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Form Finding Lab.
Princeton University

Exhibit: Alternative Skies at the Venice Architecture Biennale, Italy

We are participating in the 19th International Architecture Exhibition of Fondazione La Biennale di Venezia with the project Alternative Skies

Led by Professor Wesam Al- Asali (IE University) and designed in collaboration with us, Romina Canna (IE UNiversity)  and Robin Oval (ParisTech), the installation explores the roof as both structure and symbol—challenging conventional boundaries between craft, culture and computation.

From woven willow to timber shells and tile vaults, the work highlights the wisdom of traditional craftspeople like Salcador Gomis Avino, Ángel María Lopez and Carlos Fontales—and the power of collective intelligence to shape a more sustainable future.

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Publication: Digital guidework for augmented thin-tile vaulting construction

Masonry vaults are among the most elegant and efficient structural forms, yet their construction has long been slowed down by costly falsework and guidework.
Our team explored a new path: augmented reality as digital guidework. Instead of rigid scaffolding, masons can now follow holographic projections that provide just the right amount of visual support—keeping builders in control of their analog craft.
In field tests, this approach improved productivity by ~30% while achieving remarkable accuracy (within 1% of the vault span). Looking ahead, interactive mixed-reality could further boost precision, speed, and even training opportunities.
This work shows how centuries-old craftsmanship and cutting-edge technology can merge to keep masonry vaulting not just viable, but visionary. Read more about our findings here https://lnkd.in/d4-KcCxS

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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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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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