Ground-coupled Ground Penetrating Radar Research Articles

A Set of Ground Penetrating Radar Measures from Quarries

A Set of Ground Penetrating Radar Measures from Quarries

Comparative Study of Ground Penetrating Radar Systems with Time Delay Methods for Accurate Pavement Thickness and Dielectric Constant Estimation

The applicability of using ground penetrating radar (GPR) as a non-destructive technology for estimating the thickness and dielectric constant of pavement layers has been extensively documented in the literature. Time delay methods (TDMs) have been the most commonly used by researchers. However, various studies have reported different results with respect to the suitability of different GPR systems and TDMs for dielectric constant and thickness estimation. Thus, this study aims to compare the use of ground-coupled GPR and air-coupled GPR (AC-GPR) for estimating the dielectric constant of pavement materials. In the context of TDMs, the study examines three methodologies for assessing the traveled distance in the investigated layer. The study involves laboratory experiments using asphalt and dry sand. Also, it comprises a sensitivity analysis to examine the effect of different experimentation setups. The study employed two GSSI antennas: 51600S (GC-51600S) and GSSI 42000S (AC-42000S). Using the manufacturer’s recommended operation setups, the results generally indicated the possibility of simplifying the TDM calculation by considering the 42000S and 51600S antennas as monostatic for pavement materials with different dielectric constants (≥3) with an error rate within 5%. Moreover, the study indicates that using AC-GPR may be inappropriate for common midpoint analysis using the default mounting setups. These findings provide valuable insights into the suitability and limitations of different GPR systems and calculation methods for accurately estimating pavement’s dielectric constant and thickness.

Evaluation of Air-Cavities behind Concrete Tunnel Linings Using GPR Measurements

The primary objectives of this study are to investigate the interference of ground penetrating radar (GPR) signals with air-cavities behind concrete linings subjected to three experimental parameters (GPR antenna frequency, thickness of concrete lining, and thickness of air-cavities), and their effects on the evaluation of important engineering parameters (thickness of concrete, and length and thickness of air-cavities) for tunnel structure inspection using GPR. For these purposes, first, a concrete model specimen is fabricated in the laboratory, which simulates air-cavity defects with various thicknesses (0 mm, 30 mm, 100 mm, 200 mm, and 300 mm) behind concrete with four different thicknesses (100 mm, 200 mm, 300 mm, and 400 mm) in tunnel structures. Second, a series of GPR survey is performed on the fabricated concrete model specimen using three ground-coupled GPR antennas with nominal center frequencies of 400 MHz, 900 MHz, and 1600 MHz. Third, a practical signal interpretation method is proposed to evaluate air-cavities in concrete tunnel linings subjected to the three experimental parameters. It is demonstrated that the cross-correlation analysis is effective for identifying air-cavities behind concrete, and for characterizing the length of the air-cavities. In addition, the thicknesses of air-cavities are estimated by a new approach in this study, measuring the apparent time delay of partially overlapped electromagnetic wave reflections within air-cavities. The results in this study provide a fundamental basis to improve GPR signal interpretation techniques for the evaluation of air-cavities behind concrete tunnel linings.

Automated and Standardized Nondestructive Evaluation of Bridge Decks Using "insight" System

A common form of deterioration in reinforced concrete bridge decks is corrosion induced deck delamination that is invisible during visual inspection. Current evaluation techniques rely on visual, chain drag and manual NDE technics. Due to inefficient data collection, processing, analysis and interpretation, these manual evaluation techniques cannot provide a thorough evaluation of the structure’s condition. The presented study focuses on a novel NDE solution called “insight” which has brought artificial intelligence, robotic automation, autonomous driving and many more advanced technologies to nondestructive evaluation of bridge decks and other concrete structures. By doing so, comprehensive NDE projects can take place with higher speeds and spatial resolution, in more cost-effective and efficient manners resulting in more widespread and affordable projects. The Infratek insight solution consists of two platforms, the High Definition (HD) and the High Speed (HS). For the HD platform, results include delamination maps from Impact Echo (IE), attenuation (depth corrected amplitude) and cover depth maps from Ground-Coupled Ground Penetrating Radar (GPR), modulus distribution maps from Ultrasonic Surface Waves (USW), and resistivity maps from Electrical Resistivity (ER). ER and GPR are used to assess the corrosion potential throughout the bridge deck. IE is the primary delamination evaluation tool, while USW enables evaluation of concrete degradation through a modulus measurement. For the HS platform, results include delamination maps from automated high-speed sounding and infrared thermography (IR), attenuation (depth corrected) amplitude and cover depth maps from air-coupled GPR, crack mapped bridge deck image from cameras coupled with machine learning crack mapping technics, surface profiles from laser profilers and additional beneficial information from 360 imaging, and LiDAR. Furthermore, the results are analyzed in a holistic approach using all the collected NDE data. Maps and data-driven recommendations are portrayed in an interactive web portal with options to generate standard inspection and other custom reports.

Asphalt pavement characterization by GPR using an air-coupled antenna array

Ground penetrating radar (GPR) has become an effective tool for asphalt pavement inspection. However, a ground-coupled GPR system cannot facilitate a high-speed survey due to the complex road environment and traffic condition. In this paper, an air-coupled antenna array is designed for measurement of common midpoint (CMP) GPR data. From the CMP data, an improved velocity spectrum algorithm considering the refraction on the pavement surface is proposed to simultaneously estimate the thickness and dielectric permittivity of the pavement layer. The results of a laboratory experiment demonstrate that the improved velocity spectrum method can greatly enhance the accuracy of the velocity and thickness estimations, compared with the traditional inversion method. A field measurement conducted on a highway pavement shows that the maximum error of the thickness estimation of the asphalt layer is less than 10 mm (7.1%). It is concluded that the developed CMP technique can be used for quantitative characterization of asphalt pavement.

Recognition of void defects in airport runways using ground-penetrating radar and shallow CNN

The interpretation of ground-penetrating radar (GPR) data usually depends on manual work, thus yielding unsatisfactory accuracy when processing complicated cases. To address this issue, the finite-difference time-domain method was adopted to explore the void-related features in GPR images, and guide the field survey to locate the void area. On this basis, a dataset including 811 void features was established from 10 airport runways using an 800 MHz ground-coupled GPR system. Data augmentation was used to enlarge the dataset and train four configurations of a mixed convolution neural network (CNN) model with feature extractors (ResNet18 and ResNet50) and object detectors (YOLOv2 and Faster R-CNN). To make the detection process automatic, an incremental random sampling (IRS) approach was used to generate images from GPR data and then fed into the trained model. The experimental results demonstrated that the shallow ResNet18-YOLOv2 with the IRS method is a promising strategy for void detection in airport runways.

A Preliminary Numerical Study to Compare the Physical Method and Machine Learning Methods Applied to GPR Data for Underground Utility Network Characterization

In the field of geophysics and civil engineering applications, ground penetrating radar (GPR) technology has become one of the emerging non-destructive testing (NDT) methods thanks to its ability to perform tests without damaging structures. However, NDT applications, such as concrete rebar assessments, utility network surveys or the precise localization of embedded cylindrical pipes still remain challenging. The inversion of geometric parameters, such as depth and radius of embedded cylindrical pipes, as well as the dielectric parameters of its surrounding material, is of great importance for preventive measures and quality control. Furthermore, the precise localization is mandatory for critical underground utility networks, such as gas, power and water lines. In this context, innovative signal processing techniques associated with GPR are capable of performing physical and geometric characterization tasks. This paper evaluates the performance of a supervised machine learning and ray-based methods on GPR data. Support vector machines (SVM) classification, support vector machine regression (SVR) and ray-based methods are all used to correlate information about the radius and depth of embedded pipes with the velocity of stratified media in various numerical configurations. The approach is based on the hyperbola trace emerging in a set of B-scans, given that the shape of the hyperbola varies greatly with pipe depth and radius as well as with velocity of the medium. According to the ray-based method, an inversion of the wave velocity and pipe radius is performed by applying an appropriate nonlinear least mean squares inversion technique. Feature selection within machine learning models is also implemented on the information chosen from observed hyperbola travel times. Simulated data are obtained by means of the finite-difference time-domain (FDTD) method with the 2D numerical tool GprMax. The study is carried out on mono-static, ground-coupled GPR datasets. The preliminary study showed that the proposed machine learning methods outperforms the ray-based method for estimating radius, depth and velocity. SVR, for instance, calculates depth and radius values with mean absolute relative errors of 0.39% and 6.3%, respectively, with regard to the ground truth. A parametric comparison of the aforementioned methodologies is also included in the performance analysis in terms of relative error.

Evaluation of Pavement Stripping Using Ground-Penetrating Radar: A Case Study

Non-destructive testing techniques are highly valuable in the present day scenario as it can save money and time for the qualitative evaluation of any site. This paper presents a case study utilizing one such technique, employing a ground-coupled Ground-Penetrating Radar (GPR). The GPR was used to estimate the damages to the pavement and underlying subgrade soil at Cochin International Airport Limited (CIAL), Cochin, Kerala, India. The state of Kerala received 75% more than expected rainfall in the monsoon of 2018 and hence, faced one of the worst natural disasters in the form of floods between July and August. Therefore, a subsurface survey was performed at CIAL to analyse the pavement area and identify any detrimental effects caused by the floods. The subsurface survey scope was to image the subsurface to an approximate depth of 2–3 m to map the anomalies and quantitatively evaluate the affected areas of the runway, taxiways, and the link-ways. Non-destructive testing was opted not to affect the regular operations at the airport. This investigation involved the use of remote measurement methods, and therefore, all the findings presented here are the result of the measurement and interpretation of GPR data. The analysis was performed using a dedicated data analytics software called RADAN7. Based on the analysis, a quantitative criterion called the “stripping index” has been computed to define the degree of damages sustained by the pavement and its underlying layers. The results are both graphically and quantitatively discussed in this paper.

Ground Penetrating Radar Data Processing for Concrete Bridge Deck Evaluation

Abstract This paper presents ground-coupled ground penetrating radar (GPR) data-processing procedures for concrete bridge deck evaluation. GPR signals are analyzed in the depth range from the concr...

In-situ recognition of moisture damage in bridge deck asphalt pavement with time-frequency features of GPR signal

A complete solution, including an effective non-destructive evaluation (NDE) method and an automatic recognition model, was provided for the rapid diagnosis of moisture damage in the asphalt pavement by using ground-penetrating radar (GPR) signals. A ground-coupled 2.3 GHz antenna was used to conduct a GPR survey on an asphalt pavement of a bridge deck, where the moisture damage areas were detected and visually recognized in processed GPR B-scan images and further validated in subsequent pavement coring. Field GPR traces of the asphalt layer were read and classified to build a dataset which included 8215 moisture damage and 8215 normal pavement traces. A 28-element time-frequency feature vector was extracted and further reduced to an 11-element sensitive feature vector via the linear discriminant analysis (LDA) method. Principal component analysis (PCA) was adopted to decompose the feature vector into the PCs (principal components), which was used to train a BP-ANN model. The result indicates the high accuracy of the ANN model with sensitive feature vectors, i.e., 95.3% for normal and 92.4% for moisture classification. Finally, the ANN model was used to evaluate the GPR survey data, and its result is consistent with the GPR B-scan feature. These findings suggest that the ground-coupled GPR system with 2.3 GHz antenna and the recognition model will enable an innovative quality evaluation system for asphalt pavement.

Mapping of Sewer Lines Using GPR: A Case Study in Tunisia

Many infrastructure enhancement projects require underground utility mapping before starting any excavation processes, especially in urban areas. In fact, mapping of an area provides a general overview of the infrastructure above and underground. This mapping can be done by Ground Penetrating Radar (GPR), which is commonly used as a Non-Destructive Testing (NDT) technique that allows, among others, the detection and localization of buried utilities without any damage to the surface. This paper presents the results of mapping sewer lines in an urban region of the city of Tunis, Tunisia, using GPR. In this study a ground-coupled GPR system with a frequency of 400MHz was used to detect and locate the sewer lines in the study area. In order to reduce the amount of collected data over the large study area, some subareas were selected where GPR testing was conducted and data were analyzed. Then, sewer lines were interpolated over the whole area using the GPR results and the locations of manholes visible from the surface. It was shown that using this technique mapping of the pipes in the entire area can be achieved. All data presented in this study were collected by the Office of Topography and Cadastre (OTC) in Tunisia.

Frequency-Domain Reverse-Time Migration of Ground Penetrating Radar Based on Layered Medium Green's Functions

A frequency-domain reverse-time migration (RTM) algorithm based on the layered medium dyadic Green's function (DGF) is proposed for high-resolution and efficient subsurface imaging using ground penetrating radar (GPR). Different from the conventional RTM, which is performed by the finite-difference time domain (FDTD) method, both the forward and backward extrapolation wavefields in this frequency-domain RTM are computed by the multiplication between the frequency spectra of the excitation source or the recorded GPR data and the layered medium DGFs. The final image is reconstructed by the direct summation of all the wavefields for all the sampling frequencies in the frequency domain, instead of transforming them back to the time domain and processing them. Two-dimensional (2-D) or 3-D numerical and laboratory experiments show that the proposed frequency-domain RTM algorithm can yield almost the same imaging results as the conventional time domain RTM algorithm but requires less than two orders of magnitude in computational costs. The proposed GPR imaging method is verified to be competent for the fast 3-D imaging of potential larva rocks under the lunar surface, which can provide critical information for the regolith-drilling task of the Chang-E 5 lunar exploration mission of China. Real-time subsurface imaging by RTM could be fulfilled and be widely applied to engineering geophysics, such as urban utility detection, through-wall imaging, and ice monitoring by ground-coupled or air-borne GPR.

Automatic detection of steel rebar in bridge decks from ground penetrating radar data

This paper develops a fully automatic algorithm to detect steel rebar in concrete bridge decks from ground penetrating radar (GPR) data. The surveyed GPR data from reinforced concrete (RC) structure carries significant amount of information, such as rebar locations, dielectric constant of concrete, corrosion conditions of steel rebars, and etc. However, current methodologies to identify rebar hyperbolas from GPR data are either manual or interactive, which is time-consuming. An unsupervised, efficient, and robust pattern recognition algorithm is proposed. In the algorithm, a template of a rebar hyperbola is selected first from the data, and is updated automatically to achieve better performance of rebar detection during the data processing. A rebar hyperbola in the GPR data is detected from the local minimum points on the map of sum of square difference (SSD) with an adaptive threshold value. An output file is created and saved, containing the amplitudes of rebar reflection and the geometrical locations of the detected rebars. The proposed algorithm is tested with ground-coupled GPR data collected from several decommissioned RC slabs, which are saw cut from a highway bridge. The overall percentage of accurate rebar hyperbola detection is 88.0%. Finally, the reflection amplitudes extracted from the rebar hyperbolas are used to create a corrosion indicator map, which shows good agreement with the results of half-cell potential maps.

Concrete Bridge Deck Deterioration Assessment Using Ground Penetrating Radar (GPR)

Ground penetrating radar (GPR) has become an effective means for assessing deterioration in concrete bridge decks. While success has been demonstrated, the method is still not adopted widely. Constant technical development is making such high speed GPR mapping more affordable with systems more widely available and easier to deploy. The American Society for Testing and Materials (ASTM) has a standard procedure for performing bridge deck deterioration using GPR. The current standard, initially written for air-launched GPR devices and then modified to include ground-coupled GPRs, has many simplifying assumptions that could lead to fallacious evaluations. Both field experience and numerical simulations indicate that ground-coupled GPR systems are preferable to air-launched GPRs in this application, delivering larger signal-to-noise and higher spatial resolution data, which enhance extraction of both electromagnetic wave velocity and attenuation. We describe advances in analysis and interpretation that go beyond the current ASTM approach which ignores the impact of depth and other variables. We demonstrate these advances using a high speed, ground-coupled GPR system with examples of deck deterioration mapping. We describe the workflow for using GPR to evaluate the deterioration of concrete bridge decks, highlight the basic interpretation assumptions, demonstrate successful applications and discuss limitations with the methodology.

Utilization of air-launched ground penetrating radar (GPR) for pavement condition assessment

Ground penetrating radar (GPR) is an electromagnetic-based nondestructive geophysical tool that is widely used for pavement investigations, including the differentiation of pavement layers, the mapping of layer thicknesses, and the assessment of pavement condition. This paper presents a case study in which an air-launched GPR system consisting of two truck-mounted GSSI antennae driven at near-highway speeds was used to investigate two segments of asphalt paved roadway in central Missouri. The acquired data were interpreted; pavement layers were differentiated, the variable thicknesses of the pavement layers were mapped, bulk pavement condition were assessed, and optimum acquisition and processing parameters were developed. Core control, visual assessments, and limited ground-coupled GPR data were used to constrain and verify the reliability of the interpretations of the acquired air-launched GPR data.

Extraction of the Pavement Permittivity and Thickness From Measured Ground-Coupled GPR Data Using a Ground-Wave Technique

A ground-wave technique is introduced in this letter to directly extract the pavement permittivity and thickness from measured data of ground-coupled ground-penetrating radar (GPR). Analytic solution, numerical simulation, and experimental test are carried out to validate the method. This technique enables bistatic radar to obtain both thickness and permittivity by just one measurement, which effectively reduces measurement and computation time for GPR applications.

Determining the Optimal Frequency of Ground Penetrating Radar for Detecting Voids in Pavements

PURPOSES : The objective of this study is to determine the optimal frequency of ground penetrating radar (GPR) testing for detecting the voids under the pavement. METHODS : In order to determine the optimal frequency of GPR testing for void detection, a full-scale test section was constructed to simulate the actual size of voids under the pavement. Voids of various sizes were created by inserting styrofoam at varying depths under the pavement. Subsequently, 250-, 500-, and 800-MHz ground-coupled GPR testing was conducted in the test section and the resulting GPR signals were recorded. The change in the amplitude of these signals was evaluated by varying the GPR frequency, void size, and void depth. The optimum frequency was determined from the amplitude of the signals. RESULTS: The capacity of GPR to detect voids under the pavement was evaluated by using three different ground-coupled GPR frequencies. In the case of the B-scan GPR data, a parabolic shape occurred in the vicinity of the voids. The maximum GPR amplitude in the A-scan data was used to quantitatively determine the void-detection capacity. CONCLUSIONS: The 250-MHz GPR testing enabled the detection of 10 out of 12 simulated voids, whereas the 500-MHz testing allowed the detection of only five. Furthermore, the amplitude of GPR detection associated with 250-MHz testing is significantly higher than that of 500-MHz testing. This indicates that 250-MHz GPR testing is well-suited for the detection of voids located at depths ranging from 0.5~2.0 m. Testing at frequencies lower than 250 MHz is recommended for void detection at depths greater than 2 m.

Detection of Rockfall on a Tunnel Concrete Lining with Ground-Penetrating Radar (GPR)

Experiments were conducted using Ground-Penetrating Radar (GPR). The performance of six GPR systems was assessed in terms of: (1) remotely mapping cavities behind concrete linings, (2) detecting rockfall from the tunnel roof onto an inner lining comprising, for example, precast concrete segments. Studies conducted in Norway and the United States demonstrate that the GPR technique is a simple and reliable method that can assist stability inspection in existing Norwegian tunnels. The ground-coupled GPR systems represent a step forward in the remote detection of rockfall on tunnel concrete linings, and are particularly suited to self-standing inner linings. The analysis of the data is relatively straightforward and reasonably accurate.

Pavement thickness and stabilised foundation layer assessment using ground-coupled GPR

Experimental results from field and laboratory investigations using a ground-coupled ground penetrating radar (GPR), dielectric measurement, magnetic imaging tomography (MIT) and dynamic cone penetrometer (DCP) tests are presented. Dielectric properties of asphalt pavement and stabilised and unstabilised pavement foundation materials were evaluated in the laboratory in frozen and unfrozen conditions. Laboratory test results showed that dielectric properties of materials back-calculated from GPR in comparison to dielectric gauge measurements are strongly correlated and repeatable. For chemically stabilised materials, curing time affected the dielectric properties of the materials. Field tests were conducted on asphalt pavement test sections with different foundation materials (stabilised and unstabilised layers), drainage conditions and layer thicknesses. GPR and MIT results were used to determine asphalt layer thicknesses and were compared with measured core thicknesses, while GPR and DCP were used to assess foundation layer profiles. Asphalt thicknesses estimated from GPR showed an average error of about 11% using the dielectric gauge values as input. The average error reduced to about 4% when calibrated with cores thicknesses. MIT results showed thicknesses that are about 9% higher than estimated using GPR. Foundation layer thicknesses could not be measured using GPR due to variations in moisture conditions between the test sections, which is partly attributed to variations in gradation and drainage characteristics of the subbase layer.

Concrete bridge deck assessment: Relationship between GPR data and concrete removal depth measurements collected after hydrodemolition

A ground-coupled ground penetrating radar (GPR) system was used to assess the condition of two reinforced concrete bridge decks. After each GPR assessment was completed, the bridge deck was rehabilitated using a hydrodemolition process to remove deteriorated concrete from upper surface of the deck. LiDAR technology was used to create maps depicting the deck surface before and after the concrete removal. The objective of this work was to corroborate the GPR condition assessments by comparing the spatial distribution of the GPR and LiDAR mappings. This work illustrates that GPR data have the potential to predict concrete repair estimates.