International conference on construction engineering and project management (국제학술발표논문집)

  • 2024.07a
  • /
  • Pages.1073-1080
  • /
  • 2024
  • /
  • 2508-9048(eISSN)
Korea Institute of Construction Engineering and Management (한국건설관리학회)
권호 표지
DOI QR코드

DOI QR Code

Real-time Construction Progress Monitoring Framework leveraging Semantic SLAM

  • Wei Yi HSU (Department of Civil Engineering, National Taiwan University) ;
  • Aritra PAL (Department of Civil Engineering, National Taiwan University) ;
  • Jacob J. LIN (Department of Civil Engineering, National Taiwan University) ;
  • Shang-Hsien HSIEH (Department of Civil Engineering, National Taiwan University)
  • Published : 2024.07.29
Download PDF
⟨ Previous Next ⟩

Abstract

The imperative for real-time automatic construction progress monitoring (ACPM) to avert project delays is widely acknowledged in construction project management. Current ACPM methodologies, however, face a challenge as they rely on collecting data from construction sites and processing it offline for progress analysis. This delayed approach poses a risk of late identification of critical construction issues, potentially leading to rework and subsequent project delays. This research introduces a real-time construction progress monitoring framework that integrates cutting-edge semantic Simultaneous Localization and Mapping (SLAM) techniques. The innovation lies in the framework's ability to promptly identify structural components during site inspections conducted through a robotic system. Incorporating deep learning models, specifically those employing semantic segmentation, enables the system to swiftly acquire and process real-time data, identifying specific structural components and their respective locations. Furthermore, by seamlessly integrating with Building Information Modeling (BIM), the system can effectively evaluate and compare the progress status of each structural component. This holistic approach offers an efficient and practical real-time progress monitoring solution for construction projects, ensuring timely issue identification and mitigating the risk of project delays.

Keywords

Acknowledgement

The support of the National Science and Technology Council (NSTC), Taiwan, is gratefully acknowledged. This work was supported by the NSTC under Grant No. 112-2221-E-002 -127 -MY3.

References

  1. A. Pal, J. J. Lin, S.-H. Hsieh, and M. Golparvar-Fard, "Automated vision-based construction progress monitoring in built environment through digital twin," Dev. Built Environ., vol. 16, p. 100247, 2023, doi: 10.1016/j.dibe.2023.100247.
  2. J. Yang, A. Wilde, K. Menzel, M. Z. Sheikh, and B. Kuznetsov, "Computer Vision for Construction Progress Monitoring: A Real-Time Object Detection Approach," IFIP Adv. Inf. Commun. Technol., vol. 688 AICT, pp. 660-672, 2023, doi: 10.1007/978-3-031-42622-3_47.
  3. Z. Pucko, N. Suman, and D. Rebolj, "Automated continuous construction progress monitoring using multiple workplace real time 3D scans," Adv. Eng. Informatics, vol. 38, no. October 2017, pp. 27-40, 2018, doi: 10.1016/j.aei.2018.06.001.
  4. A. Pal and S. H. Hsieh, "Deep-learning-based visual data analytics for smart construction management," Autom. Constr., vol. 131, no. August, p. 103892, 2021, doi: 10.1016/j.autcon.2021.103892.
  5. S. Raut, S. S. Pimplikar, and K. Sawant, "Effect of Project Cost and Time Monitoring on Progress of Construction Projct," Int. J. Res. Eng. Technol., vol. 02, no. 12, pp. 796-800, 2013, doi: 10.15623/ijret.2013.0212132.
  6. Z. Liu, D. Kim, S. Lee, L. Zhou, X. An, and M. Liu, "Near Real-Time 3D Reconstruction and Quality 3D Point Cloud for Time-Critical Construction Monitoring," Buildings, vol. 13, no. 2, pp. 1-19, 2023, doi: 10.3390/buildings13020464.
  7. S. Halder, K. Afsari, J. Serdakowski, S. Devito, M. Ensafi, and W. Thabet, "Real-Time and Remote Construction Progress Monitoring with a Quadruped Robot Using Augmented Reality," Buildings, vol. 12, no. 11, p. 2027, 2022, doi: 10.3390/buildings12112027.
  8. Z. Jiang, X. Shen, M. H. Ibrahimkhil, K. Barati, and J. Linke, "Scan-Vs-Bim for Real-Time Progress Monitoring of Bridge Construction Project," ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., vol. 10, no. 4/W3-2022, pp. 97-104, 2022, doi: 10.5194/isprs-annals-X-4-W3-2022-97-2022.
  9. P. Kim, J. Chen, J. Kim, and Y. K. Cho, "SLAM-driven intelligent autonomous mobile robot navigation for construction applications," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10863 LNCS, no. January, pp. 254-269, 2018, doi: 10.1007/978-3-319-91635-4_14.
  10. L. Marcy and I. Iordanova, "Slam and Beacon Data for Automation of Indoor Construction Progress Tracking," IOP Conf. Ser. Mater. Sci. Eng., vol. 1218, no. 1, p. 012009, 2022, doi: 10.1088/1757-899x/1218/1/012009.
  11. K. Asadi et al., "Building an integrated mobile robotic system for real-time applications in construction," ISARC 2018 - 35th Int. Symp. Autom. Robot. Constr. Int. AEC/FM Hackathon Futur. Build. Things, no. Isarc, 2018, doi: 10.22260/isarc2018/0063.
  12. C. Feng, N. Fredricks, and V. R. Kamat, "Human-robot integration for pose estimation and semiautonomous navigation on unstructured construction sites," ISARC 2013 - 30th Int. Symp. Autom. Robot. Constr. Mining, Held Conjunction with 23rd World Min. Congr., vol. 48109, pp. 1317-1325, 2013, doi: 10.22260/isarc2013/0148.
  13. M. Labbe and F. Michaud, "RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation," Journal of Field Robotics, vol. 36, p. 416446, 2019, doi: 10.1002/rob.21831.
  14. A. Pal, J. J. Lin, S.-H. Hsieh, and M. Golparvar-Fard, "Activity-level construction progress monitoring through semantic segmentation of 3D-informed orthographic images," Autom. Constr., vol. 157, p. 105157, 2024, doi: 10.1016/j.autcon.2023.105157.