Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network

Precast construction is a productivity-improving technology in the architectural, engineering, and construction industry that improves construction efficiency by combining factory-based manufacturing and lean assembly. Many international efforts have encouraged the adoption of this approach. This st...

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Main Authors: Li, Kexin, Gan, Vincent J. L., Li, Mingkai, Gao, Maggie Y., Tiong, Robert Lee Kong, Yang, Yaowen
Other Authors: School of Civil and Environmental Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Online Access:https://hdl.handle.net/10356/179862
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author Li, Kexin
Gan, Vincent J. L.
Li, Mingkai
Gao, Maggie Y.
Tiong, Robert Lee Kong
Yang, Yaowen
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Li, Kexin
Gan, Vincent J. L.
Li, Mingkai
Gao, Maggie Y.
Tiong, Robert Lee Kong
Yang, Yaowen
author_sort Li, Kexin
collection NTU
description Precast construction is a productivity-improving technology in the architectural, engineering, and construction industry that improves construction efficiency by combining factory-based manufacturing and lean assembly. Many international efforts have encouraged the adoption of this approach. This study presents an integrated Building Information Modelling (BIM) with technological automation interoperability to enable generative design and prefabrication for precast buildings. A generic BIM-based graph representation is established to explicitly formulate buildings' spatial and geometric features. Following this, a graph-constrained layout generator is developed, with a generative modelling algorithm and graph convolutional neural network, to extract pairwise spatial-geometric features for generating the optimal precast layout. This is followed by semantic enrichment of BIM data (i.e., Industry Foundation Classes) with precast data schema to facilitate data transformation for prefabrication automation until site delivery. The holistic approach presented in this study empowers pre-construction planning optimisation and fabrication automation in precast construction.
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spelling ntu-10356/1798622024-08-30T15:33:39Z Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network Li, Kexin Gan, Vincent J. L. Li, Mingkai Gao, Maggie Y. Tiong, Robert Lee Kong Yang, Yaowen School of Civil and Environmental Engineering Engineering Building information modelling Deep learning Precast construction is a productivity-improving technology in the architectural, engineering, and construction industry that improves construction efficiency by combining factory-based manufacturing and lean assembly. Many international efforts have encouraged the adoption of this approach. This study presents an integrated Building Information Modelling (BIM) with technological automation interoperability to enable generative design and prefabrication for precast buildings. A generic BIM-based graph representation is established to explicitly formulate buildings' spatial and geometric features. Following this, a graph-constrained layout generator is developed, with a generative modelling algorithm and graph convolutional neural network, to extract pairwise spatial-geometric features for generating the optimal precast layout. This is followed by semantic enrichment of BIM data (i.e., Industry Foundation Classes) with precast data schema to facilitate data transformation for prefabrication automation until site delivery. The holistic approach presented in this study empowers pre-construction planning optimisation and fabrication automation in precast construction. Ministry of Education (MOE) Published version This research is supported by the Ministry of Education Singapore under the Academic Research Fund Tier 1 (A-8001207-00-00). Any opinions, findings and conclusions or recommendations expressed in the material are those of the author(s) and do not reflect the views of the grantors. 2024-08-28T02:34:01Z 2024-08-28T02:34:01Z 2024 Journal Article Li, K., Gan, V. J. L., Li, M., Gao, M. Y., Tiong, R. L. K. & Yang, Y. (2024). Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network. Developments in the Built Environment, 18, 100418-. https://dx.doi.org/10.1016/j.dibe.2024.100418 2666-1659 https://hdl.handle.net/10356/179862 10.1016/j.dibe.2024.100418 2-s2.0-85189520697 18 100418 en A-8001207-00-00 Developments in the Built Environment © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/bync/4.0/). application/pdf
spellingShingle Engineering
Building information modelling
Deep learning
Li, Kexin
Gan, Vincent J. L.
Li, Mingkai
Gao, Maggie Y.
Tiong, Robert Lee Kong
Yang, Yaowen
Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network
title Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network
title_full Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network
title_fullStr Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network
title_full_unstemmed Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network
title_short Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network
title_sort automated generative design and prefabrication of precast buildings using integrated bim and graph convolutional neural network
topic Engineering
Building information modelling
Deep learning
url https://hdl.handle.net/10356/179862
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