Wireless particle sensor is able to directly and timely achieve the motion of a particle at different locations in ballast bed. Hence, it has been increasingly employed for the dynamic performance assessment and disease detection of ballast bed in recent years, such as the detection of mud pumping, the assessment of effect of geogrid reinforcement, and the optimization of tamping parameters. Since the railway tends to have higher speed and larger axle load, and meanwhile it is usually a progressive development process from the onset of state deterioration to considerable disease, we carried out indoor full-scaled model test and DEM numerical simulation to analyze the effects of loading magnitude and frequency, local void beneath the ballast bed and moisture content variation on particle responses. The results show that the motion characteristics of particles under the sleeper are positively correlated with loading magnitude and frequency, and the particles’ accelerations are more sensitive to the change of wheel-rail force under high frequency. Due to the variation of particle contact state, particle motion usually shows spatiotemporal fluctuation. Then a comprehensive indicator integrated from particle’s multi-dimensional characteristics is more stable in reflecting changes of the supporting status and moisture content. In addition, the measured particle’s motion characteristics and macroscopic stiffness are also adopted to perform dual-scale calibration of parameters in DEM models of ballast bed to improve the accuracy of numerical models.
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