The Allertal salt structure, used as repository for nuclear waste, was explored from the surface by seismic measurements, ground openings and boreholes. Due to the accuracy of these investigations, the internal salt structure is known. The complex geological structures (bent, folded, sometimes faulted layering of salt, potassium, clay, and anhydrite) in low-conductive salt can be mapped if distance and direction of reflecting objects are known. In focus of interest is the distribution of anhydrite layers in the center of the salt structure. Underground, GPR (Ground penetrating radar) is an efficient and precise nondestructive tool for the exploration of salt deposits. With geophysical results and some geological knowledge a three dimensional model of the salt structures can be created.
Unevenness of road surfaces are a source of discomfort for the users, and to some extend even of safety hazards. Road administrations therefore put requirements on evenness in tenders. It seems accepted in the literature that roads in asphalt naturally present “long” wavelengths that are not so present in concrete roads. In this paper is presented how Ground Penetrating Radar (GPR) was used for investigating the influence of compaction of bituminous layers on unevenness with “long” wavelengths.
The discovery of written records mentioning an underground crypt beneath the floors of the Church of St. Margaret in Dol pri Ljubljani, Slovenia, lead to further investigations of the area. During a small-scale renovation of the church floor, an area filled with construction waste was discovered, suggesting the underground rooms may have been filled in during one of the previous restorations. A GPR study was carried out inside the church in order to determine whether any air-filled chambers were still present beneath the church floor. Results showed the presence of an underground air-filled chamber, the existence of which was confirmed with a small telescopic camera, lowered through a drilled hole in the floor.
The information of the type of structure, materials and thickness of different layers, that constitute a transport infrastructure, particularly the airfields, is essential for the assessment of its bearing capacity and to detect distresses. In this context, the Ground Penetrating Radar (GPR) is particularly useful, as it provides information on a continuous way, and is possible to register this information in digital BIM models. This paper presents 3D digital model of the infrastructure that allows for a better visualization of the variations of the thicknesses and can be used to add other monitoring information, such as load tests or structural characteristics, to support the pavement management system. Recommendations are presented and future perspectives are also herein referred.
This study proposed integrating CFD and FDTD to a numerically simulate the GPR response of pipe leakage in coastal sandy soil. CFD was used to model the water seepage of diffident pipe defects, while FDTD was applied to simulate radar wave propagation through the wet soil. It was observed that under the combined action of gravity and soil viscous resistance, the leakage area presents an ellipsoid with serrated edges. The corresponding radargram presented a bowl-shape strong reflection. The study proved the feasibility of using CFD and FDTD to construct more realistic seepage condition, and provide the reference for detecting pipe leakage.
Apparently complex reflection patterns can be common in GPR structural surveys. The main reasons for that are the regular geometrical shapes of structural and non-structural elements and the high frequency systems employed. These imply a greater direct and indirect signal interaction with materials: typical examples are multiples created by relatively thin layers, not otherwise visible with low frequency systems. This paper presents a case study where a radargram collected on a masonry wall of a historical building exhibited crisscross or “X” reflection patterns. It was the only wall in the building where these patterns were observed. Based on assumptions relying on a priori knowledge of the building structure gained by means of GPR scans conducted on adjacent walls, numerical modelling was employed to verify a hypothesis that could explain the observed data.
We investigate the influence of a horizontally polarized wave's oblique incidence on the direction of arrival (DOA) estimation with a dipole array antenna in a borehole. According to a computer simulation, the horizontally polarized wave component of the reflected wave from the conducting cylinder influences the wave's azimuth angle estimation. We conducted a field experiment in soil with a similar situation to the computer simulation. The experimental results confirmed the phenomena predicted by the computer simulation.
Several lab tests are applied to determine relations between GPR data and physical properties of agricultural soils. Soil water content and porosity were related to different parameters: the wave velocity, the frequency of the received signal and changes in the signal amplitude. Experimental data was compared to theoretical and empirical models.
Among the commonly used non-destructive techniques, the Ground Penetrating Radar (GPR) is one of the most widely adopted today for assessing pavement conditions in France. However, conventional radar systems and their forward processing methods have shown their limitations for the physical and geometrical characterization of very thin layers such as tack coats. However, the use of Machine Learning methods applied to GPR with an inverse approach showed that it was numerically possible to identify the tack coat characteristics despite masking effects due to low timefrequency resolution noted in the raw B-scans. Thus, we propose in this paper to apply the inverse approach based on Machine Learning, already validated in previous works on numerical data, on two experimental cases with different pavement structures. The first case corresponds to a validation on known pavement structures on the Gustave Eiffel University (Nantes, France) with its pavement fatigue carousel and the second case focuses on a new real road in Vend{é}e department (France). In both case studies, the performances of SVM/SVR methods showed the efficiency of supervised learning methods to classify and estimate the emulsion proportioning in the tack coats.
This paper presents a Ground Penetrating Radar (GPR) system based on a drone, or Unmanned Aerial System (UAS). UAS-based GPR systems could represent the future in the field of GPR sensors, since such a platform enables automatized, remote surveys in all kinds of terrain. The prototype illustrated in this work have been developed using a commercial drone and a Vector Network Analyzer (VNA) based radar sensor. In order to test the system performances, joint experimental tests have been performed by using the prototype and a conventional GPR. The comparison between results obtained with the two sensors, are promising: the UAS-based prototype successfully detected underground targets. Although the conventional GPR outperforms the UAS-based prototype, the latter offers greater flexibility and versatility on non-regular areas.
In this contribution we describe an uncommon technique for making use of the marker points in GPR prospecting. This technique is based on a “stop-and-go” of the antennas described in the follow, and has been applied on three pre-historic Tumuli in the countryside of Parabita, a small town distant 40 Km from Lecce, Italy. The topography of the Tumuli has been evaluated from a photogrammetric reconstruction, which has allowed to account for the variations of height with respect to the surrounding planking level.
The article presents possibility of use of fully non-destructive method in order to rebars corrosion assessment in concrete slab. The tests were carried out on the slab with visible reinforcement corrosion using profometer (semi-destructive method because it requires contact of electrode with the rebar) and using ground penetrating radar, without contact to the rebars. The results were corelated - in the place where corrosion of rebar is clearly visible there is the biggest potential difference (based on profometer measurements) and the higher level of amplitude attenuation (based on GPR investigations). It means we can use only GPR method without support of semi-destructive tools in order to rebars corrosion location and quantitative assessment of the degree of reinforcement deterioration.
In recent years, the effects of emerging diseases have caused significant worries among environmentalists and communities, requiring putting efforts into the monitoring and management of natural resources. In this regard, tree roots are one of the most vital and fragile organs of the tree, as well as one of the most complex to investigate. In this way, non-destructive testing (NDT) methods have become one of the most popular techniques for assessing and monitoring tree roots, as opposed to conventional destructive techniques. In this context, ground penetrating radar (GPR) applications have proved to be precise and effective for investigating and mapping tree roots. The inhomogeneity of the soil, however, is a significant obstacle towards the GPR identification of tree roots, and a deep learning (DL)-based method has been recently proposed to tackle this issue. This research, therefore, aims to improve upon the above-mentioned approach, by customising two convolutional neural networks (CNN) methods for the analysis of GPR spectrograms. In this study, the GPR signal is first processed in both the temporal and frequency domains to filter out noise-related information, and subsequently spectrograms are generated. Afterwards, two specifically modified CNN classifiers are implemented and then compared to other DL methods, already validated for tree roots detection. The findings of this study further support the viability of the suggested methodology and open the way for the application of new approaches for evaluating tree root systems.
In this paper, we present an AI-based Graphical user interface (GUI) devoted to B-scan data visualization, interpretation and classification of GPR data over debonded areas in the pavement structure. Two independent GUIs perform the processing of GPR data at two levels: gPRocessor and gprDetector. gPRocessor enables first an automatic preprocessing of the GPR data. It allows the operator to view, modify and update the data from any radar configuration in either time or frequency domains. The output B-scan processed data can be exported per user requirements. In the second stage, gprDetector implements the interlayer debonding detection on a scan-by-scan basis. Various supervised and unsupervised machine learning methods with suitable feature engineering techniques allow to classify A-scan data as either debonding or healthy zone. gprDetector includes a tool to label the data for defining the pseudo-ground-truth to be used for the performance assessment of the classification methods. Graphical results may be displayed at each processing stage to provide detailed information about the data. Both GUI tools were tested on the field data base in [1].
This work presents a combined study of complementary non-destructive techniques, GPR (Ground-Penetrating Radar) and Light Detection and Ranging (LiDAR). to analyze pathologies in a heritage building. The GPR survey was conducted with 2.3 GHz antennas. The Terrestrial Laser Scanner (TLS) used has a ±2 mm accuracy. LiDAR revealed a lateral thrust of the main nave of the hermitage, and moisture at the side wall of the altar. GPR allowed to determine the presence of moisture inside the wall. The joint interpretation of all the data allowed to highlight moisture as the cause of stone degradation, thus affecting the structural integrity of the nave. The interpretations thus obtained enables to define most appropriate maintenance interventions.
This paper presents a case study performed upon searching the foundation remnants of an approximately 400 years old lost wooden church, near Breb village in Maramure, Romania, in the old area known as Copăciş. The site was investigated using a ground penetrating radar complemented with an aerial survey that offered 3D features.
This paper analyses the electromagnetic (EM) wave scattering from an object buried between two random rough interfaces. We present a rigorous numerical EM model based on the boundary integral equation method that separates the different sensed media, which is discretized by the method of moments. The resulting linear system is resolved by a fast method called GPILE (Generalized Propagation Inside Layer Expansion), which makes it possible to reduce the complexity of the inversion of the impedance matrix and to isolate the different contributions to the overall scattering process. This method is then applied to a roadway sensed by GPR, with a view to calculating its time-domain response. The practical objective of this analysis is to study the impact of an embedded object inside a complex multilayered medium on the GPR signal.
LAYERS research project* proposes to explore, using geophysical methods, such as ground penetrating radar (GPR) and electrical resistivity, in the thickness of the walls and in the soil depth around buildings of heritage interest, and the layers in which the history of constructive and decorative elements, such as azulejos, may be revealed. In this paper, the results of some GPR and electrical resistivity tomography profiles carried out at Museu do Azulejo are presented. Preliminary results point to the identification of tombstones thickness, pathway detection of water supply system between two cloisters, and the possible presence of an underground water tank.
We conducted ground penetrating radar (GPR) surveys to detect the presence of simulated clandestine burials at the Amsterdam Research Initiative for Subsurface Taphonomy and Anthropology (ARISTA) test facility. Our aim is to determine the characteristic responses of the simulated clandestine burials in this man-made sandy environment (reclaimed land) and use them to provide recommendations for forensic investigations. We performed GPR surveys over three simulated clandestine burials at ARISTA during four non-consecutive days. The acquired data represent common-offset data to investigate changes to burial detectability depending on central antenna frequency (250 MHz and 500 MHz), different GPR instruments (NOGGIN or pulseEKKO), changes to survey grid orientation relative to burials, and increased soil moisture content in the survey area. In common-offset radargrams the burial anomalies take on many forms, appearing as disruptions to existing features (direct-wave arrivals and soil horizons) and as isolated reflection events (hyperbolic events and burial-length horizontal anomalies). In time slices, the burials are characterized by high- or low-amplitude rectangular anomalies. When used in conjunction, the radargrams and time slices produce characteristic responses consistent with the locations of the burials, regardless of the survey grid orientation. Increased soil moisture at the site improves the detectability of the burials.
Non-metallic pipes are widely used in the urban underground pipe network. To map the pipeline system, obtaining the accurate locations and directions of the pipelines is an essential challenge. In this work, Ground Penetrating Radar (GPR) is used to detect buried non-metallic pipes since it is an efficient and non-destructive method for buried non-metallic pipes. The main objective of this paper is to develop a simple, efficient and automated method to locate non-metallic pipelines, by considering the estimation of the buried depth and the distortion of hyperbola shown on the B-scan. The radargrams of buried non-metallic pipes with several angles (0°, 30°, 60° and 90°) between the GPR survey line and the pipeline were compared numerically and experimentally. Edge detection with Canny operator was used for the automatic extraction of the hyperbolas reflected by buried pipes. The pixels of the edge detection image were adapted to the detection area. So, the buried depth of the pipes could be estimated by picking the pixel point of the hyperbola vertex accurately. The estimation results showed good accuracy with good shaped and distorted hyperbolas. It can also be extended to the detection of other buried objects.
