Nonlinear Dynamic Modeling and Form-finding of Class1 Three-Bar Tensegrity in Random Structures: Genetic Algorithm Approach

This article focuses on the derivation of nonlinear dynamic equations and form-finding using genetic algorithms for class 1 tensegrity structure. The structures under consideration feature a triangular cross-section, with a sphere enclosing them. The nonlinear dynamic equations of the system are obtained by applying the Lagrangian approach and the finite element method, considering the nodal positions as the generalized coordinates. The proposed approach illustrates how to develop large-scale, detailed dynamic models with different tensegrity structures.  The form-finding approach employs a simple framework capable of identifying both regular and irregular tensegrity configurations without limitation on dimensions. Stable tensegrity structures are created by applying specific restrictions to random configurations. The genetic algorithm and multi-objective functions are used to determine the fitness function and create these structures. Three separate scenarios with both defined and random connection matrices and member types—bars and cables—evaluate the performance of the proposed method for arbitrary architectures. The resulting models are validated with regard to force density. The vibration behavior of the final models under harmonic loads is investigated via modal analysis. The simulation results demonstrate the efficacy of the proposed method in precisely determining the vibration characteristics of tensegrity structures through the application of an intelligent form-finding methodology. This method is capable of managing both regular and irregular tensegrity structures in stochastic conditions. This approach is capable of handling both regular and irregular tensegrity structures in stochastic conditions.