Reducing the number of different nodes in space frame structures through clustering and optimization

Liu, Y; Lee, TU; Koronaki, A; Pietroni, N; Xie, YM

Reducing the number of different nodes in space frame structures through clustering and optimization

Liu, Y Lee, TU Koronaki, A Pietroni, N

Xie, YM

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Engineering Structures, 2023, 284, pp. 116016
Issue Date:: 2023-06-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Publication versionAdobe PDF (3.53 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Liu, Y
dc.contributor.author	Lee, TU
dc.contributor.author	Koronaki, A
dc.contributor.author	Pietroni, N https://orcid.org/0000-0002-8271-2102
dc.contributor.author	Xie, YM
dc.date.accessioned	2023-05-31T03:15:02Z
dc.date.available	2023-05-31T03:15:02Z
dc.date.issued	2023-06-01
dc.identifier.citation	Engineering Structures, 2023, 284, pp. 116016
dc.identifier.issn	0141-0296
dc.identifier.issn	1873-7323
dc.identifier.uri	http://hdl.handle.net/10453/170563
dc.description.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.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Engineering Structures
dc.relation.isbasedon	10.1016/j.engstruct.2023.116016
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering, 0912 Materials Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Civil Engineering
dc.title	Reducing the number of different nodes in space frame structures through clustering and optimization
dc.type	Journal Article
utslib.citation.volume	284
utslib.for	0905 Civil Engineering
utslib.for	0912 Materials Engineering
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-05-31T03:14:51Z
pubs.publication-status	Accepted
pubs.volume	284

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/170563