1887
Special Issue: Ground Penetrating Radar (GPR) Numerical Modelling Research and Practice
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

Abstract

Timely and accurate detection of water pipe leakage is critical to preventing the loss of freshwater and predicting potential hazards induced by the change in underground water conditions, thereby developing mitigation strategies to improve the resilience of pipeline infrastructure. Ground‐penetrating radar (GPR) has been widely applied to investigating ground conditions and detecting pipe leakage. However, due to uncertainties of complex underground environments and time‐lapse change, a proper interpretation of GPR data has been a challenging task. This paper aims to leverage hydromechanical (HM) modelling to predict electromagnetic (EM) responses of water leakage detection in diverse leakage cases. A high‐fidelity 3D digital model of an actual pipeline network, hosting pipes with various sizes and materials, was reconstructed to represent the complex geometry and various mediums. The interoperability between the digital model and the numerical models utilized in HM and EM simulations was improved to better capture the irregular pipelines. Based on Kriging interpolation and the volumetric complex refractive index model, a linking technique was employed to replicate material heterogeneity caused by water intrusion. Thus, a framework was developed to accommodate the interoperability among digital modelling, HM modelling and finite‐difference time‐domain forward modelling. Moreover, sensitivity studies were conducted to evaluate the influences of different time stages, leak positions and pipe types on GPR responses. In GPR B‐scans, the presence of hyperbolic motion and horizontal reflections serve as indicators to estimate the location and scale of water leakage. Specifically, a downward‐shifting hyperbola indicates that the pipeline is submerged by leaked water, whereas the emergence of horizontal reflection is linked to the wetting front of saturated areas. The developed framework can be expanded for complicated applications, such as unknown locations and unforeseen failure modes of pipelines. It will increase the efficiency and accuracy of traditional interpretations of GPR‐based water leakage detection and thus enable automated interpretations by data‐driven methods.

Loading

Article metrics loading...

/content/journals/10.1002/nsg.12281
2024-04-23
2024-05-22
Loading full text...

Full text loading...

/deliver/fulltext/nsg/22/2/nsg12281.html?itemId=/content/journals/10.1002/nsg.12281&mimeType=html&fmt=ahah

References

  1. Ayala‐Cabrera, D., Campbell, E., Carreño‐Alvarado, E.P., Izquierdo, J. & Pérez‐García, R. (2014) Water leakage evolution based on GPR interpretations. Procedia Engineering, 89, 304–310. https://doi.org/10.1016/j.proeng.2014.11.192
    [Google Scholar]
  2. Birchak, J.R., Gardner, C.G., Hipp, J.E. & Victor, J.M. (1974) High dielectric constant microwave probes for sensing soil moisture. Proceeding of the IEEE, 62, (1), 93–98. https://doi.org/10.1109/PROC.1974.9388
    [Google Scholar]
  3. Cataldo, A., Persico, R., Leucci, G., De Benedetto, E., Cannazza, G., Matera, L. & De Giorgi, L. (2014) Time domain reflectometry, ground penetrating radar and electrical resistivity tomography: a comparative analysis of alternative approaches for leak detection in underground pipes. NDT & E International, 62, 14–28. https://doi.org/10.1016/j.ndteint.2013.10.007
    [Google Scholar]
  4. Charlton, M. & Mulligan, M. (2001) Efficient detection of mains water leaks using ground‐penetrating radar (GPR). Subsurface and Surface Sensing Technologies and Applications III, 4491, 375–386. https://doi.org/10.1117/12.450183
    [Google Scholar]
  5. Cheung, B.W.‐Y. & Lai, W.W.‐L. (2018) Field validation of water pipe leak by spatial and time‐lapsed measurement of GPR wave velocity. In: 2018 17th International Conference on Ground Penetrating Radar (GPR), Rapperswil, Switzerland. New York City, IEEE. pp. 1–4. https://doi.org/10.1109/ICGPR.2018.8441668
  6. Cheung, B.W.Y. & Lai, W.W.L. (2019) Field validation of water‐pipe leakage detection through spatial and time‐lapse analysis of GPR wave velocity. Near Surface Geophysics, 17, (3), 231–246. https://doi.org/10.1002/nsg.12041
    [Google Scholar]
  7. De Coster, A., Pérez Medina, J.L., Nottebaere, M., Alkhalifeh, K., Neyt, X., Vanderdonckt, J. & Lambot, S. (2019) Towards an improvement of GPR‐based detection of pipes and leaks in water distribution networks. Journal of Applied Geophysics, 162, 138–151. https://doi.org/10.1016/j.jappgeo.2019.02.001
    [Google Scholar]
  8. Fontanazza, C.M., Notaro, V., Puleo, V., Nicolosi, P. & Freni, G. (2015) Contaminant intrusion through leaks in water distribution system: experimental analysis. Procedia Engineering, 119, 426–433. https://doi.org/10.1016/j.proeng.2015.08.904
    [Google Scholar]
  9. Gyekenyesi, A.L., Dong, L., Carnalla, S. & Shinozuka, M. (2012) GPR survey for pipe leakage detection: experimental and analytical study. In: Proceedings Volume Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security, 12–15 March 2012, San Diego, CA, USA. Bellingham, WA, USA, SPIE8437(84370F), 1–7. https://doi.org/10.1117/12.917407
  10. Hu, Q., Zheng, Z., Liu, H. & Chen, B. (2020) Application of 3D ground penetrating radar to leakage detection of urban underground pipes. Journal of Tongji University (Natural Science), 48(7), 972–981.
    [Google Scholar]
  11. Huang, M.Q., Ninić, J. & Zhang, Q.B. (2021) BIM, machine learning and computer vision techniques in underground construction: current status and future perspectives. Tunnelling and Underground Space Technology, 108, 103677. https://doi.org/10.1016/j.tust.2020.103677
    [Google Scholar]
  12. ITASCA . (2019) FLAC3D 7.0. Minneapolis, USA: ITASCAhttps://www.itascacg.com/software/flac3d
  13. Kingdom, B., Liemberger, R. & Marin, P. (2006) The challenge of reducing non‐revenue water (NRW) in developing countries. Washington, DC, USA: The World Bank.
    [Google Scholar]
  14. Lai, W.L., Kou, S.C. & Poon, C.S. (2012) Unsaturated zone characterization in soil through transient wetting and drying using GPR joint time–frequency analysis and grayscale images. Journal of Hydrology, 452–453, 1–13. https://doi.org/10.1016/j.jhydrol.2012.03.044
    [Google Scholar]
  15. Lau, P.K.‐W., Cheung, B.W.‐Y., Lai, W.W.‐L. & Sham, J.F.‐C. (2021) Characterizing pipe leakage with a combination of GPR wave velocity algorithms. Tunnelling and Underground Space Technology, 109, 1–12. https://doi.org/10.1016/j.tust.2020.103740
    [Google Scholar]
  16. Liu, H., Huang, Z., Yue, Y., Cui, J. & Hu, Q. (2022) Characteristics analysis of ground penetrating radar signals for groundwater pipe leakage environment. Journal of Electronics & Information Technology, 44(4), https://doi.org/10.11999/JEIT211213
    [Google Scholar]
  17. Liu, Y. & Shi, Z. (2022) Mapping water pipeline leakage by ground‐penetrating radar diffraction imaging. Geophysics, 87(4), WB1–WB7. https://doi.org/10.1190/geo2021‐0230.1
    [Google Scholar]
  18. Loeffler, O. & Bano, M. (2004) Ground penetrating radar measurements in a controlled vadose zone: influence of the water content. Vadose Zone Journal, 3(4), 1082–1092. https://doi.org/10.2136/vzj2004.1082
    [Google Scholar]
  19. Maser, K. & Zarghamee, M. (1998) Leak and condition evaluation of a buried aqueduct. In: Proceedings Volume 3398, Nondestructive Evaluation of Utilities and Pipelines II, San Antonio, TX. Bellingham, WA, USA, SPIE. pp. 200–208. https://doi.org/10.1117/12.302526
  20. McNee, R. (2010) Rhinoceros 3D, Version 6.0. https://www.rhino3d.com
  21. Murphy, B., Yurchak, R. & Müller, S. (2022) GeoStat‐Framework/PyKrige: v1.7.0. http://pykrige.readthedocs.io/
  22. Nikolenko, S.I. (2019) Synthetic data for deep learning. ArXiv preprint: 1909.11512. https://doi.org/10.48550/arXiv.1909.11512
  23. Ocaña‐Levario, S.J., Carreño‐Alvarado, E.P., Ayala‐Cabrera, D. & Izquierdo, J. (2018) GPR image analysis to locate water leaks from buried pipes by applying variance filters. Journal of Applied Geophysics, 152, 236–247. https://doi.org/10.1016/j.jappgeo.2018.03.025
    [Google Scholar]
  24. Oliver, M.A. & Webster, R. (1990) Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information System, 4(3), 313–332. https://doi.org/10.1080/02693799008941549
    [Google Scholar]
  25. Özkap, K., Pekşen, E., Kaplanvural, İ. & Çaka, D. (2020) 3D scanner technology implementation to numerical modeling of GPR. Journal of Applied Geophysics, 179, 1–8. https://doi.org/10.1016/j.jappgeo.2020.104086
    [Google Scholar]
  26. Patsia, O., Giannopoulos, A. & Giannakis, I. (2021) A digital twin of the GSSI 2000 MHz palm antenna developed using multi‐parametric optimisation. In: 2021 11th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), Valletta, Malta. New York City, IEEE. pp. 1–5. https://doi.org/10.1109/IWAGPR50767.2021.9843157
  27. Qin, H., Zhang, D., Tang, Y. & Wang, Y. (2021) Automatic recognition of tunnel lining elements from GPR images using deep convolutional networks with data augmentation. Automation in Construction, 130, 103830. https://doi.org/10.1016/j.autcon.2021.103830
    [Google Scholar]
  28. Stampolidis, A., Soupios, P., Vallianatos, F. (2003) Detection of leaks in buried plastic water distribution pipes in urban places—a case study. In: 2nd International Workshop on Advanced GPR, Delft, the Netherlands. New York City, IEEE. https://doi.org/10.1109/AGPR.2003.1207303
  29. Topp, G.C., Davis, J.L. & Annan, A.P. (1980) Electromagnetic determination of soil water content: measurements in coaxial transmission lines. Water Resources Research, 16(3), 8.
    [Google Scholar]
  30. Warren, C., Giannopoulos, A. & Giannakis, I. (2016) gprMax: open source software to simulate electromagnetic wave propagation for ground penetrating radar. Computer Physics Communications, 209, 163–170. https://doi.org/10.1016/j.cpc.2016.08.020
    [Google Scholar]
  31. Wilson, V., Power, C., Giannopoulos, A., Gerhard, J. & Grant, G. (2009) DNAPL mapping by ground penetrating radar examined via numerical simulation. Journal of Applied Geophysics, 69(3‐4), 140–149. https://doi.org/10.1016/j.jappgeo.2009.08.006
    [Google Scholar]
  32. Water Supplies Department (WSD) of Hong Kong . (2012) Manual of mainlaying practice. Hong Kong: Water Supplies Department (WSD). https://www.wsd.gov.hk/en/publications‐and‐statistics/guidelines‐reports‐drawings‐specifications/mainlaying‐practice/index.html
/content/journals/10.1002/nsg.12281
Loading
/content/journals/10.1002/nsg.12281
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): 3D; finite‐difference; ground‐penetrating radar; modelling; water saturation

Most Cited This Month Most Cited RSS feed

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