise when dealing
with multi-dimensional design spaces without necessarily replacing the designer with the machine.
The presented research follows a human-centred design framework in which the machine assists the
human designer in generating, evaluating, and clustering large sets of design options. Through a case
study, this paper suggests ways of making use of interactive tools that do not overlook the performance
criteria or personal prefer


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