Contained in:
Book Chapter

Improving BIM Authoring Process Reproducibility with Enhanced BIM Logging

  • Suhyung Jang
  • Ghang Lee

This paper presents an enhanced BIM logger designed to capture both geometry and attribute changes of building element geometries, thereby offering a transparent source of representation of the BIM authoring process. The authors developed the logger and reproduction algorithm using the Revit C# API based on the analysis of information required to define building elements and associated attributes. The enhanced BIM log was evaluated through a case study of Villa Savoye designed by Le Corbusier. Despite negligible discrepancies, the results show that the enhanced BIM log can accurately represent the BIM authoring process capturing and reproducing 92.45% of the building elements from the original BIM model. Future research can focus on expanding the scope of logging and probing the potential of automating the BIM authoring process using these enhanced BIM logs

  • Keywords:
  • Building information modeling (BIM),
  • BIM log mining,
  • BIM authoring software,
  • Custom BIM log,
  • Authoring process reproducibility,
+ Show More

Suhyung Jang

Yonsei University, Korea (the Republic of)

Ghang Lee

Yonsei University, Korea (the Republic of) - ORCID: 0000-0002-3522-2733

  1. Aalst, W. van der, Adriansyah, A., Medeiros, A. K. A. de, Arcieri, F., Baier, T., Blickle, T., Bose, J. C., Brand, P. van den, Brandtjen, R., & Buijs, J. (2011). Process mining manifesto. International Conference on Business Process Management, 169–194.
  2. Autodesk. (2022). About Journal Files. Autodesk REVIT 2022 Help. https://help.autodesk.com/view/RVT/2022/ENU/?guid=GUID-477C6854-2724-4B5D-8B95-9657B636C48D
  3. Bose, R. J. C., Mans, R. S., & van der Aalst, W. M. (2013). Wanna improve process mining results? 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 127–134.
  4. Forcael, E., Martinez-Rocamora, A., Sepulveda-Morales, J., Garcia-Alvarado, R., Nope-Bernal, A., & Leighton, F. (2020). Behavior and Performance of BIM Users in a Collaborative Work Environment. Applied Sciences-Basel, 10(6), 2199. DOI: 10.3390/app10062199
  5. Gao, W., Wu, C., Huang, W., Lin, B., & Su, X. (2021). A data structure for studying 3D modeling design behavior based on event logs. In Automation in Construction (Vol. 132). DOI: 10.1016/j.autcon.2021.103967
  6. Jang, S., Lee, G., Shin, S., & Roh, H. (2023). Lexicon-based content analysis of BIM logs for diverse BIM log mining use cases. Advanced Engineering Informatics, 57, 102079. DOI: 10.1016/j.aei.2023.102079
  7. Jang, S., Shin, S., & Lee, G. (2021). Logging Modeling Events to Enhance the Reproducibility of a Modeling Process. ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, 38, 256–263.
  8. Kouhestani, S., & Nik-Bakht, M. (2020). IFC-based process mining for design authoring. In Automation in Construction (Vol. 112, p. 103069). DOI: 10.1016/j.autcon.2019.103069
  9. Lin, J.-R., & Zhou, Y.-C. (2020). Semantic classification and hash code accelerated detection of design changes in BIM models. In Automation in Construction (Vol. 115). DOI: 10.1016/j.autcon.2020.103212
  10. Messner, J., Anumba, C., Dubler, C., Goodman, S., Kasprzak, C., Kreider, R., Leicht, R., Saluja, C., & Zikic, N. (2019). BIM Project Execution Planning Guide (v. 2.2). Computer Integrated Construction Research Program, Pennsylvania State University. https://openlibrary-repo.ecampusontario.ca/jspui/handle/123456789/768
  11. Pan, Y., & Zhang, L. (2020). BIM log mining: Learning and predicting design commands. Automation in Construction, 112, 103107. DOI: 10.1016/j.autcon.2020.103107
  12. Pan, Y., & Zhang, L. (2021). A BIM-data mining integrated digital twin framework for advanced project management. In Automation in Construction (Vol. 124). DOI: 10.1016/j.autcon.2021.103564
  13. Sacks, R., Eastman, C., Lee, G., & Teicholz, P. (2018). BIM handbook: A guide to building information modeling for owners, designers, engineers, contractors, and facility managers. John Wiley & Sons.
  14. Shin, S. (2023). A BIM object-based BIM modeling productivity measurement method using BIM log mining. Yonsei University.
  15. Shin, S., Jang, S., Roh, H., & Lee, G. (2022). A Critical Review of Measuring the Modeling Productivity of Building Information Modeling. Proceedings of the 19th International Conference on Computing in Civil & Building Engineering (ICCCBE), Capetown, South Africa. DOI: 10.1007/978-3-031-35399-4_33
  16. Suriadi, S., Andrews, R., ter Hofstede, A. H., & Wynn, M. T. (2017). Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs. Information Systems, 64, 132–150.
  17. Yarmohammadi, S., & Castro-Lacouture, D. (2018). Automated performance measurement for 3D building modeling decisions. In Automation in Construction (Vol. 93, pp. 91–111). DOI: 10.1016/j.autcon.2018.05.011
  18. Yarmohammadi, S., Pourabolghasem, R., & Castro-Lacouture, D. (2017). Mining implicit 3D modeling patterns from unstructured temporal BIM log text data. Automation in Construction, 81, 17–24. DOI: 10.1016/j.autcon.2017.04.012
  19. Zhang, L., & Ashuri, B. (2018). BIM log mining: Discovering social networks. Automation in Construction, 91, 31–43. DOI: 10.1016/j.autcon.2018.03.009
PDF
  • Publication Year: 2023
  • Pages: 508-514

XML
  • Publication Year: 2023

Chapter Information

Chapter Title

Improving BIM Authoring Process Reproducibility with Enhanced BIM Logging

Authors

Suhyung Jang, Ghang Lee

DOI

10.36253/979-12-215-0289-3.49

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

153

Fulltext
downloads

96

Views

Export Citation

1,348

Open Access Books

in the Catalogue

2,262

Book Chapters

3,790,127

Fulltext
downloads

4,421

Authors

from 924 Research Institutions

of 65 Nations

65

scientific boards

from 348 Research Institutions

of 43 Nations

1,248

Referees

from 380 Research Institutions

of 38 Nations