[1] Z. Pučko, N. Šuman, and D. Rebolj,(2018). "Automated continuous construction progress monitoring using multiple workplace real time 3D scans," Advanced Engineering Informatics, vol. 38, pp. 27-40, https://doi.org/10.1016/j.aei.2018.06.001.
[2] W. S. Alaloul, A. H. Qureshi, M. A. Musarat, and S. Saad,(2021). "Evolution of close-range detection and data acquisition technologies towards automation in construction progress monitoring," Journal of Building Engineering, vol. 43, p. 102877, https://doi.org/10.1016/j.jobe.2021.102877.
[3] H. Son and C. Kim,(2010). "3D structural component recognition and modeling method using color and 3D data for construction progress monitoring," Automation in Construction, vol. 19, no. 7, pp. 844-854, https://doi.org/10.1016/j.autcon.2010.03.003.
[4] M. Golparvar-Fard, J. Bohn, J. Teizer, S. Savarese, and F. Peña-Mora,(2011). "Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques," Automation in Construction, vol. 20, no. 8, pp. 1143-1155, https://doi.org/10.1016/j.autcon.2011.04.016.
[5] M. Kopsida, I. Brilakis, and P. A. Vela, (2015) "A review of automated construction progress monitoring and inspection methods," In: Proc. of the 32nd CIB W78 Conference 2015. City: 421-431, https://doi.org/10.17863/CAM.92941.
[6] S. El-Omari and O. Moselhi,(2011). "Integrating automated data acquisition technologies for progress reporting of construction projects," Automation in Construction, vol. 20, no. 6, pp. 699-705, https://doi.org/10.1016/j.autcon.2010.12.001.
[7] K. Han, J. Degol, and M. Golparvar-Fard,(2018). "Geometry-and appearance-based reasoning of construction progress monitoring," Journal of Construction Engineering and Management, vol. 144, no. 2, p. 04017110, https://doi.org/10.1061/(ASCE)CO.1943-7862.0001428.
[8] H. Omar, L. Mahdjoubi, and G. Kheder,(2018). "Towards an automated photogrammetry-based approach for monitoring and controlling construction site activities," Computers in Industry, vol. 98, pp. 172-182, https://doi.org/10.1016/j.compind.2018.03.012.
[9] V. K. Reja, K. Varghese, and Q. P. Ha,(2022). "Computer vision-based construction progress monitoring," Automation in Construction, vol. 138, p. 104245, https://doi.org/10.1016/j.autcon.2022.104245.
[10] B. Sherafat et al.,(2020). "Automated methods for activity recognition of construction workers and equipment: State-of-the-art review," Journal of Construction Engineering and Management, vol. 146, no. 6, p. 03120002, https://doi.org/10.1061/(ASCE)CO.1943-7862.0001843.
[11] "Remote Monitoring of Dynamic Construction Processes Using Automated Equipment Tracking," in Construction Research Congress 2012, pp. 1360-1369.
[12] A. Khosrowpour, J. C. Niebles, and M. Golparvar-Fard,(2014). "Vision-based workface assessment using depth images for activity analysis of interior construction operations," Automation in Construction, vol. 48, pp. 74-87, https://doi.org/10.1016/j.autcon.2014.08.003.
[13] "Joint Reasoning of Visual and Text Data for Safety Hazard Recognition," in Computing in Civil Engineering 2017, pp. 450-457.
[14] "Vision-Based Construction Activity Analysis in Long Video Sequences via Hidden Markov Models: Experiments on Earthmoving Operations," in Construction Research Congress 2018, pp. 164-173.
[15] D. Roberts and M. Golparvar-Fard,(2019). "End-to-end vision-based detection, tracking and activity analysis of earthmoving equipment filmed at ground level," Automation in Construction, vol. 105, p. 102811, https://doi.org/10.1016/j.autcon.2019.04.006.
[16] K. Liu and M. Golparvar-Fard,(2015). "Crowdsourcing Construction Activity Analysis from Jobsite Video Streams," Journal of Construction Engineering and Management, vol. 141, no. 11, p. 04015035, doi:10.1061/(ASCE)CO.1943-7862.0001010.
[17] H. Kim, Y. Ham, W. Kim, S. Park, and H. Kim,(2019). "Vision-based nonintrusive context documentation for earthmoving productivity simulation," Automation in Construction, vol. 102, pp. 135-147, https://doi.org/10.1016/j.autcon.2019.02.006.
[18] A. Pal, J. J. Lin, S.-H. Hsieh, and M. Golparvar-Fard,(2024). "Activity-level construction progress monitoring through semantic segmentation of 3D-informed orthographic images," Automation in Construction, vol. 157, p. 105157, https://doi.org/10.1016/j.autcon.2023.105157.
[19] A. S. Chris, A. Rashidi, B. Samanta, C.-F. Cheng, A. Davenport Mark, and V. Anderson David,(2018). "A productivity forecasting system for construction cyclic operations using audio signals and a Bayesian approach," Construction Research Congress 2018, https://doi.org/10.1061/9780784481264.029.
[20] J. Cao, T. Zhao, J. Wang, R. Wang, and Y. Chen,(2017). "Excavation equipment classification based on improved MFCC features and ELM," Neurocomputing, vol. 261, pp. 231-241, https://doi.org/10.1016/j.neucom.2016.03.113.
[21] Y. Nakanishi, T. Kaneta, and S. Nishino,(2022). "A review of monitoring construction equipment in support of construction project management," (in English), Frontiers in Built Environment, Review vol. 7, https://doi.org/10.3389/fbuil.2021.632593.
[22] Z. Ma and S. Liu,(2018). "A review of 3D reconstruction techniques in civil engineering and their applications," Advanced Engineering Informatics, vol. 37, pp. 163-174, https://doi.org/10.1016/j.aei.2018.05.005.
[23] M. Kopsida and I. Brilakis,(2020). "Real-time volume-to-plane comparison for mixed reality–based progress monitoring," Journal of Computing in Civil Engineering, vol. 34, no. 4, p. 04020016, https://doi.org/10.1061/(ASCE)CP.1943-5487.0000896.
[24] K. Mirzaei, M. Arashpour, E. Asadi, H. Masoumi, Y. Bai, and A. Behnood,(2022). "3D point cloud data processing with machine learning for construction and infrastructure applications: A comprehensive review," Advanced Engineering Informatics, vol. 51, p. 101501, https://doi.org/10.1016/j.aei.2021.101501.
[25] C. Poullis,(2013). "A framework for automatic modeling from point cloud data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp. 2563-2575, https://doi.org/10.1109/TPAMI.2013.64.
[26] V. K. Reja, S. Goyal, K. Varghese, B. Ravindran, and Q. P. Ha,(2024). "Hybrid self-supervised learning-based architecture for construction progress monitoring," Automation in Construction, vol. 158, p. 105225, https://doi.org/10.1016/j.autcon.2023.105225.
[27] M. Sindhu Pradeep, V. K. Reja, and K. Varghese,(2024). "ConXR: A Comparative Participatory Platform for Construction Progress Monitoring," Journal of The Institution of Engineers (India): Series A, https://doi.org/10.1007/s40030-024-00799-0.
[28] Z. Wang et al.,(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, vol. 35, no. 1, p. 04020056, https://doi.org/10.1061/(ASCE)CP.1943-5487.0000933.
[29] A. R. ElQasaby, F. K. Alqahtani, and M. Alheyf, "State of the art of BIM integration with sensing technologies in construction progress monitoring," Sensors, vol. 22, no. 9. doi: https://doi.org/10.3390/s22093497
[30] W. Wei, Y. Lu, T. Zhong, P. Li, and B. Liu,(2022). "Integrated vision-based automated progress monitoring of indoor construction using mask region-based convolutional neural networks and BIM," Automation in Construction, vol. 140, p. 104327, https://doi.org/10.1016/j.autcon.2022.104327.
[31] M. Golparvar-Fard, F. Peña-Mora, C. A. Arboleda, and S. J. J. o. c. i. c. e. Lee,(2009). "Visualization of construction progress monitoring with 4D simulation model overlaid on time-lapsed photographs," Journal of Computing in Civil Engineering, vol. 23, no. 6, pp. 391-404, https://doi.org/10.1061/(ASCE)0887-3801(2009)23:6(391).
[32] H. Kim and N. Kano,(2008). "Comparison of construction photograph and VR image in construction progress," Automation in Construction, vol. 17, no. 2, pp. 137-143, https://doi.org/10.1016/j.autcon.2006.12.005.
[33] C. Kim, B. Kim, and H. Kim,(2013). "4D CAD model updating using image processing-based construction progress monitoring," Automation in Construction, vol. 35, pp. 44-52, https://doi.org/10.1016/j.autcon.2013.03.005.
[34] A. Dimitrov and M. Golparvar-Fard,(2014). "Vision-based material recognition for automated monitoring of construction progress and generating building information modeling from unordered site image collections," Advanced Engineering Informatics, vol. 28, no. 1, pp. 37-49, https://doi.org/10.1016/j.aei.2013.11.002.
[35] B. Ekanayake, A. Ahmadian Fard Fini, J. K. W. Wong, and P. Smith,(2022). "A deep learning-based approach to facilitate the as-built state recognition of indoor construction works," Construction Innovation, vol. ahead-of-print, no. ahead-of-print, https://doi.org/10.1108/CI-05-2022-0121.
[36] J. Xue, X. Hou, and Y. Zeng,(2022). "Rough registration of BIM element projection for construction progress tracking," IEEE Access, vol. 10, pp. 8305-8316, https://doi.org/10.1109/ACCESS.2022.3144150.
[37] G. A.S and J. B. Edayadiyil,(2022). "Automated progress monitoring of construction projects using Machine learning and image processing approach," Materials Today: Proceedings, vol. 65, pp. 554-563, https://doi.org/10.1016/j.matpr.2022.03.137.
[38] J. K. W. Wong, F. Bameri, A. Ahmadian Fard Fini, and M. Maghrebi,(2023). "Tracking indoor construction progress by deep-learning-based analysis of site surveillance video," Construction Innovation, vol. ahead-of-print, no. ahead-of-print, https://doi.org/10.1108/CI-10-2022-0275.
[39] A. B. Ersoz and O. Pekcan,(2025). "UAV-based automated earthwork progress monitoring using deep learning with image inpainting," Automation in Construction, vol. 175, p. 106211, https://doi.org/10.1016/j.autcon.2025.106211.
[40] S. Yoon and H. Kim, "Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period," Sensors, vol. 25, no. 2. doi: 10.3390/s25020574
[41] W. P. Chua and C. C. Cheah, "Deep-Learning-Based Automated Building Construction Progress Monitoring for Prefabricated Prefinished Volumetric Construction," Sensors, vol. 24, no. 21. doi: 10.3390/s24217074
[42] A. Ostadreza and V. Shahhosseini,(2024). "Automated Construction Progress Monitoring Using Image Segmentation Trained on a Synthetic Dataset, 3D Reconstruction, and BIM," Available at SSRN 5049036,
[43] R. Zhang, R. Deng, Z. Zhang, and Y. Mao,(2025). "Vision-based real-time progress tracking and productivity analysis of the concrete pouring process," Developments in the Built Environment, vol. 21, p. 100609, https://doi.org/10.1016/j.dibe.2025.100609.
[44] J. K. W. Wong, F. Bameri, A. Ahmadian Fard Fini, and M. Maghrebi,(2025). "Tracking indoor construction progress by deep-learning-based analysis of site surveillance video," Construction Innovation, vol. 25, no. 2, pp. 461-489,
[45] H. Zhang, J. Yan, J. Yang, W. Meng, and S. Chen,(2025). "Two-stage point cloud registration using multi-scale edge convolution for digital twin-based bridge construction progress monitoring," Automation in Construction, vol. 178, p. 106415, https://doi.org/10.1016/j.autcon.2025.106415.
[46] K. Lawani, F. Sadeghineko, M. Tong, and M. Bayraktar,(2025). "Methodology for retrospectively developing a BIM model from point cloud scans using ongoing building project as case study," Journal of Engineering, Design and Technology, vol. 23, no. 4, pp. 1243-1261,
[47] F. Pfitzner, S. Hu, A. Braun, A. Borrmann, and Y. Fang,(2025). "Monitoring concrete pouring progress using knowledge graph-enhanced computer vision," Automation in Construction, vol. 174, p. 106117, https://doi.org/10.1016/j.autcon.2025.106117.
[48] S. R. Ghanbari, Hossein; Nasihatkon, Behrooz; Sadeghi, Naimeh "High-Resolution Annotated Concrete Column Images for Object Detection," ed. Mendeley Data, V1, 2024.
[49] N. Ngoc-Thoan, D.-Q. T. Bui, C. N. Tran, and D.-H. Tran,(2024). "Improved detection network model based on YOLOv5 for warning safety in construction sites," International Journal of Construction Management, vol. 24, no. 9, pp. 1007-1017,
[50] M. Laakso and A. Kiviniemi,(2012). "The IFC standard: A review of history, development, and standardization, information technology," ITcon, vol. 17, no. 9,
[51] D. Deng, (2020) "DBSCAN Clustering Algorithm Based on Density," In: 2020 7th International Forum on Electrical Engineering and Automation (IFEEA). City: 949-953, https://doi.org/10.1109/IFEEA51475.2020.00199.