Journal of the Korea institute for structural maintenance and inspection (한국구조물진단유지관리공학회 논문집)

Korea Institute for Structural Maintenance Inspection (한국구조물진단유지관리공학회)
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Development of Thickness Measurement Method From Concrete Slab Using Ground Penetrating Radar

GPR 기반 콘크리트 슬래브 시공 두께 검측 기법 개발

  • Received : 2022.03.13
  • Accepted : 2022.05.16
  • Published : 2022.06.30
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Abstract

In this paper, we proposed a thickness measurement method of concrete slab using GPR, and the verification of the suggested algorithm was carried out through real-scale experiment. The thickness measurement algorithm developed in this study is to set the relative dielectric constant based on the unique shape of parabola, and time series data can be converted to thickness information. GPR scanning were conducted in four types of slab structure for noise reduction, including finishing mortar, autoclaved lightweight concrete, and noise damping layer. The thickness obtained by GPR was compared with Boring data, and the average error was 1.95 mm. In order to investigate the effect of finishing materials on the slab, additional three types of finishing materials were placed, and the following average error was 1.70 mm. In addition, sampling interval from device, the effect of radius on the shape of parabola, and Boring error were comprehensively discussed. Based on the experimental verification, GPR scanning and the suggested algorithm have a great potential that they can be applied to the thickness measurement of finishing mortar from concrete slab with high accuracy.

국내의 공동주택 보급률 증가에 따라 층간소음으로 인한 문제가 증가하고 있다. 이를 예방하기 위하여 바닥 충격음 차단 구조에 대한 수요가 높아지고 있으며 해당 구조에 대한 성능 인증이 이뤄지고 있지만 소음 차단 성능이 현장에서는 재현되지 않는다는 문제점이 있다. 해당 구조가 제 성능을 발휘하기 위해서는 일정 두께 이상의 마감 모르타르 타설이 필요하며, 해당 구조의 시공 적정성 판정을 위하여 GPR을 이용한 두께 측정 실험을 진행하였다. 본 연구에서 개발한 두께 측정 알고리즘은 측정된 데이터를 기반으로 상대유전율을 설정할 수 있어 정확한 두께 값을 측정할 수 있다. 네 종류의 바닥 충격음 차단 인증 구조에서 GPR 두께 측정 실험을 진행하였으며, GPR 데이터와 천공 측정 데이터 간 평균 오차는 1.95mm로 나타났다. 또한 마감재 유무가 측정값에 미치는 영향을 조사하기 위하여 총 3가지 종류의 마감재를 배치하고 실험을 진행으며, 평균 오차는 1.70mm로 나타났다. 추가적으로 장비의 샘플링 오차, 개발 알고리즘 변수, 천공 오차등을 종합적으로 고려하였을 때, GPR 계측 및 제안 알고리즘은 매우 높은 정확도로 슬래브 마감 모르타르의 두께 측정에 적용할 수 있음을 확인하였다.

Keywords

Acknowledgement

이 논문은 국토교통과학기술진흥원에서 지원 하는 국토교통기술촉진연구사업(No. 21CTAP-C164349-01) 지원에 의해 수행되었습니다. 이에 감사드립니다.

References

  1. Lee, J. W. (2013), The Actual condition and current status of noise between floors in apartment houses. Journal of The Korean Society for Noise and Vibration Engineering, 23(3), 4-8.
  2. Kim, G. H. (2013), Reduction of noise between floors by improving the ceiling structure. Journal of Journal of The Korean Society for Noise and Vibration Engineering, 23(3), 26-29.
  3. Ministry of Land, Infrastructure and Transport. Notification, No. 2018-585.
  4. Ministry of Land, Infrastructure and Transport. Notification, No. 2020-212.
  5. Yu, H., Joung, I. S., Lim, B., and Nam, M. J. (2021), Introduction to Useful Attributes for the Interpretation of GPR Data and an Analysis on Past Cases. Geophysics and Geophysical Exploration, 24(3), 113-130. https://doi.org/10.7582/GGE.2021.24.3.113
  6. Fauchard, C., Derobert, X., Cariou, J., and Cote, P. (2003), GPR performances for thickness calibration on road test sites. NDT & E International, 36(2), 67-75. https://doi.org/10.1016/S0963-8695(02)00090-7
  7. Al-Qadi, I. L., and Lahouar, S. (2005), Measuring layer thicknesses with GPR-Theory to practice. Construction and Building Materials, 19(10), 763-772. https://doi.org/10.1016/j.conbuildmat.2005.06.005
  8. Le Bastard, C., Baltazart, V., Wang, Y., and Saillard, J. (2007), Thin-pavement thickness estimation using GPR with high-resolution and superresolution methods. IEEE Transactions on Geoscience and Remote Sensing, 45(8), 2511-2519. https://doi.org/10.1109/TGRS.2007.900982
  9. Liu, H., and Sato, M. (2014), In situ measurement of pavement thickness and dielectric permittivity by GPR using an antenna array. NDT & E International, 64, 65-71. https://doi.org/10.1016/j.ndteint.2014.03.001
  10. ASTM D4748-10, "Standard Test Method for Determining the Thickness of Bound Pavement Layers Using Short-Pulse Radar", 2020.
  11. Rhee, J., Shim, J., Lee, S., and Lee, K. H. (2020), A consideration on the electromagnetic properties of road pavement using ground penetrating radar (GPR). KSCE Journal of Civil and Environmental Engineering Research, 40(3), 285-294.
  12. Yoon, J. S., Baek, J., Choi, Y. W., Choi, H., and Lee, C. M. (2016), Signal pattern analysis of ground penetrating radar for detecting road cavities. International Journal of Highway Engineering, 18(6), 61-67. https://doi.org/10.7855/IJHE.2016.18.6.061
  13. Park, S., Kim, J., Kim, W., Kim, H., and Park, S. (2019), A study on the prediction of buried rebar thickness using CNN based on GPR heatmap image data. Journal of the Korea Institute for Structural Maintenance and Inspection, 23(7), 66-71. https://doi.org/10.11112/JKSMI.2019.23.7.66
  14. Lee, H. H. (2016), Image processing of GPR detection data. Journal of the Korea Institute for Structural Aaintenance and Inspection, 20(4), 104-110. https://doi.org/10.11112/jksmi.2016.20.4.104
  15. Lee, H. H. (2016), Cavity Detection of Chamber by GPR. Journal of the Korea Institute for Structural Maintenance and Inspection, 20(2), 86-93. https://doi.org/10.11112/JKSMI.2016.20.2.086
  16. Van Der Kruk, J., Arcone, S. A., and Liu, L. (2007), Fundamental and higher mode inversion of dispersed GPR waves propagating in an ice layer. IEEE Transactions on Geoscience and Remote Sensing, 45(8), 2483-2491. https://doi.org/10.1109/TGRS.2007.900685
  17. Qin, H., Tang, Y., Wang, Z., Xie, X., and Zhang, D. (2021), Shield tunnel grouting layer estimation using sliding window probabilistic inversion of GPR data. Tunnelling and Underground Space Technology, 112, 103913. https://doi.org/10.1016/j.tust.2021.103913
  18. Hu, J., Vennapusa, P. K., White, D. J., and Beresnev, I. (2016), Pavement thickness and stabilised foundation layer assessment using ground-coupled GPR. Nondestructive Testing and Evaluation, 31(3), 267-287. https://doi.org/10.1080/10589759.2015.1111890
  19. EVANS, R. (2008), A review of pavement assessment using ground penetrating radar (GPR) in 12th International Conference on Ground Penetrating Radar, June 16-19, 2008. Birmingham, UK.
  20. De Coster, A., Van der Wielen, A., Gregoire, C., and Lambot, S. (2018), Evaluation of pavement layer thicknesses using GPR: A comparison between full-wave inversion and the straight-ray method. Construction and Building Materials, 168, 91-104. https://doi.org/10.1016/j.conbuildmat.2018.02.100
  21. Grasmueck, M., Weger, R., and Horstmeyer, H. (2005), Full resolution 3D GPR imaging. Geophysics, 70(1), K12-K19. https://doi.org/10.1190/1.1852780
  22. KS L 5220 (2018), Dry ready mixed cement mortar, Korean Agency for Technology and Standards.
  23. KS F 4039 (2019), Foamed concrete for cast-in-site, Korean Agency for Technology and Standards.