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  • “human in the loop” machine learning
  • Researchers propose three main algorithms related to these operations: combinatorial equilibrium modeling is used in the generation stage (similar to Geometric Equilibrium through 3D Graphical Statics approach), self-organising maps to clusters the outputs, and gradient-boosted trees to learn user preferences and incorporate subjective criteria during the election stage.
  • generation, clustering, evaluation, election, and regeneration
  • combine human cognition with machine-driven computation, joining humans’ subjective evaluation and selection capacity with machines’ ability to handle large datasets
  • structural design framework that allows generating multiple novels, diversified, and structurally informed design options that could be handled masterfully by joining algorithms of Machine Learning and human decision-making to select one design option fulfilling all the designer’s qualitative and quantitative criteria
  • having a non-typological approach towards design is an important requirement
  • design exercises that rely solely on parametrization will encounter a significant challenge to adopt an approach that goes beyond typologies


    Ochoa, Karla Saldana, Patrick Ole Ohlbrock, Pierluigi D’Acunto, and Vahid Moosavi. n.d. “Beyond Typologies, beyond Optimization: Exploring Novel Structural Forms at the Interface of Human and Machine Intelligence.” International Journal of Architectural Computing, 25.


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