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

Abstract

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.

Application of clustering algorithms to void recognition by 3D ground penetrating radar

“…The basic detection principle of 3D ground penetrating radar is to send high-frequency electromagnetic waves in the form of pulses underground, which are reflected when they encounter underground target bodies with electrical differences in the process of propagation in the underground medium (Huai et al, 2019;Allroggen et al, 2020;Domenzain et al, 2020;Tang et al, 2022a). The model of electromagnetic wave propagation and reflection in a medium is shown in Figure 1.…”

Crack Unet: Crack Recognition Algorithm Based on Three-Dimensional Ground Penetrating Radar Images

“…The convolutional neural network, which has emerged in recent years, can complete the task of recognizing those similar features in other unmarked images by learning from the correctly marked images [ 17 , 18 , 19 , 20 , 21 ]. Haifeng Li, et al proposed a two-stage recognition method for GPR-RCNN [ 22 ].…”

3D Ground-Penetrating Radar Void Detection Method Based on Spatial Convergence

“…This method has realized the highly-accurate recognition of objects such as void, pipeline, subsidence,etc. Pham et al, Lei et al, and Wang Hui et al, have studied hyperbolic detection in B-SCAN images in different scenarios based on faster region convolutional neural network [15]. Yang Bisheng et al [16] studied the characteristic hyperbolic detection method of typical targets in urban underground space based on the single-level target detection (YOLOV3) network model, Zhang et al [17] studied the characteristic hyperbolic detection of water damage targets on asphalt pavement through the RESNET50 and YOLOV2 network.…”