Detection of air voids in concrete by radar in transmission mode

Trela, Ch.; Kind, Thomas; Schubert, M.

doi:10.1109/iwagpr.2015.7292650

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…The images used for the damage classification were the output of a GPR campaign. Such technology is a generally non-destructive screening method [27] used in civil engineering applications [28], specifically for assessing a tunnel's structural conditions [29]. It is used in a wide range of applications including concrete void location [30], underground utility tracking [31], railway ballast optimization and evaluation [32], and landmine detection [33].…”

Section: Instrumentation: Ground Penetrating Radarmentioning

confidence: 99%

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Chiaia

Marasco

Aiello

2022

Front. Struct. Civ. Eng.

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In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.

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Section: Instrumentation: Ground Penetrating Radarmentioning

confidence: 99%

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Chiaia

Marasco

Aiello

2022

Front. Struct. Civ. Eng.

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“…GPR is a very versatile technique that has been successfully employed in many infrastructure condition assessment and geophysical survey applications . These applications include pavement thickness measurement [16], defect detection in concrete or composite bridge decks [17,25], reinforcing steel location in concrete [22,23,34], archaeological investigation [18,19], detection of landmines [28], snow thickness measurement and/or subglacial topography detailing [27,31], tree root detection [20,21,26], irrigation and soil water content monitoring [36,37], detection of cracks in concrete or pipe walls [24,30], and underground/concrete void detection [29,38]. GPR has also been used for buried container and pipe/utility detection [15,32,33,35], but these applications include the detection of metal pipes or pipes filled with water, which are much easier to detect using GPR compared to non-metallic buried pipes, which may not have water inside it.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Easy Detection of Buried FRP Composite/Non-Metallic Pipes Using Ground-Penetrating Radar

Kavi,

Halabe

2023

Sensors

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Pipelines remain the safest means of transporting natural gas and petroleum products. Nonetheless, the pipeline infrastructure in the US is facing major challenges, especially in terms of corrosion of steel/metallic pipes and excavation damage of onshore pipelines (leading to oil spills, explosions, and deaths). Corrosion of metallic pipelines can be avoided by using non-corrosive materials such as plastic pipes for low-pressure applications and glass-fiber-reinforced polymer (GFRP) composite pipes for transporting high-pressure oil and natural gas. However, buried non-metallic pipelines are not easily detectable, which can lead to increased excavation damage during construction and rehabilitation work. Alternative strategies for making buried non-metallic pipes easily locatable using ground-penetrating radar (GPR) were investigated in this study. Results from this study have shown that using carbon fabric or an aluminum foil overlay on non-metallic pipes before burying in soil significantly increases the reflected GPR signal amplitude, thereby making it easier to locate such pipelines. The reflected GPR signal amplitude for pipe sections with carbon fabric or aluminum foil overlays was found to have increased by a factor of up to 4.5 over the control samples. The results also highlight the importance of selecting the appropriate antenna frequency for GPR surveys, since wet silt loam soil and clay significantly reduce the penetration depths of the radar signals produced by the GPR antennae.

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“…Ground penetrating radar (GPR) is a non-intrusive and nondestructive sensing modality which has been used in many civilian and military applications, including mine detection [1], bridge and tunnel assessment [2], utility mapping [3], ballast assessment [4], and void detection [5]. For the detection of landmines and improvised explosive devices, the GPR system transmits electromagnetic waves to sense electrical inhomogeneities exhibited by the buried targets.…”

Section: Introductionmentioning

confidence: 99%

GPR Target Detection by Joint Sparse and Low-Rank Matrix Decomposition

Tivive

Bouzerdoum

Abeynayake

2019

IEEE Trans. Geosci. Remote Sensing

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Ground penetrating radar (GPR) uses electromagnetic waves to image, locate, and identify changes in electric and magnetic properties in the ground. The received signal comprises not only the target echoes but also strong reflections from the rough, uneven ground surface, which impair subsurface inspections and visualization of buried objects. In this paper, a background clutter mitigation and target detection method using low-rank and sparse priors is proposed for GPR data. The radar signal is decomposed into the sum of a low-rank component and a sparse component, plus noise. The low-rank component captures the ground surface reflections and background clutter, whereas the sparse component contains the target reflections. The effectiveness of the proposed method is evaluated on real radar signals collected from buried landmines and improvised explosive devices. The experimental results show that the proposed method successfully removes the background clutter and estimates the target signals.

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Detection of air voids in concrete by radar in transmission mode

Cited by 7 publications

References 6 publications

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

An Approach for Easy Detection of Buried FRP Composite/Non-Metallic Pipes Using Ground-Penetrating Radar

GPR Target Detection by Joint Sparse and Low-Rank Matrix Decomposition

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