1887
Volume 66, Issue 4
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Adverse geologies are often encountered during tunnel construction, which could seriously endanger the construction. To ensure the safety, it is essential to detect adverse geologies and their water‐bearing situation ahead the tunnel face. Ground‐penetrating radar is a suitable instrument, but the accurate interpretation of its detection results is difficult. In this paper, at first, an improved back projection imaging algorithm is proposed, which can make reflection waves closer to the real geological boundaries with few artificial clutters. And then, forward modelling of ground‐penetrating radar is carried out for typical adverse geologies, such as karst caves, faults, fractured rock masses, fracture network, and water‐bearing body. Their corresponding response features are obtained, accumulating experience for geological interpretation. The above two methods provide the basis for target identification and geological interpretation. In the last part, the application of the above two methods in several engineering cases are given, and their effectiveness is verified.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12613
2018-02-07
2020-05-28
Loading full text...

Full text loading...

References

  1. BurkholderR.J. and BrowneK.E.2010. Coherence factor enhancement of through‐wall radar images. IEEE Antennas & Wireless Propagation Letter9(1), 842–845.
    [Google Scholar]
  2. CardarelliE., MarroneC. and OrlandoL.2003. Evaluation of tunnel stability using integrated geophysical methods. Journal of Applied Geophysics52, 93–102.
    [Google Scholar]
  3. HalmanJ.I., ShubertK. and RuckG.T.1998. SAR processing of ground‐penetrating radar data for buried UXO detection: results from a surface‐based system. IEEE Transactions on Antennas & Propagation46(7), 1023–1027.
    [Google Scholar]
  4. HanD., LiD. and ShiX.2011. Effect of application of transient electromagnetic method in detection of water‐inrushing structures in coal mines. Procedia Earth & Planetary Science3, 455–462.
    [Google Scholar]
  5. GiannopoulosA.1997. The Investigation of Transmission‐Line Matrix and Finite‐Difference Time‐Domain Methods for the Forward Problem of Ground Probing Radar [dissertation]. Yorkshire, UK: University of York.
    [Google Scholar]
  6. GiannopoulosA.2005. Modelling ground penetrating radar by GprMax. Construction & Building Materials19, 755–762.
    [Google Scholar]
  7. GennarelliG. and SoldovieriF.2015. Multipath ghosts in radar imaging: physical insight and mitigation strategies. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing8(3), 1078–1086.
    [Google Scholar]
  8. GennarelliG., VivoneG., BracaP., SoldovieriF. and AminM.G.2016. Comparative analysis of two approaches for multipath ghost suppression in radar imaging. IEEE Geoscience & Remote Sensing Letters13(9), 1–5.
    [Google Scholar]
  9. LiS., LiS., ZhangQ., XueY., LiuB., SuM., et al. 2010. Predicting geological hazards during tunnel construction. Journal of Rock Mechanics and Geotechnical Engineering2, 232–242.
    [Google Scholar]
  10. LiS., LiuB., SunH., NieL., ZhongS., SuM., et al. 2014a. State of art and trends of advanced geological prediction in tunnel construction. Chinese Journal of Rock Mechanics & Engineering33(6), 1090–1113. Chinese.
    [Google Scholar]
  11. LiS., SunH., LuX. and LiX.2014b. Three‐dimensional modeling of transient electromagnetic responses of water‐bearing structures in front of a tunnel face. Journal of Environmental & Engineering Geophysics19(1), 13–32.
    [Google Scholar]
  12. LiY., LiS., LiuB., XuL., ZhangF. and NieL.2016. Imaging method of ground penetrating radar for rock fracture detection based on improved back projection algorithm. Chinese Journal of Geotechnical Engineering38(8), 1425–1433. Chinese.
    [Google Scholar]
  13. LingT., ZhangS. and ShengranL.I.2012. Hilbert–Huang transform method for detection signal of tunnel geological prediction using ground penetrating radar. Chinese Journal of Rock Mechanics & Engineering31(7), 1422–1428. Chinese.
    [Google Scholar]
  14. LiuB., LiS., LiS., ZhangQ., XueY. and ZhongS.2009. Study of application of complex signal analysis to predicting karst‐fractured ground water with GPR. Rock & Soil Mechanics30(7), 2191–2196. Chinese.
    [Google Scholar]
  15. NelsonS.O.1996. Determining dielectric properties of coal and limestone by measurements on pulverized samples. Journal of Microwave Power & Electromagnetic Energy31(4), 215–220.
    [Google Scholar]
  16. SattelG., SanderB.K., AmbergF. and KashiwaT.1996. Tunnel seismic prediction TSP‐some case histories. Tunnels and Tunneling4, 24–30.
    [Google Scholar]
  17. SeyfL. and YaldizE.2012. A simulator based on an energy‐efficient GPR algorithm modified for the scanning of all types of regions. Turkish Journal of Electrical Engineering & Computer Sciences20(3), 381–389.
    [Google Scholar]
  18. WarrenC., GiannopoulosA. and GiannakisI.2016. gprMax: open source software to simulate electromagnetic wave propagation for ground penetrating radar. Computer Physics Communications209, 163–170.
    [Google Scholar]
  19. XiangL., ZhouH., ShuZ., TanS., LiangG. and ZhuJ.2013. GPR evaluation of the Damaoshan highway tunnel: a case study. NDT & E International59, 68–76.
    [Google Scholar]
  20. XueG., YanY., LiX. and DiQ.2007. Transient electromagnetic S‐inversion in tunnel prediction. Geophysical Research Letters34(18), 529–538.
    [Google Scholar]
  21. YeeK.S.1966. Numerical solution of initial boundary value problems involving maxwell's equations in isotropic media. IEEE Transactions on Antennas & Propagation14(3), 302–307.
    [Google Scholar]
  22. ZetikR., SachsJ. and ThomäR.2005. Modified cross‐correlation back projection for UWB imaging: numerical examples. IEEE International Conference on Ultra‐Wideband.
    [Google Scholar]
  23. ZhangF., XieX. and HuangH.2010. Application of ground penetrating radar in grouting evaluation for shield tunnel construction. Tunnelling and Underground Space Technology25(2), 99–107.
    [Google Scholar]
  24. ZhouL., HuangC. and SuY.2012. A fast back‐projection algorithm based on cross correlation for GPR imaging. IEEE Geoscience & Remote Sensing Letters9(2), 228–232.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12613
Loading
/content/journals/10.1111/1365-2478.12613
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Forward modelling and imaging , Ground‐penetrating radar and Tunnel ahead prospecting
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error