Symmetry and Variance: Generative Parametric Modelling of Historical Brick Wall Patterns
This work addresses the need for generating large datasets of architectural heritage styles for machine learning and robotic fabrication, though it is incremental as it applies existing computational methods to a specific domain.
The study tackled the problem of analyzing and generating historical brick wall patterns by using photogrammetry-based point cloud models from the Anatolian Seljuk period to extract stochastic parameters and parametric shape rules, enabling the recreation of existing and hypothetical designs for machine learning and robotic production.
This study integrates artificial intelligence and computational design tools to extract information from architectural heritage. Photogrammetry-based point cloud models of brick walls from the Anatolian Seljuk period are analysed in terms of the interrelated units of construction, simultaneously considering both the inherent symmetries and irregularities. The real-world data is used as input for acquiring the stochastic parameters of spatial relations and a set of parametric shape rules to recreate designs of existing and hypothetical brick walls within the style. The motivation is to be able to generate large data sets for machine learning of the style and to devise procedures for robotic production of such designs with repetitive units.