Statistical study on the derailment factor considering the weak correlation of lateral-vertical wheel-rail forces of a vehicle-bridge system

Abstract

This paper proposes a stochastic method to solve the weak correlation of lateral-vertical forces when the train crosses a bridge. This method treats the Kalker creep equation with Taylor series and uses the moment statistical method to approximate the numerical characteristics of the nonlinear terms of the equation. The variance recursive method, mean recursive method, and lateral linear creep force modulation function are proposed. Based on these methods, the approximate numerical solutions for the standard deviation of the lateral linear creep force and the correlation coefficient of the lateral-vertical forces are obtained and verified by Monte Carlo method (MCM). Under certain preconditions, the derailment factor considering the correlation of the wheel-rail forces obeys the Approximate Cauchy Distribution, which is verified by the Kolmogorov–Smirnov test. The correctness of the proposed method is validated comparing the similarity of the derailment factor extreme value cumulative distribution function curve obtained by MCM. The case of a 6-car Pioneer EMU running through a 3-span 32 m simply-supported bridge is concerned by the proposed method and it is found that there is a significant difference with different traffic intense of the railway.

KEYWORDS:

• Vehicle-bridge system
• weak correlation
• variance recursive method
• mean recursive method
• lateral creep force modulation function
• extreme value cumulative distribution function

Acknowledgments

Support for this work from Fundamental scientific research business expenses of Beijing Jiaotong University (2021CZ108). Support for this work from the National Natural Science Foundation of China (Nos.52178101), Chinese National Science and Technology support programme ‘Study on evaluation of driving safety Index of train-bridge coupling system based on random vibration analysis’.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

If used for academic communication, the data and part of the codes contained in the paper could be obtained from the corresponding author.

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 52178101] and Fundamental Scientific Research Business Expenses of Beijing Jiaotong University [grant number 2021CZ108].