GPR measurement of the diameter of steel bars in concrete specimens based on the stationary wavelet transform

Zhan, Runtao; Xie, Hai

doi:10.1784/insi.2009.51.3.151

Cited by 14 publications

(6 citation statements)

References 17 publications

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“…This method, similar to the aforementioned ones, is evaluated for a specific host medium and uses simplified transmitters and receivers with no conclusive results. In [36] a discrete and stationary wavelet transform is used in order to estimate the diameter of the rebar assuming that the velocity of the medium is known. Direct knowledge of the velocity of the host medium also allows the usage of hyperbola fitting, with the radius of the rebar as the only unknown [37].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

Giannakis

Giannopoulos

Warren

2019

IEEE Trans. Geosci. Remote Sensing

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The simulation, or forward modeling, of Ground Penetrating Radar (GPR) is becoming a more frequently used approach to facilitate interpretation of complex real GPR data, and as an essential component of full-waveform inversion (FWI). However, general full-wave 3D electromagnetic (EM) solvers, such as ones based on the Finite-Difference Time-Domain (FDTD) method, are still computationally demanding for simulating realistic GPR problems. We have developed a novel near realtime, forward modeling approach for GPR that is based on a machine learning (ML) architecture. The ML framework uses an innovative training method which combines a predictive principal component analysis technique, a detailed model of the GPR transducer, and a large dataset of modeled GPR responses from our FDTD simulation software. The ML-based forward solver is parameterized for a specific GPR application, but the framework can be applied to many different classes of GPR problems. To demonstrate the novelty and computational efficiency of our ML-based GPR forward solver, we used it to carry out FWI for a common infrastructure assessment application-determining the location and diameter of reinforcement bars in concrete. We tested our FWI with synthetic and real data, and found a good level of accuracy in determining the rebar location, size, and surrounding material properties from both datasets. The combination of the near real-time computation, which is orders of magnitude less than what is achievable by traditional full-wave 3D EM solvers, and the accuracy of our ML-based forward model is a significant step towards commercially-viable applications of FWI of GPR.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

Giannakis

Giannopoulos

Warren

2019

IEEE Trans. Geosci. Remote Sensing

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“…This technology is currently used in construction practices to locate reinforcement before drilling into a reinforced concrete member. In laboratory tests, GPR has not yet proven capable of identifying the diameter of reinforcement with sufficient accuracy for application to this project, but it has shown promise in locating reinforcement (Zhan and Xie 2009); (Chang, Lin and Lien 2009); (He, et al 2009). By using GPR to locate vertical stirrup spacing, a conservative assumption on stirrup diameter (#3 bars) and number of legs (2) could be made to more safely and accurately assess the shear capacity of the structure in lieu of measuring shear response under controlled loading.…”

Section: Sensor Technology Developmentmentioning

confidence: 99%

Advanced Bridge Capacity and Structural Integrity Assessment Methodology

Gries¹,

Giles²,

Kuchma³

et al. 2013

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The bridge is a basic element of all surface transportation networks. In military theaters of operation, transportation routes that cross bridges are essential for deploying personnel, supplies, and heavy equipment, as well as for facilitating communications. It is essential that the structural capacity of each bridge along a military route be assessed in order to avoid overloading the bridge or unnecessarily hindering military operations by overestimating or underestimating its capacity. For reinforced concrete structures, information about the number, size, and orientation of steel reinforcement is necessary to make a strength assessment. Since reinforcement is not visible externally, making an accurate assessment without design drawings is extremely difficult. The objective of this project was to develop more reliable means of in-field capacity assessment of reinforced concrete bridges by making improved estimates of the level of longitudinal and shear reinforcement. The proposed assessment procedure is based on comparing measured structural response under controlled loading conditions to predicted structural response from analysis. This report presents results from a preliminary sensitivity study of the analytically predicted response of simply supported reinforced concrete T-beam girders that have varying levels of longitudinal and shear reinforcement. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

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“…E-mail: a.giannopoulos@ed.ac.uk C. Warren is with the Department of Mechanical and Construction Engineering, Northumbria University, Newcastle, NE1 8ST, UK. E-mail: craig.warren@northumbria.ac.uk relationship between the diameter of the rebar and the received GPR signal [10], [16], [17]. However, these methods are based on simplified assumptions and they fail to provide a universal and reliable solution [11].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Scheme for Estimating the Diameter of Reinforcing Bars Using Ground Penetrating Radar

Giannakis

Giannopoulos

Warren

2021

IEEE Geosci. Remote Sensing Lett.

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GPR measurement of the diameter of steel bars in concrete specimens based on the stationary wavelet transform

Cited by 14 publications

References 17 publications

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

Advanced Bridge Capacity and Structural Integrity Assessment Methodology

A Machine Learning Scheme for Estimating the Diameter of Reinforcing Bars Using Ground Penetrating Radar

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