Detectability of concealed cracks in the asphalt pavement layer using air-coupled ground-penetrating radar

Wang, Siqi; Leng, Zhen; Sui, Xin

doi:10.1016/j.measurement.2022.112427

Cited by 17 publications

(2 citation statements)

References 37 publications

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“…This differs from the stepped frequency antenna used in the field validation in this study. This central frequency was selected because of its high vertical resolution and sufficient penetration depth [30]. The pulse antenna could provide a simpler waveform than the stepped frequency antenna in the time domain.…”

Section: B Antennasmentioning

confidence: 99%

Real-Time Asphalt Pavement Layer Thickness Prediction Using Ground-Penetrating Radar Based on a Modified Extended Common Mid-Point (XCMP) Approach

Wang,

Leng,

Sui

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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The conventional surface reflection method has been widely used to measure the asphalt pavement layer dielectric constant using ground-penetrating radar (GPR). This method may be inaccurate for in-service pavement thickness estimation with dielectric constant variation through the depth, which could be addressed using the extended common mid-point method (XCMP) with air-coupled GPR antennas. However, the factors affecting the XCMP method on thickness prediction accuracy haven't been studied. Manual acquisition of key factors is required, which hinders its real-time applications. This study investigates the affecting factors and develops a modified XCMP method to allow automatic thickness prediction of in-service asphalt pavement with non-uniform dielectric properties through depth. A sensitivity analysis was performed, necessitating the accurate estimation of time of flights (TOFs) from antenna pairs. A modified XCMP method based on edge detection was proposed to allow real-time TOFs estimation, then dielectric constant and thickness predictions. Field tests using a multichannel GPR system were performed for validation. Both the surface reflection and XCMP setups were conducted. Results show that the modified XCMP method is recommended with a mean prediction error of 1.86%, which is more accurate than the surface reflection method (5.73%).

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Section: B Antennasmentioning

confidence: 99%

Real-Time Asphalt Pavement Layer Thickness Prediction Using Ground-Penetrating Radar Based on a Modified Extended Common Mid-Point (XCMP) Approach

Wang,

Leng,

Sui

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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“…This project used 3D GPR to detect the inner damage affecting pavement structures [38,39]. The 3D GPR used was produced by 3D Radar (Trondheim, Norway) as Figure 1 a).…”

Section: Data Collection For the Pavement Internal Crack Ratementioning

confidence: 99%

Clustering of Asphalt Pavement Maintenance Sections Based on 3D Ground-Penetrating Radar and Principal Component Techniques

Liu¹,

Zheng²,

et al. 2023

Buildings

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Asphalt pavement maintenance section classification is an important prerequisite for accurately determining asphalt pavement maintenance needs and formulating accurate maintenance plans. This paper introduces the three-dimensional (3D) ground-penetrating radar (GPR) pavement internal crack rate index on the basis of an original road surface performance data matrix, and the dimensionality of the road section classification data matrix was reduced through the principal component technique. An analysis of variance was used to compare the significance of the differences in the results for road section classification using different clustering methods and different clustering data and to investigate the influence of the clustering method, principal component technique and crack rate index on the maintenance road section classification results. The results showed that the principal component technique could reduce the dimensionality of the data matrix by 33% and retain more than 84% of the information. There was a genetic relationship between the clustering data and the technical characteristics of the classified sub-sections, and the internal crack rate was important for the characterisation of internal defects in asphalt pavement sub-sections and the determination of maintenance needs. The results of section classification varied considerably between clustering methods, and the choice of clustering method had a relationship to the pavement maintenance objectives. The dynamic clustering method combined with principal component analysis could significantly improve the significance of the differences in the clustering results, effectively improving the division of maintenance sections.

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Corrosion assessment using ground penetrating radar in reinforced concrete structures: Influential factors and analysis methods

Faris,

Zayed,

Abdelkader

et al. 2023

Automation in Construction

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Detectability of concealed cracks in the asphalt pavement layer using air-coupled ground-penetrating radar

Cited by 17 publications

References 37 publications

Real-Time Asphalt Pavement Layer Thickness Prediction Using Ground-Penetrating Radar Based on a Modified Extended Common Mid-Point (XCMP) Approach

Real-Time Asphalt Pavement Layer Thickness Prediction Using Ground-Penetrating Radar Based on a Modified Extended Common Mid-Point (XCMP) Approach

Clustering of Asphalt Pavement Maintenance Sections Based on 3D Ground-Penetrating Radar and Principal Component Techniques

Corrosion assessment using ground penetrating radar in reinforced concrete structures: Influential factors and analysis methods

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