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Book Chapter

A Review of Computer Vision-Based Progress Monitoring for Effective Decision Making

  • Roy Lan
  • Tulio Sulbaran

Construction Progress Monitoring (CPM) is a significant aspect of project management aimed to align planned design with the actual construction on site, the process ensures that the project is well within the control of the stakeholders involved and ensures the project is completed complying with the construction documents, on time, and within budget. Despite how central progress monitoring is to attaining project success and advances in technology, the progress monitoring is majorly implemented manually, which requires manual retrieving and processing of site data to compare with the planned design. This manual process is both time-consuming and prone to errors. Automating the task of progress monitoring involving real-time data acquisition and timely information retrieval can assist the project managers for effective decision making to the successful delivery of the project. Thus, the objective of this research was to assess the impact of computer vision (CV) – based progress monitoring as a driver for effective decision-making in project management. A qualitative methodology was implemented for this research using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to review and analyze studies on the application of computer vision (CV). The study reviews studies of CV based CPM process, highlighting its benefits against the traditional method of progress and the limitation to its adoption. Research findings from this paper provide an increased understanding and have a broader scope on the application of computer vision-based progress monitoring

  • Keywords:
  • Computer Vision,
  • Construction progress monitoring,
  • Decision-making,
  • Project management,
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Roy Lan

The University of Texas at San Antonio, United States - ORCID: 0000-0001-8168-7438

Tulio Sulbaran

The University of Texas at San Antonio, United States

  1. Ahmed, S. (2019). A Review on Using Opportunities of Augmented Reality and Virtual Reality in Construction Project Management. Organization, Technology and Management in Construction: An International Journal, 11(1), 1839–1852. DOI: 10.2478/otmcj-2018-0012
  2. Araújo, A. G., Pereira Carneiro, A. M., & Palha, R. P. (2020). Sustainable construction management: A systematic review of the literature with meta-analysis. Journal of Cleaner Production, 256, 120350. DOI: 10.1016/j.jclepro.2020.120350
  3. Benyeogor, M. S., Olatunbosun, A., & Kumar, S. (2020). Airborne System for Pipeline Surveillance Using an Unmanned Aerial Vehicle. European Journal of Engineering Research and Science, 5(2), 178–182. DOI: 10.24018/ejers.2020.5.2.1761
  4. Braun, A., Tuttas, S., Borrmann, A., & Stilla, U. (2020). Improving progress monitoring by fusing point clouds, semantic data and computer vision. Automation in Construction, 116. DOI: 10.1016/j.autcon.2020.103210
  5. Chan, A. P. C., Scott, D., & Chan, A. P. L. (2004). Factors Affecting the Success of a Construction Project. Journal of Construction Engineering and Management, 130(1), 153–155. DOI: 10.1061/ASCE0733-93642004130:1153
  6. Ekanayake, B., Wong, J. K.-W., Fini, A. A. F., & Smith, P. (2021). Computer vision-based interior construction progress monitoring: A literature review and future research directions. Automation in Construction, 127, 103705. DOI: 10.1016/j.autcon.2021.103705
  7. Harris, J. D., Quatman, C. E., Manring, M. M., Siston, R. A., & Flanigan, D. C. (2014). How to Write a Systematic Review. The American Journal of Sports Medicine, 42(11), 2761–2768. DOI: 10.1177/0363546513497567
  8. Hannan Qureshi, A., Alaloul, W. S., Wing, W. K., Saad, S., Ammad, S., & Musarat, M. A. (2022). Factors impacting the implementation process of automated construction progress monitoring. Ain Shams Engineering Journal, 13(6), 101808. DOI: 10.1016/j.asej.2022.101808
  9. Golparvar-Fard, M., Peña-Mora, F., Gutgsell, J., & Savarese, S. (2009). Application of D 4 AR-A 4-Dimensional augmented reality model for automating construction progress monitoring data collection. In Journal of Information Technology in Construction (ITcon) (Vol. 14). http://www.itcon.org/2009/13
  10. Hamledari, H., McCabe, B., & Davari, S. (2017). Automated computer vision-based detection of components of under-construction indoor partitions. Automation in Construction, 74, 78–94. DOI: 10.1016/j.autcon.2016.11.009
  11. Hui, L., & Brilakis, I. (2013). Real-Time Bricks Counting for Construction Progress Monitoring. In Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering, Los Angeles, 818–824.
  12. Ibrahim, Y. M., Lukins, T. C., Zhang, X., Trucco, E., & Kaka, A. P. (2009). Towards automated progress assessment of workpackage components in construction projects using computer vision. Advanced Engineering Informatics, 23(1), 93–103. DOI: 10.1016/j.aei.2008.07.002
  13. Jeon, S., Hwang, J., Kim, G. J., & Billinghurst, M. (2006). Interaction Techniques in Large Display Environments using Hand-held Devices.
  14. Khan, K. S. mb, Kunz, R., Kleijnen, J., & Antes, G. (2003). Five steps to conducting a systematic review. 96
  15. Kim, C., Kim, C., & Son, H. (2013). Automated construction progress measurement using a 4D building information model and 3D data. Automation in Construction, 31, 75–82. DOI: 10.1016/j.autcon.2012.11.041
  16. Kim, D., Liu, M., Lee, S. H., & Kamat, V. R. (2019). Remote proximity monitoring between mobile construction resources using camera-mounted UAVs. Automation in Construction, 99, 168–182. DOI: 10.1016/j.autcon.2018.12.014
  17. Kim, P., Chen, J., Kim, J., & Cho, Y. K. (2018). SLAM-driven intelligent autonomous mobile robot navigation for construction applications. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10863 LNCS, 254–269. DOI: 10.1007/978-3-319-91635-4_14
  18. Kopsida, M., & Vela, P. A. (2015). A Review of Automated Construction Progress Monitoring and Inspection Methods. 32nd CIB W78 Conference on Construction IT, Tokyo, Japan, 421–431.
  19. Luong, D. L., Tran, D. H., & Nguyen, P. T. (2021). Optimizing multi-mode time-cost-quality trade-off of construction project using opposition multiple objective difference evolution. International Journal of Construction Management, 21(3), 271–283. DOI: 10.1080/15623599.2018.1526630
  20. Mahami, H., Nasirzadeh, F., Ahmadabadian, A. H., & Nahavandi, S. (2019). Automated progress controlling and monitoring using daily site images and building information modelling. Buildings, 9(3). DOI: 10.3390/buildings9030070
  21. Martínez-Aires, M. D., López-Alonso, M., & Martínez-Rojas, M. (2018). Building information modeling and safety management: A systematic review. Safety Science, 101, 11–18. DOI: 10.1016/j.ssci.2017.08.015
  22. McCabe, B. Y., Hamledari, H., Shahi, A., Zangeneh, P., & Azar, E. R. (2017). Roles, Benefits, and Challenges of Using UAVs for Indoor Smart Construction Applications. Congress on Computing in Civil Engineering, Proceedings, 2017-June, 349–357. DOI: 10.1061/9780784480830.043
  23. Memarzadeh, M., Heydarian, A., Golparvar-Fard, M., & Niebles, J. C. (2012). Real-time and Automated Recognition and 2D Tracking of Construction Workers and Equipment from Site Video Streams. In Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, 429–436.
  24. Meža, S., Turk, Ž., & Dolenc, M. (2015). Measuring the potential of augmented reality in civil engineering. Advances in Engineering Software, 90, 1–10. DOI: 10.1016/j.advengsoft.2015.06.005
  25. Moragane, H. P. M. N. L. B., Perera, B. A. K. S., Palihakkara, A. D., & Ekanayake, B. (2022). Application of computer vision for construction progress monitoring: a qualitative investigation. Construction Innovation. DOI: 10.1108/CI-05-2022-0130
  26. Munn, Z., Stern, C., Aromataris, E., Lockwood, C., & Jordan, Z. (2018). What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences. BMC Medical Research Methodology, 18(1), 5. DOI: 10.1186/s12874-017-0468-4
  27. Omar, T., & Nehdi, M. L. (2016). Data acquisition technologies for construction progress tracking. In Automation in Construction (Vol. 70, pp. 143–155). Elsevier B.V. DOI: 10.1016/j.autcon.2016.06.016
  28. Osuizugbo, I. C., Okolie, K. C., Oshodi, O. S., & Oyeyipo, O. O. (2022). Buildability in the construction industry: A systematic review. Construction Innovation. DOI: 10.1108/CI-05-2022-0112
  29. Paneru, S., & Jeelani, I. (2021). Computer vision applications in construction: Current state, opportunities & challenges. In Automation in Construction (Vol. 132). Elsevier B.V. DOI: 10.1016/j.autcon.2021.103940
  30. Rehman, M., Shafiq, M. T., & Ullah, F. (2022). Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review. Buildings, 12(7). DOI: 10.3390/buildings12071037
  31. Reja, V. K., Varghese, K., & Ha, Q. P. (2022). Computer vision-based construction progress monitoring. In Automation in Construction (Vol. 138). Elsevier B.V. DOI: 10.1016/j.autcon.2022.104245
  32. Rohani, M., Fan, M., & Yu, C. (2014). Advanced visualization and simulation techniques for modern construction management. Indoor and Built Environment, 23(5), 665–674. DOI: 10.1177/1420326X13498400
  33. Sami Ur Rehman, M., Shafiq, M. T., & Ullah, F. (2022). Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review. Buildings, 12(7), 1037
  34. Sultana, F., Sufian, A., & Dutta, P. (2018). Advancements in Image Classification using Convolutional Neural Network. 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN).
  35. Shamseer, L., Moher, D., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A., & the PRISMA-P Group. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: Elaboration and explanation. BMJ, 349(jan02 1), g7647–g7647. DOI: 10.1136/bmj.g7647
  36. Wang, X., Love, P. E. D., Kim, M. J., Park, C. S., Sing, C. P., & Hou, L. (2013). A conceptual framework for integrating building information modeling with augmented reality. Automation in Construction, 34, 37–44. DOI: 10.1016/j.autcon.2012.10.012
  37. Wang, Z., Zhang, Q., Yang, B., Wu, T., Lei, K., Zhang, B., & Fang, T. (2021). Vision-Based Framework for Automatic Progress Monitoring of Precast Walls by Using Surveillance Videos during the Construction Phase. Journal of Computing in Civil Engineering, 35(1). DOI: 10.1061/(asce)cp.1943-5487.0000933
  38. Zhu, Z., German, S., & Brilakis, I. (2010). Detection of large-scale concrete columns for automated bridge inspection. Automation in Construction, 19(8), 1047–1055. DOI: 10.1016/j.autcon.2010.07.016
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  • Publication Year: 2023
  • Pages: 856-864

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  • Publication Year: 2023

Chapter Information

Chapter Title

A Review of Computer Vision-Based Progress Monitoring for Effective Decision Making

Authors

Roy Lan, Tulio Sulbaran

DOI

10.36253/979-12-215-0289-3.85

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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