Research on deterioration mechanism of graded gravel in high-speed railway subgrade layer based on machine vision

Abstract

This study aims to investigate the macro-micro deterioration mechanism of graded gravel under cyclic loading, which is of great significance in revealing the generation mechanism of high-speed railway subgrade defects in service and guiding the subgrade reinforcement. Initially, a precise-rapid quantification method for particle shape based on machine vision was established. To further improve the existing traditional semantic segmentation model (U-Net), a novel semantic segmentation model named VGG16-UNet-CA was established, which incorporated the U-Net, VGG16 model, Bilinear interpolation, and Coordinate attention (CA). Moreover, the high-precision binary images obtained by the established model were imported into Open Source Computer Vision Library (OpenCV) to calculate the particle shape indicators rapidly. Additionally, the cyclic loading tests were carried out to characterize the macro deterioration of graded gravel based on the mechanical indicator dynamic stiffness K, and the key factor of deterioration was explored through the established machine vision method. Finally, biaxial compression models with different deterioration degree were established by Discrete Element Method (DEM) to explore the micro deterioration mechanism. It was found that the segmentation accuracy indicators F1-score, Mean Pixel Accuracy (MPA) and Mean Intersection over Union (MIoU) of VGG16-UNet-CA were 98.56 %, 97.89 % and 98.01 %, which were higher than U-Net, SegNet, PSPNet, and DeepLabv3+. During the cyclic loading, the changes of the equivalent particle diameter De and slenderness ratio Ei of coarse particles in three filler groups were not significant, while the average values of roundness Rc increased by 28 %, 31 % and 25 %, indicating that the coarse particle abrasion was the key factor of deterioration. In the DEM simulation, with the increase of Rc, the mechanical strength of graded gravel decreased and the micro indicators (e.g., sliding rate, strong force chains, and anisotropy parameter an) inside the fillers gradually decreased, while the particle rotation continuously increased. Consequently, the interlocking ability of coarse particles decreased, leading to the bearing capacity and structural stability of particle skeleton reduced. This study not only provides a novel approach to investigate the deterioration mechanism of graded gravel under cyclic loading, which contributes to revealing the generation mechanism of high-speed railway subgrade defects in service, but also provides theoretical support for subgrade reinforcement.