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Book Chapter

Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes

  • Shirin Malihi
  • Frederic Bosche
  • Martin Bueno Esposito

3D spatial data is increasingly employed to generate Building Information Models (BIMs) by extension digital twins for various applications in the architecture, engineering, and construction (AEC) sector such as project monitoring, engineering analyses, retrofit planning, etc. The outputted models of Scan-to-BIM processes should satisfy pre-defined levels of quality. In the case of emerging automated Scan-to-BIM solutions, users however currently need to check all generated geometry manually, which is time-consuming. What would help users is if the automated systems could also provide a level of confidence in the detection and modelling of each element. In this paper three generic indicators are defined for analysing the reliability of the generated 3D models: Icoverage estimates the portion of the surface of the modelled element that can be explained by the input point cloud. Idistance defines the closeness of the generated element models to the input point cloud. The confidence of the generated 3D local models can be computed by combining the two aforementioned indices. The proposed indicators are assessed using actual examples and comparisons are conducted between automatically generated 3D BIM models and 3D models generated manually by a BIM modeler

  • Keywords:
  • BIM,
  • point cloud,
  • confidence,
  • indoor modelling,
  • wall,
  • digital twin,
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Shirin Malihi

University of Edinburgh, United Kingdom - ORCID: 0000-0003-1499-8791

Frederic Bosche

University of Edinburgh, United Kingdom - ORCID: 0000-0002-4064-8982

Martin Bueno Esposito

University of Edinburgh, United Kingdom

  1. Akula, M., Lipman, R. R., Franaszek, M., Saidi, K. S., Cheok, G. S., & Kamat, V. R. (2013). Real-time drill monitoring and control using building information models augmented with 3D imaging data. Automation in Construction, 36, 1–15. DOI: 10.1016/j.autcon.2013.08.010
  2. Armeni, I., Sener, O., Zamir, A. R., Jiang, H., Brilakis, I., Fischer, M., & Savarese, S. (n.d.). 3D Semantic Parsing of Large-Scale Indoor Spaces (a) Raw Point Cloud (b) Space Parsing and Alignment in Canonical 3D Space (c) Building Element Detection Enclosed Spaces. http://buildingparser.stanford.edu/
  3. Bassier, M., & Vergauwen, M. (2020). Unsupervised reconstruction of Building Information Modeling wall objects from point cloud data. Automation in Construction, 120, 103338. DOI: 10.1016/J.AUTCON.2020.103338
  4. Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020). Towards a semantic Construction Digital Twin: Directions for future research. Automation in Construction, 114(March), 103179. DOI: 10.1016/j.autcon.2020.103179
  5. Bosché, F., Ahmed, M., Turkan, Y., Haas, C. T., & Haas, R. (2015a). 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 components. Automation in Construction, 49, 201–213. DOI: 10.1016/j.autcon.2014.05.014
  6. Bosché, F., Ahmed, M., Turkan, Y., Haas, C. T., & Haas, R. (2015b). 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 components. Automation in Construction, 49, 201–213. DOI: 10.1016/j.autcon.2014.05.014
  7. Bueno, M., Bosché, F., González-Jorge, H., Martínez-Sánchez, J., & Arias, P. (2018). 4-Plane congruent sets for automatic registration of as-is 3D point clouds with 3D BIM models. Automation in Construction, 89, 120–134. DOI: 10.1016/j.autcon.2018.01.014
  8. BuildingSMART. (n.d.).
  9. I. Giannakis, G., N. Lilis, G., Angel Garcia, M., D. Kontes, G., Valmaseda, C., & V. Rovas, D. (2015, December 7). A Methodology to Automatically Generate Geometry And Material Inputs for Energy Performance Simulation from Ifc Bim Models. DOI: 10.26868/25222708.2015.2363
  10. Nikoohemat, S., Diakité, A., Zlatanova, S., & Vosselman, G. (2019). INDOOR 3D MODELING AND FLEXIBLE SPACE SUBDIVISION FROM POINT CLOUDS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W5, 285–292. DOI: 10.5194/isprs-annals-IV-2-W5-285-2019
  11. Open3D. (n.d.). No Title. http://www.open3d.org/docs/release/tutorial/geometry/voxelization.html
  12. Pan, Y., Braun, A., Brilakis, I., & Borrmann, A. (2022). Enriching geometric digital twins of buildings with small objects by fusing laser scanning and AI-based image recognition. Automation in Construction, 140, 104375. DOI: 10.1016/J.AUTCON.2022.104375
  13. Perez-Perez, Y., Golparvar-Fard, M., & El-Rayes, K. (2021). Scan2BIM-NET: Deep Learning Method for Segmentation of Point Clouds for Scan-to-BIM. Journal of Construction Engineering and Management, 147(9), 4021107. DOI: 10.1061/(ASCE)CO.1943-7862.0002132
  14. Rashdi, R., Martínez-Sánchez, J., Arias, P., & Qiu, Z. (2022). Scanning Technologies to Building Information Modelling: A Review. Infrastructures 2022, Vol. 7, Page 49, 7(4), 49. DOI: 10.3390/INFRASTRUCTURES7040049
  15. Rocha, G., Mateus, L., Malinverni, S., & Pierdicca, R. (2021). A Survey of Scan-to-BIM Practices in the AEC Industry—A Quantitative Analysis. ISPRS International Journal of Geo-Information 2021, Vol. 10, Page 564, 10(8), 564. DOI: 10.3390/IJGI10080564
  16. Skrzypczak, I., Oleniacz, G., Leśniak, A., Zima, K., Mrówczyńska, M., & Kazak, J. K. (2022). Scan-to-BIM method in construction: assessment of the 3D buildings model accuracy in terms inventory measurements. Https://Doi.Org/10.1080/09613218.2021.2011703, 50(8), 859–880. DOI: 10.1080/09613218.2021.2011703
  17. Thomson, C., & Boehm, J. (2015). Automatic geometry generation from point clouds for BIM. Remote Sensing, 7(9), 11753–11775. DOI: 10.3390/rs70911753
  18. Valero, E., Mohanty, D. D., Ceklarz, M., Tao, B., Bosche, F., Giannakis, G. I., Fenz, S., Katsigarakis, K., N. Lilis, G., Rovas, D., & Papanikolaou, A. (2021, November 2). An Integrated Scan-to-BIM Approach for Buildings Energy Performance Evaluation and Retrofitting. DOI: 10.22260/ISARC2021/0030
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  • Publication Year: 2023
  • Pages: 1137-1146

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  • Publication Year: 2023

Chapter Information

Chapter Title

Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes

Authors

Shirin Malihi, Frederic Bosche, Martin Bueno Esposito

DOI

10.36253/979-12-215-0289-3.113

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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