Numerical investigation of GPR performance over voxelized heterogeneous railway ballast: Influence of layer composition and antenna frequency

Conventional homogeneous ballast models used in Ground Penetrating Radar (GPR) railway simulations cannot adequately represent the scattering behaviour and waveform variability associated with realistic ballast heterogeneity. Accurate assessment of railway ballast and substructure layers therefore requires modelling approaches capable of capturing stone-scale electromagnetic interactions. Three-dimensional numerical simulations of GPR responses were performed over both homogeneous and voxelized heterogeneous ballast models to investigate the influence of layer composition and antenna frequency. The heterogeneous ballast was generated by procedurally placing 3D stone geometries within a voxel grid using randomized rigid-body transformations and overlap criteria. A total of 147 modular sub-blocks were assembled into a high-resolution computational domain representing realistic ballast heterogeneity. Simulations were conducted using 1 GHz and 400 MHz dipole antennas across three geotechnical scenarios consisting of ballast, sand, and clay layers. B-scan radargrams, zoomed A-scan comparisons, and windowed similarity percentages were used to evaluate waveform agreement. Results show that homogeneous models, although computationally efficient, fail to reproduce scattering and attenuation effects caused by stone-scale dielectric variability. At 1 GHz, heterogeneous ballast produces waveform distortions that obscure thin-layer detection and reduce similarity to 10%–45% in several time windows. In contrast, 400 MHz provides more stable penetration but lower resolution, with similarity values generally ranging between 75%–100%. These findings emphasize the importance of frequency-aware modelling strategies and demonstrate how voxel-based 3D representations can improve the interpretation of GPR responses. The proposed approach supports more effective survey design, antenna selection, and data interpretation in railway substructure condition assessment.