| id |
ijac202119313 |
| authors |
Saldana Ochoa, Karla; Ohlbrock, Patrick Ole; D’Acunto, Pierluigi; Moosavi, Vahid |
| year |
2021 |
| title |
Beyond typologies, beyond optimization: Exploring novel structural forms at the interface of human and machine intelligence |
| source |
International Journal of Architectural Computing 2021, Vol. 19 - no. 3, 466–490 |
| summary |
This article presents a computer-aided design framework for the generation of non-standard structural forms in static equilibrium that takes advantage of the interaction between human and machine intelligence. The design framework relies on the implementation of a series of operations (generation, clustering, evaluation, selection, and regeneration) that allow to create multiple design options and to navigate in the design space according to objective and subjective criteria defined by the human designer. Through the interaction between human and machine intelligence, the machine can learn the nonlinear correlation between the design inputs and the design outputs preferred by the human designer and generate new options by itself. In addition, the machine can provide insights into the structural performance of the generated structural forms. Within the proposed framework, three main algorithms are used: Combinatorial Equilibrium Modeling for generating of structural forms in static equilibrium as design options, Self-Organizing Map for clustering the generated design options, and Gradient-Boosted Trees for classifying the design options. These algorithms are combined with the ability of human designers to evaluate non-quantifiable aspects of the design. To test the proposed framework in a real-world design scenario, the design of a stadium roof is presented as a case study. |
| keywords |
Structural design, machine learning, topology, graphic statics, form-finding, Combinatorial Equilibrium Modeling, Self-Organizing Map, Gradient-Boosted Trees |
| series |
journal |
| email |
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| references |
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| last changed |
2024/04/17 14:29 |
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