controlled by input parameters set by the designer. Hence, the algorithms that makes this system perform functions can be thought of as co-designers. (Radziszewski, Cudzik, & Kacper, 2018) This model has been experimented by on column designs, the AI system was trained on detailed configurations of the Roman Corinthian order capitals. Automated design variations were created by tiny deformations of the initial design, the ability to deform the model was set by the designers as a constraint for the system to work under. The successfully trained neural network was able to produce both purposeful and random design variations to the new capital forms. “Neural networks, that are less predictable, intuitive, resembling human-like decision-making process, became a way of data computing that is able to extend the set of architectural computational design tools�. (Radziszewski, Cudzik, & Kacper, 2018)
Fig 11. Capitals automatically designed with machine learning
4. RESEARCH METHODOLOGY 4.1.
Case Studies
4.1.1. Case Study 1 - New Delve Generative Design Tool Designers: Sidewalk Labs Description: The use of different platforms and different methods of data collection by architects, engineers and business department leads to the fragmentation of data. This makes the process inefficient and sometimes even expensive, limiting variation in design possibilities. (Hickman, 2020)
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