Please use this identifier to cite or link to this item:
Title: As-built BIM reconstruction of piping systems using PipeNet
Authors: Xie, Yuan
Li, Siyi
Liu, Tianrui
Cai, Yiyu
Keywords: Engineering::Mechanical engineering
Issue Date: 2023
Source: Xie, Y., Li, S., Liu, T. & Cai, Y. (2023). As-built BIM reconstruction of piping systems using PipeNet. Automation in Construction, 147, 104735-.
Project: IAF-ICP 
Journal: Automation in Construction
Abstract: 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needed for efficient BIM-enabled facility management of buildings that were built before the existence of BIM or with outdated BIM.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.
ISSN: 0926-5805
DOI: 10.1016/j.autcon.2022.104735
Schools: School of Mechanical and Aerospace Engineering 
Interdisciplinary Graduate School (IGS) 
Research Centres: Surbana Jurong - NTU Corporate Lab
Rights: © 2023 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:IGS Journal Articles
MAE Journal Articles

Citations 20

Updated on Jun 24, 2024

Page view(s)

Updated on Jun 15, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.