Wavelet Packet Analysis of Ground–Penetrating Radar Simulated Signal for Tunnel Cavity Fillings

Abstract

Quality defects, such as noncompactness, cavity, and water leakage, typically appear behind tunnel linings given the lack of standardization and loose management in tunnel construction. Thus, a method for acquiring characteristic information from tunnel lining cavity fillings using frequency band energy of wavelet packet transform (FBEWPT) was proposed in this study to master the reflection characteristics of ground-penetrating radar (GPR) signal. A forward simulation of tunnel lining cavity fillings, including air, wet clay, silt, and water, was explored through finite-difference time-domain method. The spectrum characteristics of tunnel cavity fillings were analyzed from time, frequency, and time-frequency domains. Results demonstrate that amplitude reflection clearly occurs at the inhomogeneous interface when GPR electromagnetic waves collide with the tunnel lining cavity fillings. However, the degree of attenuations is different considering the various dielectric constants of objects. These relative dielectric constants reveal the reflection interface position and other information between lining and fillings. With the increase in the difference in relative dielectric constants for lining cavity fillings, the attenuation of FBEWPT for GPR signals increasingly strengthens, and the variance and standard deviation of the FBEWPT for GPR signals constantly decrease, thereby reflecting the strong reflection and attenuation of GPR signals that appear in the propagation process. This study provides a favourable reference for obtaining GPR signal spectrum interpretation for tunnel lining cavity fillings in actual projects.

The Use of Wavelet Analysis to Improve the Accuracy of Pavement Layer Thickness Estimation Based on Amplitudes of Electromagnetic Waves

“…The scope of applications of wavelet analysis of GPR signals in road infrastructure includes the use of wavelet analysis, among others, in the assessment of the backfill of the tunnel [ 31 ]. The tests were carried out using a 400 MHz ground-coupled antenna, and the GPR signal was compared to the Daubechies 4 wavelet.…”

Advancing towards semi-automatic labeling of GPR images to improve visualizations of pipes and leaks in water distribution networks using multi-agent systems and machine learning techniques

“…GPR is a non-destructive testing method which is deployable in a variety of environmental conditions (e.g., urban environments [9]) and does not require disruption in the operation of the critical infrastructure inspected; e.g., water supplies in WDN [1]. GPR is examined in this research as an alternative to destructive testing to obtain relevant information regarding buried assets of WDN due to its potential to detect components (e.g., pipes) and their integrity with great detail (e.g., pipe with water leak [10], clogged pipe [11], deteriorated pipe [12]). Among the main advantages of the GPR as a non-destructing method, its inspection ability for successfully inspect different pipe materials has been evidenced in several studies (see [11,12,13]).…”

“…GPR is examined in this research as an alternative to destructive testing to obtain relevant information regarding buried assets of WDN due to its potential to detect components (e.g., pipes) and their integrity with great detail (e.g., pipe with water leak [10], clogged pipe [11], deteriorated pipe [12]). Among the main advantages of the GPR as a non-destructing method, its inspection ability for successfully inspect different pipe materials has been evidenced in several studies (see [11,12,13]). GPR signals are capable of detecting materials, both metallic and non-metallic [14] (e.g., PVC, asbestos cement, polyethylene), which are commonly used in assets of WDNs.…”