Volume 21 Issue 3
Aug.  2021
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LIU Lin-ya, CUI Wei-tao, QIN Jia-liang, LIU Quan-min, SONG Li-zhong. Effects of rail pad viscoelasticity on vibration and structure-borne noise of railway box girder[J]. Journal of Traffic and Transportation Engineering, 2021, 21(3): 134-145. doi: 10.19818/j.cnki.1671-1637.2021.03.007
Citation: LIU Lin-ya, CUI Wei-tao, QIN Jia-liang, LIU Quan-min, SONG Li-zhong. Effects of rail pad viscoelasticity on vibration and structure-borne noise of railway box girder[J]. Journal of Traffic and Transportation Engineering, 2021, 21(3): 134-145. doi: 10.19818/j.cnki.1671-1637.2021.03.007

Effects of rail pad viscoelasticity on vibration and structure-borne noise of railway box girder

doi: 10.19818/j.cnki.1671-1637.2021.03.007
Funds:

National Natural Science Foundation of China 51968025

National Natural Science Foundation of China 52068030

National Natural Science Foundation of China 52008169

Natural Science Foundation of Jiangxi Province 20192ACBL20009

Science Foundation for Young Scholars of Jiangxi Province 20202BABL214048

Science and Technology Research Project of Jiangxi Education Department GJJ200658

More Information
  • Author Bio:

    LIU Lin-ya(1973-), male, professor, PhD, lly1949@163.com

  • Received Date: 2020-12-11
    Available Online: 2021-08-27
  • Publish Date: 2021-08-27
  • Taking the WJ-7B rail pad for high-speed railways as the research object, the dynamic properties of rail pad at different temperatures were tested through the dynamics mechanical property test, and the viscoelastic properties of rail pads were characterized by the temperature-frequency equivalent principle, Williams-Landel-Ferry (WLF) formula, and high-order fractional derivative fraction Voigt and Maxwell model in parallel (FVMP) model. The model was substituted into a finite element-boundary element model specially designed for the bridge vibration and structure-borne noise prediction, and the results were compared with those obtained through the Kelvin-Voigt (KV) model to analyze the effects of rail pad viscoelasticity on the box girder vibration and structure-borne noise. Research results show that the rail pad viscoelasticity is a temperature- and frequency-dependent dynamic parameter. The rail pad stiffness is positively correlated with the frequency and negatively correlated with the temperature, whereas the damping is negatively correlated with both the frequency and temperature. The damping changes significantly at frequencies within 1-100 Hz, but it varies slightly at frequencies above 100 Hz. The experimental dynamic parameters of rail pad are in good agreement with the high-order fractional derivative FVMP model fitting values. Therefore, the high-order fractional derivative FVMP model can accurately describe the dynamic viscoelastic behavior of rail pad under wide ranges of temperatures and frequencies. When only the frequency-dependent properties of rail pad are considered, the vibration of bridge intensifies at 25-63 Hz and weakens at 80-200 Hz. At the peak frequency of 63 Hz, the acceleration vibration levels of top plate, web, and bottom plate increase by 5.62, 0.91, and 2.94 dB, respectively. In the transverse direction of the bridge, the sound radiation increases obviously at the vertical near-field points of all bridge plates and near the ground under the bridge. When both the temperature- and frequency-dependent properties of rail pad are considered, as the temperature drops, the bridge vibration weakens continuously at 31.5-50.0 Hz and then intensifies progressively at 63-200 Hz. In the transverse direction of bridge, the sound radiation decreases diagonally above the top plate, at the vertical near-field points of web and bottom plate, and near the ground under the bridge. When the temperature drops from 20 ℃ to -20 ℃, the overall sound pressure level reduces by approximately 2 dB at most. Neglecting the rail pad viscoelasticity will lead to the deviations in the predictions of bridge vibration and structure-borne noise. The rail pad viscoelasticity should be considered in the simulation analysis to improve the prediction accuracy. 5 tabs, 15 figs, 31 refs.

     

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