Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Geometric and structural assessment and reverse engineering of a steel-framed building using 3D laser scanning

  • Arum Jang (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Sanggi Jeong (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Hunhee Cho (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Donghwi Jung (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Young K. Ju (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Ji-sang Kim (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Donghyuk Jung (School of Civil, Environmental and Architectural Engineering, Korea University)
  • Received : 2023.12.15
  • Accepted : 2024.03.13
  • Published : 2024.05.25
⟨ Previous Next ⟩

Abstract

In the construction industry, there has been a surge in the implementation of high-tech equipment in recent years. Various technologies are being considered as potential solutions for future construction projects. Building information modeling (BIM), which utilizes advanced equipment, is a promising solution among these technologies. The need for safety inspection has also increased with the aging structures. Nevertheless, traditional safety inspection technology falls short of meeting this demand as it heavily relies on the subjective opinions of workers. This inadequacy highlights the need for advancements in existing maintenance technology. Research on building safety inspection using 3D laser scanners has notably increased. Laser scanners that use light detection and ranging (LiDAR) can quickly and accurately acquire producing information, which can be realized through reverse engineering by modeling point cloud data. This study introduces an innovative evaluation system for building safety using a 3D laser scanner. The system was used to assess the safety of an existing three-story building by implementing a reverse engineering technique. The 3D digital data are obtained from the scanner to detect defects and deflections in and outside the building and to create an as-built BIM. Subsequently, the as-built structural model of the building was generated using the reverse engineering approach and used for structural analysis. The acquired information, including deformations and dimensions, is compared with the expected values to evaluate the effectiveness of the proposed technique.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1A5A1032433).

References

  1. Bae, J., Jang, A., Park, M.J., Lee, J. and Ju, Y.K. (2022), "Assessment of concrete macrocrack depth using infrared thermography", Steel Compos. Struct., 43(4), 501-509. https://doi.org/10.12989/scs.2022.43.4.501.
  2. Bae, J., Lee, J., Jang, A., Ju, Y.K. and Park, M.J. (2022), "SMART SKY Eye system for preliminary structural safety assessment of buildings using unmanned aerial vehicles", Sensors, 22(7), 2762. https://doi.org/10.3390/s22072762.
  3. Cheok, G.S., Stone, W.C., Lipman, R.R. and Witzgall, C. (2000), "Ladars for construction assessment and update", Automat. Constr., 9(5-6), 463-477. https://doi.org/10.1016/S0926-5805(00)00058-3.
  4. Clarke, T.A. and Robson, S. (1993), "Building a digital close range three dimensional measuring system for less than £ 5000", Photogramm. Rec., 14(82), 675-680. https://doi.org/10.1111/j.1477-9730.1993.tb00777.x.
  5. Construction, M.H. (2014), "The business value of BIM for construction in major global markets: How contractors around the world are driving innovation with building information modeling", Smart Market Report; Dodge Data & Analytics, Bedford, MA, USA.
  6. Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C.M. and Dario, P. (2020), "Visual-based defect detection and classification approaches for industrial applications-A survey", Sensors, 20(5), 1459. https://doi.org/10.3390/s20051459.
  7. Dang, L.M., Wang, H., Li, Y., Nguyen, L.Q., Nguyen, T.N., Song, H.K. and Moon, H. (2022), "Deep learning-based masonry crack segmentation and real-life crack length measurement", Constr. Build. Mater., 359, 129438. https://doi.org/10.1016/j.conbuildmat.2022.129438.
  8. Duarte, D., Nex, F., Kerle, N. and Vosselman, G. (2017), "Towards a more efficient detection of earthquake induced facade damages using oblique UAV imagery", Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci., 42, 93-100. https://doi.org/10.5194/isprs-archives-XLII-2-W6-93-2017.
  9. Fraser, C.S. (1992), "Photogrammetric measurement to one part in a million", Photogramm. Eng. Remote Sens., 58, 305-310.
  10. Gordon, S.J. and Lichti, D.D. (2007), "Modeling terrestrial laser scanner data for precise structural deformation measurement", J. Survey. Eng., 133(2), 72-80. https://doi.org/10.1061/(ASCE)0733-9453(2007)133:2(72).
  11. Ham, N. and Lee, S.H. (2018), "Empirical study on structural safety diagnosis of large-scale civil infrastructure using laser scanning and BIM", Sustainab., 10(11), 4024. https://doi.org/10.3390/su10114024.
  12. Jahanshahi, M.R. and Masri, S.F. (2013), "A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation", Smart Mater. Struct., 22(3), 035019. https://doi.org/10.1088/0964-1726/22/3/035019.
  13. Jang, A., Jeong, S., Park, M.J. and Ju, Y.K. (2023), "Structural evaluation by reverse engineering with 3D laser scanner", ce/papers, 6(5), 308-314. https://doi.org/10.1002/cepa.1989.
  14. Jang, A., Ju, Y.K. and Park, M.J. (2022), "Structural stability evaluation of existing buildings by reverse engineering with 3D laser scanner", Remote Sens., 14(10), 2325. https://doi.org/10.3390/rs14102325.
  15. Jang, A., Kim, J., Park, M.J., Ju, Y.K. and Kim, S.J. (2022), "Analysis of machine learning for detect concrete crack depths using infrared thermography technique", IABSE Symposium Prague 2022: Challenges for Existing and Oncoming Structures, Prague, Czech Republic, May.
  16. Kim, S.Y., Kwon, D.Y., Jang, A., Ju, Y.K., Lee, J.S. and Hong, S. (2023), "A review of UAV integration in forensic civil engineering: From sensor technologies to geotechnical, structural and water infrastructure applications", Measure., 224, 113886. https://doi.org/10.1016/j.measurement.2023.113886.
  17. KISTEC (2017), Detailed Guideline for Safety Inspection and Precision Safety Inspection, Korea Infrastructure Safety Corporation, Jinju, Korea.
  18. Mader, D., Blaskow, R., Westfeld, P. and Weller, C. (2016), "Potential of UAV-based laser scanner and multispectral camera data in building inspection", Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci., 41, 1135-1142. https://doi.org/10.5194/isprs-archives-XLI-B1-1135-2016.
  19. Nex, F., Duarte, D., Steenbeek, A. and Kerle, N. (2019), "Towards real-time building damage mapping with low-cost UAV solutions", Remote Sens., 11(3), 287. https://doi.org/10.3390/rs11030287.
  20. Park, M.J. and Ju, Y.K. (2022), "Finite element model for the steel-polymer composite floor filled with phase-change amorphous polymers at elevated temperatures", Constr. Build. Mater., 319, 126059. https://doi.org/10.1016/j.conbuildmat.2021.126059.
  21. Park, M.J., Kim, J., Jeong, S., Jang, A., Bae, J. and Ju, Y.K. (2022), "Machine learning-based concrete crack depth prediction using thermal images taken under daylight conditions", Remote Sens., 14(9), 2151. https://doi.org/10.3390/rs14092151.
  22. Prak, R., Park, J.H., Jeong, S., Jang, A., Park, M.J., Kang, T.H.K. and Ju, Y.K. (2023), "Determination and evaluation of dynamic properties for structures using UAV-based video and computer vision system", Comput. Concrete, 31(5), 457-468. https://doi.org/10.12989/cac.2023.31.5.457.
  23. Puente, I., Lindenbergh, R., Van Natijne, A., Esposito, R. and Schipper, R. (2018), "Monitoring of progressive damage in buildings using laser scan data", Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci., 42, 923-929. https://doi.org/10.5194/isprs-archives-XLII-2-923-2018.
  24. Raja, B.N.K., Miramini, S., Duffield, C., Sofi, M., Mendis, P. and Zhang, L. (2020), "The influence of ambient environmental conditions in detecting bridge concrete deck delamination using infrared thermography (IRT)", Struct. Control Health Monit., 27(4), e2506. https://doi.org/10.1002/stc.2506.
  25. Sankarasrinivasan, S., Balasubramanian, E., Karthik, K., Chandrasekar, U. and Gupta, R. (2015), "Health monitoring of civil structures with integrated UAV and image processing system", Procedia Comput. Sci., 54, 508-515. https://doi.org/10.1016/j.procs.2015.06.058.
  26. Sivasuriyan, A., Vijayan, D.S., Gorski, W., Wodzynski, L., Vaverkova, M.D. and Koda, E. (2021), "Practical implementation of structural health monitoring in multi-story buildings", Build., 11(6), 263. https://doi.org/10.3390/buildings11060263.
  27. Sony, S., Laventure, S. and Sadhu, A. (2019), "A literature review of next-generation smart sensing technology in structural health monitoring", Struct. Control Health Monit., 26(3), e2321. https://doi.org/10.1002/stc.2321.
  28. Standard A.K.D. (2019), 41 (KDS 41) Korean Design Standard 41, Architectural Institute of Korea, Seoul, Korea.