Form-Finding and Physical Property Predictions of Tensegrity Structures Using Deep Neural Networks

TL;DR

This paper introduces a deep neural network method to predict the form and physical properties of tensegrity structures, improving accuracy over traditional methods affected by manufacturing imperfections and errors.

Contribution

The work presents a novel data-driven DNN framework that predicts tensegrity configurations and physical properties without solving equilibrium equations, validated on multiple structures.

Findings

Accurately predicts geometric configurations and physical properties.

Reduces errors compared to traditional form-finding methods.

Applicable to various real-world tensegrity structures.

Abstract

In the design of tensegrity structures, traditional form-finding methods utilize kinematic and static approaches to identify geometric configurations that achieve equilibrium. However, these methods often fall short when applied to actual physical models due to imperfections in the manufacturing of structural elements, assembly errors, and material non-linearities. In this work, we develop a deep neural network (DNN) approach to predict the geometric configurations and physical properties-such as nodal coordinates, member forces, and natural frequencies-of any tensegrity structures in equilibrium states. First, we outline the analytical governing equations for tensegrity structures, covering statics involving nodal coordinates and member forces, as well as modal information. Next, we propose a data-driven framework for training an appropriate DNN model capable of simultaneously predicting tensegrity forms and physical properties, thereby circumventing the need to solve equilibrium equations. For validation, we analyze three tensegrity structures, including a tensegrity D-bar, prism, and lander, demonstrating that our approach can identify approximation systems with relatively very small output errors. This technique is applicable to a wide range of tensegrity structures, particularly in real-world construction, and can be extended to address additional challenges in identifying structural physics information.