Symmetry and Variance: Generative Parametric Modelling of Historical Brick Wall Patterns

TL;DR

This paper presents a method combining AI and computational design to analyze and generate historical brick wall patterns by capturing symmetries and irregularities for applications in machine learning and robotic fabrication.

Contribution

It introduces a novel approach to extract stochastic parameters and parametric rules from heritage brick walls, enabling the creation of large datasets and robotic production procedures.

Findings

Successful extraction of symmetry and irregularity parameters from photogrammetry data

Generation of both real and hypothetical brick wall designs within the style

Potential for machine learning and robotic fabrication applications

Abstract

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.