Chain mail structures in architecture: a systematic, multi-scalar design exploration

Chain mail structures, known for flexibility and adaptability, hold increasing promise for architectural applications, including transportable and reconfigurable systems. However, there is a dearth of knowledge on both systematic methods to design them, and complex behaviours of interlocking modules that comprise the structure. Preliminary studies, in response to this research gap, demonstrate the chain mail’s structural potential as programmable architecture. Nevertheless, to validate our models, we must move from the small scale to recognisably viable structures at an architectural scale.

Acknowledging the multiscale prototype’s significance for developing new architectural systems, this study scales up chain mail structures from a small 1:10 scale to larger 1:2 and 1:1 scales. Employing a Research-Through-Design approach, we systematically addressed the challenges, focusing on module fabrication and prototype construction through analogue computation. Fabrication adjustments involve changing materials and modifying designs to suit manufacturing techniques. Additional design elements and process steps are needed to facilitate programming the larger scale structures due to the increased weight during construction. The research culminated in a full-scale saddle-like structure, illustrating the feasibility of direct scaling from smaller to larger scales and the expansive architectural potential of chain mail structures.

In conclusion, the study successfully identified and responded to specific challenges related to the fabrication and construction of upscaled chain mail prototypes, aligning solutions with practical contexts. In doing so, this research contributes a set of considerations to enable more systematic design approaches for chain mail structural systems in architecture. At the same time, scaling up uncovers the inherent intelligence of these structures, providing a foundation for both empirical testing through analogue experimentation, and developing a predictive framework for their development and application in the field.