Abstract
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 f(0) of HRGG fillers, varying in compactness K, was initially determined. The correlation between f(0) 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 K(20). 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(d)(max), K(rd), and K(20) all obtained optimal states when f(ov) was set as f(0) 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 R(2) 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.