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Related Experiment Video

Updated: Aug 16, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Evaluation of Void Defects behind Tunnel Lining through GPR forward Simulation.

Xianlong Wu^1,2, Xiaohua Bao^1,2, Jun Shen^1,2

¹College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China.

Sensors (Basel, Switzerland)

|December 23, 2022

Summary

Ground-penetrating radar (GPR) effectively detects voids in tunnel linings. This study simulated GPR detection using the finite difference time domain (FDTD) method, verifying its accuracy for identifying defects and ensuring tunnel safety.

Keywords:

finite difference time domain (FDTD)forward modeling ground-penetrating radar (GPR)tunnel lining void

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Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

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Published on: August 31, 2022

Area of Science:

Geotechnical Engineering
Geophysics
Non-Destructive Testing

Background:

Tunnel lining voids are common defects that compromise structural integrity and safety.
Effective detection methods are crucial for maintaining tunnel infrastructure.
Ground-penetrating radar (GPR) offers a non-destructive approach for subsurface investigations.

Purpose of the Study:

To investigate the application of GPR for detecting voids in tunnel linings.
To develop and validate a forward model for simulating GPR void detection using the finite difference time domain (FDTD) method.
To analyze the influence of void characteristics (size, fill material) on GPR responses.

Main Methods:

Utilized the finite difference time domain (FDTD) method to create a forward model for GPR simulation.
Analyzed simulated GPR images using a binarization method to compare void areas.
Considered both plain and reinforced concrete linings with air-filled and water-filled voids of various sizes.
Validated the model's rationality with measured GPR data.

Main Results:

GPR void detection model demonstrated accuracy when compared with field data.
Observed void response modes include hyperbolic, bowl-shaped, and strip-shaped, dependent on void width.
Water-filled voids exhibited an increased response range and produced virtual images compared to air-filled voids.
Steel reinforcement caused interference, but 3D GPR accurately located void centers.

Conclusions:

The FDTD-based GPR forward model is a reliable tool for simulating tunnel lining void detection.
GPR can effectively identify and characterize voids in tunnel linings, differentiating between air and water filling.
Despite interference from reinforcement, 3D GPR provides accurate void localization, crucial for tunnel safety assessments.