Driving safety assessment method for high-speed maglev-bridge systems based on probabilistic tail distribution

In response to inaccurate safety assessments in high-speed maglev-bridge systems due to the neglected cumulative train operation effect of the traditional methods, a probabilistic safety assessment method based on the tail distribution of driving safety indicators was developed. A high-speed maglev-track bridge coupled stochastic dynamic model was established using the square power good lattice point method, multibody dynamics, and the modal superposition method. The probabilistic characteristic parameters of driving safety indicators (mean and standard deviation) were determined using the equal probability method and extreme-value theory. An explicit mapping relationship between the probabilistic characteristic parameters and the extreme-value cumulative distribution function (CDF) was constructed. According to a predefined confidence level, the extreme-value estimate of the safety indicator was obtained. By comparing this with the current normative threshold, a driving safety assessment method based on the probabilistic tail distribution characteristics was proposed. Finally, taking the case of a high-speed maglev train passing over a 24.768 m track beam, a driving safety assessment was conducted under different operating conditions such as varying train speeds. The results indicate that by employing the square power good lattice point method to construct a highly uniform set of track irregularity samples, the computational requirements can be met with only 131 samples. Compared to the 5 000 samples required by the Monte Carlo method, this significantly improves both the computational efficiency and accuracy of the probabilistic characteristic parameters for driving safety indicators. Furthermore, the extreme-value distribution proposed can effectively characterize the tail distribution characteristics of driving safety indicators. Its distribution curve well fits the maximum value samples, and the estimated extreme values can fully envelope the results generated by the Monte Carlo method. The number of failed samples evaluated based on the tail distribution model is less than 10, whereas it is as high as 2 280 under the normal distribution assumption. The method effectively avoids the limitation of traditional methods in failing to account for the cumulative effect of train operations, thereby providing a scientific basis for the safety assessment of high-speed maglev-bridge systems.