Fabrication-Aware Joint Clustering in Freeform Space-Frames
Fabrication-Aware Joint Clustering in Freeform Space-Frames
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DOI：
10.3390/buildings13040962
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发表时间：
2023-04
期刊：
影响因子：
3.8
通讯作者：
A. Koronaki;P. Shepherd;M. Evernden
中科院分区：
文献类型：
--
作者：
A. Koronaki;P. Shepherd;M. Evernden
大型双曲空间框架结构节点的几何可变性会对其施工时间和成本产生重大影响。本文提出了一种新的框架来评估空间框架结构的施工复杂性，作为其节点的几何变化和制造的一个因素，以促进制造过程的知情设计。k-means算法用于将空间框架接头聚类到制造批次中，提供变异性分布的概述。开发了一种新的初始化方法，使算法能够适应特定于项目的输入，大大提高了集群的紧凑性。将聚类结果与不同制造工艺的性质叠加提供了对替代制造选项的构造复杂性的准确估计。该方法被应用到一个大规模的案例研究，以证明在实践中的好处。在设计开发的早期阶段，对替代制造方案进行了评估，从而对制造过程进行了明智的设计，从而有效地建造了大型复杂结构。
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.