Measurement of cyclic response of railway embankments and underlying soft peat foundations to heavy axle loads

HendryMichael, T; Martin, C. Derek; Lee, BarbourS.

doi:10.1139/cgj-2012-0118

Cited by 39 publications

(10 citation statements)

References 13 publications

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“…In the past few decades, the dynamic response of railway subgrades was mainly studied based on field tests and/or laboratory model tests [6][7][8][9][10][11], which are uneconomical and time-consuming. Recently, with the development of computer technology, numerical simulations have gradually become an important research technique.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Stress of Subgrade Bed Layers Subjected to Train Vehicles with Large Axle Loads

Liu

et al. 2018

Shock and Vibration

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The dynamic responses of subgrade bed layers are the key factors affecting the service performance of a heavy-haul railway. A 3D train-track-subgrade interaction finite element (FE) model was constructed using the ABAQUS code, where different vertical irregular track spectra were simulated by modifying the vertical node coordinates of the FE mesh of the rail. Then, the dynamic stresses in the subgrade bed layers subjected to heavy-haul trains were studied in detail. The results showed the following: (1) the transverse distribution of the dynamic stress transformed from a bimodal pattern to a unimodal pattern with increasing depth; (2) the pass of adjacent bogies of adjacent carriages can be simplified once loaded on the subgrade since the dynamic stresses are maintained around the peak value during the pass of the adjacent bogies; (3) the dynamic stress at the bottom of the subgrade bed surface layer was more sensitive to the train axle load compared with that at the subgrade surface because the dynamic stresses induced by the two rails were gradually overlaid with increasing depth; (4) the maximum dynamic stress at the subgrade bed bottom was reduced by approximately 70% compared with that at the subgrade surface; (5) the vertical track irregularities intensified the vertical excitation between the train vehicle wheels and rails, and the maximum dynamic stress at the subgrade surface under the action of the irregular heavy-haul track spectrum increased by 23% compared with the smooth rail condition; and (6) the possible maximum dynamic stress (σdm) at the subgrade surface under the action of irregular track spectra can be predicted using the triple standard deviation principle of a normally distributed random variable, i.e., σdm = μ + 3σ (where μ and σ are the expectation and standard deviation of σdm, respectively).

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Section: Introductionmentioning

confidence: 99%

Dynamic Stress of Subgrade Bed Layers Subjected to Train Vehicles with Large Axle Loads

Liu

et al. 2018

Shock and Vibration

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“…The tests were conducted using a DDS-70 electromagnetic triaxial test system. To simulate the slow drainage relative to the traffic loading duration, laboratory tests were conducted under undrained conditions [32,37].…”

Section: Laboratory Triaxial Testsmentioning

confidence: 99%

Determination of traffic-load-influenced depths in clayey subsoil based on the shakedown concept

Tang

Chen

Sang

et al. 2015

Soil Dynamics and Earthquake Engineering

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“…By analyzing the model, level-crossing properties as well as peak statistics are studied and compared with the observed data [5]. The deformation and pore-water pressure response within peat foundations are below three different railway embankments in response to cyclic heavy axle loading [6]. Wireless network structure was proposed with differential GPS and GSM [7].…”

Section: Introductionmentioning

confidence: 99%

Rail Track Irregularity Detection Method Based on Computer Vision and Gesture Analysis

Rong

Song²,

Dang³

et al. 2016

Int. J. Onl. Eng.

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Abstract-In this paper, rail track irregularity detection system based on computer vision and SVD analysis is proposed and located in the train's operator cabin near the front. Images are captured by FLEA3 camera of PointGrey, and vibration signals are collected by sensor device MPU6050 integrating 3-axis accelerometer and 3-axis gyroscope. Root mean square of gray-scale threshold Pulse Coupled Neural Network (RMS-PCNN) is used for segmentation of the rail track's image in a single loop, and the improved coupled map lattice(CML) is used for filtering the image and signifying the rail track. After perspective, the track radius can be fetched by analysis of regression. Vibration signal filtered by SVD-unscented Kalman filter(UKF) can reflect the wagon movements. In unscented Kalman filter, Cholesky is replaced by SDV in UT(unscented transform), which can solve negative definite matrix caused by covariance matrix on account of calculation error and round-off error. Also numerical stability is improved under the guarantee of filtering accuracy and the same complexity level of algorithm based on SVD-UKF. Looking up the radius record table, the corresponding threshold in gyroscope signal can be selected, and Compared to the super elevation, the invisible irregularity defects of rail bed will be found out.

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Measurement of cyclic response of railway embankments and underlying soft peat foundations to heavy axle loads

Cited by 39 publications

References 13 publications

Dynamic Stress of Subgrade Bed Layers Subjected to Train Vehicles with Large Axle Loads

Dynamic Stress of Subgrade Bed Layers Subjected to Train Vehicles with Large Axle Loads

Determination of traffic-load-influenced depths in clayey subsoil based on the shakedown concept

Rail Track Irregularity Detection Method Based on Computer Vision and Gesture Analysis

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