Ground Penetrating Radar Measurements Research Articles

Coupled full-waveform inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic parameters: A synthetic study

Coupled inversion is a promising technique for determining soil hydraulic properties from time-lapse geophysical measurements. In this inversion approach, a hydrological model is coupled with ground penetrating radar (GPR) forward modelling to avoid interpretation errors from data processing. In this study, a workflow for coupled GPR full-waveform inversion (CFWI) is proposed that combines the benefits of coupled inversion and full-waveform inversion (FWI) to estimate soil hydraulic properties from time-lapse horizontal borehole GPR measurements. In particular, a synthetic modelling study is presented that compares the performance of coupled inversion of full waveforms and first arrival time data from zero-offset profile (ZOP) borehole measurements obtained during an infiltration event for estimating hydraulic parameters and the thickness of the first layer for a 2-layer soil profile. It was found that the thickness of the first layer could be more accurately estimated by CFWI because of the additional information contained in reflected and refracted waves from the air–soil layer boundary. Furthermore, the hydraulic parameters estimated by the coupled inversion of GPR travel times slightly deviated from true values and showed a relatively large uncertainty, whereas the results of CFWI precisely matched the known water retention and hydraulic conductivity functions. Despite these promising results, several challenges should be solved before CFWI can be applied to experimental GPR data. First, conducting CFWI needs accurate conceptual setups for the hydro(geo-)logical model. Furthermore, a robust approach should be developed to accurately estimate an effective source wavelet from the experimental ZOP data, and a more sophisticated hydrological modelling approach has to be considered to better estimate the distribution of soil electrical conductivity during the infiltration event. Finally, the accuracy and efficiency of current GPR modelling methods may need to be improved.

Automatic modelling of urban subsurface with ground-penetrating radar using multi-agent classification method

ABSTRACT The subsurface of urban cities is becoming increasingly congested. In-time records of subsurface structures are of vital importance for the maintenance and management of urban infrastructure beneath or above the ground. Ground-penetrating radar (GPR) is a nondestructive testing method that can survey and image the subsurface without excavation. However, the interpretation of GPR relies on the operator’s experience. An automatic workflow was proposed for recognizing and classifying subsurface structures with GPR using computer vision and machine learning techniques. The workflow comprises three stages: first, full-cover GPR measurements are processed to form the C-scans; second, the abnormal areas are extracted from the full-cover C-scans with coefficient of variation-active contour model (CV-ACM); finally, the extracted segments are recognized and classified from the corresponding B-scans with aggregate channel feature (ACF) to produce a semantic map. The selected computer vision methods were validated by a controlled test in the laboratory, and the entire workflow was evaluated with a real, on-site case study. The results of the controlled and on-site case were both promising. This study establishes the necessity of a full-cover 3D GPR survey, illustrating the feasibility of integrating advanced computer vision techniques to analyze a large amount of 3D GPR survey data, and paves the way for automating subsurface modeling with GPR.

Ground-Penetrating Radar Signal Characterization for Non-destructive Evaluation of Low-Range Concrete Sub-surface Boundary Conditions

The use of nondestructive sensing technologies is critical in the evaluation of concrete conditions and structural integrity. In our previous experimental studies, we showed the use of the ground-penetrating radar (GPR)-based nondestructive evaluation method to examine low-range concrete subsurface boundary conditions. As the characterization of GPR signals based on multiple factors is crucial for furthering the accurate conditions of concrete subsurfaces, we investigate the GPR signals in this letter by simulating electromagnetic wave propagation on concrete models through the use of the finite-difference time-domain method. The effects of concrete relative permittivity, the standoff distance of GPR from the top surface of concrete, the operating frequency of GPR, and the depth height of multilayered concrete on the GPR measurements were all investigated, and conclusions were established. Through the outcomes of this investigation, we will be able to choose the most appropriate operating frequency for the GPR for collecting measurements on concrete infrastructures at the most effective standoff distance to the surface and subsurface depths under a variety of relative permittivity conditions in various locations.

An approach for the reconstruction of a traditional masonry-wooden building located in an archeological area. Part I: Methodology

This study, presented in a pair of articles, defines a comprehensive methodological approach to the reconstruction of a traditional masonry-timber mansion building constructed in the 1880s of the Turkish house typology on Istanbul’s historical peninsula area that was intentionally demolished in 1948. A historical process research was carried out in the first stage of the study, after which ground penetration radar measurements and an archeological excavation were carried out to determine any possible remains or ruins of the structure, and the original architectural features of the demolished building were ascertained from the obtained data. The proposed stages in the current paper can be considered a comprehensive approach to the determination of the authentic properties of demolished or destroyed buildings in historical areas, given that the methodology allows for the integration of construction features obtained separately and independently through different activities, such as excavations, georadar measurements and historical surveys. The result is a versatile approach to the complete and realistic reconstruction of historical buildings.

Улаанбаатар Орчмын “Сонгино” Идэвхитэй Хагарал Дээрхи Георадарын Судалгааны Анхан Шатны Үр Дүнгээс

The seismic activity observed in the vicinity of Ulaanbaatar (UB) city not only has been increased since 2005, but it is also organized, at least, on two perpendicular directions which determine two active faults: Emeelt fault (40 km long, situated 15 km NW of UB) and Hustai fault (80 km long, less than 30 km West of UB). The Songino fault (10 km long, situated 20 km West of UB) is located between Emeelt and Hustai fault. Their length and morphology indicate they can produce earthquakes of magnitude 6.5 to 7.5. Most of the Mongolian population (1.5 million over 3 million) is concentrated around UB city which is the main political and economical centre of the country. Hence, the study of seismic hazard is of first importance for the country. Since the last large earthquake, the fault geomorphology has been smoothed due to erosion processes and a low slip rate. The exact location of the fault is thus hidden in a several meter strip. In such a context, the Ground Penetrating Radar (GPR) method gives good and useful results and can help to characterize faults by identifying offsets of radar reflectionsand buried fluvial channel deposits. In order to image and characterize these faults, we have conducted many GPR campaigns in 2010, 2012 and 2013 over active areas around UB. In our study we present only some preliminary results of GPR measurements performed along the Songino fault (in 2013). The objective is to understand its geometry (dip, length, orientation) and the type of displacements and amplitude. In this survey we used 250 MHz, 500 MHz shielded antenna and RTA 50 MHz unshielded antenna and also used a differential GPS in order to measure the topography. One trench has been dug in Songino area.

Detecting the ground-dependent structural damages in a historic mosque by employing GPR

This study aims to employ one of the most significant geophysical methods, known as the ground penetrating radar (GPR) method, in the determination of reasons for the underground cavities, the size of deformations and fractures in the ground and structure, the geometric behavior patterns, and the soil-structure interaction and changes in a historic structure. In addition to the instrumental examination, an observational investigation of the current situation of the mosque was also carried out. İskender Pasha Mosque, which is a rare mosque having a tabhane (guest house), is one of the important historical mosques regarded as built-in Mimar Sinan style in Diyarbakır, Turkey in 1552. The instrumental examination part of the study was conducted totally in 13 different regions of the mosque and its courtyard. Within this scope, 8 GPR measurements were taken on the ground of the mosque and its immediate environment and 5 GPR measurements were taken on the walls of the mosque. The GPR measurements indicated that there are serious subsidence anomalies under the mosque and its courtyard. Besides, there is a water table under the portico of the mosque which may lead to big problems in the future. In addition, observationally detected structural deteriorations were supported by geophysical GPR measurements. It was revealed that problems dealing with the leveling irregularities on the floor of the harim, portico, and courtyard are directly related to the anomalies under the ground. Moreover, it was achieved from both instrumental and observational investigations that the anomalies under the ground are directly the reasons for the anomalies in the walls of the mosque. GPR results demonstrated that this technique can be used effectively to determine anomalies in both the carrier system of the historic structures and the ground under the historic structures without any damage.

Standoff High-Frequency Electromagnetic Induction Response of Unsaturated Sands: A Tank-Scale Feasibility Study

Standoff electromagnetic induction (EMI) measurements of complex conductivity and complex permittivity for engineering soil properties have the potential to revolutionize the way the US Army handles route planning and infrastructure assessment. An unmanned aerial system (UAS) based EM platform for soil interrogation would have wide reaching impact in a variety of applications including: civil infrastructure inspection, in-theater ingress and egress routing, reduction of false positives in IED detection, and permafrost mapping, among many others. Traditional frequency domain EMI instruments assess conductivity at low-frequencies, generally in the range of 1–20 kHz; however, recent advancements have resulted in instrumentation targeting a broadband range of frequencies, from 10 kHz through 20 MHz. This advancement, known as high-frequency electromagnetic induction (HFEMI) allows the potential to evaluate frequency domain relaxation effects in soils by acquiring both the in phase and quadrature response of the secondary field from the soil. Relaxation phenomena such as induced polarization and dielectric permittivity are related to important soil properties that can potentially be exploited using this HFEMI system. While conductivity measurements using the quadrature component of the EMI response are well established in EMI instrumentation, understanding of the relationship between direct electrical measurements and standoff HFEMI measurements is lacking. In an effort to illuminate this relationship between various electrical and electromagnetic methods at a scale suitable for soil property estimation, we perform side-by-side measurements using galvanic geoelectrical methods (ERT, IP), electromagnetics, time-domain reflectometry (TDR) and ground penetrating radar (GPR). We compare HFEMI obtained quadrature and in-phase responses to ERT, IP, TDR and GPR measurements. A tank-scale test cell was developed for comparison of the above methods and allowed assessment of sand at varying saturation levels. Further, the HFEMI response at varying heights above the sand surface was also assessed. Qualitative observations are reported in an initial attempt to relate the HFEMI response to important soil parameters.

Using Ground Penetrating Radar to Monitor Seasonal Moisture Fluctuations in Base Layers of Existing Roads

A high moisture content in the unbound aggregate layers of pavements can lead to severe functional and structural damage, especially in pavement constructed in freeze-thaw environments, such as Minnesota. In these climates, the freezing temperatures can contribute to the formation of severe crack-heaving issues, and the early spring-thaw conditions can significantly reduce a pavement’s bearing capacity. A good understanding of subsurface seasonal moisture fluctuation is, therefore, essential for implementing effective maintenance and road load management techniques. Unfortunately, traditional pavement moisture testing approaches are expensive, time-consuming, invasive, limited in spatial coverage, and negatively affect the traffic flow. The present study builds on previous efforts to characterize moisture and frost conditions in pavement base layers using ground penetrating radar (GPR) and moisture sensing instruments. The paper offers a simple methodology for monitoring moisture variations in the base layer and assessing the moisture susceptibility of aggregate base materials from single-offset GPR measurements. The methodology was validated by comparing the GPR data with moisture content readings collected from four different pavement test sections over a period of approximately two years. Overall, the moisture contents obtained from the GPR were comparable with those obtained from sensors installed in the pavement section. However, the GPR approach offered the added advantage of covering long stretches of pavement in short times and with minimal impact on the pavement and traffic.

GPR mapping with mobile mapping sensing and tracking technologies

Ground Penetrating Radar (GPR) is commonly used for detecting and locating underground utilities, as well as identifying water leakages and other buried objects without the need for excavation. To precisely depict the subsurface by GPR, an accurate geo-referencing technique on each GPR scan is also essential. Traditional geo-referencing of GPR scans requires a survey wheel to walk along a predetermined grid, or relies on the Global Navigational Satellite Systems (GNSS) to work under open sky environments where enough satellites are available (Böniger and Tronicke, 2010; Chang, 2016). In this study, we attempt to use a mobile mapping system (MMS) backpack guided by SLAM as the geo-referencing device for synchronizing GPR scans in areas with bad or no GNSS, and is free of control points. The study develops a method to synchronise the SLAM positioning and GPR measurements and then validate the proposed method's precision and accuracy with a site case. It was proved that the integrationed time and labour required in the traditional gridding are minimized for improving the GPR survey efficiency, facilitating the large-scale survey in dense urban areas where good GNSS coverage is unavailable.

Missing Data Recovery via Deep Networks for Limited Ground Penetrating Radar Measurements

Missing data problem frequently occurs during data acquisition in ground-penetrating radar (GPR) and recovery of the missing entries prior to any processing is vital in GPR imaging. Existing missing data recovery methods are based on low-rank matrix completion or the recently proposed deep generative networks. However, the former approaches suffer from producing satisfying results under severe missing data cases and the latter require a large amount of data for training. This study proposes two methods based on deep networks for the missing data recovery. The first method uses pyramid-context encoder network (PEN-Net) architecture which consists of three parts: attention transfer network, guided Pyramid-context encoder, and a multi-scale decoder. Although the method needs training, it requires considerably less data compared to the existing U-Net based method. The second method, deep image prior (DIP), is a regularization based data recovery method which uses an untrained network as a prior. This method does not need any training, network weights are initialized randomly and updated during the iterations to minimize the cost function. Different experiments are reported for both pixel and column-wise missing cases in simulated and real data. The simulated data results show that the proposed methods have a noticeably better performance than conventional methods for the challenging pixel-wise case around 17–27% and moderate level column-wise missing case around 15%. Besides, they can also deal with extreme column-wise missing data cases where the conventional methods fail completely. Real data results further verify the superiority of the proposed methods.

UAV and GPR Data Integration in Glacier Geometry Reconstruction: A Case Study from Irenebreen, Svalbard

Although measurements of thickness and internal structure of glaciers are substantial for the understanding of their evolution and response to climate change, detailed data about polythermal glaciers, are scarce. Here, we present the first ground-penetrating radar (GPR) measurement data of Irenebreen, and high-resolution DEM and orthomosaic, obtained from unmanned aerial vehicle (UAV) photogrammetry. A combination of GPR and UAV data allowed for the reconstruction of the glacier geometry including thermal structure. We compare different methods of GPR signal propagation speed determination and argue that a common midpoint method (CMP) should be used if possible. Our observations reveal that Irenebreen is a polythermal glacier with a basal temperate ice layer, the volume of which volume reaches only 12% of the total glacier volume. We also observe the intense GPR signal scattering in two small zones in the ablation area and suggest that intense water percolation occurs in these places creating local areas of temperate ice. This finding emphasizes the possible formation of localised temperate ice zones in polythermal glaciers due to the coincidence of several factors. Our study demonstrates that a combination of UAV photogrammetry and GPR can be successfully applied and should be used for the high-resolution reconstruction of 3D geometries of small glaciers.

Orthogonal Dual Polarization GPR Measurement for Detection of Buried Vertical Fault

This short letter proposes a ground-penetrating radar (GPR) measurement technique to better detect vertical fault that occurs in a base rock under a surface layer. The technique exploits polarization-dependent radar response of the linear feature of the fault end that appears on the featureless base rock surface. A set of GPR measurements of which polarizations are orthogonal to each other are carried out in a fashion that one polarization is parallel to the putative target fault line, while another is normal to it. Taking the difference of instantaneous power A-scope data of the measurements cancels contribution from the featureless base rock surface while the contribution from the fault fracture end is elicited, thanks to its strong polarization-dependent radar response. The technique was described by an analytic model, its feasibility was demonstrated by finite difference time domain (FDTD) simulation, and was verified by a field measurement experiment.

Pavement Moisture Content Prediction: A Deep Residual Neural Network Approach for Analyzing Ground Penetrating Radar

The non-destructive detection and monitoring of moisture content in asphalt concrete (AC) pavement is important, as moisture may cause adhesion failures between aggregates and asphalt, resulting in AC stripping and raveling. This paper introduces a novel deep residual network–Mois-ResNets–to predict the internal moisture content of AC pavement from ground-penetrating radar (GPR) measurements. A GPR signal database was established from field tests and numerical simulations. A developed heterogeneous numerical model was used to generate synthetic GPR signals by simulating asphalt pavements at various configurations, moisture contents, volumetrics, and dielectric properties. The Mois-ResNets model, which contains a short-time Fourier transform followed by a deep residual network, was trained to minimize the error between predicted moisture content level and ground-truth data. Testing results show that Mois-ResNets can achieve a classification accuracy of 91% on testing datasets, outperforming conventional machine learning methods. The proposed Mois-ResNets has the potential for using GPR measurements and deep learning methods for pavement internal moisture content prediction.

Using Geometry Data from Ground Penetrating Radar to Improve Pavement Layers Auscultation Using Seismic Surface Waves Inversion Method

In order to reduce the unknown parameters during the inversion of the dispersion curve in the aim to obtain more precise Vs and VP profiles, the Multi-Channel Analysis of Surface Waves (MASW) method was combined with the Ground Penetrating Radar (GPR) method. First, a 2D GPR profile was made on the pavement, then 1D MASW profiles spaced by 0.2 m apart in the perpendicular direction and centered on the intersection with the Radar profile. The GPR measurements, made with a 1600 Hz antenna, allowed to calculate the dielectric permittivities then velocities in order to convert the times profiles to depth. Thus, the thicknesses of the layers are directly read on the radar profile. MASW measurements were performed by simulating the Multi-Channel Simulation with One Receiver (MSOR) method in Land Streamer mode. Six 4.5 Hz pointless geophones are connected to the seismograph consisting of an Arduino Due microcontroller and a nano-computer type Raspberry Pi 4. The results of the dispersion analysis showed a fundamental mode located between approximately 70 Hz and 200 Hz and an inverted dispersion, characteristic of pavements with higher frequencies propagating at higher velocities. The results show that the integration of the number of layers and the thicknesses obtained from the GPR measurements in the inversion parameters makes it possible to obtain a more precise Vs and VP velocity profile. All profiles, seismic and radar, have shown that velocities decrease with depth. The detected heterogeneities appear to be related to differences in water content inside the pavement.

ГЕОДИНАМІКА

The purpose of the research was to verify the extraordinary big theoretical penetration depth of new developed georadar in the real conditions of karsts areas. The new kind of the Ground Penetrating Radar (GPR) – Roteg – was developed in 2013 (RTG-Tengler 2013). Its technical parameters (pulse peak on the transmitting antenna 20 kV or more, and the sensitivity of the receiving antenna at least 2 uV, i.e. the real signal detection level around 20 uV) express that the real signal detection sensitivity is 9 orders, i.e. 180 dB. Such sensitivity means that the real penetration depth should be two orders bigger than in the case of common GPR´s. We tested the real penetration depths in the suitable environmental conditions over the caves in the Moravian Karst and in Slovenia near Postojna cave. The measurements results showed that reflections from known caves are reliably detectable at depths of 40-210 m below the surface. Reflections from the geological structures up to a depth of 480 m, in which the bottom of Lift II is probably located, were observable on the profile above the Hranická abyss. The new kind of Roteg georadar with 20 kV pulses on the transmitting antennas was able to detect reflections from the Devon – Brno Granite contact at a depth of up to 850 m in the case of optimal conditions in the karst without soil cover (in the Malá Dohoda quarry in the Moravian Karst). The radarogram showed a change in the lithology between the Vilémovice – Lažánky limestone and layers of clasts in the bottom of limestone strata. Both of the tests mentioned above confirmed the extraordinary big penetration depth of the GPR signal which exceeded 500 m in karst conditions when using the maximum power on transmitting antennas. The quite new kind of GPR called Roteg with the extraordinary high voltage on transmitting antenna and pulse instead of harmonic signal generation of transmitted signal allows reach two orders bigger penetration depths than the common GPRs.. The new kind of Roteg GPR makes it possible to obtain data (especially from karst areas) from depths that were previously only accessible by seismic methods or boreholes. GPR measurements are orders of magnitude cheaper and much faster in the field.

Accurate Tree Roots Positioning and Sizing Over Undulated Ground Surfaces by Common Offset GPR Measurements

Tree roots detection is a popular application of the Ground-penetrating radar (GPR). Normally, the ground surface above the tree roots is assumed to be flat, and standard processing methods based on hyperbolic fitting are applied to the hyperbolae reflection patterns of tree roots for detection purposes. When the surface of the land is undulating (not flat), these typical hyperbolic fitting methods becomes inaccurate. This is because, the reflection patterns change with the uneven ground surfaces. When the soil surface is not flat, it is inaccurate to use the peak point of an asymmetric reflection pattern to identify the depth and horizontal position of the underground target. The reflection patterns of the complex shapes due to extreme surface variations results in analysis difficulties. Furthermore, when multiple objects are buried under an undulating ground, it is hard to judge their relative positions based on a B-scan that assumes a flat ground. In this paper, a roots fitting method based on electromagnetic waves (EM) travel time analysis is proposed to take into consideration the realistic undulating ground surface. A wheel-based (WB) GPR and an antenna-height-fixed (AHF) GPR System are presented, and their corresponding fitting models are proposed. The effectiveness of the proposed method is demonstrated and validated through numerical examples and field experiments.

Nondestructive Inspection of Tree Trunks Using a Dual-Polarized Ground-Penetrating Radar System

In recent years, trees in European countries have been increasingly endangered by emerging infectious diseases (EIDs). In the United Kingdom, this has been observed to affect whole woodlands and forests, threatening the existence of some types of trees. Although quarantine measures have been taken to limit the spreading of such diseases, this has not yet been effectively controlled leading to millions of trees affected by EIDs. Ground-penetrating radar (GPR) has proven effective in identifying critical features on diseased trees for detection of EIDs spread. However, the irregular shape of tree trunks and their complex internal structure represent real challenges for conventional GPR measurements and data processing methodologies. In this research, a dual-polarised GPR system is used to detect internal decay in tree trunks using novel signal processing methodologies. A polarimetric correlation filter based on Bragg Scattering on a 3D Pauli feature vector and an arc-shaped Kirchhoff migration are discussed in detail. The proposed polarimetric correlation filter is utilised to enhance the signal-to-noise ratio (SNR) of B-scans due to bark and tree trunk high-loss properties of tree trunks. Meanwhile, an arc-shaped Kirchhoff migration algorithm is performed to counteract the influence of the bark irregularity. The proposed data processing framework is successfully validated with measurements on a real tree trunk, where cross-sections were subsequently cut for comparison purposes. Outcomes from the proposed methodology demonstrate a high consistency with the features observed on the tree trunk cross-sections, indicating the reliability of the proposed detection scheme for assessing tree-decay associated with EIDs.

After the Indian Ocean Tsunami (IOT): Natural beach recovery, Meulaboh, Sumatra, Indonesia

Ground-penetrating radar (GPR) offers an efficient and non-invasive method of identifying and characterising subsurface features. It has previously been used to investigate both tsunami deposits and marine erosion surfaces from tsunamis as well as the structure of the structure of prograding beaches. The present study investigates beach deposits at Meulaboh, western coast of Aceh Province in Sumatra Island of Indonesia, to estimate the volume of sediment that has been deposited since the 2004 Indian Ocean Tsunami, using the GPR with an antenna of 200 MHz. Two profiles perpendicular to the coastline were collected, one 93 m long and the other 30 m long, to capture the internal profile of beach ridge deposition. From the GPR measurement the amount of 1,190,191,716 tons of sediment redeposited along the 1092 m coastline since the 2004 tsunami, with a prograding length of 73 m per year. As beaches provide a good form of tsunami protection the rapid beach recovery and the return of a large amount of sediment helps provide much needed coastal protection to the area.

Snow Depth Retrieval With an Autonomous UAV-Mounted Software-Defined Radar

We present results from a field campaign to measure seasonal snow depth at Cameron Pass, Colorado, using a synthetic ultrawideband software-defined radar (SDRadar) implemented in commercially available Universal Software Radio Peripheral (USRP) software-defined radio hardware and flown on a small hexacopter unmanned aerial vehicle (UAV). We coherently synthesize an ultrawideband signal from stepped frequency 50-MHz subpulses across 600–2100-MHz frequency bands using a novel nonuniform nonlinear synthetic wideband waveform reconstruction technique that minimizes sweep time and completely eliminates problematic grating lobes and other processing artifacts traditionally seen in stepped waveform synthesis. We image seasonal snow across two transects: a 400-m open Meadow Transect and a 380-m forested transect. We present a surface detection algorithm that fuses data from LiDAR, global navigation satellite system (GNSS)/global positioning system (GPS), and features in the radargram itself to obtain high precision estimates of both snow and ground surface reflections, and thus total snow depth, represented as two-way travel time. The measurements are validated against independent ground-based ground-penetrating radar measurements with correlations coefficients as high as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho = 0.9$ </tex-math></inline-formula> demonstrated. Finally, we compare backscattered radar data collected by the UAV-SDRadar while hovering proximal to a known snow pit with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measured snow dielectric profiles and demonstrate imaging of snow stratigraphy.

Effect of Moisture Content on Calculated Dielectric Properties of Asphalt Concrete Pavements from Ground-Penetrating Radar Measurements

Moisture presence in asphalt concrete (AC) pavement is a major cause of damage to the pavement. In recent decades, an increasing need exists for non-destructive detection and monitoring of the moisture content in AC pavement. This paper provides a simulated approach to quantify the effect of internal moisture content on AC pavement dielectric properties using ground-penetrating radar (GPR). A heterogeneous numerical model was developed to simulate AC pavement with internal moisture at various saturation levels. The numerical model was validated using GPR surveys on cold-in-place recycling treated pavements. An empirical formula was derived from the simulation to correlate the dielectric constant with the moisture content for non-dry AC pavement. The results validated the proposed model and, hence, demonstrated the ability of GPR to monitor moisture variation in AC pavements.