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Form Finding Lab.
Princeton University

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

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The construction industry is a major contributor to environmental impact—but also a powerful lever for change. Our latest research introduces a novel methodology for optimizing gridshell structures using the NSGA II algorithm and the improved Multi-body Rope Approach (i-MRA). This approach reduces material use, minimizes waste, and simplifies construction, all while meeting structural standards. By streamlining the design process, we aim to make sustainable, efficient gridshells more accessible from the earliest design stages.

J.Melchiorre, A.M. Bertetto, S. Adriaenssens, G. C. Marano, (2025) ’Form-finding and metaheuristic multiobjective optimization methodology for sustainable gridshells with reduced construction complexity and waste’, Automation in Construction, 177, p.106315,DOI: 10.1016/j.autcon.2025.106315

 

 

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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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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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