Analysis of Local Vibration Characteristics and Influencing Factors of the High-Speed Railway Box Beam

ICTE 2015 ◽  
2015 ◽  
Author(s):  
Wenjun Luo ◽  
Xinyuan Zhang
2009 ◽  
Vol 2009.18 (0) ◽  
pp. 109-112
Author(s):  
Yuta ICHIKURA ◽  
Masao NAGAI ◽  
Ryuzo HAYASHI ◽  
Ryohei SHIMAMUNE ◽  
Yoshitaka YASUI ◽  
...  

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Biao Zhou ◽  
Fengshou Zhang ◽  
Xiongyao Xie

A series of field vibration tests were carried out at an underground metro station underneath the high speed railway by installing accelerometers both on the side wall of the metro station and in the surrounding soil. Within the frequency domain of 0–200 Hz, the attenuation, transmission, and frequency response properties of vibration for both the underground structure and the surrounding soil were analyzed and compared. The attenuation index is found to be decreased with the increase of underground structure stiffness. The existence of damping and coupling effect of the surrounding soil, as well as the interference of axle spectrum from excitation sources, makes it very challenging to separate the frequency response characteristics of structures from soil at FFT (Fast Fourier Transform) spectrum. The combined NExT (Natural Excitation Technique) and HHT (Hilbert–Huang Transform) method are thus used to study the waveforms and propagation velocities of vibration waves in underground structure and surrounding soil. The wave types and their speeds are determined and used for evaluating the structural elastic modulus. Compared with the attenuation index or natural frequency, wave velocity is easier to be recognized, is sensitive to the change of the structural stiffness, and requires limited number of sensors in the field. Based on the properties of the vibration characteristics studied in this work, the wave velocity based method is recommended for the health monitoring of underground structures.


2012 ◽  
Vol 178-181 ◽  
pp. 1956-1960
Author(s):  
Xiao Yan Shen ◽  
Hao Xue Liu ◽  
Jia Liu

In order to scientifically decide the percentage of vehicle entering expressway rest area, based on analyzing the influencing factors relating to the percent of mainline traffic stopping, a BP neural network prediction model for it was put forward. Finally, The Xinzheng Rest Area (XRA) was taken as an example for verifying the feasibility of the prediction model and determining the influence degree of the Shijiazhuang-Wuhan high-speed railway on the percentage of mainline vehicles entering XRA. The result shows that the model had a high precision and reliability.


2013 ◽  
Vol 16 (1) ◽  
pp. 26-31 ◽  
Author(s):  
Hae Young Ji ◽  
Kang Ho Lee ◽  
Jae Chul Kim ◽  
Dong Hyoung Lee ◽  
Kyoung Ho Moon

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