Makine öğrenmesi ile yapısal alternatiflerin tasarım-hedef uzamının araştırılması ve çoklu kriterle optimizasyonu

Increasing implementations of digital workflows within design processes generate exponentially growing data in each phase. Therefore, decision making within a design space with growing complexity is expected to be a great challenge for designers in the future. Hence, this research aimed to seek the potentials of complex relations between data within design space and objective space of structural design problems for proposing a novel approach to augment capabilities of digital tools by artificial intelligence. As a method, a machine learning-based framework was proposed that can help designers to understand the trade-offs between initial structural design alternatives to make informed decisions. The proposed framework was tested in three stages: probabilistic, deterministic, and integrated; all of which allow users to conduct optimization studies with the help of different machine learning models. Finally, the proposed approaches were presented in case studies, and potentials/limitations of the models were discussed including future projections.