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Form Finding Lab.
Princeton University

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Publication: Digital guidework for augmented thin-tile vaulting construction

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Masonry vaults are structurally efficient but often dismissed as uneconomical due to the need for extensive falsework. Even thin-tile vaulting, which avoids centering, still relies on time-intensive guidework to shape the geometry.

We propose a new approach: replacing physical guidework with augmented reality (AR). This method provides builders with precise digital visual cues, allowing them to stay in control of their analog craft while benefiting from digital support.

Tested through a prototype and outdoor demonstrator, this AR-based guidework showed a 30% improvement in construction time and shape accuracy within 1% of the span. Future research could evolve this static holographic system into an interactive mixed-reality tool—enhancing not only productivity and precision but also training and design processes.

R. Oval, V. Paris, R. Pastrana, E.P.G. Bruun, S. Gomis Aviño, S. Adriaenssens (2025), W. Al Asali,’Digital guidework for augmented thin-tile vaulting construction’, Developments in the Built Environment,DOI: 10.1016/j.dibe.2025.100738.R. Oval, V. Paris, R. Pastrana, E.P.G. Bruun, S. Gomis Aviño, S. Adriaenssens (2025), W. Al Asali,’Digital guidework for augmented thin-tile vaulting construction’, Developments in the Built Environment,DOI: 10.1016/j.dibe.2025.100738.

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Publication: Design of purely compressive shells under vertical and horizontal loads through Machine Learning-driven form-finding

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

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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.

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Publication: Form-finding and metaheuristic multiobjective optimization methodology for sustainable gridshells with reduced construction complexity and waste

This project combines kirigami engineering with artificial intelligence to reimagine the design of space frames for building components such as roofs, floors, and walls.
Kirigami is the art of cutting and arranging sheets to produce 3D objects. With kirigami principles, thin steel sheets can be cut, deployed, and connected to create stiff and easy-to-assemble space frames. A neural network equivariant to wallpaper group symmetries generates spinvalence cut patterns that endow a space frame with remarkable mechanical performance, distinctive architectural expression, and a mesmerizing interplay of light and shadow.
The exhibition is open until November 23rd at the mezanino level of Palazzo Mora, Venice, Italy. Keep up with our work at kirigami-strata.ai

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