Real-Time Density and Thickness Estimation of Thin Asphalt Pavement Overlay During Compaction Using Ground Penetrating Radar Data

Wang, Siqi; Zhao, Shan; Al‐Qadi, Imad L.

doi:10.1007/s10712-019-09556-6

Cited by 47 publications

(14 citation statements)

References 27 publications

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“…The model was built in two dimensions [18], [19], which could significantly reduce the computational expense compared to three-dimensional numerical models. This can meet the simulation accuracy because the locations and amplitudes of interface reflections are the same between two-dimensional and three-dimensional models; the area of impact of the aircoupled antenna on the pavement surface can be assumed to be identical.…”

Section: A Geometric and Electrical Propertiesmentioning

confidence: 99%

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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: A Geometric and Electrical Propertiesmentioning

confidence: 99%

“…This method has been widely implemented due to its simplicity; only one antenna with a transmitter and receiver is required [16]- [18]. However, it assumes the uniformity of layer dielectric property through depth, which may cause inaccurate dielectric constant and thickness estimations for inservice pavement [19]- [22].…”

Section: Introductionmentioning

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 reflection coefficient method does not consider electromagnetic wave diffraction and can only realize point measurement [ 13 ]. The premise assumption of the constant thickness method is the asphalt pavement thickness being a constant value, which has a large error with the actual situation of the project [ 14 ]. The common mid-point method is one of the dielectric constant detection methods, which is able to be carried out in a large area, with well-documented theoretical calculation model and accurate results [ 15 ].…”

Section: Dielectric Constant Detection Principle Of 3d Ground-penetra...mentioning

confidence: 99%

Low Compaction Level Detection of Newly Constructed Asphalt Pavement Based on Regional Index

Tang¹,

Huang

et al. 2022

Sensors

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In order to improve the prediction accuracy regarding low compaction level of asphalt pavement, this paper carries out indoor tests to detect the voids and dielectric constants of AC-13, AC-16 and AC-25 asphalt mixtures, obtaining their relationship equations via linear fitting and determining the dielectric constant judgment threshold of low compaction level segregation risk points ε1. Based on the common mid-point method, three-dimensional ground-penetrating radar is used to obtain the dielectric constant of the physical engineering test section. The researcher can draw the distribution map of the low compaction level segregation risk area according to the judgment threshold ε1 of the rough segregation risk points; divide the connected risk areas; determine the regional convex hull; and calculate the regional indicators such as the regional area, the ratio of the convex risk points and the mean value of the regional dielectric constant. The response surface analysis method is used to acquire the model of risk area index and core void ratio. The model is employed to predict and verify the core void ratio in the risk area of the road section and verify the accuracy of the model. The results show that the error range between the predicted voids and the measured voids is −0.4%~+0.4%, and the mean absolute value of the error is 0.25%. Compared with the mean measured voids of 6.63%, the relative error is 3.77%, indicating that the model can accurately predict the regional low compaction level segregation degree.

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“…Pavement compaction quality control is critical to the longterm service performance of the road [1][2][3], and its quality inspection and control methods mainly include in-situ tests (modulus and density test) and intelligent compaction (IC) technology [4]. These in-situ test methods do not necessarily provide accurate real-time compaction information and as a result of that damage to pavement structures could be the consequence [2,5]. For instance, IC technology assumes the dynamic response of the compacted layer can be acquired through the acceleration sensor installed on the * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Experimental and numerical simulation study on flow behavior of asphalt mixture using smart aggregate

Chen

Wang

2023

Smart Mater. Struct.

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A meso-scale mechanical analysis approach, which is essentially based on the use of smart aggregate sensor, was employed to inquire the dynamic responses of smart aggregate in cold recycled asphalt mixtures during gyratory compaction, including as well an investigation over the critical influence factors. The discrete element method (DEM) was further applied to study the interaction behaviors of smart aggregate with the surrounding particles. The laboratory test results showed that smart aggregate is an effective method to monitor the contact force and rotation variation between the aggregates, and the changes in the mechanical and physical properties of the mixtures can be characterized by the monitoring results of smart aggregate. In addition, the results of DEM analyses show that the mixtures with 55% coarse aggregates have higher normal force and lower kinetic energy during gyratory compaction, revealing the influence mechanism of the effect of coarse aggregate content on smart aggregate monitoring results at the meso-scale.

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Real-Time Density and Thickness Estimation of Thin Asphalt Pavement Overlay During Compaction Using Ground Penetrating Radar Data

Cited by 47 publications

References 27 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

Low Compaction Level Detection of Newly Constructed Asphalt Pavement Based on Regional Index

Experimental and numerical simulation study on flow behavior of asphalt mixture using smart aggregate

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