Automated progress monitoring in pipeline construction: a systematic review

    Muhammad Hassaan Farooq Khan Info
    Wesam Salah Alaloul Info
    Muhammad Ali Musarat Info
    Abdul Hannan Qureshi Info
DOI: https://doi.org/10.3846/jcem.2025.24800

Abstract

Automated progress monitoring for pipeline construction is an evolving research domain among researchers which can provide effective visualisation and control of related projects. Although automation has been widely reviewed in building construction, a focused review on pipeline construction is lacking despite unique challenges and a growing need for automated monitoring. Hence, a systematic review of available methodologies and technologies was necessary to assess the achievement level of automation attained in progress monitoring practice in pipeline construction. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement, relevant studies in the area were identified by using five databases: Scopus, Web of Science, ScienceDirect, American Society of Civil Engineers, and Emerald. Keyword analysis was performed by developing a keyword network, and descriptive statistics were provided. The review examines a wide range of technologies and methods for automated progress monitoring, with a focus on data acquisition tools, monitoring techniques, and their integration within unnecessary article pipeline construction scenarios. A technological process overview was developed to outline the complete monitoring workflow, while a conceptual visual representation illustrated the potential impact of tools selection and integration strategies on successful project completion, and its broader impact on sustainability and economy.

Keywords:

pipeline construction, MEP, automated progress monitoring, BIM, data acquisition tools, integration technologies

How to Cite

Khan, M. H. F., Alaloul, W. S., Musarat, M. A., & Qureshi, A. H. (2026). Automated progress monitoring in pipeline construction: a systematic review. Journal of Civil Engineering and Management, 32(4), 489–512. https://doi.org/10.3846/jcem.2025.24800

Share

Published in Issue
May 11, 2026
Abstract Views
0

References

Alaloul, W. S., Liew, M. S., & Zawawi, N. (2016). Coordination process in construction projects management. In Engineering Challenges for Sustainable Future: Proceedings of the 3rd International Conference on Civil, Offshore and Environmental Engineering (ICCOEE 2016), Kuala Lumpur, Malaysia. https://doi.org/10.1201/b21942-29

Alaloul, W. S., Liew, M., Zawawi, N. A. W. A., & Kennedy, I. B. (2020). Industrial Revolution 4.0 in the construction industry: Challenges and opportunities for stakeholders. Ain Shams Engineering Journal, 11(1), 225–230. https://doi.org/10.1016/j.asej.2019.08.010

Alaloul, W. S., Qureshi, A. H., Musarat, M. A., & Saad, S. (2021). Evolution of close-range detection and data acquisition technologies towards automation in construction progress monitoring. Journal of Building Engineering, 43, Article 102877. https://doi.org/10.1016/j.jobe.2021.102877

Alaloul, W. S., Alzubi, K. M., Malkawi, A. B., Al Salaheen, M., & Musarat, M. A. (2022). Productivity monitoring in building construction projects: a systematic review. Engineering, Construction and Architectural Management, 29(7), 2760–2785. https://doi.org/10.1108/ECAM-03-2021-0211

Albeaino, G., & Gheisari, M. (2021). Trends, benefits, and barriers of unmanned aerial systems in the construction industry: A survey study in the United States. ITcon, 26, 84–111. https://doi.org/10.36680/j.itcon.2021.006

Alizadehsalehi, S., & Yitmen, I. (2016). The impact of field data capturing technologies on automated construction project progress monitoring. Procedia Engineering, 161, 97–103. https://doi.org/10.1016/j.proeng.2016.08.504

Alizadehsalehi, S., & Yitmen, I. (2021). Digital twin-based progress monitoring management model through reality capture to extended reality technologies (DRX). Smart and Sustainable Built Environment, 12(1), 200–236. https://doi.org/10.1108/SASBE-01-2021-0016

Altaf, M., Alaloul, W. S., Musarat, M. A., & Qureshi, A. H. (2023). Life cycle cost analysis (LCCA) of construction projects: sustainability perspective. Environment, Development and Sustainability, 25(11), 12071–12118. https://doi.org/10.1007/s10668-022-02579-x

Álvares, J. S., Costa, D. B., & Melo, R. R. S. d. (2018). Exploratory study of using unmanned aerial system imagery for construction site 3D mapping. Construction Innovation, 18(3), 301–320. https://doi.org/10.1108/CI-05-2017-0049

Allied Market Research. (2022). Pipeline construction market. https://www.alliedmarketresearch.com/pipeline-construction-market-A16396

Asadi, K., Suresh, A. K., Ender, A., Gotad, S., Maniyar, S., Anand, S., Noghabaei, M., Han, K., Lobaton, E., & Wu, T. (2020). An integrated UGV-UAV system for construction site data collection. Automation in Construction, 112, Article 103068. https://doi.org/10.1016/j.autcon.2019.103068

Asadi, K., Haritsa, V. R., Han, K., & Ore, J.-P. (2021). Automated object manipulation using vision-based mobile robotic system for construction applications. Journal of Computing in Civil Engineering, 35(1), Article 04020058. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000946

Azzahra, Z. F., Utama, N. I., Suakanto, S., & Setiawan, T. D. (2024). A blockchain-based monitoring system for oil pipeline distribution with IoT technology. In 2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics (ICoABCD 2024), Bali, Indonesia. https://doi.org/10.1109/ICoABCD63526.2024.10704345

Bang, S., Kim, H., & Kim, H. (2017). UAV-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching. Automation in Construction, 84, 70–80. https://doi.org/10.1016/j.autcon.2017.08.031

Barton, P. K., Fryer, B. G., & Highfield, D. (1983). Building services integration. E & FN Spon.

Bhaddurgatte, R., Kumar, B. V., & Kusuma, S. (2019). Machine learning and prediction-based resource management in IoT considering Qos. International Journal of Recent Technology and Engineering, 8(2), 687–694. https://doi.org/10.35940/ijrte.B1705.078219

Bognot, J. R., Candido, C. G., Blanco, A. C., & Montelibano, J. R. Y. (2018). Building construction progress monitoring using unmanned aerial system (UAS), low-cost photogrammetry, and geographic information system (GIS). ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 41–47. https://doi.org/10.5194/isprs-annals-IV-2-41-2018

Bosché, F., Guillemet, A., Turkan, Y., Haas, C. T., & Haas, R. (2014). Tracking the built status of MEP works: Assessing the value of a Scan-vs-BIM system. Journal of Computing in Civil Engineering, 28(4), Article 05014004. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000343

Bosché, F., Ahmed, M., Turkan, Y., Haas, C. T., & Haas, R. (2015). The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components. Automation in Construction, 49, 201–213. https://doi.org/10.1016/j.autcon.2014.05.014

Bramer, W. M., Rethlefsen, M. L., Kleijnen, J., & Franco, O. H. (2017). Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study. Systematic Reviews, 6, Article 245. https://doi.org/10.1186/s13643-017-0644-y

Braun, A., & Borrmann, A. (2019). Combining inverse photogrammetry and BIM for automated labeling of construction site images for machine learning. Automation in Construction, 106, Article 102879. https://doi.org/10.1016/j.autcon.2019.102879

Chan, T. H. H. (2017). Real-time multi-sensory monitoring by automated routing of UAV in civil/building construction site and cavern development: to improve safety, health and productivity [BSc thesis]. City University of Hong Kong, Hong Kong.

Charlton, A. (2012). Book review: Doing your literature review: Traditional and systematic techniques. Evaluation Journal of Australasia, 12(2), 54–55. https://doi.org/10.1177/1035719X1201200208

Chi, S., & Caldas, C. H. (2012). Image-based safety assessment: Automated spatial safety risk identification of earthmoving and surface mining activities. Journal of Construction Engineering and Management, 138(3), 341–351. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000438

Choi, H.-W., Kim, H.-J., Kim, S.-K., & Na, W. S. (2023). An overview of drone applications in the construction industry. Drones, 7(8), Article 515. https://doi.org/10.3390/drones7080515

Czerniawski, T., Nahangi, M., Haas, C., & Walbridge, S. (2016). Pipe spool recognition in cluttered point clouds using a curvature-based shape descriptor. Automation in Construction, 71, 346–358. https://doi.org/10.1016/j.autcon.2016.08.011

Dave, B., Kubler, S., Främling, K., & Koskela, L. (2016). Opportunities for enhanced lean construction management using Internet of Things standards. Automation in Construction, 61, 86–97. https://doi.org/10.1016/j.autcon.2015.10.009

Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In D. A. Buchanan, & A. Bryman (Eds.), The Sage handbook of organizational research methods (pp. 671–689). Sage Publications Ltd.

Dib, H., Adamo-Villani, N., & Issa, R. R. (2013). A GIS-based visual information model for buildng construction project management. International Journal of Construction Management, 13(1), 1–18. https://doi.org/10.1080/15623599.2013.10773202

Du, J., Zou, Z., Shi, Y., & Zhao, D. (2018). Zero latency: Real-time synchronization of BIM data in virtual reality for collaborative decision-making. Automation in Construction, 85, 51–64. https://doi.org/10.1016/j.autcon.2017.10.009

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, Article 103705. https://doi.org/10.1016/j.autcon.2021.103705

Ekanayake, E., Shen, G. Q., Kumaraswamy, M. M., & Owusu, E. K. (2022). Identifying supply chain vulnerabilities in industrialized construction: An overview. International Journal of Construction Management, 22(8), 1464–1477. https://doi.org/10.1080/15623599.2020.1728487

Fiz, J. I., Martín, P. M., Cuesta, R., Subías, E., Codina, D., & Cartes, A. (2022). Examples and results of aerial photogrammetry in archeology with UAV: Geometric documentation, high resolution multispectral analysis, models and 3D printing. Drones, 6(3), Article 59. https://doi.org/10.3390/drones6030059

Gharbia, M., Chang-Richards, A., Lu, Y., Zhong, R. Y., & Li, H. (2020). Robotic technologies for on-site building construction: A systematic review. Journal of Building Engineering, 32, Article 101584. https://doi.org/https://doi.org/10.1016/j.jobe.2020.101584

Golparvar-Fard, M., Bohn, J., Teizer, J., Savarese, S., & Peña-Mora, F. (2011a). Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques. Automation in Construction, 20(8), 1143–1155. https://doi.org/10.1016/j.autcon.2011.04.016

Golparvar-Fard, M., Peña-Mora, F., & Savarese, S. (2011b). Integrated sequential as-built and as-planned representation with D 4 AR tools in support of decision-making tasks in the AEC/FM industry. Journal of Construction Engineering and Management, 137(12), 1099–1116. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000371

Golparvar-Fard, M., Pena-Mora, F., & Savarese, S. (2015). Automated progress monitoring using unordered daily construction photographs and IFC-based building information models. Journal of Computing in Civil Engineering, 29(1), Article 04014025. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000205

Gómez, C., & Green, D. R. (2017). Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping. Arabian Journal of Geosciences, 10(9), Article 202. https://doi.org/10.1007/s12517-017-2989-x

Gong, J., & Caldas, C. H. (2011). An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations. Automation in Construction, 20(8), 1211–1226. https://doi.org/10.1016/j.autcon.2011.05.005

Halder, S., & Afsari, K. (2023). Robots in inspection and monitoring of buildings and infrastructure: A systematic review. Applied Sciences, 13(4), Article 2304. https://doi.org/10.3390/app13042304

Hamledari, H., McCabe, B., Davari, S., Shahi, A., Rezazadeh Azar, E., & Flager, F. (2017). Evaluation of computer vision-and 4D BIM-based construction progress tracking on a UAV platform. In Proceedings of the 6th CSCE/ASCE/CRC International Construction Specialty Conference, Vancouver, Canada.

Han, K., Degol, J., & Golparvar-Fard, M. (2018). Geometry-and appearance-based reasoning of construction progress monitoring. Journal of Construction Engineering and Management, 144(2), Article 04017110. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001428

Han, K. K., & Golparvar-Fard, M. (2017). Potential of big visual data and building information modeling for construction performance analytics: An exploratory study. Automation in Construction, 73, 184–198. https://doi.org/10.1016/j.autcon.2016.11.004

Hu, H., Wen, Y., Chua, T.-S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, 652–687. https://doi.org/10.1109/ACCESS.2014.2332453

Huang, H., Lo, Y., Zhu, J., Ge, S., & Zhang, C. (2020). Semantic enhanced as-built BIM updating based on vSLAM and Image processing. In Construction Research Congress 2020: Computer Applications, Tempe, Arizona, USA. https://doi.org/10.1061/9780784482865.082

Jacob-Loyola, N., Muñoz-La Rivera, F., Herrera, R. F., & Atencio, E. (2021). Unmanned aerial vehicles (UAVs) for physical progress monitoring of construction. Sensors, 21(12), Article 4227. https://doi.org/10.3390/s21124227

Jalilian, E., Koles, S., & Kochatt, B. (2024). Managing scaleup: installation of a novel integrity monitoring system through challenging terrain. In 15th International Pipeline Conference, Calgary, Alberta, Canada. https://doi.org/10.1115/IPC2024-131772

Javadnejad, F., Simpson, C. H., Gillins, D. T., Claxton, T., & Olsen, M. J. (2017). An assessment of UAS-based photogrammetry for civil integrated management (CIM) modeling of pipes. In Pipelines 2017 (pp. 112–123), Phoenix, Arizona, USA. https://doi.org/10.1061/9780784480885.012

Kalasapudi, V. S., Turkan, Y., & Tang, P. (2014). Toward automated spatial change analysis of MEP components using 3D point clouds and as-designed BIM models. In 2014 2nd International Conference on 3D Vision, Tokyo, Japan. IEEE. https://doi.org/10.1109/3DV.2014.105

Karimi, S., & Iordanova, I. (2021). Integration of BIM and GIS for construction automation, a systematic literature review (SLR) combining bibliometric and qualitative analysis. Archives of Computational Methods in Engineering, 28, 4573–4594. https://doi.org/10.1007/s11831-021-09545-2

Khallaf, R., & Khallaf, M. (2021). Classification and analysis of deep learning applications in construction: A systematic literature review. Automation in Construction, 129, Article 103760. https://doi.org/10.1016/j.autcon.2021.103760

Khoshnevis, B. (2004). Automated construction by contour crafting – related robotics and information technologies. Automation in Construction, 13(1), 5–19. https://doi.org/10.1016/j.autcon.2003.08.012

Kim, C., Lee, J., Kym, C., & Son, H. (2012). Automated pipeline extraction for modeling from laserscanned data. In Proceedings of the 29th International Symposium of Automation and Robotics in Construction (ISARC 2012), Eindhoven, Netherlands. https://doi.org/10.22260/ISARC2012/0019

Kim, C., Son, H., & Kim, C. (2013). Automated construction progress measurement using a 4D building information model and 3D data. Automation in Construction, 31, 75–82. https://doi.org/10.1016/j.autcon.2012.11.041

Kim, Y., Nguyen, C. H. P., & Choi, Y. (2020). Automatic pipe and elbow recognition from three-dimensional point cloud model of industrial plant piping system using convolutional neural network-based primitive classification. Automation in Construction, 116, Article 103236. https://doi.org/10.1016/j.autcon.2020.103236

Kimoto, K., Endo, K., Iwashita, S., & Fujiwara, M. (2005). The application of PDA as mobile computing system on construction management. Automation in Construction, 14(4), 500–511. https://doi.org/10.1016/j.autcon.2004.09.003

Kolaei, A. Z., Hedayati, E., Khanzadi, M., & Amiri, G. G. (2022). Challenges and opportunities of augmented reality during the construction phase. Automation in Construction, 143, Article 104586. https://doi.org/10.1016/j.autcon.2022.104586

Kopsida, M., & Brilakis, I. (2020). Real-time volume-to-plane comparison for mixed reality–based progress monitoring. Journal of Computing in Civil Engineering, 34(4), Article 04020016. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000896

Korman, T. M., Fischer, M. A., & Tatum, C. (2003). Knowledge and reasoning for MEP coordination. Journal of Construction Engineering and Management, 129(6), 627–634. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:6(627)

Kropp, C., Koch, C., & König, M. (2018). Interior construction state recognition with 4D BIM registered image sequences. Automation in Construction, 86, 11–32. https://doi.org/10.1016/j.autcon.2017.10.027

Lee, Y.-C., Ma, J. W., & Leite, F. (2023). A parametric approach towards semi-automated 3D as-built modeling. ITcon, 28, 806–825. https://doi.org/10.36680/j.itcon.2023.041

Li, Y., & Liu, C. (2019). Applications of multirotor drone technologies in construction management. International Journal of Construction Management, 19(5), 401–412. https://doi.org/10.1080/15623599.2018.1452101

Lin, J. J., Han, K. K., & Golparvar-Fard, M. (2015). A framework for model-driven acquisition and analytics of visual data using UAVs for automated construction progress monitoring. In Proceedings of the 2015 International Workshop on Computing in Civil Engineering (pp. 156–164), Austin, Texas, USA. https://doi.org/10.1061/9780784479247.020

Lin, Z., Petzold, F., & Ma, Z. (2019). A real-time 4D augmented reality system for modular construction progress monitoring. In Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC 2019) (pp. 743–748), Banff, Canada. https://doi.org/10.22260/ISARC2019/0100

Lombard, N. F., Byland, W. W., Henry, G. J., Liga, M. V., & Price, V. N. (2020). Seeing from above, What’s below: How drones can be used in pipeline design and construction. In Pipelines 2020 (pp. 392–401), San Antonio, Texas, USA. https://doi.org/10.1061/9780784483213.044

Lopez, R., Love, P. E., Edwards, D. J., & Davis, P. R. (2010). Design error classification, causation, and prevention in construction engineering. Journal of Performance of Constructed Facilities, 24(4), 399–408. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000116

Love, P. E., Sing, C. P., Wang, X., Edwards, D. J., & Odeyinka, H. (2013). Probability distribution fitting of schedule overruns in construction projects. Journal of the Operational Research Society, 64(8), 1231–1247. https://doi.org/10.1057/jors.2013.29

Mantel, S. J., & Meredith, J. R. (2009). Project management: A managerial approach. John Wíley and Sons, Inc.

Mehrbod, S., Staub-French, S., Mahyar, N., & Tory, M. (2019). Beyond the clash: Investigating BIM-based building design coordination issue representation and resolution. ITcon, 24, 33–57.

Memarzadeh, M., Golparvar-Fard, M., & Niebles, J. C. (2013). Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors. Automation in Construction, 32, 24–37. https://doi.org/10.1016/j.autcon.2012.12.002

Miao, B., Giordano, L., & Chan, S. H. (2021). Long-distance renewable hydrogen transmission via cables and pipelines. International Journal of Hydrogen Energy, 46(36), 18699–18718. https://doi.org/https://doi.org/10.1016/j.ijhydene.2021.03.067

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of Internal Medicine, 151(4), 264–269. https://doi.org/10.1371/journal.pmed.1000097

Mondejar, M. E., Avtar, R., Diaz, H. L. B., Dubey, R. K., Esteban, J., Gómez-Morales, A., Hallam, B., Mbungu, N. T., Okolo, C. C., & Prasad, K. A. (2021). Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet. Science of The Total Environment, 794, Article 148539. https://doi.org/10.1016/j.scitotenv.2021.148539

Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106, 213–228. https://doi.org/10.1007/s11192-015-1765-5

Moselhi, O., Bardareh, H., & Zhu, Z. (2020). Automated data acquisition in construction with remote sensing technologies. Applied Sciences, 10(8), Article 2846. https://doi.org/10.3390/app10082846

Munoz-Morera, J., Maza, I., Fernandez-Aguera, C. J., Caballero, F., & Ollero, A. (2015). Assembly planning for the construction of structures with multiple UAS equipped with robotic arms. In 2015 International Conference on Unmanned Aircraft Systems (ICUAS), Denver, Colorado, USA. https://doi.org/10.1109/ICUAS.2015.7152396

Nahangi, M., & Haas, C. T. (2014). Automated 3D compliance checking in pipe spool fabrication. Advanced Engineering Informatics, 28(4), 360–369. https://doi.org/10.1016/j.aei.2014.04.001

Nahangi, M., Yeung, J., Haas, C. T., Walbridge, S., & West, J. (2015). Automated assembly discrepancy feedback using 3D imaging and forward kinematics. Automation in Construction, 56, 36–46. https://doi.org/10.1016/j.autcon.2015.04.005

Navon, R., & Sacks, R. (2007). Assessing research issues in automated project performance control (APPC). Automation in Construction, 16(4), 474–484. https://doi.org/10.1016/j.autcon.2006.08.001

Ng, K. K., Chen, C.-H., Lee, C. K., Jiao, J. R., & Yang, Z.-X. (2021). A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives. Advanced Engineering Informatics, 47, Article 101246. https://doi.org/10.1016/j.aei.2021.101246

Norton Jr., J. W., King, T. W., Kuhns, T., Berg, G., Trifone, L., Boshart, S., Aliva, A., St. John, B., Wang, W., & Wolf, C. (2021). Piloting innovative approaches to pipe structural renewal and monitoring methods. In Pipelines 2021 (pp. 426–435). https://doi.org/10.1061/9780784483626.047

Oates, B. J., & Capper, G. (2009). Using systematic reviews and evidence-based software engineering with masters students. In 13th International Conference on Evaluation and Assessment in Software Engineering (EASE), Durham University, UK. https://doi.org/10.14236/ewic/EASE2009.10

Omar, T., & Nehdi, M. L. (2016). Data acquisition technologies for construction progress tracking. Automation in Construction, 70, 143–155. https://doi.org/10.1016/j.autcon.2016.06.016

Oyewola, D. O., & Dada, E. G. (2022). Exploring machine learning: A scientometrics approach using bibliometrix and VOSviewer. SN Applied Sciences, 4(5), Article 143. https://doi.org/10.1007/s42452-022-05027-7

Parpulova, N., & Zinoviev, V. (2021). Cybersecurity in the transportation of energy resources. Godishnik na UNSS, 2, 131–146. https://doi.org/10.37075/YB.2021.2.10

Perez-Perez, Y., Golparvar-Fard, M., & El-Rayes, K. (2021). Segmentation of point clouds via joint semantic and geometric features for 3D modeling of the built environment. Automation in Construction, 125, Article 103584. https://doi.org/10.1016/j.autcon.2021.103584

Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), Article 12. https://doi.org/10.3390/publications9010012

Prasad, A. (2024). Real-time physical and financial monitoring of cross-country natural gas pipeline infrastructure projects: A digital way. In S. Adeyinka-Ojo (Ed.), Digital project management – Strategic theory and practice. IntechOpen. https://doi.org/10.5772/intechopen.1007164

Qureshi, A. H., Alaloul, W. S., Manzoor, B., Musarat, M. A., Saad, S., & Ammad, S. (2020). Implications of machine learning integrated technologies for construction progress detection under industry 4.0 (IR 4.0). In 2020 Second International Sustainability and Resilience Conference: Technology and Innovation in Building Designs (51154), Sakheer, Bahrain. IEEE. https://doi.org/10.1109/IEEECONF51154.2020.9319974

Rachman, A., Zhang, T., & Ratnayake, R. M. C. (2021). Applications of machine learning in pipeline integrity management: A state-of-the-art review. International Journal of Pressure Vessels and Piping, 193, Article 104471. https://doi.org/https://doi.org/10.1016/j.ijpvp.2021.104471

Rahimian, F. P., Seyedzadeh, S., Oliver, S., Rodriguez, S., & Dawood, N. (2020). On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning. Automation in Construction, 110, Article 103012. https://doi.org/10.1016/j.autcon.2019.103012

Rakha, T., & Gorodetsky, A. (2018). Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones. Automation in Construction, 93, 252–264. https://doi.org/10.1016/j.autcon.2018.05.002

Rao, A. S., Radanovic, M., Liu, Y., Hu, S., Fang, Y., Khoshelham, K., Palaniswami, M., & Ngo, T. (2022). Real-time monitoring of construction sites: Sensors, methods, and applications. Automation in Construction, 136, Article 104099. https://doi.org/10.1016/j.autcon.2021.104099

Rasoolinejad, M., Dönmez, A. A., & Bažant, Z. P. (2020). Fracture and size effect suppression by mesh reinforcement of concrete and justification of empirical shrinkage and temperature reinforcement in design codes. Journal of Engineering Mechanics, 146(10), Article 04020120. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001850

Raykar, P., & Ghadge, A. (2016). Analyzing the critical factors influencing the time overrun and cost overrun in construction project. International Journal of Engineering Research, 5(1), 21–25.

Regona, M., Yigitcanlar, T., Xia, B., & Li, R. Y. M. (2022). Opportunities and adoption challenges of AI in the construction industry: a PRISMA review. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), Article 45. https://doi.org/10.3390/joitmc8010045

Reja, V. K., & Varghese, K. (2019). Impact of 5G technology on IoT applications in construction project management. Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC 2019) (pp. 209–217), Banff, Canada. https://doi.org/10.22260/ISARC2019/0029

Reja, V. K., Varghese, K., & Ha, Q. P. (2022). Computer vision-based construction progress monitoring. Automation in Construction, 138, Article 104245. https://doi.org/10.1016/j.autcon.2022.104245

Riley, D. R., Varadan, P., James, J. S., & Thomas, H. R. (2005). Benefit-cost metrics for design coordination of mechanical, electrical, and plumbing systems in multistory buildings. Journal of Construction Engineering and Management, 131(8), 877–889. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:8(877)

Sami Ur Rehman, M., Shafiq, M. T., & Ullah, F. (2022). Automated computer vision-based construction progress monitoring: A systematic review. Buildings, 12(7), Article 1037. https://doi.org/10.3390/buildings12071037

Sarkis-Onofre, R., Catalá-López, F., Aromataris, E., & Lockwood, C. (2021). How to properly use the PRISMA statement. Systematic Reviews, 10(1), Article 117. https://doi.org/10.1186/s13643-021-01671-z

Scott, S., & Assadi, S. (1999). A survey of the site records kept by construction supervisors. Construction Management & Economics, 17(3), 375–382. https://doi.org/10.1080/014461999371574

Shah, R. K. (2016). An exploration of causes for delay and cost overrun in construction projects: A case study of Australia, Malaysia & Ghana. Journal of Advanced College of Engineering and Management, 2(1), 41–55. https://doi.org/10.3126/jacem.v2i0.16097

Shahi, A., Cardona, J. M., Haas, C. T., West, J. S., & Caldwell, G. L. (2012). Activity-based data fusion for automated progress tracking of construction projects. In Proceedings of Construction Research Congress 2012: Construction Challenges in a Flat World. The American Society of Civil Engineers. https://doi.org/10.1061/9780784412329.085

Shahi, A., West, J. S., & Haas, C. T. (2013). Onsite 3D marking for construction activity tracking. Automation in Construction, 30, 136–143. https://doi.org/10.1016/j.autcon.2012.11.027

Shahinmoghadam, M., & Motamedi, A. (2019). Review of BIM-centred IoT deployment–state of the art, opportunities, and challenges. Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC 2019) (pp. 1268–1275), Banff, Canada. https://doi.org/10.22260/ISARC2019/0170

Shen, X., Lu, M., Wei, B., & Chen, W. (2011). Field evaluation of ZigBee-based wireless sensor networks for automated resource tracking on construction sites. In Annual Conference of the Canadian Society for Civil Engineering 2011 (CSCE 2011), Ottawa, Ontario, Canada.

Son, H., & Kim, C. (2016). Automatic segmentation and 3D modeling of pipelines into constituent parts from laser-scan data of the built environment. Automation in Construction, 68, 203–211. https://doi.org/10.1016/j.autcon.2016.05.010

Son, H., Kim, C., & Kim, C. (2015). Fully automated as-built 3D pipeline extraction method from laser-scanned data based on curvature computation. Journal of Computing in Civil Engineering, 29(4), Article B4014003. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000401

Taneja, S., Akinci, B., Garrett, J. H., Soibelman, L., Ergen, E., Pradhan, A., Tang, P., Berges, M., Atasoy, G., & Liu, X. (2011). Sensing and field data capture for construction and facility operations. Journal of Construction Engineering and Management, 137(10), 870–881. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000332

Tang, S., Shelden, D. R., Eastman, C. M., Pishdad-Bozorgi, P., & Gao, X. (2019). A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends. Automation in Construction, 101, 127–139. https://doi.org/10.1016/j.autcon.2019.01.020

Teo, Y. H., Yap, J. H., An, H., Yu, S. C. M., Zhang, L., Chang, J., & Cheong, K. H. (2022). Enhancing the MEP coordination process with BIM technology and management strategies. Sensors, 22(13), Article 4936. https://doi.org/10.3390/s22134936

Turkan, Y. (2012). Automated construction progress tracking using 3D sensing technologies [PhD thesis]. University of Waterloo.

Turkan, Y., Bosche, F., Haas, C. T., & Haas, R. (2012). Automated progress tracking using 4D schedule and 3D sensing technologies. Automation in Construction, 22, 414–421. https://doi.org/10.1016/j.autcon.2011.10.003

Turkan, Y., Bosché, F., Haas, C. T., & Haas, R. (2013). Tracking secondary and temporary concrete construction objects using 3D imaging technologies. In Proceedings of Computing in Civil Engineering (2013) (pp. 749–756). The American Society of Civil Engineers. https://doi.org/10.1061/9780784413029.094

Tyagi, A. K., Fernandez, T. F., Mishra, S., & Kumari, S. (2020). Intelligent automation systems at the core of Industry 4.0. In A. Abraham, V. Piuri, N. Gandhi, P. Siarry, A. Kaklauskas, & A. Madureira (Eds.), Intelligent Systems Design and Applications (ISDA 2020). Vol. 1351: Advances in intelligent systems and computing. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_1

Valderrama-Zurián, J.-C., Aguilar-Moya, R., Melero-Fuentes, D., & Aleixandre-Benavent, R. (2015). A systematic analysis of duplicate records in Scopus. Journal of Informetrics, 9(3), 570–576. https://doi.org/10.1016/j.joi.2015.05.002

Vasenev, A., Hartmann, T., & Doree, A. G. (2014). A distributed data collection and management framework for tracking construction operations. Advanced Engineering Informatics, 28(2), 127–137. https://doi.org/10.1016/j.aei.2014.01.003

Wang, Q., & Kim, M.-K. (2019). Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018. Advanced Engineering Informatics, 39, 306–319. https://doi.org/10.1016/j.aei.2019.02.007

Wang, J., Wang, X., Shou, W., Chong, H.-Y., & Guo, J. (2016). Building information modeling-based integration of MEP layout designs and constructability. Automation in Construction, 61, 134–146. https://doi.org/10.1016/j.autcon.2015.10.003

Wang, Q., Tan, Y., & Mei, Z. (2020). Computational methods of acquisition and processing of 3D point cloud data for construction applications. Archives of Computational Methods in Engineering, 27, 479–499. https://doi.org/10.1007/s11831-019-09320-4

Wang, B., Yin, C., Luo, H., Cheng, J. C., & Wang, Q. (2021). Fully automated generation of parametric BIM for MEP scenes based on terrestrial laser scanning data. Automation in Construction, 125, Article 103615. https://doi.org/10.1016/j.autcon.2021.103615

Wang, B., Wang, Q., Cheng, J. C., & Yin, C. (2022). Object verification based on deep learning point feature comparison for scan-to-BIM. Automation in Construction, 142, Article 104515. https://doi.org/10.1016/j.autcon.2022.104515

Xu, Q., Song, K., Liu, H., Zheng, W., Dong, S., & Zhang, L. (2024). Remote real-time monitoring and early warning of pipeline status under landslide conditions based on stress–strain analysis. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 10(1), Article 04023060. https://doi.org/10.1061/AJRUA6.RUENG-1078

Yang, J., Arif, O., Vela, P. A., Teizer, J., & Shi, Z. (2010). Tracking multiple workers on construction sites using video cameras. Advanced Engineering Informatics, 24(4), 428–434. https://doi.org/10.1016/j.aei.2010.06.008

Yin, X., Liu, H., Chen, Y., & Al-Hussein, M. (2019). Building information modelling for off-site construction: Review and future directions. Automation in Construction, 101, 72–91. https://doi.org/10.1016/j.autcon.2019.01.010

Yin, C., Wang, B., Gan, V. J., Wang, M., & Cheng, J. C. (2021). Automated semantic segmentation of industrial point clouds using ResPointNet++. Automation in Construction, 130, Article 103874. https://doi.org/10.1016/j.autcon.2021.103874

Zhang, J., Luo, H., & Xu, J. (2022). Towards fully BIM-enabled building automation and robotics: A perspective of lifecycle information flow. Computers in Industry, 135, Article 103570. https://doi.org/10.1016/j.compind.2021.103570

Zhao, X., Jin, Y., Selvaraj, N. M., Ilyas, M., & Cheah, C. C. (2023). Platform-independent visual installation progress monitoring for construction automation. Automation in Construction, 154, Article 104996. https://doi.org/10.1016/j.autcon.2023.104996

Zhong, B., Wu, H., Ding, L., Love, P. E. D., Li, H., Luo, H., & Jiao, L. (2019a). Mapping computer vision research in construction: Developments, knowledge gaps and implications for research. Automation in construction, 107, Article 102919. https://doi.org/https://doi.org/10.1016/j.autcon.2019.102919

Zhong, B., Wu, H., Li, H., Sepasgozar, S., Luo, H., & He, L. (2019b). A scientometric analysis and critical review of construction related ontology research. Automation in Construction, 101, 17–31. https://doi.org/https://doi.org/10.1016/j.autcon.2018.12.013

View article in other formats

CrossMark check

CrossMark logo

Published

2026-05-11

Issue

Section

Articles

How to Cite

Khan, M. H. F., Alaloul, W. S., Musarat, M. A., & Qureshi, A. H. (2026). Automated progress monitoring in pipeline construction: a systematic review. Journal of Civil Engineering and Management, 32(4), 489–512. https://doi.org/10.3846/jcem.2025.24800

Share