We are excited to share our latest research on advancing the design of shell structures under challenging loading conditions. Traditionally, form-finding methods for masonry vaults focus on vertical loads, but extreme wind or seismic forces introduce significant design and safety complexities.
Our new paper explores an alternative to computationally intensive optimisation steps in Membrane Equilibrium Analysis. Using machine learning regression techniques—XGBoost, Random Forests, and k-Nearest Neighbours—we identify optimal Airy Stress Function parameters to improve efficiency and maintain structural integrity.
Case study results show that these methods can reduce computational demands while achieving material-efficient designs, with k-Nearest Neighbours delivering the best performance in our tests.
Read more about our findings here: https://lnkd.in/eEnu8sEW

Publication: Architectural swarms for responsive façades and creative expression
Living architectures, like beehives and ant bridges, adapt to their environments through self-organization of swarming agents. Most human-made architectures are static, and can’t adapt to changing conditions.
That’s why Princeton engineers designed the Swarm Garden, a modular architectural facade that integrates swarm intelligence and robotics. Each module, resembling flowers, uses buckling sheet technology to open and close in response to environmental stimuli.
A paper published in Science Robotics demonstrates two applications. In one study, the team applied a Swarm Garden prototype to an office window to illustrate adaptive shading, where the robotic flowers open and close in response to sunlight. The second study explored creative expressions in interior design where the robotic flowers responded to human interaction during a public exhibition.
M. Alhafnawi, J. Bendarkawi, Y. Tafesse, L. Stein-Montalvo, A. Jones, V. Chow, S. Adriaenssens, R. Nagpal. (2026) ‘Architectural swarms for responsive façades and creative expression.’ Science Robotics, DOI: 10.1126/scirobotics.ady7233

