Challenges of Automating Interior Construction Progress Monitoring
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 9
Abstract
Automated interior construction progress monitoring (ICPM) has gained increasing academic attention. This emerging research field faces numerous technical challenges that have been noted in previous studies but lack a holistic examination to analyze these challenges and their potential impacts. This study addresses this gap by conducting a systematic review of ICPM technical challenges, collecting related literature from Scopus, Web of Science, and ScienceDirect databases, and utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) framework for filtering and selecting literature. The filtration results in 44 strongly related technical papers. Alongside summarizing these challenges, the study explores their impacts on the entire ICPM automation process and proposes innovative solutions. Specifically, this study highlights the key phases of ICPM automation development, including data acquisition, 3D reconstruction, as-planned modeling, as-built modeling, progress comparison, and progress quantification, and subsequently identifies challenges for each phase, totaling 11 major challenges composed of 41 subchallenges. The data acquisition phase is found to have the most and most severe challenges, and challenges in other phases also impact the automation system performance. This review encompasses the identification of potential issues and proposes corresponding solutions, enabling future researchers to anticipate challenges and develop more advanced and user-friendly monitoring systems.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
No data, models, or code were generated or used during the study.
Acknowledgments
The authors acknowledge the financial support from China Scholarship Council (File No. 202210210002) and the Adelaide University China Fee Scholarship.
References
Abdel-Monem, M., and T. Hegazy. 2013. “Enhancing construction as-built documentation using interactive voice response.” J. Constr. Eng. Manage. 139 (7): 895–898. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000648.
Adán, A., B. Quintana, S. A. Prieto, and F. Bosché. 2018. “Scan-to-BIM for ‘secondary’ building components.” Adv. Eng. Inf. 37 (Aug): 119–138. https://doi.org/10.1016/j.aei.2018.05.001.
Ahmed, A., S. C. L. Koh, B. Kayis, and S. Amornsawadwatana. 2007. “A review of techniques for risk management in projects.” Benchmarking: Int. J. 14 (1): 22–36. https://doi.org/10.1108/14635770710730919.
Alaloul, W. S., 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.” J. Build. Eng. 43 (Nov): 102877. https://doi.org/10.1016/j.jobe.2021.102877.
Ali, A. K., O. J. Lee, D. Lee, and C. Park. 2021. “Remote indoor construction progress monitoring using extended reality.” Sustainability 13 (4): 1–24. https://doi.org/10.3390/su13042290.
Alizadehsalehi, S., and I. Yitmen. 2016. “The impact of field data capturing technologies on automated construction project progress monitoring.” Procedia Eng. 161 (Jan): 97–103. https://doi.org/10.1016/j.proeng.2016.08.504.
Alizadehsalehi, S., and I. Yitmen. 2018. “A concept for automated construction progress monitoring: Technologies adoption for benchmarking project performance control.” Arab. J. Sci. Eng. 44 (5): 4993–5008. https://doi.org/10.1007/s13369-018-3669-1.
Alzubi, K. M., W. S. Alaloul, M. A. Salaheen, A. H. Qureshi, M. A. Musarat, and A. O. Baarimah. 2021. “Automated monitoring for construction productivity recognition.” In Proc., 3rd Int. Sustainability and Resilience Conf.: Climate Change, ISRC 2021, 489–494. New York: IEEE.
Ambrus, R., S. Claici, and A. Wendt. 2017. “Automatic room segmentation from unstructured 3D data of indoor environments.” IEEE Rob. Autom. 2 (2): 749–756. https://doi.org/10.1109/LRA.2017.2651939.
Amer, F., and M. Golparvar-Fard. 2018. “Crowd-sourced visual data collection for monitoring indoor construction in 3D.” In Proc., 2nd European and Mediterranean Structural Engineering and Construction Conf., EURO-MED-SEC-2 2018. Beirut, Lebanon: ISEC Press.
Asadi, K., H. Ramshankar, M. Noghabaei, and K. Han. 2019. “Real-time image localization and registration with BIM using perspective alignment for indoor monitoring of construction.” J. Comput. Civ. Eng. 33 (5): 04019031. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000847.
Bhatla, A., S. Y. Choe, O. Fierro, and F. Leite. 2012. “Evaluation of accuracy of as-built 3D modeling from photos taken by handheld digital cameras.” Autom. Constr. 28 (Dec): 116–127. https://doi.org/10.1016/j.autcon.2012.06.003.
Bosché, F. 2010. “Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction.” Adv. Eng. Inf. 24 (1): 107–118. https://doi.org/10.1016/j.aei.2009.08.006.
Braun, A., S. Tuttas, U. Stilla, and A. Borrmann. 2018. “Process- and computer vision-based detection of as-built components on construction sites.” In Proc., 35th Int. Symp. on Automation and Robotics in Construction. Bochum, Germany: Ruhr-Universität Bochum.
Bulat, A., and G. Tzimiropoulos. 2017. “How far are we from solving the 2D and 3D face alignment problem? (and a dataset of 230,000 3D facial landmarks).” In Proc., 2017 IEEE International Conference on Computer Vision, 1021–1030. New York: IEEE.
Chang, V., and G. Wills. 2016. “A model to compare cloud and noncloud storage of big data.” Future Gener. Comput. Syst. 57 (Apr): 56–76. https://doi.org/10.1016/j.future.2015.10.003.
Chen, G., H. Wang, K. Chen, Z. Li, Z. Song, Y. Liu, W. Chen, and A. Knoll. 2022. “A survey of the four pillars for small object detection: Multiscale representation, contextual information, super-resolution, and region proposal.” IEEE Trans. Syst. Man Cybern Syst. 52 (2): 936–953. https://doi.org/10.1109/TSMC.2020.3005231.
Chen, L., and Y. Wang. 2017. “Automatic key frame extraction in continuous videos from construction monitoring by using color, texture, and gradient features.” Autom. Constr. 81 (Sep): 355–368. https://doi.org/10.1016/j.autcon.2017.04.004.
Dai, F., and M. Lu. 2010. “Assessing the accuracy of applying photogrammetry to take geometric measurements on building products.” J. Constr. Eng. Manage. 136 (2): 242–250. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000114.
Dai, F., A. Rashidi, I. Brilakis, and P. Vela. 2013. “Comparison of image-based and time-of-flight-based technologies for three-dimensional reconstruction of infrastructure.” J. Constr. Eng. Manage. 139 (1): 69–79. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000565.
Deng, H., H. Hong, D. Luo, Y. Deng, and C. Su. 2020. “Automatic indoor construction process monitoring for tiles based on BIM and computer vision.” J. Constr. Eng. Manage. 146 (1): 04019095. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001744.
Ekanayake, B., A. A. F. Fini, and J. K. W. Wong. 2021a. “Technical challenges for automated indoor construction progress monitoring.” In Proc., 44th AUBEA Conf.: Construction Education: Live the FutureGeelong. Ultimo, NSW, Australia: Open Publication of UTS Scholars.
Ekanayake, B., A. A. F. 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.” Constr. Innovation-England.
Ekanayake, B., J. K. W. Wong, A. A. F. Fini, and P. Smith. 2021b. “Computer vision-based interior construction progress monitoring: A literature review and future research directions.” Autom. Constr. 127 (Jul): 103705. https://doi.org/10.1016/j.autcon.2021.103705.
Eulitz, M., and G. Reiss. 2015. “3D reconstruction of SEM images by use of optical photogrammetry software.” J. Struct. Biol. 191 (2): 190–196. https://doi.org/10.1016/j.jsb.2015.06.010.
Fernandes, L. J., A. P. Barbosa-Póvoa, and S. Relvas. 2011. “Supply chain risk management review and a new framework for petroleum supply chains.” In Proc., Quantitative Financial Risk Management, 227–264. Berlin: Springer.
Fugate, B., F. Sahin, and J. T. Mentzer. 2006. “Supply chain management coordination mechanisms.” J. Bus. Logist. 27 (2): 129–161. https://doi.org/10.1002/j.2158-1592.2006.tb00220.x.
Golparvar-Fard, M., 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.” Autom. Constr. 20 (8): 1143–1155. https://doi.org/10.1016/j.autcon.2011.04.016.
Golparvar-Fard, M., F. Peña-Mora, and S. Savarese. 2015. “Automated progress monitoring using unordered daily construction photographs and IFC-based building information models.” J. Comput. Civ. Eng. 29 (1): 04014025. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000205.
Gurtu, A., and J. Johny. 2021. “Supply chain risk management: Literature review.” Risks 9 (1): 16. https://doi.org/10.3390/risks9010016.
Guven, G., and E. Ergen. 2021. “Tracking major resources for automated progress monitoring of construction activities: Masonry work case.” Constr. Innovation 21 (4): 648–667. https://doi.org/10.1108/CI-05-2020-0081.
Ham, Y., K. K. Han, J. J. Lin, and M. Golparvar-Fard. 2016. “Visual monitoring of civil infrastructure systems via camera-equipped unmanned aerial vehicles (UAVs): A review of related works.” Vis. Eng. 4 (1): 1–8. https://doi.org/10.1186/s40327-015-0029-z.
Ham, Y., and M. Kamari. 2019. “Automated content-based filtering for enhanced vision-based documentation in construction toward exploiting big visual data from drones.” Autom. Constr. 105 (Sep): 102831. https://doi.org/10.1016/j.autcon.2019.102831.
Hamledari, H., and B. McCabe. 2016. “Automated visual recognition of indoor project-related objects: Challenges and solutions.” In Proc., Construction Research Congress 2016: Old and New Construction Technologies Converge in Historic San Juan, CRC 2016, 2573–2582. Reston, VA: ASCE.
Hamledari, H., B. McCabe, and S. Davari. 2017a. “Automated computer vision-based detection of components of under-construction indoor partitions.” Autom. Constr. 74 (Feb): 78–94. https://doi.org/10.1016/j.autcon.2016.11.009.
Hamledari, H., B. McCabe, S. Davari, and A. Shahi. 2017b. “Automated schedule and progress updating of IFC-based 4D BIMs.” J. Comput. Civ. Eng. 31 (4): 04017012. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000660.
Hamledari, H., B. McCabe, S. Davari, A. Shahi, E. Rezazadeh Azar, and F. Flager. 2017c. “Evaluation of computer vision- and 4D BIM-based construction progress tracking on a UAV platform.” In Proc., 6th CSCE-CRC Int. Construction Specialty Conf. 2017—Held as Part of the Canadian Society for Civil Engineering Annual Conf. and General Meeting 2017, 621–630. Vancouver, BC, Canada: Canadian Society for Civil Engineering.
Hamledari, H., E. Rezazadeh Azar, and B. McCabe. 2018. “IFC-based development of as-built and as-is BIMs using construction and facility inspection data: Site-to-BIM data transfer automation.” J. Comput. Civ. Eng. 32 (2): 04017075. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000727.
Hamledari, H., S. Sajedi, B. McCabe, and M. Fischer. 2021. “Automation of inspection mission planning using 4D BIMs and in support of unmanned aerial vehicle-based data collection.” J. Constr. Eng. Manage. 147 (3): 04020179. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001995.
Hamma-adama, M., and T. Kouider. 2019. “Comparative analysis of BIM adoption efforts by developed countries as precedent for new adopter countries.” Curr. J. Appl. Sci. Technol. 36 (2): 1–15. https://doi.org/10.9734/cjast/2019/v36i230224.
Han, K., J. Degol, and M. Golparvar-Fard. 2017. “Geometry- and appearance-based reasoning of construction progress monitoring.” J. Constr. Eng. Manage. 144 (2): 04017110. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001428.
Han, K., and M. Golparvar-Fard. 2017. “Crowdsourcing BIM-guided collection of construction material library from site photologs.” Vis. Eng. 5 (1): 1–13. https://doi.org/10.1186/s40327-017-0052-3.
Han, K. K., and M. Golparvar-Fard. 2014. “Multi-sample image-based material recognition and formalized sequencing knowledge for operation-level construction progress monitoring.” In Computing in civil and building engineering, 364–372. Reston, VA: ASCE.
Han, K. K., and M. Golparvar-Fard. 2015. “Appearance-based material classification for monitoring of operation-level construction progress using 4D BIM and site photologs.” Autom. Constr. 53 (May): 44–57. https://doi.org/10.1016/j.autcon.2015.02.007.
Hasan, S., and R. Sacks. 2023. “Integrating BIM and multiple construction monitoring technologies for acquisition of project status information.” J. Constr. Eng. Manage. 149 (7): 04023051. https://doi.org/10.1061/JCEMD4.COENG-12826.
Hosseini, M. R., M. Maghrebi, A. Akbarnezhad, I. Martek, and M. Arashpour. 2018a. “Analysis of citation networks in building information modeling research.” J. Constr. Eng. Manage. 144 (8): 04018064. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001492.
Hosseini, M. R., I. Martek, E. K. Zavadskas, A. A. Aibinu, M. Arashpour, and N. Chileshe. 2018b. “Critical evaluation of off-site construction research: A scientometric analysis.” Autom. Constr. 87 (Mar): 235–247. https://doi.org/10.1016/j.autcon.2017.12.002.
Hu, D., V. J. L. Gan, and C. Yin. 2023. “Robot-assisted mobile scanning for automated 3D reconstruction and point cloud semantic segmentation of building interiors.” Autom. Constr. 152 (Aug): 104949. https://doi.org/10.1016/j.autcon.2023.104949.
Huang, H. Y., Y. C. Chou, and S. Chang. 2009. “A dynamic system model for proactive control of dynamic events in full-load states of manufacturing chains.” Int. J. Prod. Res. 47 (9): 2485–2506. https://doi.org/10.1080/00207540701484913.
Ibrahim, A., A. Sabet, and M. Golparvar-Fard. 2019. “BIM-driven mission planning and navigation for automatic indoor construction progress detection using robotic ground platform.” In Proc., 2019 European Conf. on Computing in Construction, 182–189. Chania, Greece: European Council on Computing in Construction.
Jiang, F., L. Ma, T. Broyd, and K. Chen. 2021. “Digital twin and its implementations in the civil engineering sector.” Autom. Constr. 130 (Oct): 103838. https://doi.org/10.1016/j.autcon.2021.103838.
Jung, J., C. Stachniss, S. Ju, and J. Heo. 2018. “Automated 3D volumetric reconstruction of multiple-room building interiors for as-built BIM.” Adv. Eng. Inf. 38 (Oct): 811–825. https://doi.org/10.1016/j.aei.2018.10.007.
Kim, H., H. Kim, W. Hong Yong, and H. Byun. 2018. “Detecting construction equipment using a region-based fully convolutional network and transfer learning.” J. Comput. Civ. Eng. 32 (2): 04017082. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000731.
Kim, J. 2020. “Visual analytics for operation-level construction monitoring and documentation: State-of-the-art technologies, research challenges, and future directions.” Front. Built Environ. 6 (Nov): 575738. https://doi.org/10.3389/fbuil.2020.575738.
Kopsida, M., and I. Brilakis. 2020. “Real-time volume-to-plane comparison for mixed reality-based progress monitoring.” J. Comput. Civ. Eng. 34 (4): 04020016. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000896.
Kropp, C., C. Koch, M. Konig, and I. Brilakis. 2012. “A framework for automated delay prediction of finishing works using video data and BIM-based construction simulation.” In Proc., 14th Int. Conf. on Computing in Civil and Building Engineering. Moscow: Moscow State Univ. of Civil Engineering.
Kropp, C., C. Koch, and M. König. 2014. “Drywall state detection in image data for automatic indoor progress monitoring.” In Proc., 2014 Int. Conf. on Computing in Civil and Building Engineering, 347–354. Reston, VA: ASCE.
Kropp, C., C. Koch, and M. König. 2018. “Interior construction state recognition with 4D BIM registered image sequences.” Autom. Constr. 86 (Feb): 11–32. https://doi.org/10.1016/j.autcon.2017.10.027.
Kropp, C., M. König, and C. Koch. 2013. “Object recognition in BIM registered videos for indoor progress monitoring.” In Proc., 20th Int. Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2013. Vienna, Austria: European Group for Intelligent Computing in Engineering.
Kurbis, A. G., B. Laschowski, and A. Mihailidis. 2022. “Stair recognition for robotic exoskeleton control using computer vision and deep learning.” In Proc., 2022 Int. Conf. on Rehabilitation Robotics (ICORR). New York: IEEE.
Laakso, M., and A. Kiviniemi. 2012. “The IFC standard: A review of history, development, and standardization, information technology.” ITcon 17 (9): 134–161.
Li, Y., W. Li, S. Tang, W. Darwish, Y. Hu, and W. Chen. 2020. “Automatic indoor as-built building information models generation by using low-cost RGB-D sensors.” Sensors 20 (1): 293. https://doi.org/10.3390/s20010293.
Lim, G., and N. Doh. 2021. “Automatic reconstruction of multilevel indoor spaces from point cloud and trajectory.” Sensor 21 (10): 3493. https://doi.org/10.3390/s21103493.
Liu, Y., P. Sun, N. Wergeles, and Y. Shang. 2021. “A survey and performance evaluation of deep learning methods for small object detection.” Expert Syst. Appl. 172 (Jun): 114602. https://doi.org/10.1016/j.eswa.2021.114602.
Louis, M., and M. Pagell. 2019. “Categorizing supply chain risks: Review, integrated typology and future research.” Revisiting Supply Chain Risk 7: 329–366. https://doi.org/10.1007/978-3-030-03813-7_20.
Maalek, R., J. Ruwanpura, and K. Ranaweera. 2014. “Evaluation of the state-of-the-art automated construction progress monitoring and control systems.” In Proc., 2014 Construction Research Congress: Construction in a Global Network, CRC 2014, 1023–1032. Reston, VA: ASCE.
Maalek, R., and F. Sadeghpour. 2013. “Accuracy assessment of ultra-wide band technology in tracking static resources in indoor construction scenarios.” Autom. Constr. 30 (Mar): 170–183. https://doi.org/10.1016/j.autcon.2012.10.005.
MacDonald, J. 2014. “Systematic approaches to a successful literature review.” J. Can. Health Lib. Assoc. 34 (1): 46–47. https://doi.org/10.5596/c13-009.
Macher, H., T. Landes, and P. Grussenmeyer. 2017. “From point clouds to building information models: 3D semiautomatic reconstruction of indoors of existing buildings.” Appl. Sci. 7 (10): 1030. https://doi.org/10.3390/app7101030.
Mahami, H., F. Nasirzadeh, A. H. Ahmadabadian, and S. Nahavandi. 2019. “Automated progress controlling and monitoring using daily site images and building information modelling.” Buildings 9 (3): 70. https://doi.org/10.3390/buildings9030070.
Majrouhi Sardroud, J. 2012. “Influence of RFID technology on automated management of construction materials and components.” Scientia Iranica 19 (3): 381–392. https://doi.org/10.1016/j.scient.2012.02.023.
McCabe, B. Y., H. Hamledari, A. Shahi, P. Zangeneh, and E. R. Azar. 2017. “Roles, benefits, and challenges of using UAVs for indoor smart construction applications.” In Proc., 2017 ASCE International Workshop on Computing in Civil Engineering, IWCCE 2017, 349–357. Reston, VA: ASCE.
Memon Zubair, A., A. Majid Muhd Zaimi, and M. Mustaffar. 2012. “An automatic project progress monitoring model by integrating AutoCAD and digital photos.” In Computing in civil engineering (2005), 1–13. Reston, VA: ASCE.
Mengiste, E., B. Garcia de Soto, and T. Hartmann. 2024. “Automated integration of as-is point cloud information with as-planned BIM for interior construction.” Int. J. Constr. Manage. 24 (2): 137–150. https://doi.org/10.1080/15623599.2023.2211487.
Meyer, T., A. Brunn, and U. Stilla. 2022. “Change detection for indoor construction progress monitoring based on BIM, point clouds, and uncertainties.” Autom. Constr. 141 (Sep): 104442. https://doi.org/10.1016/j.autcon.2022.104442.
Moragane, H. P., B. A. Perera, A. D. Palihakkara, and B. Ekanayake. 2022. “Application of computer vision for construction progress monitoring: A qualitative investigation.” Constr. Innovation 24 (2): 446–469. https://doi.org/10.1108/CI-05-2022-0130.
Murali, S., P. Speciale, M. R. Oswald, and M. Pollefeys. 2017. “Indoor Scan2BIM: Building information models of house interiors.” In Proc., 2017 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 6126–6133. New York: IEEE.
Nightingale, A. 2009. “A guide to systematic literature reviews.” Surgery—Oxford Int. Ed. 27 (9): 381–384. https://doi.org/10.1016/j.mpsur.2009.07.005.
Omar, H., L. Mahdjoubi, and G. Kheder. 2018. “Towards an automated photogrammetry-based approach for monitoring and controlling construction site activities.” Comput. Ind. 98 (Jun): 172–182. https://doi.org/10.1016/j.compind.2018.03.012.
Omar, T., and M. L. Nehdi. 2016. “Data acquisition technologies for construction progress tracking.” Autom. Constr. 70 (Oct): 143–155. https://doi.org/10.1016/j.autcon.2016.06.016.
Page, M. J., et al. 2021. “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews.” Syst. Rev. 10 (1): 89. https://doi.org/10.1186/s13643-021-01626-4.
Pal, A., J. J. Lin, S.-H. Hsieh, and M. Golparvar-Fard. 2024. “Activity-level construction progress monitoring through semantic segmentation of 3D-informed orthographic images.” Autom. Constr. 157 (Jan): 105157. https://doi.org/10.1016/j.autcon.2023.105157.
Paneru, S., and I. Jeelani. 2021. “Computer vision applications in construction: Current state, opportunities and amp; challenges.” Autom. Constr. 132 (Dec): 103940. https://doi.org/10.1016/j.autcon.2021.103940.
Patel, T., B. H. W. Guo, and Y. Zou. 2021. “A scientometric review of construction progress monitoring studies.” Eng. Constr. Archit. Manage. 29 (9): 3237–3266. https://doi.org/10.1108/ECAM-10-2020-0799.
Pazhoohesh, M., and C. Zhang. 2015. “Automated construction progress monitoring using thermal images and wireless sensor networks.” In Vol. 101 of Proc., Annual Conf. Canadian Society for Civil Engineering, 593–602. Vancouver, BC, Canada: Canadian Society for Civil Engineering.
Pazhoohesh, M., C. Zhang, A. Hammad, Z. Taromi, and A. Razmjoo. 2021. “Infrared thermography for a quick construction progress monitoring approach in concrete structures.” Archit. Struct. Constr. 1 (2–3): 91–106. https://doi.org/10.1007/s44150-021-00008-7.
Pour Rahimian, F., S. Seyedzadeh, S. Oliver, S. Rodriguez, and N. Dawood. 2020. “On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning.” Autom. Constr. 110 (Feb): 103012. https://doi.org/10.1016/j.autcon.2019.103012.
Pučko, Z., N. Šuman, and D. Rebolj. 2018. “Automated continuous construction progress monitoring using multiple workplace real time 3D scans.” Adv. Eng. Inf. 38 (Oct): 27–40. https://doi.org/10.1016/j.aei.2018.06.001.
Qureshi, A. H., W. S. Alaloul, W. K. Wing, S. Saad, S. Ammad, and M. A. Musarat. 2022. “Factors impacting the implementation process of automated construction progress monitoring.” Ain Shams Eng. J. 13 (6). https://doi.org/10.1016/j.asej.2022.101808.
Radopoulou, S. C., M. Sun, F. Dai, I. Brilakis, and S. Savarese. 2012. “Testing of depth-encoded Hough voting for infrastructure object detection.” In Computing in civil engineering, 309–316. Reston, VA: ASCE.
Reja, V. K., K. Varghese, and Q. P. Ha. 2022. “Computer vision-based construction progress monitoring.” Autom. Constr. 138 (Jun): 104245. https://doi.org/10.1016/j.autcon.2022.104245.
Ren, S., K. He, R. Girshick, and J. Sun. 2016. “Faster R-CNN: Towards real-time object detection with region proposal networks.” IEEE Trans. Pattern Anal. Mach. Intell. 39 (6): 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031.
Roh, S., Z. Aziz, and F. Peña-Mora. 2011. “An object-based 3D walk-through model for interior construction progress monitoring.” Autom. Constr. 20 (1): 66–75. https://doi.org/10.1016/j.autcon.2010.07.003.
Seagate. 2022. “Surveillance storage calculator.” Accessed October 18, 2022. https://www.seagate.com/au/en/video-storage-calculator/.
Sgrenzaroli, M., J. Ortiz Barrientos, G. Vassena, A. Sanchez, A. Ciribini, S. Mastrolembo Ventura, and S. Comai. 2022. “Indoor mobile mapping systems and (BIM) digital models for construction progress monitoring.” In Proc., 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission I, 121–127. Riveria, France: International Society for Photogrammetry and Remote Sensing.
Shahi, A., A. Aryan, J. S. West, C. T. Haas, and R. C. G. Haas. 2012. “Deterioration of UWB positioning during construction.” Autom. Constr. 24 (Jul): 72–80. https://doi.org/10.1016/j.autcon.2012.02.009.
Shahi, A., J. S. West, and C. T. Haas. 2013. “Onsite 3D marking for construction activity tracking.” Autom. Constr. 30 (Mar): 136–143. https://doi.org/10.1016/j.autcon.2012.11.027.
Shirowzhan, S., S. Sepasgozar, and C. Liu. 2018. “Monitoring physical progress of indoor buildings using mobile and terrestrial point clouds.” In Proc., Construction Research Congress 2018: Construction Information Technology, CRC 2018, 602–611. Reston, VA: ASCE.
Sinopoli, J. 2010. “Design, construction, and renovations.” Smart Build. Syst. Architects Owners Build. 139–158. https://doi.org/10.1016/B978-1-85617-653-8.00013-2.
Sodhi, M. S., B.-G. Son, and C. S. Tang. 2012. “Researchers’ perspectives on supply chain risk management.” Prod. Oper. Manage. 21 (1): 1–13. https://doi.org/10.1111/j.1937-5956.2011.01251.x.
Son, H., F. Bosché, and C. Kim. 2015. “As-built data acquisition and its use in production monitoring and automated layout of civil infrastructure: A survey.” Adv. Eng. Inf. 29 (2): 172–183. https://doi.org/10.1016/j.aei.2015.01.009.
Tajeen, H., and Z. Zhu. 2014. “Image dataset development for measuring construction equipment recognition performance.” Autom. Constr. 48 (Dec): 1–10. https://doi.org/10.1016/j.autcon.2014.07.006.
Tan, M., and Q. V. Le. 2020. “Efficient Net rethinking model scaling for convolutional neural networks.” Preprint, submitted May 28, 2019. http://arxiv.org/abs/1905.11946.
Tang, S. J., X. M. Li, X. W. Zheng, B. Wu, W. X. Wang, and Y. J. Zhang. 2022. “BIM generation from 3D point clouds by combining 3D deep learning and improved morphological approach.” Autom. Constr. 141 (Sep): 104422. https://doi.org/10.1016/j.autcon.2022.104422.
Tommasi, T., N. Patricia, B. Caputo, and T. Tuytelaars. 2017. “A deeper look at dataset bias.” Domain Adaptat. Comput. Vision Appl. 37–55. https://doi.org/10.1007/978-3-319-58347-1_2.
Tong, K., Y. Wu, and F. Zhou. 2020. “Recent advances in small object detection based on deep learning: A review.” Image Vis. Comput. 97 (May): 103910. https://doi.org/10.1016/j.imavis.2020.103910.
Turkan, Y., F. Bosche, C. T. Haas, and R. Haas. 2012. “Automated progress tracking using 4D schedule and 3D sensing technologies.” Autom. Constr. 22 (Mar): 414–421. https://doi.org/10.1016/j.autcon.2011.10.003.
Turkan, Y., F. Bosché, T. Haas Carl, and R. Haas. 2013. “Tracking secondary and temporary concrete construction objects using 3D imaging technologies.” In Computing in civil engineering, 749–756. Reston, VA: ASCE.
van Eck, N. J., and L. Waltman. 2014. “CitNetExplorer: A new software tool for analyzing and visualizing citation networks.” J. Informetrics 8 (4): 802–823. https://doi.org/10.1016/j.joi.2014.07.006.
Vanem, E. 2012. “Ethics and fundamental principles of risk acceptance criteria.” Saf. Sci. 50 (4): 958–967. https://doi.org/10.1016/j.ssci.2011.12.030.
Wang, S. K., and H. M. Chen. 2020. “A construction progress on-site monitoring and presentation system based on the integration of augmented reality and BIM.” In Proc., 37th Int. Symp. on Automation and Robotics in Construction: From Demonstration to Practical Use—To New Stage of Construction Robot, ISARC 2020, Int. Association on Automation and Robotics in Construction (IAARC), 147–154. Kitakyushu, Japan: International Association for Automation and Robotics in Construction.
Wei, W., 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.” Autom. Constr. 140 (Aug): 104327. https://doi.org/10.1016/j.autcon.2022.104327.
Wong, J. K. W., F. Bameri, A. Ahmadian Fard Fini, and M. Maghrebi. 2023. “Tracking indoor construction progress by deep-learning-based analysis of site surveillance video.” Constr. Innovation. https://doi.org/10.1108/CI-10-2022-0275.
Wu, H., J. Zuo, H. Yuan, G. Zillante, and J. Wang. 2019. “A review of performance assessment methods for construction and demolition waste management.” Resour. Conserv. Recycl. 150 (Nov): 104407. https://doi.org/10.1016/j.resconrec.2019.104407.
Xie, L., and R. Wang. 2017. “Automatic indoor building reconstruction from mobile laser scanning data.” In Proc., ISPRS Geospatial Week 2017, 417–422. Wuhan, China: International Society for Photogrammetry and Remote Sensing.
Xie, L., R. Wang, Z. Ming, and D. Chen. 2019. “A layer-wise strategy for indoor as-built modeling using point clouds.” Appl. Sci. 9 (14): 2904. https://doi.org/10.3390/app9142904.
Xiong, X., A. Adan, B. Akinci, and D. Huber. 2013. “Automatic creation of semantically rich 3D building models from laser scanner data.” Autom. Constr. 31 (May): 325–337. https://doi.org/10.1016/j.autcon.2012.10.006.
Yang, J., M.-W. Park, P. A. Vela, and M. Golparvar-Fard. 2015. “Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future.” Adv. Eng. Inf. 29 (2): 211–224. https://doi.org/10.1016/j.aei.2015.01.011.
Yang, P., N. Xiong, and J. Ren. 2020. “Data security and privacy protection for cloud storage: A survey.” IEEE Access 8: 131723–131740. https://doi.org/10.1109/ACCESS.2020.3009876.
Zafari, F., A. Gkelias, and K. K. Leung. 2019. “A survey of indoor localization systems and technologies.” IEEE Commun. Surv. Tutorials 21 (3): 2568–2599. https://doi.org/10.1109/COMST.2019.2911558.
Zhang, C., and D. Arditi. 2013. “Automated progress control using laser scanning technology.” Autom. Constr. 36 (Dec): 108–116. https://doi.org/10.1016/j.autcon.2013.08.012.
Zhao, J., E. Pikas, O. Seppänen, and A. Peltokorpi. 2021. “Using real-time indoor resource positioning to track the progress of tasks in construction sites.” Front. Built Environ. 7 (Apr): 661166. https://doi.org/10.3389/fbuil.2021.661166.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 150 • Issue 9 • September 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Published online: Jun 17, 2024
Published in print: Sep 1, 2024
Discussion open until: Nov 17, 2024
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.