Bo Zang

Interlayer voids threaten pavement integrity, but their ground-penetrating radar (GPR) signatures and measurable dimensions depend on the acquisition conditions and target geometry. This study combines two-dimensional finite-difference time-domain simulations, a full-scale wet-sand experiment, and a preliminary field case study. Nine numerical configurations represent air- or water-filled voids. Physical targets comprise polystyrene blocks and water-bag-filled polycarbonate boxes surveyed using stepped-frequency 3D-GPR. Under the adopted processing and display convention, the two physical target groups show contrasting polarity patterns, and larger targets generally have clearer lateral boundaries. Quantitative analysis shows that dimensional accuracy is configuration-specific and does not support a universal size threshold. Under the investigated acquisition and interpretation conditions, thickness cannot be reliably estimated for either the air-like or water-rich targets. Field coring and endoscopy, supplemented by the reported excavation measurements, verify one void with a plan extent of approximately 5.2 m × 1.2 m and a depth range of 0.44–0.54 m. This case supports preliminary correspondence between controlled and field signatures, rather than general field-accuracy benchmarks.

The size and gradation of aggregates strongly influence the performance of asphalt concrete. They directly affect the deformation characteristics and fatigue resistance of asphalt concrete pavements. During production, transportation, and storage, granular aggregates are accumulated in bulk. When allowed to settle naturally, gradation segregation may occur. This leads to an uneven spatial distribution of coarse and fine particles. As a result, traditional screening methods cannot achieve rapid and accurate gradation detection. This paper proposes an intelligent identification method for aggregate heap gradation based on binocular machine vision and deep learning. We use a binocular stereoscopic vision system to reconstruct the three-dimensional model of the heap. The actual height of the heap is determined by acquiring a disparity map using Semi-Global Block Matching stereoscopic matching. Top-view images of the conical heap are captured, and deep learning algorithms segment aggregate particles and quantify the area proportion of coarse particles on the surface. By analyzing the concentration gradient of coarse particles in radial zones, we develop a multiple linear regression model linking surface distribution to the overall coarse aggregate gradation. Experimental results show that the proposed method is much more efficient than the manual screening method in the laboratory environment. The average relative error for aggregate pile height from three-dimensional reconstruction is less than 5%. The coefficient of determination for the prediction model of aggregate gradation is 0.970. This study thus provides a low-cost, high-efficiency approach for detecting aggregate gradation.

Three-dimensional ground-penetrating radar can quickly visualize the internal condition of the road; however, it faces challenges such as data splicing difficulties and image noise interference. Scanning antenna and lane size differences, as well as equipment and environmental interference, make the radar image difficult to interpret, which affects disease identification accuracy. For this reason, this paper focuses on road radar image splicing and noise reduction. The primary research includes the following: (1) We make use of backward projection imaging algorithms to visualize the internal information of the road, combined with a high-precision positioning system, splicing of multi-lane data, and the use of bilinear interpolation algorithms to make the three-dimensional radar data uniformly distributed. (2) Aiming at the defects of the low computational efficiency of the traditional adaptive median filter sliding window, a Deep Q-learning algorithm is introduced to construct a reward and punishment mechanism, and the feedback reward function quickly determines the filter window size. The results show that the method is outstanding in improving the peak signal-to-noise ratio, compared with the traditional algorithm, improving the denoising performance by 2–7 times. It effectively suppresses multiple noise types while precisely preserving fine details such as 0.1–0.5 mm microcrack edges, significantly enhancing image clarity. After processing, images were automatically recognized using YOLOv8x. The detection rate for transverse cracks in images improved significantly from being undetectable in mixed noise and original images to exceeding 90% in damage detection. This effectively validates the critical role of denoising in enhancing the automatic interpretation capability of internal road cracks.

To accurately predict the relative permittivity of cement-stabilized base materials, a study on the dielectric mixing model for cracked base materials was conducted. Based on the electromagnetic mixing theory of multiphase composites, a comprehensive dielectric mixing model of cement-stabilized base materials was derived. The volume ratios and relative permittivity values of the specimen constituents in different cracking states of the cement-stabilized base were determined using industrial CT and a Percometer relative permittivity meter, with comprehensive consideration given to the effects of different initial porosities and crack widths on the dielectric properties. Based on the volumetric and dielectric properties of the base material specimens in both intact and cracked states, as well as the error analysis between the predicted and measured values of the relative permittivity constant, the u-optimal solution of the dielectric mixing model for cement-stabilized base material was determined to be 1. Consequently, an optimization dielectric mixing model for semi-rigid base course materials in a cracked state was developed. The optimization model proposed is suitable for predicting the dielectric properties of cement-stabilized base material with crack widths generally greater than 3 mm during the service life of semi-rigid base course in engineering practice.