Reducing the number of different nodes in space frame structures through clustering and optimization
Yuanpeng Liu, Ting-Uei Lee, Antiopi Koronaki, Nico Pietroni, Yi Min Xie
Abstract
Space frame structures are increasingly adopted in contemporary free-form architectural designs due to their elegant appearance and excellent structural performance. However, a space frame structure in a doubly-curved form typically comprises nodes of different shapes. This often requires extensive node customization, hence incurring high manufacturing costs. In this study, we propose a new clustering–optimization framework to reduce the number of different nodes in space frame structures. In clustering, nodes are divided into different groups, with similar shapes grouped together, using an enhanced k-means clustering technique. In optimization, nodes within the same group are transformed towards congruence while closely approximating the target surface. Together, by interleaving clustering and optimization, our method can minimize the node shape variety under a user-defined error threshold. The effectiveness of the method is validated through a variety of numerical examples. The potential practical application of our method is demonstrated by re-designing a complex, free-form architectural project.