Abstract
A method for analysing the vehicle–bridge interaction system with enhanced objectivity is proposed in the paper, which considers the time-variant and random characteristics and allows finding the power spectral densities (PSDs) of the system responses directly from the PSD of track irregularity. The pseudo-excitation method is adopted in the proposed framework, where the vehicle is modelled as a rigid body and the bridge is modelled using the finite element method. The vertical and lateral wheel–rail pseudo-excitations are established assuming the wheel and rail have the same displacement and using the simplified Kalker creep theory, respectively. The power spectrum function of vehicle and bridge responses is calculated by history integral. Based on the dynamic responses from the deterministic and random analyses of the interaction system, and the probability density functions for three safety factors (derailment coefficient, wheel unloading rate, and lateral wheel axle force) are obtained, and the probabilities of the safety factors exceeding the given limits are calculated. The proposed method is validated by Monte Carlo simulations using a case study of a high-speed train running over a bridge with five simply supported spans and four piers.
Highlights
The viaduct bridges are widely used in high-speed railway systems, in order to control the foundation settlement and to reduce the workload in railway maintenance
A method for analysing the vehicle–bridge interaction system with enhanced objectivity is proposed in the paper, which considers the time-variant and random characteristics and allows finding the power spectral densities (PSDs) of the system responses directly from the PSD of track irregularity
Since the parameters used in calculations usually differ from the actual values [1], probabilistic analysis is necessary for the vehicle–bridge interaction system
Summary
The viaduct bridges are widely used in high-speed railway systems, in order to control the foundation settlement and to reduce the workload in railway maintenance. Wetzel et al [5] studied the random dynamic responses of trains under wind load by a subset simulation method. Zhang et al [16] analysed the nonstationary random responses of interaction systems subjected to lateral horizontal earthquakes by PEM and the precise integration method (PIM) and obtained the time-dependent power spectral density (PSD) functions and standard deviations of the responses. He et al [17] proposed an efficient analysis framework for the interaction system subjected to wind loads and studied the nonstationary effects on the vibration responses. For a given wheel set, the equilibrium equations of the interaction system subjected to pseudo-excitation are expressed as follows:
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