Fabrication-Aware Synthetic Dataset Generation and Compact Geometric Encoding for Architectural Robotic Assembly of Brick Wall Designs

Bridging the gap between Artificial Intelligence (AI)-driven generative design and robotic fabrication remains a critical challenge in architectural automation. While Generative Artificial Intelligence (GenAI) tools have advanced conceptual design workflows, their practical deployment in physical construction is hindered by the absence of structured, fabrication-aware datasets to train suitable AI models. This study introduces GDRF (Geometric Data for Robotic Fabrication), an automated pipeline for the generation, evaluation, and encoding of structurally feasible brick wall designs, enabling the creation of machine-learning-compatible data tailored for architectural robotic assembly. We developed a six-stage process that combines parametric modeling, algorithmic design generation, physics-based simulation, data encoding and storage, toolpath generation and assembly simulation, and physical robotic assembly with a robot. Over 33,000 wall configurations were synthetically generated and evaluated for structural stability, of which approximately 52% met the feasibility criteria. Stable and failed designs were identified through displacement-based criteria and encoded using dot-product-based rotational representation, reducing dimensionality while preserving critical geometric features. Comparative analysis revealed that brute-force generation produced more consistent outcomes, while random sampling achieved slightly higher local diversity. This study delivered a data pipeline and the BrickNet dataset, providing a foundation for future research in generative design, structural prediction, and autonomous robotic assembly.