To address the uncertainty of optimal vibratory frequency f_ov of high-speed railway graded gravel (HRGG) and achieve high-precision prediction of the f_ov, the following research was conducted. Firstly, commencing with vibratory compaction experiments and the hammering modal analysis method, the resonance frequency f₀ of HRGG fillers, varying in compactness K, was initially determined. The correlation between f₀ and f_ov was revealed through vibratory compaction experiments conducted at different vibratory frequencies. This correlation was established based on the compaction physical–mechanical properties of HRGG fillers, encompassing maximum dry density ρd_max, stiffness K_rd, and bearing capacity coefficient K₂₀. Secondly, the gray relational analysis algorithm was used to determine the key feature influencing the f_ov based on the quantified relationship between the filler feature and f_ov. Finally, the key features influencing the f_ov were used as input parameters to establish the artificial neural network prediction model (ANN-PM) for f_ov. The predictive performance of ANN-PM was evaluated from the ablation study, prediction accuracy, and prediction error. The results showed that the ρ_dmax, K_rd, and K₂₀ all obtained optimal states when f_ov was set as f₀ for different gradation HRGG fillers. Furthermore, it was found that the key features influencing the f_ov were determined to be the maximum particle diameter d_max, gradation parameters b and m, flat and elongated particles in coarse aggregate Q_e, and the Los Angeles abrasion of coarse aggregate LAA. Among them, the influence of d_max on the ANN-PM predictive performance was the most significant. On the training and testing sets, the goodness-of-fit R² of ANN-PM all exceeded 0.95, and the prediction errors were small, which indicated that the accuracy of ANN-PM predictions was relatively high. In addition, it was clear that the ANN-PM exhibited excellent robust performance. The research results provide a novel method for determining the f_ov of subgrade fillers and provide theoretical guidance for the intelligent construction of high-speed railway subgrades.

Graded aggregates used in high-speed railway subgrades often undergo micro breakage (i.e., corner and edge spalling) under cyclic loading, whereas the mechanism linking local particle spalling to contact-network evolution and permanent deformation remains unclear. To address the lack of a physically based micro breakage criterion and a continuous local shape-updating scheme in existing DEM approaches, this study proposes an energy-driven micro breakage method. The method uses the relationship between contact elastic energy and fracture energy for newly created free surfaces as the breakage criterion. It also employs a radial function representation to simulate local particle shape evolution. The proposed method is subsequently validated through multilevel tests and shows reasonable agreement with the experimental results for particle micro breakage and the associated mechanical responses. Furthermore, dynamic triaxial simulation results show that micro breakage exhibits a distinct cumulative characteristic and increases with loading frequency and amplitude. Meanwhile, micro breakage drives particle rearrangement and new contact formation, leading to skeleton densification and weakening of the strong force-chain network, which in turn accelerates plastic deformation accumulation. These coupled processes constitute an important mechanism governing the progressive degradation of graded aggregates under cyclic loading. This study clarifies the associated particle-scale mechanism and provides a reference for performance optimization.