Year: 2026
Type: article
Abstract: 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.
Source: Frontiers in Materials
Authors: Yanzhen Li, Bo Zang, Zhiyong Huang, Jiaming Tang
Institution: South China University of Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7118659531