A machine learning-based prediction model for architectural heritage: The case of domed Sinan mosques

Abstract

This study presents a machine learning-based prediction model (PM) customized to predict missing components of historical mosques. Domed mosques built by Architect Sinan during the Classical Ottoman Period (16th century) are selected due to their distinctive features and stylistic similarities. The model development process includes data collection (46 domed Sinan mosques), data preparation and refinement, training, testing, and validation. The Pix2Pix method is used to train and validate the machine learning models, and the Structural Similarity (SSIM) metric is used to objectively evaluate the outcomes. Preliminary results indicate that the success of the PMs is not directly proportional to the number of input components. Instead, factors such as overall mass organization, the curvature of the dome, and the number of balconies on the minaret play crucial roles in determining the success of the outcomes.