A Review of Computer Vision-Based Progress Monitoring for Effective Decision Making Roy Lan The University of Texas at San Antonio, United States Tulio Sulbaran The University of Texas at San Antonio, United States This is a section of CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality (DOI: 10.36253/979-12-215-0289-3) by Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi Firenze University Press Florence 2023 https://doi.org/10.36253/10.36253/979-12-215-0289-3.85

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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

 Computer Vision Construction progress monitoring Decision-making Project management

It is available online at https://doi.org/10.36253/10.36253/979-12-215-0289-3.85

References 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. 10.2478/otmcj-2018-0012 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. 10.1016/j.jclepro.2020.120350 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. 10.24018/ejers.2020.5.2.1761 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. 10.1016/j.autcon.2020.103210 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. 10.1061/ASCE0733-93642004130:1153 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. 10.1016/j.autcon.2021.103705 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. 10.1177/0363546513497567 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. 10.1016/j.asej.2022.101808 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 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. 10.1016/j.autcon.2016.11.009 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. 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. 10.1016/j.aei.2008.07.002 Jeon, S., Hwang, J., Kim, G. J., & Billinghurst, M. (2006). Interaction Techniques in Large Display Environments using Hand-held Devices. Khan, K. S. mb, Kunz, R., Kleijnen, J., & Antes, G. (2003). Five steps to conducting a systematic review. 96 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. 10.1016/j.autcon.2012.11.041 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. 10.1016/j.autcon.2018.12.014 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. 10.1007/978-3-319-91635-4_14 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. 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. 10.1080/15623599.2018.1526630 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). 10.3390/buildings9030070 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. 10.1016/j.ssci.2017.08.015 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. 10.1061/9780784480830.043 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. Meža, S., Turk, Ž., & Dolenc, M. (2015). Measuring the potential of augmented reality in civil engineering. Advances in Engineering Software, 90, 1–10. 10.1016/j.advengsoft.2015.06.005 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. 10.1108/CI-05-2022-0130 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. 10.1186/s12874-017-0468-4 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. 10.1016/j.autcon.2016.06.016 Osuizugbo, I. C., Okolie, K. C., Oshodi, O. S., & Oyeyipo, O. O. (2022). Buildability in the construction industry: A systematic review. Construction Innovation. 10.1108/CI-05-2022-0112 Paneru, S., & Jeelani, I. (2021). Computer vision applications in construction: Current state, opportunities & challenges. In Automation in Construction (Vol. 132). Elsevier B.V. 10.1016/j.autcon.2021.103940 Rehman, M., Shafiq, M. T., & Ullah, F. (2022). Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review. Buildings, 12(7). 10.3390/buildings12071037 Reja, V. K., Varghese, K., & Ha, Q. P. (2022). Computer vision-based construction progress monitoring. In Automation in Construction (Vol. 138). Elsevier B.V. 10.1016/j.autcon.2022.104245 Rohani, M., Fan, M., & Yu, C. (2014). Advanced visualization and simulation techniques for modern construction management. Indoor and Built Environment, 23(5), 665–674. 10.1177/1420326X13498400 Sami Ur Rehman, M., Shafiq, M. T., & Ullah, F. (2022). Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review. Buildings, 12(7), 1037 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). 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. 10.1136/bmj.g7647 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. 10.1016/j.autcon.2012.10.012 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). 10.1061/(asce)cp.1943-5487.0000933 Zhu, Z., German, S., & Brilakis, I. (2010). Detection of large-scale concrete columns for automated bridge inspection. Automation in Construction, 19(8), 1047–1055. 10.1016/j.autcon.2010.07.016