Rule-Based Scan-to-BIM Mapping Pipeline in the Plumbing System
Source abstract
Abstract
The number of scan-to-BIM projects that convert scanned data into Building Information Modeling (BIM) for facility management applications in the Mechanical, Electrical and Plumbing (MEP) fields has been increasing. This conversion features an application purpose-oriented process, so the Scan-to-BIM work parameters to be applied vary in each project. Inevitably, a modeler manually adjusts the BIM modeling parameters according to the application purpose, and repeats the Scan-to-BIM process until the desired result is achieved. This repetitive manual process has adverse consequences for project productivity and quality. If the Scan-to-BIM process can be formalized based on predefined rules, the repetitive process in various cases can be automated by re-adjusting only the parameters. In addition, the predefined rule-based Scan-to-BIM pipeline can be stored and reused as a library. This study proposes a rule-based Scan-to-BIM Mapping Pipeline to support application-oriented Scan-to-BIM process automation, variability and reusability. The application target of the proposed pipeline method is the plumbing system that occupies a large number of MEPs. The proposed method was implemented using an automatic generation algorithm, and its effectiveness was verified.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: 3D Surveying and Cultural Heritage · BIM and Construction Integration · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Korea
Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul 06974, Korea
Department of Engineering Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
Department of Construction Management, University of North Florida, Jacksonville, FL 32224, USA
Funding
Korea Agency for Infrastructure Technology Advancement
References (30)
Show source-provided references
1. Xiao, Y.-Q., Li, S.-W., and Hu, Z.-Z. (2019). Automatically Generating a MEP Logic Chain from Building Information Models with Identification Rules. Appl. Sci., 9.10.3390/app9112204 ↗
2. Deep Learning Approach to Point Cloud Scene Understanding for Automated Scan to 3D Reconstruction10.1061/(ASCE)CP.1943-5487.0000842 ↗
3. A framework for in-situ geometric data acquisition using laser scanning for BIM modelling10.1016/j.jobe.2019.101073 ↗
4. Stojanovic, V., Trapp, M., Richter, R., and Döllner, J. (2019, January 2–3). A Service Oriented Indoor Point Cloud Processing. Proceedings of the International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Strasbourg, France.10.5194/isprs-archives-XLII-2-W17-339-2019 ↗
5. Pepe, M., Costantino, D., and Garofalo, A.R. (2020). An Efficient Pipeline to Obtain 3D Model for HBIM and Structural Analysis Purposes from 3D Point Clouds. Appl. Sci., 10.10.3390/app10041235 ↗
6. Wang, Q., Guo, J., and Kim, M.-K. (2019). An Application Oriented Scan-to-BIM Framework. Remote Sens., 11.10.3390/rs11030365 ↗
7. Leite, F. (2018). Automated Approaches Towards BIM-Based Intelligent Decision Support in Design, Construction, and Facility Operations. Lecture Notes in Computer Science, Springer. Workshop of the European Group for Intelligent Computing in Engineering.10.1007/978-3-319-91638-5_15 ↗
8. Bassier, M., Vergauwen, M., and Van Genechten, B. (2016, January 25–26). Automated Semantic Labelling of 3D Vector Models for Scan-to-BIM. Proceedings of the Annual International Conference on Architecture and Civil Engineering (ACE 2016), Singapore.10.5176/2301-394X_ACE16.83 ↗
9. Automated continuous construction progress monitoring using multiple workplace real time 3D scans10.1016/j.aei.2018.06.001 ↗
10. Automatic Planning for Scanning: Optimizing 3D Laser Scanning Operations Using BIM and TLS
11. Wang, Q., Cheng, J.C.P., and Sohn, H. (2016, January 18–21). Automatic Reconstruction of As-built BIM from Laser Scanned Data of Precast Concrete Elements for Dimensional Quality Assessment. Proceedings of the 33rd International Symposium on Automation and Robotics in Construction (ISARC), Auburn, AL, USA.10.22260/ISARC2016/0015 ↗
12. Yin, C., Wang, B., and Cheng, J.C.P. (2019, January 7–8). Deep Learning-based Scan-to-BIM Framework for Complex MEP Scene using Laser Scanning Data. Proceedings of the 4th International Conference on Civil and Building Engineering Informatics (ICCBEI), Miyagi, Japan.
13. Guo, J., and Wang, Q. (2019). Dimensional Quality Inspection of Prefabricated MEP Modules with 3D Laser Scanning. Computing in Civil Engineering 2019, American Society of Civil Engineers.10.1061/9780784482438.022 ↗
14. Ham, N., and Lee, S.-H. (2018). Empirical Study on Structural Safety Diagnosis of Large-Scale Civil Infrastructure Using Laser Scanning and BIM. Sustainability, 10.10.3390/su10114024 ↗
15. Geometric quality inspection of prefabricated MEP modules with 3D laser scanning10.1016/j.autcon.2019.103053 ↗
16. Wang, K., and Cheng, J.C. (2019, January 21–24). Integrating Hardware-In-the-Loop Simulation and BIM for Planning UAV-based As-built MEP Inspection with Deep Learning Techniques. Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC), Edmonton, AB, Canada.10.22260/ISARC2019/0042 ↗
17. Modeling and analysis of the impact of BIM-based field data capturing technologies on automated construction progress monitoring10.1007/s40999-018-0320-1 ↗
18. García, O.F., Diaz-Severiano, J.A., Gomez-Jauregui, V., Del Val, C.M., Iglesias, J.L., Royano, A.P., and González, C.O. (2020, January 20–21). Modelling As-Built MEP Facilities in a BIM Environment. Proceedings of the Lecture Notes in Mechanical Engineering, Logroño, Spain.
19. Performance evaluation of automated 3D image-scan-based reverse engineering in architectural MEP pipe object modeling10.1007/s41324-016-0066-2 ↗
20. Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring10.1016/j.autcon.2017.09.021 ↗
21. Chen, J., and Cho, Y.K. (2018, January 5–7). Point-to-point Comparison Method for Automated Scan-vs-bim Deviation Detection. Proceedings of the 2018 17th International Conference on Computing in Civil and Building Engineering, Tampere, Finland.
22. Iglesias, J.L., Severiano, J.A.D., Amorocho, P.E.L., Del Val, C.M., Gomez-Jauregui, V., García, O.F., Royano, A.P., and González, C.O. (2020, January 20–21). Revision of Automation Methods for Scan to BIM. Proceedings of the Lecture Notes in Mechanical Engineering, Logroño, Spain.
23. Scan planning and route optimization for control of execution of as-designed bim
24. Scan-to-BIM for ‘secondary’ building components10.1016/j.aei.2018.05.001 ↗
25. Scan-to-BIM Methodology Adapted for Different Application
26. Scan-to-BIM vs. 3D Ideal Model HBIM: Parametric Tools to Study Domes Geometry
27. Scan-to-graph: Semantic enrichment of existing building geometry10.1016/j.autcon.2020.103286 ↗
28. Czerniawski, T., and Leite, F. (2019, January 17–19). Semantic Segmentation of Building Point Clouds Using Deep Learning: A Method for Creating Training Data Using BIM to Point Cloud Label Transfer. Proceedings of the Computing in Civil Engineering 2019, Atlanta, GA, USA.10.1061/9780784482421.052 ↗
29. Study on 3D Reverse Engineering-based MEP Facility Management Improvement Method
30. The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components10.1016/j.autcon.2014.05.014 ↗