A sampling investigation of GPR wave propagation velocity data to improve migration processing of concrete rebars

In this study, a demonstration of the potential of ground-penetrating radar (GPR) for improving location of rebars is given. To this purpose, a high-frequency GPR system with a 2000 MHz dual-polarised antenna (HH/VV polarisations) has been used to collect data on a reinforced concrete floor of dimensions 1m×0.80m (longitudinal and transversal acquisitions). The high-dense grid mesh of rebars and the use of the hyperbola fitting method allowed for the acquisition of a dataset of wave propagation velocity values. Hence, an analysis of the statistic distribution of propagation velocity values was carried out to assess the data dispersion throughout the area. A data sampling approach was then proposed and velocity values were sampled at different percentages and in an evenly-distributed manner throughout the inspected area. Corresponding values of velocity were therefore used for data migration purposes and C-scan maps were produced as a combination of longitudinal/transversal acquisitions and HH/VV polarisations. The optimum sampling rate of wave propagation velocity was then assessed by way of comparison of the migrated maps

Using ground penetrating radar methods to investigate reinforced concrete structures

This paper provides an overview of the existing literature on the subject of ground penetrating radar (GPR) methods for the investigation of reinforced concrete structures. An overview of the use of concrete and reinforced concrete in civil engineering infrastructures is given. A review of the main destructive and non-destructive testing methods in the field is presented, and an increase in the use of GPR to reinforced concrete structures is highlighted. It was also observed that research in some application areas has been predominantly or exclusively carried out at a laboratory scale, and that similarly, other more application-oriented research has been developed only on real-life structures. The effectiveness of GPR in these areas is demonstrated. Furthermore, a case study is presented on a new methodological and data processing approach for the assessment of reinforced concrete structures using a high-frequency dual-polarised antenna system. Results have proven the advantages of using the proposed methodology and GPR system in order to improve the detectability of rebars, including secondary bottom lines of reinforcement. The horizontal polarisation was proven to be more stable compared to the vertical. Finally, it has been demonstrated that a more accurate location of the rebars in a high-density grid mesh arrangement can be obtained by means of data migration processing with a scan spacing of 5 cm and wave velocity information through the use of the hyperbola fitting method from at least 30% of the targets

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

Ground-penetrating radar (GPR) for non-destructive characterization of reinforced concrete structures

Characterizing reinforced concrete structures is an overall evaluation process, including inspection, investigation, and testing of the concrete structures. To investigate existing concrete structures in service is preferred to use non-destructive techniques. For non-destructive investigation of reinforced concrete structures, Ground Penetrating radar (GPR) is a preferred instrument and is worldwide accepted for its ability to detect defects and provision of reliable images of the substructure of concrete structures. Ground penetrating radar (GPR) emits electromagnetic energy waves into the surface of concrete due to encounters with materials having different dielectric constants; waves deflect back and make the display of substructure on radargram. This thesis work is aimed to find the location of different types of construction defects, foreign objects, and reinforcement details in concrete structures. For this purpose, PS 1000 X-SCAN CONCRETE SCANNER is used to obtain image scans from the thick concrete testing wall, retaining wall and precast RC slabs. GPR scans are analyzed using Profis Detection Software by HILTI to get the required results. Detection and location of construction defects, rebar mapping, concrete cover depth and location of other anomalies and metal objects in the sub-structure of concrete are beneficial for the maintenance and repair of concrete structures. The service life of concrete structures can be predicted from GPR scanning results using the evaluation of defects leading to failure of the concrete structures

Imaging reinforced concrete: A comparative study of Ground Penetration Radar and Rebarscope

Geophysical techniques have been playing a very vital role in subsurface imaging in the recent past. Technology has been making it both reliable and convenient to utilize non-destructive geophysics techniques like Ground Penetration Radar, Induction current based Rebarscope, Seismic methods, ERT, etc. The applications range from shallow subsurface investigation of Bridge decks to old tunnels, mapping of rabars in a pre-existing construction and analyzing the concrete strength. The thesis constitutes of a comparative study and analysis of a Ground Penetration Radar system and a Rebarscope. Individual parameters obtained directly from the study and obtained indirectly from the study shall be analyzed for a better quantitative understanding of their variation and errors to optimize the utility of the instruments individually. Data obtained from both Ground Penetration Radar system and Rebarscope would be compared for accuracy in determining the rebar depth. For the experiments, pre-designed concrete slabs are constructed with rebars at various depths and defects in concrete. Furthermore, a combination of both the instruments is used to minimize errors and to achieve better control over the intrinsic and extrinsic errors of the instruments to undertake real world studies with better dependency. A calibration, comparative and combination study of Ground Penetration Radar and Rebarscope is important for the very purpose of better understanding of the quality of concrete, especially in its initial stages of degradation. The amplitude variation in the signal and dielectric permittivity of the concrete indicates concrete quality. The study illustrate the superiority of the Ground Penetration Radar system, but in cases of highly varying degradation and construction errors Rebarscope plays key role in accurate depth estimation of the reinforcement rebars. The study highlights some limitations of GPR surveys and proceeds to address the limitations by utilizing a Rebarscope in combination with GPR system --Abstract, page iii

Comprehensive Bridge Deck Deterioration Mapping of Nine Bridges by Nondestructive Evaluation Technologies Final Report, January 2011

The primary objective of this research was to demonstrate the benefits of NDT technologies for effectively detecting and characterizing deterioration in bridge decks. In particular, the objectives were to demonstrate the capabilities of ground-penetrating radar (GPR) and impact echo (IE), and to evaluate and describe the condition of nine bridge decks proposed by Iowa DOT. The first part of the report provides a detailed review of the most important deterioration processes in concrete decks, followed by a discussion of the five NDT technologies utilized in this project. In addition to GPR and IE methods, three other technologies were utilized, namely: half-cell (HC) potential, electrical resistivity (ER), and ultrasonic surface waves (USW) method. The review includes a description of the principles of operation, field implementation, data analysis, and interpretation; information regarding their advantages and limitations in bridge deck evaluations and condition monitoring are also implicitly provided.. The second part of the report provides descriptions and bridge deck evaluation results from the nine bridges. The results of the NDT surveys are described in terms of condition assessment maps and are compared with the observations obtained from the recovered cores or conducted bridge deck rehabilitation. Results from this study confirm that the used technologies can provide detailed and accurate information about a certain type of deterioration, electrochemical environment, or defect. However, they also show that a comprehensive condition assessment of bridge decks can be achieved only through a complementary use of multiple technologies at this stage,. Recommendations are provided for the optimum implementation of NDT technologies for the condition assessment and monitoring of bridge decks

NON-DESTRUCTIVE TESTING FOR QUALITY ASSURANCE OF CONCRETE & PERFORMANCE PREDICTION OF BRIDGE DECKS WITH MACHINE LEARNING

Non-destructive testing (NDT) methods are particularly valuable in the quality assurance (QA) process since they do not interfere with production of concrete and reduce testing time and cost. NDTs can provide early warnings in meeting strength requirements at early ages of concrete as well as long term strength. NDTs are also valuable in providing evaluation of health of in-service infrastructures such as bridge and pavement. The results of this study can be used for potential adoption of an NDT-based QA plan. Their adoption in QA will provide the opportunity to test a larger portion of concrete during assessment without a significant increase in QA cost and testing time. To achieve that purpose, the selected NDTs should be fast, accurate, reliable and simple to run. The NDT methods explored in this study included infrared thermography, ultrasonic pulse velocity (UPV), fundamental resonance frequency, rebound hammer, ground penetrating radar (GPR), and ultrasonic pulse echo (UPE). Different sets of NDTs were selected in each experimental study undertaken in this dissertation appropriate to the research objectives and goals in each case. For strength gain monitoring, (i.e., maturity modeling during early ages of hydration), the suggested NDTs need to provide an assessment of the mechanical properties of concrete. To assess the concrete quality during production and/or construction the selected NDTs should rapidly identify potential issues concerning uniformity and/or the presence of production and placement defects. For evaluating the condition of concrete bridge decks with asphalt overlays, GPR response was used to detect layer thickness and concrete quality and to evaluate reinforcement condition. For addressing the transition from lab to field results, machine learning modeling was used to predict the structure condition. Therefore, two artificial neural network (ANN) models were proposed and assessed in this study to predict the condition of bridge decks in Maryland and Massachusetts. Thus, the objectives of this research were to identify and assess alternative NDT methods that can be used in: i) monitoring and/or estimating strength gain (i.e., maturity modeling) in concrete; ii) evaluating concrete uniformity and production quality; iii) detecting and measuring the extent of delamination in concrete slab representing small scale field conditions; iv) evaluating GPR in assessing the condition of pavement layers, concrete quality and reinforcement in bridge decks; and v) employing machine learning modeling to predict the condition of bridge decks.

Deep learning processing and interpretation of ground penetrating radar data using a numerical equivalent of a real GPR transducer

Ground-Penetrating Radar (GPR) is a popular non-destructive electromagnetic (EM) technique that is used in diverse applications across different fields, most commonly geophysics and civil engineering. One of the most common applications of GPR is concrete scanning, where it is used to detect structural elements and support the assessment of its condition. However, in any GPR application, the data have no resemblance to the characteristics of targets of interest and a means of extracting information from the data regarding the targets is required. Interpreting the GPR data, to infer key properties of the subsurface and to locate the targets is a difficult and challenging task and is highly dependent on the processing of the data and the experience of the user. Traditional processing techniques have some drawbacks, which can lead to misinterpretations of the data in addition to the interpretation being subjective to the user. Machine learning (ML) has proven its ability to solve a variety of problems and map complex relationships and in recent years, is becoming an increasingly attractive option for solving GPR and other EM problems regarding processing and interpretation. Numerical modelling has been extensively used to understand the EM wave propagation and assist in the interpretation of GPR responses. If ML is combined with numerical modelling, efficient solutions to GPR problems can be acquired. This research focuses on developing a numerical equivalent of a commercial GPR transducer and utilising this model to produce realistic synthetic training data sets for deep learning applications. The numerical model is based on the high-frequency 2000 MHz "palm" antenna from Geophysical Survey Systems, Inc. (GSSI). This GPR system is mainly used for concrete scanning, where the targets are located close to the surface. Unknown antenna parameters were found using global optimisation by minimising the mismatch between synthetic and real responses. A very good match was achieved, demonstrating that the model can accurately replicate the behaviour of the real antenna which was further validated using a number of laboratory experiments. Real data were acquired using the GSSI transducer over a sandbox and reinforced concrete slabs and the same scenarios were replicated in the simulations using the antenna model, showing excellent agreement. The developed antenna model was used to generate synthetic data, which are similar to the true data, for two deep learning applications, trained entirely using synthetic data. The first deep learning application suggested in the present thesis is background response and properties prediction. Two coupled neural networks are trained to predict the background response given as input total GPR responses, perform background removal and subsequently use the predicted background response to predict its dielectric properties. The suggested scheme not only performs the background removal processing step, but also enables the velocity calculation of the EM wave propagating in a medium using the predicted permittivity value. The ML algorithm is evaluated using a number of synthetic and measured data demonstrating its efficiency and higher accuracy compared to traditional methods. Predicting a permittivity value per A-scan included in a B-scan results in a permittivity distribution, which is used along with background removal to perform reverse-time migration (RTM). The proposed RTM scheme proved to be superior when compared with the commonly used RTM schemes. The second application was a deep learning-based forward solver, which is used as part of a full-waveform inversion (FWI) framework. A neural network is trained to predict entire B-scans given certain model parameters as input for reinforced concrete slab scenarios. The network makes predictions in real time, reducing by orders of magnitude the computational time of FWI, which is usually coupled with an FDTD forward solver. Therefore, making FWI applicable to commercial computers without the need of high-performance computing (HPC). The results clearly illustrate that ML schemes can be implemented to solve GPR problems and highlight the importance of having a digital representation of a real transducer in the simulations

Detection of corrosion of reinforced concrete on cooling towers of energy production sites

Cette thèse a été financée par Electricité de France-Recherche et Développement (EDF R&D). L'objectif est le développement d'une méthodologie pour une meilleure estimation de l'état de corrosion des armatures du béton des aéroréfrigérants, soumis à la carbonatation atmosphérique, sur la base d'une double approche: le radar géophysique (GPR) et la mesure de la résistance de polarisation1. Le GPR peut être utilisé pour la détection rapide des zones présentant un risque élevé de corrosion (détection des contrastes de permittivité). En plus, le GPR est utilisé pour la localisation des armatures d'acier et l'estimation de l'épaisseur d'enrobage. Cette dernière application est très importante pour cette étude. Dans les zones identifiées comme potentiellement corrodées par le GPR, il est proposé d'utiliser la mesure de la résistance de polarisation pour quantifier l'activité de corrosion. Cette étude propose une méthode opérationnelle et originale, adaptée seulement à cette problématique. Après une analyse critique des dispositifs existants pour la mesure sur site de la résistance de polarisation, un nouveau dispositif est proposé. Un modèle numérique de ce dispositif est développé. Sur la base des résultats du modèle, des abaques sont construites afin de remonter aux propriétés électrochimiques de l'acier (potentiel et courant) à partir des valeurs qui sont mesurées à la surface du béton. Le rôle des paramètres influents, physiques (courant injecté, résistivité), géométriques (enrobage, position de la sonde) et électrochimiques (état de l'acier), est examiné en détail. Ensuite, la méthode d'inversion proposée est testée en laboratoire, sur des corps d'épreuve reproduisant les conditions du site2. La fiabilité et l'efficacité du modèle dans son domaine de définition sont démontrées. Les limites et l'incertitude du protocole de mesure sont également abordées. Enfin, un premier protocole opérationnel pour l'utilisation sur site de la technique est proposé.The current thesis is the result of a study funded by Electricité de France -Research and Development (EDF R&D). It aims to develop an original methodology for a better estimation of the state of corrosion of steel reinforced concrete of cooling towers, due to atmospheric carbonation, based on a double approach: the Ground Penetrating Radar (GPR) and the electrochemical measurement of polarization resistance1. GPR can be used for detecting zones with a high risk of corrosion (detection of contrasts of permittivity). In addition, GPR is used for the location of steel rebars and the estimation of concrete cover thickness. On the zones identified by GPR with high risk of corrosion, it is proposed to use the polarization resistance measurement to define quantitatively the corrosion activity. This study proposes an original simple operative measurement mode, adapted only for this particular context. After a critical analysis of the existing devices of the polarization resistance measurement, a novel probe is proposed. A numerical model of this probe is developed. Based on the results of the model, abacuses are built in order to gather the real electrochemical proprieties of the steel reinforcement (potential and current) from those values measured on the concrete surface. The role of the influencing factors i.e. physical (injected current, resistivity), geometric (concrete cover, probe's position) and electrochemical (state of the reinforcement), are fully investigated. The proposed model is applied in a laboratory environment, by reproducing the real site conditions2. The experimental work proves its feasibility, efficiency and effectiveness (within certain limits) by confirming its theoretical principles and indicating some uncertainties during its application. Finally, a primary operational protocol for the on site utilization of the technique is proposed