Fabrication-Aware Joint Clustering in Freeform Space-Frames

Abstract

The geometrical variability in the joints of large-scale, doubly-curved space-frame structures can have a substantial impact on the time and cost of their construction. This paper proposes a novel framework to assess the construction complexity of space-frame structures as a factor of the geometrical variability and fabrication of their joints, to promote the informed design of the fabrication process. The k-means algorithm was used to cluster space-frame joints into fabrication batches, providing an overview of the variability distribution. A novel initialisation method was developed that allows the algorithm to adapt to project-specific inputs, substantially improving cluster compactness. Overlaying the clustering results with the properties of different fabrication processes provides an accurate estimation of the construction complexity of alternative fabrication options. The method was applied to a large-scale case study to demonstrate the benefits in practice. Alternative fabrication scenarios were assessed in the early stages of the design development, leading to the informed design of the fabrication process and hence to the efficient construction of large-scale, complex structures.

Aesthetic Assessment of Free-Form Space Structures Using Machine Learning Based on the Expert's Experiences

“…They designed arbitrary space frame structures and trained the artificial neural network to implement the optimal geometry for each structural node parallel to a given load. Koronaki et al [7] used machine learning algorithms to determine the requirements of the fabrication process of space-frame structures and then optimize the structure geometrically. Es-Haghi et al proposed a machine-learning algorithm for the optimization of large-scale space frames in real size with high speed and accuracy.…”