Geomechanics and Engineering

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Void detection for tunnel lining backfill using impact-echo method based on continuous wavelet transform and convolutional neural network

  • Received : 2022.12.15
  • Accepted : 2023.11.10
  • Published : 2024.01.10
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Abstract

We propose a new method for detecting voids behind tunnel concrete linings using the impact-echo method that is based on continuous wavelet transform (CWT) and a convolutional neural network (CNN). We first collect experimental data using the impact-echo method and then convert them into time-frequency images via CWT. We provide a CNN model trained using the converted images and experimentally confirm that our proposed model is robust. Moreover, it exhibits outstanding performance in detecting backfill voids and their status.

Keywords

Acknowledgement

This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 22TBIP-C162312-02).

References

  1. Aggelis, D., Shiotani, T. and Kasai, K. (2008), "Evaluation of grouting in tunnel lining using impact-echo", Tunn. Undergr. Sp. Tech., 23(6), 629-637. https://doi.org/10.1016/j.tust.2007.12.001.
  2. Cao, R., Ma, M., Liang, R. and Niu, C. (2019), "Detecting the void behind the tunnel lining by impact-echo methods with different signal analysis approaches", Appl. Sci., 9(16), 3280. https://doi.org/10.3390/app9163280.
  3. Carino, N.J. (2001), "The Impact-Echo Method: An Overview." Structures 2001. https://doi.org/10.1061/40558(2001)15.
  4. Gong, C., Ding, W., Soga, K. and Mosalam, K. (2019), "Failure mechanism of joint waterproofing in precast segmental tunnel linings". Tunn. Undergr. Sp. Tech., 84, 334-352. https://doi.org/10.1016/j.tust.2018.11.003.
  5. He, K., Zhang, X., Ren, S. and Sun, J. (2016), "Deep residual learning for image recognition", Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.90.
  6. Kwak, N.S. and Ko, T.Y. (2022), "Machine learning-based regression analysis for estimating Cerchar abrasivity index", Geomech. Eng., 29(3), 219-228. https://doi.org/10.12989/gae.2022.29.3.219.
  7. Lee, J.S., Park, J., Kim, J. and Yoon, H.K. (2022), "Study of oversampling algorithms for soil classifications by field velocity resistivity probe", Geomech. Eng., 30(3), 247-258. https://doi.org/10.12989/gae.2022.30.3.247.
  8. Liao, Y., Zeng, X. and Li, W. (2017), "Wavelet transform based convolutional neural network for gearbox fault classification", Proceedings of the 2017 Prognostics and System Health Management Conference (PHM-Harbin). https://doi.org/10.1109/phm.2017.8079274.
  9. Lilly, J.M. (2017), "Element analysis: a wavelet-based method for analysing time-localized events in noisy time series", Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473(2200), 20160776. https://doi.org/10.1098/rspa.2016.0776.
  10. Lilly, J.M. and Olhede, S.C. (2010), "On the analytic wavelet transform", IEEE T. Inform. Theory, 56(8), 4135-4156. https://doi.org/10.1109/tit.2010.2050935.
  11. Lilly, J.M. and Olhede, S.C. (2012), "Generalized morse wavelets as a superfamily of analytic wavelets", IEEE T. Signal Process., 60(11), 6036-6041. https://doi.org/10.1109/tsp.2012.2210890.
  12. Lilly, J. and Olhede, S. (2009), "Higher-order properties of analytic wavelets", IEEE T. Signal Process., 57(1), 146-160. https://doi.org/10.1109/tsp.2008.2007607.
  13. Liu, N., Li, N., Xu, C., Li, G., Song, G. and Yang, M. (2020), "Mechanism of secondary lining cracking and its simulation for the dugongling tunnel", Rock Mech. Rock Eng.. 53. 4539-4558. https://doi.org/10.1007/s00603-020-02183-3
  14. Mirzaeiabdolyousefi, M., Nikkhah, M. and Zare, S. (2022), "Assessment of time-dependent behaviour of rocks on concrete lining in a large cross-section tunnel", Geomech. Eng., 29(1), 41-51. https://doi.org/10.12989/gae.2022.29.1.041.
  15. Moon, J., An, J., Kim, H., Lee, J. and Lattner, T. (2022), "Evaluation criteria for freezing and thawing of tunnel concrete lining according to theoretical and experimental analysis", Geomech. Eng., 29(3). 349-357. https://doi.org/10.12989/gae.2022.29.3.349.
  16. Sansalone, M.J. and Streett, W.B. (1997), "Impact-echo: nondestructive evaluation of concrete and masonry", Bullbrier Press:Jersey Shore, PA, USA.
  17. Sasmal, S. and Behera, R. (2021), "Application of artificial intelligence methods for predicting transient response of foundation", Geomech. Eng., 27(3), 197-211. https://doi.org/10.12989/gae.2021.27.3.197.
  18. Sawicki, B., Piotrowski, T. and Garbacz, A. (2021), "Development of impact-echo multitransducer device for automated concrete homogeneity assessment", Materials, 14(9), 2144. https://doi.org/10.3390/ma14092144.
  19. Slavic, J., Simonovski, I. and Boltezar, M. (2003), "Damping identification using a continuous wavelet transform: Application to real data", J. Sound Vib., 262(2), 291-307. https://doi.org/10.1016/s0022-460x(02)01032-5.
  20. Song, K.I. and Cho, G.C. (2009), "Bonding state evaluation of tunnel shotcrete applied onto hard rocks using the impact-echo method", NDT &Amp E Int., 42(6), 487-500. https://doi.org/10.1016/j.ndteint.2009.02.007.
  21. Song, K.I. and Cho, G.C. (2010), "Numerical study on the evaluation of tunnel shotcrete using the impact-echo method coupled with fourier transform and short-time fourier transform", Int. J. Rock Mech. Min. Sci., 47(8), 1274-1288. https://doi.org/10.1016/j.ijrmms.2010.09.005.
  22. Xu, G., He, C., Wang, J. and Chen, Z. (2020), "Study on the mechanical behavior of a secondary tunnel lining with a yielding layer in transversely isotropic rock stratum", Rock Mech. Rock Eng., 53, 2957-2979. https://doi.org/10.1007/s00603-020-02107-1.
  23. Yao, F., Chen, G. and Abula, A. (2018), "Research on signal processing of segment-grout defect in tunnel based on impact-echo method", Constr. Build. Mater., 187, 280-289. https://doi.org/10.1016/j.conbuildmat.2018.07.192.