scholarly journals Nonintrusive Depth Estimation of Buried Radioactive Wastes Using Ground Penetrating Radar and a Gamma Ray Detector

2019 ◽  
Vol 11 (2) ◽  
pp. 141 ◽  
Author(s):  
Ikechukwu Ukaegbu ◽  
Kelum Gamage ◽  
Michael Aspinall
Keyword(s):  
Bulk Density ◽  
Ground Penetrating Radar ◽  
Gamma Ray ◽  
Depth Estimation ◽  
Average Error ◽  
Density Estimates ◽  
Material Density ◽  
Linear Correction ◽  
Ground Penetrating ◽  
Gamma Ray Detector

This study reports on the combination of data from a ground penetrating radar (GPR) and a gamma ray detector for nonintrusive depth estimation of buried radioactive sources. The use of the GPR was to enable the estimation of the material density required for the calculation of the depth of the source from the radiation data. Four different models for bulk density estimation were analysed using three materials, namely: sand, gravel and soil. The results showed that the GPR was able to estimate the bulk density of the three materials with an average error of 4.5%. The density estimates were then used together with gamma ray measurements to successfully estimate the depth of a 658 kBq ceasium-137 radioactive source buried in each of the three materials investigated. However, a linear correction factor needs to be applied to the depth estimates due to the deviation of the estimated depth from the measured depth as the depth increases. This new application of GPR will further extend the possible fields of application of this ubiquitous geophysical tool.

scholarly journals Integration of Ground- Penetrating Radar and Gamma-Ray Detectors for Nonintrusive Characterisation of Buried Radioactive Objects

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2743
Author(s):  
Ikechukwu K. Ukaegbu ◽  
Kelum A. A. Gamage ◽  
Michael D. Aspinall
Keyword(s):  
Numerical Simulations ◽  
Relative Error ◽  
Ground Penetrating Radar ◽  
Gamma Ray ◽  
Radioactive Wastes ◽  
Soil Density ◽  
Gamma Ray Detectors ◽  
Density Relationship ◽  
Ground Penetrating ◽  
Gamma Ray Detector

The characterisation of buried radioactive wastes is challenging because they are not readily accessible. Therefore, this study reports on the development of a method for integrating ground-penetrating radar (GPR) and gamma-ray detector measurements for nonintrusive characterisation of buried radioactive objects. The method makes use of the density relationship between soil permittivity models and the flux measured by gamma ray detectors to estimate the soil density, depth and radius of a disk-shaped buried radioactive object simultaneously. The method was validated using numerical simulations with experimentally-validated gamma-ray detector and GPR antenna models. The results showed that the method can simultaneously retrieve the soil density, depth and radius of disk-shaped radioactive objects buried in soil of varying conditions with a relative error of less than 10%. This result will enable the development of an integrated GPR and gamma ray detector tool for rapid characterisation of buried radioactive objects encountered during monitoring and decontamination of nuclear sites and facilities.

scholarly journals Soil water content estimation using ground penetrating radar data via group intelligence optimization algorithms: An application in the Northern Shaanxi Coal Mining Area

2020 ◽  
pp. 014459872097336
Author(s):  
Fan Cui ◽  
Jianyu Ni ◽  
Yunfei Du ◽  
Yuxuan Zhao ◽  
Yingqing Zhou
Keyword(s):  
Dielectric Constant ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Error Rate ◽  
Ground Penetrating Radar ◽  
Volumetric Water Content ◽  
Average Error ◽  
Average Error Rate ◽  
Ground Penetrating

The determination of quantitative relationship between soil dielectric constant and water content is an important basis for measuring soil water content based on ground penetrating radar (GPR) technology. The calculation of soil volumetric water content using GPR technology is usually based on the classic Topp formula. However, there are large errors between measured values and calculated values when using the formula, and it cannot be flexibly applied to different media. To solve these problems, first, a combination of GPR and shallow drilling is used to calibrate the wave velocity to obtain an accurate dielectric constant. Then, combined with experimental moisture content, the intelligent group algorithm is applied to accurately build mathematical models of the relative dielectric constant and volumetric water content, and the Topp formula is revised for sand and clay media. Compared with the classic Topp formula, the average error rate of sand is decreased by nearly 15.8%, the average error rate of clay is decreased by 31.75%. The calculation accuracy of the formula has been greatly improved. It proves that the revised model is accurate, and at the same time, it proves the rationality of the method of using GPR wave velocity calibration method to accurately calculate the volumetric water content.

Detection of Sizes and Locations Air Voids in Reinforced Concrete Slab using Ground Penetrating Radar and Impact-Echo Methods

2015 ◽  
Vol 74 (3) ◽  
Author(s):  
Nurhayati Abdul Razak ◽  
Syahrul Fithry Senin ◽  
Roszilah Hamid
Keyword(s):  
Reinforced Concrete ◽  
Ground Penetrating Radar ◽  
Concrete Slab ◽  
Depth Estimation ◽  
Reinforced Concrete Slab ◽  
Air Voids ◽  
Impact Echo ◽  
Void Defects ◽  
Ground Penetrating ◽  
Air Void

 The presence of inevitable air void defects in reinforced concrete components due to poor quality control during construction can further aggravate the moisture and chloride penetration in concrete to accelerate the corrosion process of the reinforcing steel. Non-destructive test  (NDT) methods, Ground Penetrating Radar (GPR) and Impact-Echo (IE), are utilised tp detect the void defects. This study is to compare the accuracy and limitations of both methods in detecting the sizes and depths of the air voids. The sample is a 600 × 400 ×200 mm3 reinforced grade 40 concrete slab with embedded air voids in the sample. The air-voids are introduced in the concrete slab by positioning air-void plastic balls with diameters of 67, 45, 27, 20 and 3 mm each at the depths of 70, 80, 100, 80 and 80 mm, respectively, from the top surface of the slab. Results show that GPR can detect the air voids with sizes larger than 20 mm in diameter with error ranging from -8.9 to 30% from their actual diameters. The IE method is only able to detect the air voids depths and not the voids’ sizes. It is also observed that the void depth estimation acquired by GPR is more accurate only for large size void (67 mm), but for sizes less than that, IE is more accurate in determining their locations. Both methos should be considered for NDT application in detecting voids depending on which parameter accuracy is inticipated.  

Measuring Rebar Cover Depth in Rigid Pavements with Ground-Penetrating Radar

2005 ◽  
Vol 1907 (1) ◽  
pp. 80-85 ◽  
Author(s):  
Imad L. Al-Qadi ◽  
Samer Lahouar
Keyword(s):  
Ground Penetrating Radar ◽  
Concrete Slab ◽  
Average Error ◽  
Reinforcing Bars ◽  
Cover Depth ◽  
Rigid Pavements ◽  
Reflection Model ◽  
Nondestructive Investigation ◽  
Ground Penetrating ◽  
Processing Techniques

Ground-penetrating radar (GPR) is a nondestructive investigation tool that is usually used in flexible pavement evaluation to estimate the thicknesses of the various layers composing the pavement. GPR is also used in flexible pavements to detect subsurface distresses, such as moisture accumulation and air voids. For rigid pavements and bridge decks, GPR is used to measure the thickness of the concrete slab and detect the location of reinforcing bars (rebar). Rebar detection is typically achieved, in this case, when an experienced operator finds the rebar's classic parabolic signature in the GPR data. This paper presents image-processing techniques that can be used to detect the rebar parabolic signature automatically in GPR data collected from rigid pavements with a high-frequency ground-coupled antenna. After detection of the rebar, the reflected parabolic shape is fit to a theoretical reflection model to estimate the pavement's dielectric constant and the rebar depth. The algorithms were validated on GPR data collected from a known continuously reinforced concrete pavement section. The technique showed an average error of 2.6% on the estimated rebar cover depth.

Towards the multi-scale characterization of braided fluvial geobodies from outcrop, core, ground-penetrating radar and well log data

2018 ◽  
Vol 488 (1) ◽  
pp. 73-95 ◽  
Author(s):  
Luis Miguel Yeste ◽  
Saturnina Henares ◽  
Neil McDougall ◽  
Fernando García-García ◽  
César Viseras
Keyword(s):  
Ground Penetrating Radar ◽  
Gamma Ray ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Random Pattern ◽  
Log Data ◽  
Architectural Elements ◽  
Ground Penetrating ◽  
Gamma Ray Log

AbstractThe integrated application of advanced visualization techniques – validated against outcrop, core and gamma ray log data – was found to be crucial in characterizing the spatial distribution of fluvial facies and their inherent permeability baffles to a centimetre-scale vertical resolution. An outcrop/behind outcrop workflow was used, combining the sedimentological analysis of a perennial deep braided outcrop with ground-penetrating radar profiles, behind outcrop optical and acoustic borehole imaging, and the analyses of dip tadpoles, core and gamma ray logs. Data from both the surface and subsurface allowed the recognition of two main architectural elements – channels and compound bars – and within the latter to distinguish between the bar head and tail and the cross-bar channel. On the basis of a well-constrained sedimentological framework, a detailed characterization of the gamma ray log pattern in the compound bar allowed several differences between the architectural elements to be identified, despite a general cylindrical trend. A high-resolution tadpole analysis showed that a random pattern prevailed in the channel, whereas in the bar head and tail the tadpoles displayed characteristic patterns that allowed differentiation. The ground-penetrating radar profiles aided the 3D reconstruction of each architectural element. Thus the application of this outcrop/behind outcrop workflow provided a solid database for the characterization of reservoir rock properties from outcrop analogues.

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