Thickness Identification of Tunnel Lining Structure by Time–Energy Density Analysis based on Wavelet Transform

Abstract

The concrete thickness of the tunnel lining structure and cover depth is insufficient. Such condition seriously affects the safety and stability of the lining structure. The lining structure thickness is difficult to identify using radar profile horizon tracing method because of the strong interference of steel bars to electromagnetic wave propagation. To explore the reflection characteristics of electromagnetic wave signals at the interface between deep concrete and surrounding rock, a time-energy density analysis based on wavelet transform (TEDAWT) was proposed in this study. Ground-penetrating radar (GPR) forward modelling of the lining structure with different thicknesses of plain and reinforced concrete was carried out by using different central frequencies, namely, 1600 and 900 MHz, respectively. On this basis, the GPR detection signals for the plain and reinforced concrete lining structures were analyzed by employing the TEDAWT method. The feasibility of the TEDAWT method in GPR quantitative identification was verified using physical experiment. Results demonstrate that the identification accuracy of different thicknesses of plain and reinforced concrete structure is high regardless of the method used in either forward modelling or physical experiment, and the relative error is less than 5%. In the identification of concrete cover depth, the resolution of a 1600 MHz antenna is higher than that of a 900 MHz antenna, and the relative error is also less than 5%. The results indicate the application potential of the proposed method for quantitative identification of tunnel lining thickness by non-destructive testing. The proposed method provides not only the thickness distribution of the plain concrete structure but also the distribution of the reinforced concrete structure and concrete cover depth compared with traditional radar profile horizon tracing method.

An Automatic Recognition Method of Microseismic Signals Based on S Transformation and Improved Gaussian Mixture Model

“…e wavelet coefficients have different distribution characteristics, which are obtained by the multiscale analysis of noise and signal. erefore, on the basis of fully considering the frequency distribution characteristics of microseismic signals and the wavelet transform principle, the wavelet function db4 is selected to carry out noise reduction pretreatment for microseismic signals [38].…”

Applied research of comprehensive advance geological prediction in Daluoshan water diversion tunnel

“…Moreover, since this method is carried out on the tunnel excavation face, it also affects the tunnel construction progress 18 , 19 , making it difficult to be widely popularized. Non-destructive detection technologies are more varied, such as surface geological survey based on surface geological outcrops and research area geological structures, tunneling machine geological prediction based on parameters such as shield machine cutterhead speed, cutterhead torque, thrust, and advance speed 20 – 22 , and geophysical detection methods based on differences in physical properties and structural differences of surrounding rock 23 – 25 . Among them, the tunnel advanced geological prediction technology based on geophysical detection methods is an effective means and main method for guiding tunnel construction methods, reducing tunnel geological disasters, and ensuring normal tunnel construction due to its advantages of fast detection speed, large detection range, and low detection cost.…”

Applied Research of comprehensive advance geological prediction in Daluoshan diversion tunnel

“…However, the detection results based on the limited number of advanced drilling information have the disadvantages of high cost, small detection scope and low representativeness. At the same time, since this method is carried out on the tunnel heading face, it will also affect the tunnel construction progress [9,10], and it is di cult to popularize it in a large scale.There are many non-destructive detection technologies, such as the ground geological survey method based on the information of surface geological outcrops and geological structures in the study area, the geological prediction of tunnel boring machine (TBM) using the parameter information such as shield machine cutter head speed, cutter head torque, thrust and forward speed [11], and the geophysical detection method based on the physical and structural differences of surrounding rock [12][13][14].Among them, the tunnel advanced geological prediction technology based on geophysical exploration method is an effective means and main method to guide the tunnel construction mode, reduce tunnel geological disasters and ensure the normal construction of tunnel engineering at this stage with its advantages of fast detection speed, large detection range and low detection cost.…”