Nondestructive Evaluation of Concrete Bridge Decks with Automated Acoustic Scanning System and Ground Penetrating Radar

Sun, Hongbin; Pashoutani, Sepehr; Zhu, Jinying

doi:10.3390/s18061955

Cited by 52 publications

(25 citation statements)

References 31 publications

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“…The threshold values λ is calculated by The Visu Shrink threshold which is defined as Eq. 21: (21) where: σ n is the standard deviation of Gaussian white noise with a mean of zero. N is the length of the signal.…”

Section: Denoising With the Shearlet Transformmentioning

confidence: 99%

“…Ground-penetrating radar (GPR), as a non-destructive, sufficient detection, simple operation, rapids ampling, high-resolution geophysical tool, has raised a substantial interest in non-destructive testing workers. This tool has been considered reliable for evaluation of geological disaster survey (Tang et al 2011), concrete bridge decks (Sun et al 2018), road investigations (Poikajärvi et al 2012), dielectric structures testing (Ivashov et al 2018) and doi:/10.37763/wr.1336-4561/65.1.001012 underground structure detection (Lai et al 2018). Meanwhile, it has been gradually applied to the non destructive testing (NDT) of trees.…”

Section: Introductionmentioning

confidence: 99%

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Noise Removal in Tree Radar B-Scan Images Based on Shearlet

Wen¹,

Li²,

Jiang³

2020

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There are often many scars and hollows in ancient and famous trees. As a convenient and effective non-destructive testing tool, ground-penetrating (GPR) has a technical advantage in detecting abnormality in trees. But the tree radar images always inherit some extent of noise in them. Thus, denoising is very important to extract useful information from a tree radar image. Shearlet is a directional multi-scale framework, which has been shown effective to identify sparse anisotropic edges even in the presence of a large quantity of noise. This article presents an efficient denoising method based on shearlet applied on the tree radar images. Experimental results on forward modeling and standing trees radar data substantiate that the proposed method has the best denoising performance, especially in preserving the edge information as compared with the other methods which are based on wavelet, curvelet and contourlet.

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Section: Denoising With the Shearlet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noise Removal in Tree Radar B-Scan Images Based on Shearlet

Wen¹,

Li²,

Jiang³

2020

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“…References N M Delamination IE [20,26,27,29,30,40,41,[43][44][45]48,53,55,56,61,62,68] 17 34% GPR [19,20,[22][23][24][25][26][27]29,[31][32][33][34][35]38,40,43,45,46,48 The results are also illustrated in Figures 6-9 for clarity. The charts clearly show the NDT method(s) that is (are) most appropriate for each damage type in closure joints, as denoted by the higher percentage(s).…”

Section: Type Of Defectmentioning

confidence: 99%

Non-Destructive Evaluation of Closure Joints in Accelerated Bridge Construction using a Damage Etiology Approach

Farhangdoust

Mehrabi

2020

Applied Sciences

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In accelerated bridge construction (ABC), prefabricated bridge deck elements are merged using “closure joints.” Because of the cast-in-place nature of closure joints that are expected to go into service rapidly and problems observed for some types of closure joints, there have been some concerns about their long-term durability. This has necessitated the need for monitoring the condition of ABC closure joints using non-destructive testing (NDT) methods. Closure joints contain unique features and details that sets them apart from conventional deck panels. This requires a special treatment of closure joints when it comes to selecting the appropriate NDT technique for their health monitoring. A clear guideline for selecting an applicable NDT method for various types of closure joints has not been developed yet. For this purpose, an investigation was carried out in the Accelerated Bridge Construction University Transportation Center (ABC-UTC) at Florida International University. This paper summarizes the result of this investigation. It includes reviews of all relevant NDT methods for applicability to ABC closure joints and efforts for categorizing closure joints according to joint features that affect the use of NDT. Since the applicability of NDT methods heavily depend on the type of expected anomaly to be detected and its root causes, all potential defects and types of damage were identified and investigated using a damage sequence tree (DST). Consequently, damage etiology for ABC closure joints were established using fault tree analysis (FTA). Finally, a quantitative statistical survey was used to substantiate the selection of the NDT methods that were most applicable to the health monitoring of ABC bridges containing closure joints. The results presented in this paper can be used by bridge owners and consultants as an effective and practical guide for the selection of NDT methods for monitoring the health of ABC closure joints.

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“…Recently, ground penetrating radar (GPR) has been widely employed for early detection of underground cavities, which are most likely propagating to sinkholes, thanks to its fast scanning speed, nondestructive inspection, and 3D imaging capabilities [10][11][12]. GPR transmitters emit electromagnetic waves into the underground at several spatial positions along the scanning direction, and GPR receivers measure the reflected signals to establish 2D GPR image called a radargram, also known as a B-scan image.…”

Section: Introductionmentioning

confidence: 99%

3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability

Kang

Kim

Im³

et al. 2019

Remote Sensing

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This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones.

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Nondestructive Evaluation of Concrete Bridge Decks with Automated Acoustic Scanning System and Ground Penetrating Radar

Cited by 52 publications

References 31 publications

Noise Removal in Tree Radar B-Scan Images Based on Shearlet

Noise Removal in Tree Radar B-Scan Images Based on Shearlet

Non-Destructive Evaluation of Closure Joints in Accelerated Bridge Construction using a Damage Etiology Approach

3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability

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