Structural Engineering and Mechanics

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Dynamic response uncertainty analysis of vehicle-track coupling system with fuzzy variables

  • Ye, Ling (Engineering Research Center of Railway Environment Vibration and Noise, Ministry of Education, East China Jiaotong University) ;
  • Chen, Hua-Peng (Engineering Research Center of Railway Environment Vibration and Noise, Ministry of Education, East China Jiaotong University) ;
  • Zhou, Hang (Engineering Research Center of Railway Environment Vibration and Noise, Ministry of Education, East China Jiaotong University) ;
  • Wang, Sheng-Nan (Xiyi Road Sub-district Office, Organization Department)
  • Received : 2020.05.11
  • Accepted : 2020.06.03
  • Published : 2020.08.25
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Abstract

Dynamic analysis of a vehicle-track coupling system is important to structural design, damage detection and condition assessment of the structural system. Deterministic analysis of the vehicle-track coupling system has been extensively studied in the past, however, the structural parameters of the coupling system have uncertainties in engineering practices. It is essential to treat the parameters of the vehicle-track coupling system with consideration of uncertainties. In this paper, a method for predicting the bounds of the vehicle-track coupling system responses with uncertain parameters is presented. The uncertain system parameters are modeled as fuzzy variables instead of conventional random variables with known probability distributions. Then, the dynamic response functions of the coupling system are transformed into a component function based on the high dimensional representation approximation. The Lagrange interpolation method is used to approximate the component function. Finally, the bounds of the system's dynamic responses can be predicted by using Monte Carlo method for the interpolation polynomials of the Lagrange interpolation function. A numerical example is introduced to illustrate the ability of the proposed method to predict the bounds of the system's dynamic responses, and the results are compared with the direct Monte Carlo method. The results show that the proposed method is effective and efficient to predict the bounds of the system's dynamic responses with fuzzy variables.

Keywords

Acknowledgement

The authors are very grateful for the financial supports received from the Basic Research Program of China (Grant No. 216YFC0802002), the National Natural Science Foundation of China (Grant No. 51978263) and the Natural Science Key Foundation of Jiangxi Province (Grant No. 20192ACBL20008).

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