Computational simulation reveals that under sleeper pads reduce peak ballast pressure by up to 44% in railway turnouts, indicating reduced void formation and settlement.
Railway turnouts are subject to disproportionately high maintenance costs, partly driven by ballast void formation under asymmetrically loaded sleepers. Existing turnout models rely on detailed three-dimensional finite element track representations and do not readily support iterative long-term settlement analyses across the complete turnout geometry. This paper presents a computationally efficient physics-based vehicle–track interaction (VTI) framework extended to railway turnouts, incorporating a dual track model with equivalent foundation stiffnesses, a rail pad layer, and a crossing nose impact model. A modular sleeper dynamics model, formulated as an Euler–Bernoulli beam on a tensionless Winkler foundation, is coupled with the VTI framework to compute spatially resolved dynamic ballast pressure distributions along individual sleepers. A local pressure-based settlement model then allows the study of sleeper–ballast void formation over repeated axle passes. The framework is applied as a proof of concept to two in-service railway turnouts, one with and one without under sleeper pads, and complemented by a parametric study varying train speed, vehicle type, and rail pad stiffness. Void formation concentrates in the crossing nose region, consistent with laboratory measurements and particle-scale simulations. Under sleeper pads reduce peak ballast pressure by 38 to 44% and substantially reduce void formation across the crossing panel, thereby lowering overall track settlement and supporting the maintenance of track geometry in the turnout during operation. Vehicle type is the dominant influencing factor, with the locomotive producing up to 83% higher peak pressure than the passenger car model. The proposed framework provides an efficient tool for comparative turnout design, offering insight into the force transfer and settlement mechanisms of railway turnouts.
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Pircher et al. (2026) studied this question.
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