A novel method for determining the particle breakage contribution of high-speed railway graded aggregate and its application in vibratory compaction

Case Studies in Construction Materials(2023)

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摘要
The essence of vibratory compaction of high-speed railway graded aggregate (HRGA) materials lies in both internal particle rearrangement and breakage. However, there is a research gap on the level of particle rearrangement and breakage effects on the vibratory compaction characteristics. A novel index, the breakage contribution rate Cb, was proposed to quantify the contribution level of particle breakage to vibratory compaction characteristics based on theoretical derivation. Additionally, the relationship between the gradation, maximum dry density (MDD) and Cb was established using a high-performance machine learning (ML) model. Finally, the impact of Cb on vibratory compaction characteristics under different compaction conditions, including vibration parameters, container size, and sample thickness, was explored through laboratory vibratory compaction tests (LVCTs). The results show that the Cb is better predicted by the PSO-ANN-ML model (the comprehensive evaluation index in the testing set is 4.028). Under different compaction conditions, Cb has a significant important effect on determining the actual dry density (ADD). When Cb> 8.8%, it may lead to over-compaction, which has a negative effect on the overall quality of the compacted structure. Based on the analysis of Cb and ADD, it is recommended that the vibratory frequency should be set to the inherent frequency of the sample, while the ratio of excitation force to static load should be controlled at 1.8. The ratio of the container diameter and the sample thickness to the maximum particle size of the sample should be controlled at 4.4 and 3.5, respectively. The breakage contribution rate index proposed in the study has significant implications for the control of compaction quality and the understanding of the compaction mechanism.
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关键词
High-speed railway, Vibratory compaction, Breakage contribution rate, Machine learning, Compaction quality
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