A stochastic vibration analysis method for metro vehicle-floating slab track coupled system considering shear hinges

Abstract

Stochastic vibration analysis is the basis for vehicle-track system parameter optimization, performance evaluation, and reliability improvement. This study proposes a stochastic framework for analyzing coupled metro vehicle–floating slab track interactions. The methodology systematically integrates vehicle parameter randomness, track component variability and correlation, and random track irregularity excitations. The vehicle-track dynamic model incorporating shear hinges is employed to investigate the shear hinge mechanisms. Utilizing Cantor set mapping for dimensionality reduction of multivariate random variables, a bilateral format finite difference scheme is developed to solve the probability density evolution equation (PDEE), enabling efficient simulation of spatiotemporal stochastic response statistics under varying coefficient of variation (CV). Numerical results demonstrate that the shear hinge implementation significantly reduces vibration amplitudes (about 27.4% at the CV of 0.1) and dispersion, enhancing system dynamic stability. Increasing coefficients of variation values of vehicle parameters (primary/secondary suspension stiffness/damping) and track parameters (fastener stiffness/damping, steel spring stiffness/damping) progressively amplify dynamic responses. The proposed approach provides a robust tool for reliability assessment in metro-floating slab track system, particularly in addressing the stochastic nature of vehicle–track interactions.