Image Processing for Corrosion Quantification in Concrete Slabs using GPR data

Corrosion of steel reinforcement is a major cause of structural damage that requires repair or replacement. Early detection of steel corrosion can limit the extent of necessary repairs or replacements and costs associated with the rehabilitation works. The ground penetrating radar (GPR) method has been found to be a useful method for evaluating reinforcement corrosion in existing concrete structures. In this paper, GPR was utilized to assess corrosion of steel reinforcement in a concrete slab. A technique for accelerating reinforcement bar corrosion using direct current (DC) power supply with 5% sodium chloride (NaCl) solution was used to induce corrosion to embedded reinforcement bars (rebars) in this concrete slab. A 2 GHz GPR was used to assess the corrosion of the rebars. The analysis of the results of the GPR data obtained shows that corrosion of the rebars could be effectively localized and assessed.

Combined application of GPR and ERT for the assessment of a wall structure at the Heptapyrgion fortress (Thessaloniki, Greece)

We investigated a significant correlation between Ground-Penetrating Radar (GPR) combined with down-hole seismic technique and geotechnical data (boring log and Standard Penetrating Test) to examine geophysical data as an important aid tool in interpreting geotechnical parameter. GPR data provide geotechnical engineers with continuous layering information, whereas down-hole seismic test (DHT) delivers geotechnical parameters such as Poisson's ratio $\sigma$ , Young Modulus E, Shear modulus and Bulk moduli. By combining these data with boring log and standard penetrating Test (SPT) data, a comprehensive estimation about geotechnical parameters can be achieved. We conducted GPR measurement with 100 MHz shielded antenna in two profiles (inline and cross-line) centered at a borehole. We conducted also SPT to measure soil strength and boring to know the lithology. Intensive examination reveals that GPR technique should be applied to extend the meaning of geotechnical parameter value around and between boreholes. From case study at ITB site, we found some interesting result. Whereas both GPR and down-hole seismic data can reveal hard soils with SPT value of more than 60 corresponding to silty sand, GPR data can detect the interface between silty clay and clayey silt layers that cannot be detected from SPT. This study reveal the potential use of GPR technique as a tool for investigation of subsurface that can be elaborated to the exploration of shallow coal seams in subsurface as well to that of valuable minerals.

In Big data era, the data volume produced by ground penetrating radar (GPR) application swelling and has the characteristics of heterogeneity. This paper introduces the current status of the big data. In view of the characteristics of ground penetrating radar (GPR) application used in road disease detection, we design and realize a kind of ground penetrating radar (GPR) data management system GRBase based on Hadoop platform used in road disease detection, propose and implement a GPR data preprocessing method based on the Storm, solve the problem that the GPR data and Hadoop input format does not match. GRBase uses MySQL and HBase as storage database and enhances the radar data computational efficiency in road disease detection applications. The experiments and practice demonstrate that GRBase management platform is an effective, practical and strong scalability distributed GPR data management system.

Corrosion of steel and rebar in concrete structures is one of the most frequent reasons for civil infrastructure failures. Thus, improving the effective corrosion sensor technology can greatly reduce cost and provide safe structures with long service lives. However, assessing the corrosion condition of rebars is not simple because they are buried in concrete. In this paper, using fiber Bragg grating (FBG), a corrosion sensor for monitoring steel rebars embedded in a concrete structure is developed and validated by experiments. Based on the fact that the volume and diameter of a rebar embedded in concrete will enlarge due to corrosion, an FBG packaged with fiber-reinforced plastics (FRP) is wrapped on the steel bar. During corrosion, the increase in the bar diameter leads to the increase in fiber strain, which can be measured by the shift of the wavelength of FBG. Performances of the corrosion sensor are validated by accelerating corrosion in lab experiments. The corrosion sensor is embedded in a concrete specimen put in a 5% sodium chloride solution with a constant current. Experimental results show that the corrosion sensor can monitor the concurrence of corrosion of rebars in concrete. The corrosion extent can be quantitatively evaluated through the change in the wavelength of FBG. Therefore, the corrosion sensor developed in this paper is feasible for monitoring the early corrosion of rebars in concrete.

In this paper practical results of the long-term (1999-2007) application of Ground Penetrating Radar (GPR) technique for estimation of electrical parameters of contaminated ground layers are presented. Contaminations were caused by oil products. GPR technique includes the procedures of profiling, taking hodographs according to the Common-Midpoint (CMP) method and data optimal processing. GPR data processing was applied to acquire the estimations of values of ground layers thickness and their relative dielectric permittivity e by solving the GPR inverse problem. For data processing were used the methods of weighted least squares, the recursive Kalman filter, the maximum likelihood and the Nelder-Mead optimization. The comparative analysis of these methods allows to define the most expedient technique for the practical estimation of the ground electrical parameters using GPR data. The acquired values of e were connected with the degree of ground contamination which was attested by measurements of a floating layer thickness of oil products in the observation wells and their concentration in the ground. The monitoring data acquired in 2007 shows in comparison with the year 2006 the values of ground contamination degree estimated by GPR technique were decreased from 2.7% up to 58.8% for different observation wells. These decreases were confirmed by measurements in the observation wells.

'The project''s goals are threefold: (1) to examine the complementary site-characterization capabilities of modern, three-component shallow-seismic techniques and ground-penetrating radar (GPR) methods at depths ranging from 2 to 8 m at an existing test site; (2) to demonstrate the usefulness of the two methods when used in concert to characterize, in three-dimensions, the cone of depression of a pumping well, which will serve as a proxy site for fluid-flow at an actual, polluted site; and (3) to use the site as an outdoor mesoscale laboratory to validate existing three-dimensional ground-penetrating radar and seismic-reflection computer models developed at the Univ. of Kansas. To do this, useful seismic and GPR data are being collected along the same line(s) and within the same depth range. The principal investigators selected a site in central Kansas as a primary location and, although the site itself is not environmentally sensitive, the location chosen offers particularly useful attributes for this research and will serve as a proxy site for areas that are contaminated. As part of an effort to evaluate the strengths of each method, the authors will repeat the seismic and GPR surveys on a seasonal basis to establish how the complementary information obtained varies over time. Because the water table fluctuates at this site on a seasonal basis, variations in the two types of data over time also can be observed. Such noninvasive in-situ methods of identifying and characterizing the hydrologic flow regimes at contaminated sites support the prospect of developing effective, cost-conscious cleanup strategies in the near future. As of the end of May 1998, the project is on schedule. The first field work was conducted using both of the geophysical survey methods in October of 1997, and the second field survey employed both methods in March of 1998. One of the stated tasks is to reoccupy the same survey line on a quarterly basis for two years to examine change in both the seismic reflection data and the ground-penetrating radar (GPR) data over time. Two factors drive these changes: First, the soil-moisture conditions vary on a seasonal basis at the site. Second, the water table rises and falls on the order of one meter in response to changes in the level of the Arkansas River and in response to the many irrigation wells found nearby. At the test site in the Arkansas River alluvial valley near Great Bend, Kansas, surface material consists of unconsolidated medium- to coarse-grained sand interspersed with clay stringers and lenses deposited by the Arkansas River. A hand-augered test hole 5 meters from the seismic line revealed sand to a depth of about 1.5 meters, where a hard pan was found presumably a clay layer. At the time of the seismic and GPR surveys, the water table was at a depth of 2.1 meters, based on a measurement in a test well located 25 meters from the seismic line. A well drilled about 40 meters away from the seismic line encountered bedrock (a fine- to medium-grained Cretaceous-age sandstone) at a depth of 29 meters.'

Corrosion of steel reinforcement in concrete highway bridges due to carbonation and chloride attack is an ongoing and prevalent problem. If left undetected in time, late-stage corrosion can lead to section loss of steel rebars, an uneven distribution of internal stresses, and ultimately lead to surface cracks and spalling of the concrete. Nondestructive testing and evaluation (NDT/E) sensors like ground penetrating radar (GPR) are commonly used to detect subsurface objects and anomalies (e.g., concrete cracking, and rebars). In this paper, the feasibility of a 1.6GHz GPR device for detection of steel rebar corrosion is tested in both laboratory reinforced concrete (RC) specimens and in-situ RC structures. For this purpose, three RC specimens (12 x 12 x 5 in³ ) were cast with a No.5 steel rebar (5/8" diameter) at the center of each specimen. Two of the RC panels were corroded using the accelerated corrosion test (ACT) to achieve different levels of corrosion, while the third one was left un-corroded to serve as an intact baseline in our measurements. The RC specimens were kept in a temperature-controlled environment (73 ∼ 77◦F) for six years (2017-2023). In addition, a bridge column of a RC highway bridge underpass (Chelmsford, MA), which exhibits the signs of steel rebar corrosion, was selected for collecting in-situ GPR scans. A known intact RC bridge column was chosen and scanned to serve as the baseline for comparison. In both scenarios, B-scan images were developed from the lab RC specimens and the damaged bridge column. The changes in the reflection amplitudes of the hyperbolic-shaped rebar reflections in GPR B-scan images due to corrosion were studied in both the time and the frequency domains, and cross-validated with the results from previous research. From our experiments, it was found that a 1.6GHz GPR sensor can successfully detect and distinguish corrosion level in three RC specimens and one bridge column by the combined use of 1D and 2D analytic methods.

In this paper, we describe the application of wave-equation redatuming through downward and upward continuations of surface ground penetrating radar (GPR) data to image the target beneath a surface layer with geometric or material property heterogeneities. We first tested this technique with the use of a synthetic radar profile generated by the finite difference time domain method to show its effectiveness. We then applied it to the surface GPR data collected in several urban public transportation infrastructure sites including a project to use GPR to assess the quality of mortar consolidation behind a tunnel wall in the Xiangyin highway tunnel, Shanghai, China. The purpose of this redatuming processing is to enhance target images in later times of a GPR profile, by eliminating the contamination in the GPR profile caused by strong diffractive scattering from the steel rebars in the surface concrete slab or tunnel wall. The results for synthetic and observed GPR data show that the wave-equation redatuming technique is an effective way to eliminate unwanted diffraction signatures and ease GPR image interpretation for data collected at a site with a strongly heterogeneous surface layer.

In this study, the reduction of ground penetrating radar (GPR) signal amplitude magnitudes were measured to monitor the reinforcing steel corrosion in concrete slab. Geophysical Survey System Incorporation (GSSI) BridgeScan system which includes a SIR-3000 Windows, based on portable GPR data collection with 1.6 GHz ground-couple antenna has been used to generate electromagnetic waves and analysis of the signal on a slab sample. Four 16 mm diameter reinforcing steel were embedded in a 255 mm x 455 mm x 60 mm grade 40 concrete slab in the transverse direction and three in the longitudinal. The slab was immersed in concentrated sodium chloride solution (8 g/l) for three months to simulate the corrosive environment on the reinforced slab. The average signal amplitude decreases from 1139.75 dB (at day 7) to -404.25 dB (at day 61) and finally drop to -782.75 dB (at day 93) as the corrosion process progressing. The presence of corrosion activity on the reinforcing steel was confirmed based on the potential difference reading by Half Cell (HC) which shows that the reinforcing steel experienced probability of corrosion level by more than 90 % after 61 and 93 days. GPR method is found to be a promising approach as it possesses high sensitivity towards changes in amplitude at par with high potential changes in HC method in corrosion detection.

Subsurface conditions can be non-intrusively mapped by observing and grouping patterns of similarity within ground-penetrating radar (GPR) profiles. We have observed that the intricate and often visually indiscernible textural variability found within a complex GPR image possesses important parameters that help delineate regions of similar subsurface characteristics. In this study, we therefore examined the feasibility of using textural features extracted from GPR data to automate subsurface characterization. The textural features were matched to a fingerprint database of previous subsurface classifications of GPR textural features and the corresponding physical probings of subsurface conditions. Four textural features (energy, contrast, entropy, and homogeneity) were selected as inputs into a neural-network classifier. This classifier was tested and verified using GPR data obtained from two distinctly different field sites. The first data set contained features that indicate the presence or lack of sandstone bedrock in the upper 2 m of a shallow soil profile of fine sandy loam and loam. The second data set contained columnar patterns that correspond to the presence or the lack of vertical preferential flow paths within a deep loessial soil. The classifier automatically grouped each data set into one of the two categories. Comparing the results of classification using extracted textural features to the results obtained by visual interpretation found 93.6% of the sections that lack sandstone bedrock correctly classified in the first set of data, and 90% of the sections that contain pronounced columnar patterns correctly classified in the second set of data. The classified profile sections were mapped using integrated GPR and GPS data to show ground surface boundaries of different subsurface conditions. These results indicate that textural features extracted from GPR data can be utilized as inputs in a neural network classifier to rapidly characterize and map the subsurface into categories associated with known conditions with acceptable levels of accuracy. This approach of GPR imagery classification is to be considered as an alternative method to traditional human interpretation only in the classification of voluminous data sets, wherein the extensive time requirement would make the traditional human interpretation impractical.

The objective of this work was to evaluate the feasibility and value of expanding the MDT's Ground Penetrating Radar (GPR) program to pavement design and rehabilitation, and to network level evaluation. Phase I of this project concluded that in order to investigate the feasibility and value of these program expansions, a Phase II field evaluation project be designed and implemented to evaluate the accuracy of GPR pavement thickness data on Montana pavements, and to correlate these findings with the accuracy requirements of the individual applications. This field evaluation began with identifying 26 pavement test section of different composition and structure located throughout the diverse climatic regions of Montana. At each site, FWD and GPR data was collected, followed by coring and augering to determine the thickness of the pavement layer structure and base moisture content. This testing was carried out both in the spring of 2010 and in the fall of 2010 to capture seasonal variations. The GPR data was analyzed for thickness, and the GPR thickness data was evaluated for seasonal changes, and compared to core and plan data to investigate thickness accuracy and the effectiveness of calibration methods. Compared to cores, the average GPR bound layer thickness error was 10.3% vs. 15.2% using plan data. A GPR data checking method was developed using FWD and plan data to identify potential analysis layer analysis inconsistencies and suggest alternative interpretation. Implementation of this method reduced the GPR error to 7.6%. A sensitivity study was carried out to investigate the impact of having the more accurate GPR data. This study showed that, on average, the use of GPR reduced the pavement life prediction error by 62% when compared to using as-built plan data.

In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) or simulated (sensor-like) soil-moisture data to estimate soil moisture. We propose two simulation approaches to extend a given multi-sensor dataset which contains sparse GPR data. In the first approach, simulated GPR data is generated either by an interpolation along the time axis or by a machine learning model. The second approach includes the simulation of soil-moisture along the GPR profile. The soil-moisture estimation is improved significantly by the fusion of hyperspectral and GPR data. In contrast, the combination of simulated, sensor-like soil-moisture values and hyperspectral data achieves the worst regression performance. In conclusion, the estimation of soil moisture with hyperspectral and GPR data engages further investigations.

Geophysics has played a significant and efficient role in studying geological structures over the past decades as the goal of geophysical data acquisition is to investigate underground phenomena with the highest possible level of accuracy. The ground penetrating radar (GPR) method is used as a nondestructive method to reveal shallow structures by beaming electromagnetic waves through the Earth and recording the received reflections, albeit inevitably, along with random noise. Various types of noise affect GPR data, among the most important of which are random noise resulting from arbitrary motions of particles during data acquisition. Random noise which exists always and at all frequencies, along with coherent noise, reduces the quality of GPR data and must be reduced as much as possible. Over the recent years, discrete wavelet transform has proved to be an efficient tool in signal processing, especially in image and signal compressing and noise suppression. It also allows for obtaining an accurate understanding of the signal properties. In this study, we have used the autoregression in both wavelet and f-x domains to suppress random noise in synthetic and real GPR data. Finally, we compare noise suppression in the two domains. Our results reveal that noise suppression is conducted more efficiently in the wavelet domain due to decomposing the signal into separate subbands and exclusively applying the method parameters in autoregression modeling for each subband.

The most expensive part to maintain throughout the lifespan of a reinforced concrete bridge is the deck, largely because rehabilitation occurs after degradation is visible on a surface. However, the mechanisms that are the cause of deterioration, such as reinforcing bar corrosion, are initiated long before damage is detected via visual inspection. If rebar corrosion can be detected in its early stages, before severe deterioration has resulted, maintenance costs can be significantly reduced and the life cycle extended. Recent studies have shown that ground penetrating radar (GPR) rebar reflection amplitude attenuation correlates with active corrosion in reinforced concrete bridge decks. A significant advantage of GPR over other non-destructive evaluation (NDE) methods is its ability to be operated at highway speeds so that traffic is not disrupted. However, a well-defined GPR amplitude threshold allowing the operator to distinguish non-corroded from corroded areas of the deck has yet to be established. Because reinforcing steel corrosion is the most predominant cause of bridge deck deterioration, this research seeks to quantify the thresholds relating GPR signal amplitudes and rebar corrosion. One bridge deck removed from service, seventeen artificially corroded slabs, and one in-service bridge deck were analyzed using GPR and half-cell potential (HCP), which measures the amount of active corrosion and is currently considered the standard NDE method. A significant correlation between these two methods was found for each case. To systematically determine a threshold for the GPR so that deteriorated areas of the deck can be identified, receiver operating characteristic (ROC) curves were utilized. With an accuracy of over 87% for each scenario, this method clearly demonstrates the use of GPR for distinguishing corrosion in bridge decks.