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Computational Modeling, Optimization, and Machine-Learning Methods for Double-Layer Tensegrity Grid Structures: A Comprehensive Review
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DOI：10.1007/s11831-026-10575-x.png)
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Abstract
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A comprehensive assessment of computational techniques for analysis, optimization, and modeling of double-layer tensegrity grid structures using machine learning has been presented in this review. The importance of numerical formulation, form finding, prestress determination, and stability analysis has been emphasized using the computational mechanics approach. A proper categorization has been made, including the classical tensegrity system, tensegrity ring, and combined cable-strut systems, whose performance analysis has been evaluated in terms of structural stiffness, stability, lightness, and the role of prestress. The computational analysis has been carried out using various deterministic and metaheuristic techniques, such as genetic algorithms, mixed-integer programming, and semidefinite programming, which have been shown to yield optimal solutions while balancing computational cost, scalability, and modeling complexity. The recent applications of deep learning algorithms, including neural networks, graph neural networks, and physics-informed neural networks, for the development of surrogates and inverse problems in form finding, force prediction, and analysis to improve computational speed, have potential applications to the development of tensegrity structures. Despite the above advancements, the analysis has been hindered by the complexity of nonlinear stability analysis, the reduction in cable tensions, and the role of robustness with respect to uncertain variables.
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