GPR research on damaged road pavements built in cut and fill sections

Colagrande, S.; Ranalli, Danilo; Tallini, Marco

doi:10.1016/j.trpro.2020.02.059

Cited by 7 publications

(3 citation statements)

References 1 publication

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“…They used GPR analysis and sweep rectification for crack detection. The proposed demonstration, drawn from a sizable sample, displayed high repeatability to prove that the methodology can be regarded as competent to assess damaged roads with cut and fill portions [8]. Also,Dharneeshkar et al [7] used the commercial solutions covered in this evaluation, and a gap analysis was then carried out.…”

Section: Mei Et Almentioning

confidence: 97%

Detecting Danger: AI-Enabled Road Crack Detection for Autonomous Vehicles

Alisha,

Debnarayan,

Rachaita

et al. 2023

E3S Web Conf.

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The present article proposes the deep learning concept termed ―Faster-Region Convolutional Neural Network‖ (Faster-RCNN) technique to detect cracks on road for autonomous cars. Feature extraction, preprocessing, and classification techniques have been used in this study. Several types of image datasets, such as camera images, faster-RCNN laser images, and real-time images, have been considered. With the help of GPU (graphics processing unit), the input image is processed. Thus, the density of the road is measured and information regarding the classification of road cracks is acquired. This model aims to determine road crack precisely as compared to the existing techniques.

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Section: Mei Et Almentioning

confidence: 97%

Detecting Danger: AI-Enabled Road Crack Detection for Autonomous Vehicles

Alisha,

Debnarayan,

Rachaita

et al. 2023

E3S Web Conf.

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show abstract

“…It can be seen that when the FDTD method is used to solve the Maxwell equation, the 3D size of the element mesh (∆x, ∆y, ∆z) greatly affects the accuracy of the forward modeling. In general, considering the calculation error caused by the dispersion of electromagnetic waves after discretization, better accuracy can be obtained when the size of the discrete grid is less than 1/10 of the minimum wavelength, as shown in Equation (5).…”

Section: ∇ ×mentioning

confidence: 99%

“…Ground penetrating radar (GPR) is a technology that uses high-frequency radio electromagnetic waves to perform non-destructive testing of targets in the medium [2][3][4]. Owing to its advantages of high resolution, high efficiency, intuitive results, and non-destructive nature, it is widely used in geological exploration [5], road surface inspection [6], underground pipeline detection [7], archaeological detection [8,9], etc. In addition, GPR is also used in the detection of tunnel linings [10,11].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Void Defects behind Tunnel Lining through GPR forward Simulation

Bao

Shen

et al. 2022

Sensors

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Voids, a common defect in tunnel construction, lead to the deterioration of the lining structure and reduce the safety of tunnels. In this study, ground-penetrating radar (GPR) was used in tunnel lining void detection. Based on the finite difference time domain (FDTD) method, a forward model was established to simulate the process of tunnel lining void detection. The area of the forward image and the actual void area was analyzed based on the binarization method. Both the plain concrete and reinforced concrete lining with various sizes of air-filled and water-filled voids were considered. The rationality of the model was verified by measured data. It was observed that the response mode of voids can be hyperbolic, bowl-shaped, and strip-shaped, and this depends on the void’s width. Compared with the air-filled voids, water filling increases the response range of the voids and produces a virtual image. Although the diffracted wave caused by a steel bar will bring about significant interference to the void response, the center position of the voids can be accurately located using 3D GPR.

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PD-ITS: Pothole Detection Using YOLO Variants for Intelligent Transport System

Omar,

Kumar

2024

SN COMPUT. SCI.

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GPR research on damaged road pavements built in cut and fill sections

Cited by 7 publications

References 1 publication

Detecting Danger: AI-Enabled Road Crack Detection for Autonomous Vehicles

Detecting Danger: AI-Enabled Road Crack Detection for Autonomous Vehicles

Evaluation of Void Defects behind Tunnel Lining through GPR forward Simulation

PD-ITS: Pothole Detection Using YOLO Variants for Intelligent Transport System

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