A prediction model for railroad ballast resilient modulus considering cyclic loading duration ratio

Abstract

Railroad ballast aggregate significantly contributes to load distribution, drainage, and track resilience. Over time, the resilience degrades due to factors like ballast breakage, permanent deformation, and mud pumping, necessitating regular, resource-intensive maintenance. Thus, investigating the influential factors of resilient modulus and accurately correlating them can assist in cost-effective maintenance planning. This study used large-scale triaxial tests with varied loading waveforms to investigate their impact on resilient modulus. A new index, the Cyclic Loading Duration Ratio (CLDR), was introduced to quantify loading waveforms with different rest periods. Our experimental results indicate that the cyclic loading duration period controls the resilient modulus of granular material by varying the loading rates, while unloading duration and rest periods do not affect the resilient modulus. With increasing CLDR, resilient modulus initially decreases, but begins to ascend when CLDR falls above 0.20. Building on these findings, an empirical correlation model incorporating the CLDR for the resilient modulus of railroad ballast is proposed. The validation results show this proposed model outperforms existing ones in correlating the resilient modulus with cyclic loading waveforms.