Applicability of GPR and a rebar detector to obtain rebar information of existing concrete structures

Rathod, Harsh; Debeck, Scott; Gupta, Rishi; Chow, Brian G.

doi:10.1016/j.cscm.2019.e00240

Cited by 23 publications

(14 citation statements)

References 10 publications

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“…The acoustic method can only detect near surface delamination, while GPR signals can penetrate concrete. Rathod et al [162] combine GPR with a rebar detector (profoscope), which uses electromagnetic pulse induction technology to measure rebar diameter, spacing and cover depth which are required to assess the load capacity of bridge decks. The results suggest that GPR gives more consistent and accurate data with regards to spacing and cover.…”

Section: Concrete Bridgesmentioning

confidence: 99%

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

2021

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The non-destructive testing and diagnosis of transport infrastructures is essential because of the need to protect these facilities for mobility, and for economic and social development. The effective and timely assessment of structural health conditions becomes crucial in order to assure the safety of the transportation system and time saver protocols, as well as to reduce excessive repair and maintenance costs. Ground penetrating radar (GPR) is one of the most recommended non-destructive methods for routine subsurface inspections. This paper focuses on the on-site use of GPR applied to transport infrastructures, namely pavements, railways, retaining walls, bridges and tunnels. The methodologies, advantages and disadvantages, along with up-to-date research results on GPR in infrastructure inspection are presented herein. Hence, through the review of the published literature, the potential of using GPR is demonstrated, while the main limitations of the method are discussed and some practical recommendations are made.

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Section: Concrete Bridgesmentioning

confidence: 99%

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

2021

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“…Current non-destructive evaluation (NDE) scanning technology is based on the detection of reflected or transmitted electromagnetic, thermal or acoustic waves generated by a local source. The most used NDE techniques are magnetic imaging and ground penetrating radar (GPR) [1][2][3][4], which can image bars with the diameters of 10-20mm at depths of 100-500 mm. Low depth imaging, for depths at <20 cm, can be performed with infrared thermographics [5,6] and ultrasonics [7,8].…”

Section: Introductionmentioning

confidence: 99%

Development of muon scattering tomography for detection of reinforcement in concrete

Dobrowolska¹,

Velthuis²,

Kopp³

et al. 2022

J. Inst.

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Inspection of ageing, reinforced concrete structures is a world-wide challenge. Existing evaluation techniques in civil and structural engineering have limited penetration depth and do not allow to precisely ascertain the configuration of reinforcement within large concrete objects. The big challenge for critical infrastructure (bridges, dams, dry docks, nuclear bioshields etc.) is understanding the internal condition of the concrete and steel, not just the location of the reinforcement. Muon scattering tomography is a non-destructive and non-invasive technique which shows great promise for high-depth 3D concrete imaging. Here a method is presented to locate reinforcement meshes placed in a large-scale concrete object. A reinforcement mesh was simulated as two layers of 2 m long bars, forming a mesh. Two layers of the mesh were placed at several distances from each other inside a large concrete block. Previously, we have shown that using our autocorrelation technique for single meshes inside the concrete and using only one week worth of data taking, bars with a diameter of 7 mm and larger, could easily be detected for a 10 cm mesh spacing. The signal for 6 mm diameter bar exceeds the background and becomes very clear after two weeks of data taking. Here we show that we can detect the vertical positions of two mesh layers inside the concrete. This is a very important result for non-destructive evaluation of civil structures.

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“…In concrete reinforcement inspection, cover meter is a frequently used commercial NDT instrument because it is highly sensitive to metal targets, low‐cost, and trivially deployable (Rathod et al., 2019). Cover meters are based on electromagnetic induction (EMI).…”

Section: Introductionmentioning

confidence: 99%

Deep learning–based nondestructive evaluation of reinforcement bars using ground‐penetrating radar and electromagnetic induction data

Liu

Zhou

et al. 2021

Computer aided Civil Eng

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This paper proposes a nondestructive evaluation method based on deep learning using combined ground‐penetrating radar (GPR) and electromagnetic induction (EMI) data for autonomic and accurate estimation of the cover thickness and diameter of reinforcement bars. A real‐time object detection algorithm—You Only Look Once–version 3 (YOLO v3)—is adopted to automatically identify the reinforcement bar reflected signals from radargrams, with which the range of the cover thickness is roughly predicted. Subsequently, EMI data, accompanied with the cover thickness range, are imported to a one‐dimensional convolutional neural network (1D CNN), pretrained by calibrated EMI and GPR data, to simultaneously estimate the cover thickness and reinforcement bar diameter. Testing with the on‐site GPR data shows that YOLO v3 is superior to Single Shot Multibox Detector method in GPR hyperbolic signal identification. Testing of 1D CNN with the EMI and GPR data collected in an in‐house sand pit experiment shows that the estimation accuracy of the cover thickness and reinforcement bar diameter is, respectively, 96.8% and 90.3% with a permissible error of 1 mm. Further, an experiment with concrete specimens demonstrates that among the 22 estimated values (including the reinforcement bar diameter and cover thickness), there are 17 values accurately estimated, while the inaccurately estimated values have an error up to 2 mm. The experimental results show that the proposed method can autonomically evaluate the reinforcement bar diameter and cover thickness with a high accuracy.

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Applicability of GPR and a rebar detector to obtain rebar information of existing concrete structures

Cited by 23 publications

References 10 publications

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

Development of muon scattering tomography for detection of reinforcement in concrete

Deep learning–based nondestructive evaluation of reinforcement bars using ground‐penetrating radar and electromagnetic induction data

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