Topological form-finding analysis of regular tensegrity structure based on parallel component coding algorithm

To address the problem of form-finding in complex symmetric tensegrity structures, a topology form-finding algorithm based on a parallel component coding strategy (PCC) is proposed. Initially, parallel components are categorized into groups based on the base structure model. By integrating tensegrity structure morphological analysis theory, the spatial morphology of the structure is established through the splicing and recombining of components from different groups while accounting for constraints such as element collision, geometric stability, and the zero-bar phenomenon. Subsequently, metrics, including the symmetry index, pressure bars count ratio, and internal force uniformity, are employed to evaluate and analyze various structural topological forms. Using 12-node and 18-node spatial tensegrity structures as examples, a tensegrity structure with high topological symmetry is effectively obtained and screened according to different group numbers, thereby fully verifying the feasibility and accuracy of the algorithm. The Random Forest Classification (RF) model was introduced to form the PCC-RF algorithm. By comparing the computational efficiency of PCC and PCC-RF in the form-finding process of tensegrity structures, the results indicate that PCC-RF significantly reduces computational time and provides a new efficient approach for exploring complex and highly symmetric tensegrity structures.