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
Exhibit: Alternative Skies at the Venice Architecture Biennale, Italy
We’re pleased to share that our latest paper, “Numerical modeling of cantilevered bigon arm mechanics under gravity,” by Axel Larsson @axla.io and Sigrid Adriaenssens is now published Open Access in the Journal of the Mechanics and Physics of Solids (link in bio)
In this work, we investigate the stability regimes of reconfigurable bigon arms under gravitational loading—offering new insights into multi-stable structural systems.
