Predicting GPR Signals from Concrete Structures Using Artificial Intelligence‐Based Method

Abstract

This paper presents the application of an Artificial Intelligence-based method in analyzing the effects of environmental conditions, chloride contamination in concrete, and surface corrosion of rebars on the amplitude of Ground Penetrating Radar (GPR) signals. Six reinforced concrete slabs with different chloride contamination mixtures were fabricated and tested. GPR data were collected under various temperature and ambient humidity combinations. A total of 288 rebar picks were used for training, validation, and testing the proposed Artificial Neural Network (ANN) model. Multiple ANN model configurations with a variation in learning algorithms and the number of nodes in the hidden layer were explored to obtain the optimal model for the nondestructive data. It is shown that the “trainlm” learning algorithm produced the high accuracy prediction of the reflection amplitude of GPR signals. The sensitivity analysis was also conducted with the ANN model to investigate the effects of the input on the output parameters. Results from the sensitivity analysis revealed that the GPR reflection amplitudes were more sensitive to the changes of temperature parameter (TEM) and chloride contamination level (CCL), while they were less sensitive to the variation of ambient relative humidity (ARH) and rust condition on the rebar surface (CSR).

Evaluating the Properties of Railway Ballast Using Spectral Analysis of Ground Penetrating Radar Signal Based on Optimized Variational Mode Decomposition

“…As such, it is very clear and easy to use specific mode frequency to form an assessment index and use its shift as reliable and robust criteria to assess the changes in the properties of ballast, which obviously overperform that of the conventional methods as illustrated in Figures 2-6. This method can also be extended for quantifying the water contents in other materials such as sand, soil, and concrete which had been assessed by using conventional spectra of GPR signals [22][23][24][25][26]. Moreover, this method can be further improved by incorporating the advanced optimization and artificial intelligence methods [19-21, 26, 27].…”

A Printed U-Shaped Coplanar Waveguide Feed UWB Antenna for GPR Applications

“…An ultra-wideband (UWB) antenna is suitable for the capability of penetration at various depths. Accordingly, UWB antennas have recently received a large concentration to propose developed systems with efficacious antennas [1][2][3][4][5][6][7][8][9][10][11][12]. In this context, different GPR antennas have been presented in recent literature, such as those presented in [13][14][15][16][17][18][19][20][21][22].…”