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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.
Original language | English |
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Article number | 962 |
Journal | Buildings |
Volume | 13 |
Issue number | 4 |
DOIs | |
Publication status | Published - 4 Apr 2023 |
Bibliographical note
Funding: This study was supported by the EPSRC Centre for Decarbonisation of the Built Environment (dCarb) [Grant Ref: EP/L016869/1].Data Availability Statement: The data presented in this study are available on request from the corresponding author
Keywords
- construction
- fabrication
- geometry
- joints
- k-means
- machine learning
- space-frames
ASJC Scopus subject areas
- Building and Construction
- Architecture
- Civil and Structural Engineering
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Computational Design Optimization of Large-Scale Building Structures
Shepherd, P. (PI)
Engineering and Physical Sciences Research Council
1/07/16 → 31/12/19
Project: Research council