Abstract—This paper borrows the concept of spectral
clustering in the computer vision field, proposes an alternative
approach to optimise space frame structure. Spectral clustering
was implemented to segment the whole structure into two
subclusters. Then genetic algorithm was used to optimise
member sizes of each subcluster separately. It is hypothesized
that optimizing the structural stability for subassemblies will
largely reduce the search space, which allows greater
computational efficiency. The program has been developed in
MATLAB and tested on differently shaped space frame
structure under varied loading conditions. Results show that for
a heterogeneous structure with high a level of complexity, the
implementation of spectral clustering can separate the
enormous search space of GA down to smaller search space,
leading to faster convergence with increased the computational
efficiency, while providing an equivalent or better optimisation
Index Terms—Computational efficiency, genetic algorithm, space frame structure, spectral clustering, structural optimization.
Xinwei Zhuang was with University College London, London, WC1E 6BT UK (e-mail: email@example.com).
Sean Hanna is with University College London, London, WC1E 6BT UK (e-mail: firstname.lastname@example.org).
Cite: Xinwei Zhuang and Sean Hanna, "Space Frame Optimisation with Spectral Clustering," International Journal of Machine Learning and Computing vol. 10, no. 4, pp. 507-512, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).