The conventional crosswind stability analysis of high-speed trains is based on a deterministic approach with the final output being a characteristic wind curve (CWC). The CWC only provides the dividing line between the safe state and failure state of vehicles, thus it cannot be used to evaluate the overturning probability of vehicles subjected to strong winds. To overcome this shortcoming of the conventional CWC, a fuzzy stochastic approach is proposed that can make an effective assessment of the operational safety of high-speed trains exposed to stochastic winds. According to this methodology, the uncertain parameters existing in the system such as stochastic winds and aerodynamic coefficients are modelled as basic random variables, and the failure of the structure is considered as a fuzzy random event. An algorithm for computing the unsteady aerodynamic loads of high-speed trains exposed to stochastic winds is created and then the aerodynamic loads are applied to a dynamic model of the vehicle system in order to investigate the dynamic response. Importance sampling is used to conduct an analysis of the crosswind stability of high-speed trains based on fuzzy random reliability theory. This finally leads to the substitution of the conventional CWC by probabilistic characteristic wind curves (PCWCs). The conventional CWC is shown to be over-conservative, while the PCWCs can provide more significant reference for the safe operation of high-speed trains.
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