
Ground penetrating radar detection of steel fiber reinforced composite linings in shield tunnels: Experimental and field studies
Kang LI, Xiongyao XIE, Hao CHEN, Biao ZHOU, Changfu HUANG
Front. Struct. Civ. Eng. ›› 2025, Vol. 19 ›› Issue (4) : 541-555.
Ground penetrating radar detection of steel fiber reinforced composite linings in shield tunnels: Experimental and field studies
Steel fiber reinforced concrete-reinforced concrete (SFRC-RC) composite linings are popular in shield tunnel construction due to exceptional strength and waterproofing properties. Non-destructive testing methods are essential for assessing the quality of these linings and ensuring tunnel construction safety. This study investigates the potential and parameters of ground penetrating radar (GPR) detection for the composite linings, using the Deep Tunnel Sewerage System-Phase 2 project in Singapore as a case study. The gprMax simulations incorporated the random distribution and precise parameters of steel fibers to conduct preliminary frequency selection studies. The structural setup of the model experiments mirrored that of the actual tunnel, allowing for an analysis of GPR penetration capabilities at various frequencies. Field testing provided authentic GPR data, validating conclusions drawn from simulations and model experiments and examining GPR power attenuation patterns. Findings indicate that GPR is effective for the quality detection of composite linings. The optimal frequency for detecting SFRC-RC composite linings is 300 MHz, which resolves the interfaces of different layered media. Based on single-parameter exponential and power function fitting, empirical formulas for power attenuation quantitatively characterize GPR signal attenuation in SFRC-RC composite linings. This paper offers valuable references for GPR detection of SFRC-RC composite linings.
shield tunnel / steel fiber reinforced concrete / composite lining / ground penetrating radar / model experiment / field test
[1] |
Guo W, Feng K, Lu X, Qi M, Liu X, Fang Y, He C, Xiao M. Model test investigation on the longitudinal mechanical property of shield tunnels considering internal structure. Tunnelling and Underground Space Technology, 2023, 140: 105293
CrossRef
Google scholar
|
[2] |
Li C, Zhang W, Wang X, Pan B, Zhu H, Spencer B F. Modeling dynamic responses of a cross-river road shield tunnel under stochastic vehicle loads. Tunnelling and Underground Space Technology, 2020, 102: 103432
CrossRef
Google scholar
|
[3] |
Jiang X, Zhang Y, Zhang Z, Bai Y. Study on risks and countermeasures of shallow biogas during construction of metro tunnels by shield boring machine. Transportation Research Record: Journal of the Transportation Research Board, 2021, 2675(7): 105–116
CrossRef
Google scholar
|
[4] |
Su E, Zhang X, Wen X, Liu J, Ye F, Han X, Lei P. Visual characteristics of drivers in different directions and lengths of gradual landscape zones in extra-long highway tunnels. Tunnelling and Underground Space Technology, 2023, 137: 105136
CrossRef
Google scholar
|
[5] |
Guo Y, Wang Y, Geng F, Zhang Z. Study on dust distribution in a highway tunnel during the full-face excavation with Drilling-Blasting method. Tunnelling and Underground Space Technology, 2023, 139: 105229
CrossRef
Google scholar
|
[6] |
Lu B, Zhao W, Wang W, Jia P, Du X, Cao W, Li W. Design and optimization of secant pipe roofing structure applied in subway stations. Tunnelling and Underground Space Technology, 2023, 135: 105026
CrossRef
Google scholar
|
[7] |
Yang B, Sun Y, Yao Y, Ni L. Field measurement and analysis of subway tunnel thermal environment in severe cold region. Building and Environment, 2023, 243: 110629
CrossRef
Google scholar
|
[8] |
Wan L, Xie X, Wang L, Li P, Huang Q. Modal analysis of subway tunnel in soft soil during operation. Underground Space, 2023, 8: 181–195
CrossRef
Google scholar
|
[9] |
Zhao D, Huang Y, Chen X, Han K, Chen C, Zhao X, Chen W. Numerical investigations on dynamic responses of subway segmental tunnel lining structures under internal blasts. Tunnelling and Underground Space Technology, 2023, 135: 105058
CrossRef
Google scholar
|
[10] |
KhanlariGGhaderi MeybodiRMokhtariE. Engineering geological study of the second part of water supply Karaj to Tehran tunnel with emphasis on squeezing problems. Engineering Geology, 2012, 145–146: 145–146
|
[11] |
WangX FZhouX JHuH Y. Study on selection of the length of reinforced concrete segment for oil and gas shield tunnel with 3D numerical analysis. Applied Mechanics and Materials, 2014, 488–489: 488–489
|
[12] |
Zhang X P, Tang S H, Liu Q S, Wang H J, Li X F, Chen P, Liu H. Key technology for the construction and inspection of long-distance underwater tunnel for 1000 kV gas-insulated transmission line. Bulletin of Engineering Geology and the Environment, 2023, 82(1): 7
CrossRef
Google scholar
|
[13] |
Barla M, Insana A. Energy tunnels as an opportunity for sustainable development of urban areas. Tunnelling and Underground Space Technology, 2023, 132: 104902
CrossRef
Google scholar
|
[14] |
He S, Lai J, Wang L, Wang K. A literature review on properties and applications of grouts for shield tunnel. Construction and Building Materials, 2020, 239: 117782
CrossRef
Google scholar
|
[15] |
Ye X W, Jin T, Chen Y M. Machine learning-based forecasting of soil settlement induced by shield tunneling construction. Tunnelling and Underground Space Technology, 2022, 124: 104452
CrossRef
Google scholar
|
[16] |
Ding W, Duan C, Zhu Y, Zhao T, Huang D, Li P. The behavior of synchronous grouting in a quasi-rectangular shield tunnel based on a large visualized model test. Tunnelling and Underground Space Technology, 2019, 83: 409–424
CrossRef
Google scholar
|
[17] |
Xu J, Ding L, Luo H, Chen E J, Wei L. Near real-time circular tunnel shield segment assembly quality inspection using point cloud data: A case study. Tunnelling and Underground Space Technology, 2019, 91: 102998
CrossRef
Google scholar
|
[18] |
Shi Z, Xie X, Zeng H, Zeng K, Niu G, Xiao Z. Disaster mechanism of large-diameter shield tunnel segments under multi-source load coupling: A case study. Engineering Failure Analysis, 2024, 166: 108878
CrossRef
Google scholar
|
[19] |
Yang K, Yan Q, Zhang C. Three-dimensional mesoscale numerical study on the mechanical behaviors of SFRC tunnel lining segments. Tunnelling and Underground Space Technology, 2021, 113: 103982
CrossRef
Google scholar
|
[20] |
Oliveira J M J, Vieira C S, Silva M F A, Amorim D L N F. Fracture modelling of steel fibre reinforced concrete structures by the lumped damage mechanics: Application in precast tunnel segments. Engineering Structures, 2023, 278: 115487
CrossRef
Google scholar
|
[21] |
BenturAMindessS. Fibre Reinforced Cementitious Composites. London: Chemical Rubber Company Press, 2006
|
[22] |
Caratelli A, Meda A, Rinaldi Z. Design according to MC2010 of a fibre-reinforced concrete tunnel in Monte Lirio, Panama. Structural Concrete, 2012, 13(3): 166–173
CrossRef
Google scholar
|
[23] |
Peng M, Wang D, Liu L, Shi Z, Shen J, Ma F. Recent advances in the GPR detection of grouting defects behind shield tunnel segments. Remote Sensing, 2021, 13(22): 4596
CrossRef
Google scholar
|
[24] |
Liu H, Yue Y, Lai S, Meng X, Du Y, Cui J, Spencer B F. Evaluation of the antenna parameters for inspection of hidden defects behind a reinforced shield tunnel using GPR. Tunnelling and Underground Space Technology, 2023, 140: 105265
CrossRef
Google scholar
|
[25] |
Xiang L, Zhou H L, Shu Z, Tan S H, Liang G Q, Zhu J. GPR evaluation of the Damaoshan highway tunnel: A case study. Non-Destructive Testing and Evaluation International, 2013, 59: 68–76
CrossRef
Google scholar
|
[26] |
Qin H, Tang Y, Wang Z, Xie X, Zhang D. Shield tunnel grouting layer estimation using sliding window probabilistic inversion of GPR data. Tunnelling and Underground Space Technology, 2021, 112: 103913
CrossRef
Google scholar
|
[27] |
Han W, Jiang Y, Wang G, Liu C, Koga D, Luan H. Review of health inspection and reinforcement design for typical tunnel quality defects of voids and insufficient lining thickness. Tunnelling and Underground Space Technology, 2023, 137: 105110
CrossRef
Google scholar
|
[28] |
Li K, Xie X, Huang C, Zhou B, Duan W, Lin H, Wang C. Study on the penetration capability of GPR for the steel-fibre reinforced concrete (SFRC) segment based on numerical simulations and model test. Construction and Building Materials, 2023, 400: 132719
CrossRef
Google scholar
|
[29] |
Manhães P M B, Júnior J T A, Chen G, Anderson N L, Pereira E V, de Andrade Silva F. The use of GPR to investigate the effect of steel fiber distribution on the mechanical behavior of FRC. Construction and Building Materials, 2022, 344: 128248
CrossRef
Google scholar
|
[30] |
JolH M. Ground Penetrating Radar Theory and Applications. Amsterdam: Elsevier, 2008
|
[31] |
Wee J W, Chudnovsky A, Choi B H. Crack layer modeling of overload-induced slow crack growth retardation of high-density polyethylene. International Journal of Mechanical Sciences, 2023, 257: 108546
CrossRef
Google scholar
|
[32] |
Ezazi M, Hossaini M F, Sheikhmali R, Khosrotash M, Sharifi Teshnizi E, O’Kelly B C. Assessment of steel-fiber reinforced segmental lining of Chamshir water conveyance tunnel, Iran: Integrating laboratory experiments, field observations, and numerical analysis. Case Studies in Construction Materials, 2024, 20: e03144
CrossRef
Google scholar
|
[33] |
Maxwell J C. VIII. A dynamical theory of the electromagnetic field. Philosophical Transactions of the Royal Society of London, 1865, 155: 459–512
CrossRef
Google scholar
|
[34] |
Giannopoulos A. Modelling ground penetrating radar by gprMax. Construction and Building Materials, 2005, 19(10): 755–762
CrossRef
Google scholar
|
[35] |
Warren C, Giannopoulos A, Giannakis I. gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar. Computer Physics Communications, 2016, 209: 163–170
CrossRef
Google scholar
|
[36] |
Giannakis I, Giannopoulos A, Warren C. A realistic FDTD numerical modeling framework of ground penetrating radar for landmine detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 9(1): 37–51
CrossRef
Google scholar
|
[37] |
Warren C, Giannopoulos A. Creating finite-difference time-domain models of commercial ground-penetrating radar antennas using Taguchi’s optimization method. Geophysics, 2011, 76(2): G37–G47
CrossRef
Google scholar
|
[38] |
Yee K S. Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media. IEEE Transactions on Antennas and Propagation, 1966, 14(3): 302–307
CrossRef
Google scholar
|
[39] |
Giannopoulos A. Unsplit implementation of higher order PMLs. IEEE Transactions on Antennas and Propagation, 2012, 60(3): 1479–1485
CrossRef
Google scholar
|
[40] |
Li C, Cai L, Guo L, Chen D. Research on target recognition method of tunnel lining image based on deep learning. In: 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC). Chongqing: IEEE, 2022, 6: 1833–1837
|
[41] |
Qin H, Zhang D, Tang Y, Wang Y. Automatic recognition of tunnel lining elements from GPR images using deep convolutional networks with data augmentation. Automation in Construction, 2021, 130: 103830
CrossRef
Google scholar
|
[42] |
Go G H, Lee S J. A Study on numerical analysis for GPR signal characterization of tunnel lining cavities. Journal of the Korean Geotechnical Society, 2021, 37(10): 65–76
|
[43] |
GiannopoulosAWarrenC. gprMax: Electromagnetic simulation software. 2025 (available at the website of gprMax)
|
[44] |
PontiFBarbutoFdi GregorioP PManginiFSimeoniPTroianoM. Deep learning for applications to ground penetrating radar and electromagnetic diagnostic. In: 2019 Photonlcs and Electromagnetics Research Symposium-Spring (PIERS-Spring). Rome: IEEE, 2019: 547–551
|
[45] |
DoganMTurhan-SayanG. Investigation of the effects of buried object orientation in subsurface target detection by GPR. In: 2017 7th IEEE International Symposium on Microwave, Antenna, Propagation, and EMC Technologies (MAPE). Xi’an: IEEE, 2017: 475–479
|
[46] |
Singapore’sNational Water Agency. Deep Tunnel Sewerage System (DTSS). 2023
|
/
〈 |
|
〉 |