Non-Stationary Random Vibration Analysis of Railway Bridges Under Moving Heavy-Haul Trains
Author(s): |
Zhihui Zhu
Lidong Wang Zhiwu Yu Wei Gong Yu Bai |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | International Journal of Structural Stability and Dynamics, February 2018, n. 3, v. 18 |
Page(s): | 1850035 |
DOI: | 10.1142/s0219455418500359 |
Abstract: |
This paper presents a non-stationary random vibration analysis of railway bridges under moving heavy-haul trains by the pseudo-excitation method (PEM) considering the train-track-bridge coupling dynamics. The train and the ballasted track-bridge are modeled by the multibody dynamics and finite element (FE) method, respectively. Based on the linearized wheel-rail interaction model, the equations of motion of the train-ballasted track-bridge coupling system are then derived. Meanwhile, the excitations between the rails and wheels caused by the random track irregularity are transformed into a series of deterministic pseudo-harmonic excitation vectors by the PEM. Then, the random vibration responses of the coupling system are obtained using a step-by-step integration method and the maximum responses are estimated using the 3[Formula: see text] rule for the Gaussian stochastic process. The proposed method is validated by the field measurement data collected from a simply-supported girder bridge (SSB) for heavy-haul trains in China. Finally, the effects of train speed, grade of track irregularity, and train type on the random dynamic behavior of six girder bridges for heavy-haul railways are investigated. The results show that the vertical acceleration and dynamic amplification factor (DAF) of the midspan of the SSB girders are influenced significantly by the train speed and track irregularity. With the increase in the vehicle axle-load, the vertical deflection-to-span ratio ([Formula: see text]) of the girders increases approximately linearly, but the DAF and vertical acceleration fail to show clear trend. |
- About this
data sheet - Reference-ID
10352260 - Published on:
14/08/2019 - Last updated on:
14/08/2019