Machine Learning in Predicting Section Drawings Case of Anatolian Seljuk Kümbets

Funerary structures called kümbet emerged as a unique typology during the Anatolian Seljuk period (1077-1307). This study introduces a machine learning (ML) based model to predict sections of kümbets to complete their missing parts. The proposed ML-based model employs the Pix2Pix method, which is a subset of conditional Generative Adversarial Networks (cGAN).The model is trained over a coupled dataset (interior space and exterior shell) of section drawings. Then, the model is validated by predicting overall shape (exterior shell) for a given input (interior space). The outcomes of the validation phase are evaluated objectively by using structural similarity method (SSIM). Initial findings of the implementation show that the proposed ML-based model has the potential to be used as a design decision support tool for further restitution and renovation works.