A Genetic Algorithm Based Form-Finding for Tensegrity Structure

Abstract

A tensegrity structure consists of a set of continuous cables in tension and a set of discontinuous struts in compression. The tensegrity structure can be classified into self-stressed and pre-stressed structures. Present paper interest is in the self-stressed tensegrity structures, since they can free standing without any support while maintaining their self-equilibrium state. In the process form-finding of a tensegrity structure, some constraints are usually introduced for geometry and/or member to ensure uniqueness of the solution. The tensegrity structures are indeterminate problems in most cases. In this paper, a genetic algorithm based form-finding for tensegrity structures is presented to assist designers to obtain tensegrity structures with less design variables. A novel and versatile numerical form-finding procedure which requires only a minimal knowledge of initial structure configuration is adopted. The procedure needs only the prototype of each member, i.e. either compression or tension, and the connectivity information of members. The connectivity of members and its prototype information are encoded to form an individual population used in genetic algorithm searching problems. As for the fitness evaluation to each population, the existence of self-stressed state in each population is sought. At the end, some numerical examples are given to show the efficiency of the present study and its ability in searching new configurations of tensegrity structures.