As-built BIM reconstruction of piping systems using PipeNet
Building Information Modeling (BIM) is gradually recognized and promoted as the new standard practice in the construction industry as well as the built environment. Mechanical, electrical, and plumbing system, as a system requiring regular maintenance, takes an important place in the building operat...
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sg-ntu-dr.10356-1690122023-06-27T02:02:24Z As-built BIM reconstruction of piping systems using PipeNet Xie, Yuan Li, Siyi Liu, Tianrui Cai, Yiyu School of Mechanical and Aerospace Engineering Interdisciplinary Graduate School (IGS) Surbana Jurong - NTU Corporate Lab Engineering::Mechanical engineering Building Information Modeling 3D Reconstruction Building Information Modeling (BIM) is gradually recognized and promoted as the new standard practice in the construction industry as well as the built environment. Mechanical, electrical, and plumbing system, as a system requiring regular maintenance, takes an important place in the building operation and maintenance; its failure can cause significant impacts operationally, economically, and even environmentally. As-built BIM is needed for efficient BIM-enabled facility management of buildings that were built before the existence of BIM or with outdated BIM. Modeling of the as-built BIM is currently practiced in a very manual and tedious way, requiring a considerable amount of time and effort even for a skilled modeler. This paper proposes a solution that reconstructs the as-built BIM of piping systems in buildings from LiDAR scanned point cloud data automatically. Compared to the existing works, it requires no additional data other than the unstructured point cloud with XYZ fields, no data preprocessing, and no prior knowledge of the pipe directions or dimensions. The solution comprises two stages. The first is a novel deep learning network, PipeNet, that is able to detect pipes regardless of the size of the input data and the scale of the target scene, and predict the pipe centerline points together with other pipe parameters. In the second stage, the pipe model is reconstructed through line fitting, refinement, and graph-based connectivity analysis constrained by domain knowledge that maximizes the coherence of the piping system model. The final output is converted to the Industry Foundation Classes format which is neutrally acceptable in the BIM industry. The solution is validated on both synthetic and actual scan data, and the results demonstrate its robustness, fast speed, and high recognition rate and precision. The results are discussed in detail and further improvements such as improving the precision of recognition are suggested for future works. This study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from Surbana Jurong Pte Ltd. 2023-06-27T02:02:24Z 2023-06-27T02:02:24Z 2023 Journal Article Xie, Y., Li, S., Liu, T. & Cai, Y. (2023). As-built BIM reconstruction of piping systems using PipeNet. Automation in Construction, 147, 104735-. https://dx.doi.org/10.1016/j.autcon.2022.104735 0926-5805 https://hdl.handle.net/10356/169012 10.1016/j.autcon.2022.104735 2-s2.0-85145965229 147 104735 en IAF-ICP RIE2020 Automation in Construction © 2023 Elsevier B.V. All rights reserved. |
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Engineering::Mechanical engineering Building Information Modeling 3D Reconstruction Xie, Yuan Li, Siyi Liu, Tianrui Cai, Yiyu As-built BIM reconstruction of piping systems using PipeNet |
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Building Information Modeling (BIM) is gradually recognized and promoted as the new standard practice in the construction industry as well as the built environment. Mechanical, electrical, and plumbing system, as a system requiring regular maintenance, takes an important place in the building operation and maintenance; its failure can cause significant impacts operationally, economically, and even environmentally. As-built BIM is needed for efficient BIM-enabled facility management of buildings that were built before the existence of BIM or with outdated BIM. Modeling of the as-built BIM is currently practiced in a very manual and tedious way, requiring a considerable amount of time and effort even for a skilled modeler. This paper proposes a solution that reconstructs the as-built BIM of piping systems in buildings from LiDAR scanned point cloud data automatically. Compared to the existing works, it requires no additional data other than the unstructured point cloud with XYZ fields, no data preprocessing, and no prior knowledge of the pipe directions or dimensions. The solution comprises two stages. The first is a novel deep learning network, PipeNet, that is able to detect pipes regardless of the size of the input data and the scale of the target scene, and predict the pipe centerline points together with other pipe parameters. In the second stage, the pipe model is reconstructed through line fitting, refinement, and graph-based connectivity analysis constrained by domain knowledge that maximizes the coherence of the piping system model. The final output is converted to the Industry Foundation Classes format which is neutrally acceptable in the BIM industry. The solution is validated on both synthetic and actual scan data, and the results demonstrate its robustness, fast speed, and high recognition rate and precision. The results are discussed in detail and further improvements such as improving the precision of recognition are suggested for future works. |
author2 |
School of Mechanical and Aerospace Engineering |
author_facet |
School of Mechanical and Aerospace Engineering Xie, Yuan Li, Siyi Liu, Tianrui Cai, Yiyu |
format |
Article |
author |
Xie, Yuan Li, Siyi Liu, Tianrui Cai, Yiyu |
author_sort |
Xie, Yuan |
title |
As-built BIM reconstruction of piping systems using PipeNet |
title_short |
As-built BIM reconstruction of piping systems using PipeNet |
title_full |
As-built BIM reconstruction of piping systems using PipeNet |
title_fullStr |
As-built BIM reconstruction of piping systems using PipeNet |
title_full_unstemmed |
As-built BIM reconstruction of piping systems using PipeNet |
title_sort |
as-built bim reconstruction of piping systems using pipenet |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/169012 |
_version_ |
1772825474491219968 |