An integrated approach to dynamic analysis of railroad track transitions behavior

Mishra, Debakanta; Qian, Yu; Huang, Hai; Tutumluer, Erol

doi:10.1016/j.trgeo.2014.07.001

Cited by 62 publications

(33 citation statements)

References 18 publications

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“…The MDD anchor needs to be fixed in the non-deformable soil layer which is normally located at a depth of 3 m below the top of the sleeper. According to previous studies [95], to monitor the deformations of each layer of railway track, LVDTs were placed in the borehole at various depths: at the top of the ballast layer, top of the subgrade layer, top of the embankment fill layer, top of the upper subgrade layer and top of the lower subgrade layer, and the anchor was placed in the lower subgrade layer. However, the installation of MDDs is difficult under a railway track as the size of MDDs is large [38].…”

Section: Multidepth Deflectometers (Mdds)mentioning

confidence: 99%

State-of-the-Art Review of Railway Track Resilience Monitoring

2018

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Abstract:In recent years, railway systems have played a significant role in transportation systems due to the demand increase in conveying both cargo and passengers. Due to the harsh environments and severe loading conditions, caused by the traffic growth, heavier axles and vehicles and increase in speed, railway tracks are at risk of degradation and failure. Condition monitoring has been widely used to support the health assessment of civil engineering structures and infrastructures. In this context, it was adopted as a powerful tool for an objective assessment of the railway track behaviour by enabling real-time data collection, inspection and detection of structural degradation. According to relevant literature, a number of sensors can be used to monitor track behaviour during the train passing under harsh environments. This paper presents a review of sensors used for structural monitoring of railway track infrastructure, as well as their application to sense the performance of different track components during extreme events. The insight into track monitoring for railways serving traffic with extreme features will not only improve the track inspection and damage detection but also enable a predictive track maintenance regime in order to assist the decision-making process towards more cost-effective management in the railway industry.

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Section: Multidepth Deflectometers (Mdds)mentioning

confidence: 99%

State-of-the-Art Review of Railway Track Resilience Monitoring

2018

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“…Moreover, the transfer of cyclic stresses to a greater depth might cause differential settlements in the subgrade thus affecting track stability. The complex stress states in a real track environment were influenced by the mid-and high-frequency vibrations stemming from applied train loads (Esveld 2001, Mishra et al 2014. Damage to sleepers and ballast, and track settlements were attributed to vibrations.…”

Section: Introductionmentioning

confidence: 99%

Performance of Ballast Influenced by Deformation and Degradation: Laboratory Testing and Numerical Modeling

2020

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This paper presents a study on the deformation and degradation responses of railway ballast using largescale laboratory testing and computational modeling approaches. A series of large-scale triaxial tests were carried out to investigate the ballast breakage responses under cyclic train loading subjected to varying frequencies, f=10-40 Hz. The role of recycled rubber energy-absorbing mats (REAMs) on reducing ballast breakage was also examined. Laboratory test results show that the ballast experiences significant degradation (breakage) and deformation, while the inclusion of REAMs can reduce the ballast breakage up to about 35%. Numerical modeling using the coupled discrete-continuum approach [coupled discreteelement method-finite-difference method (DEM-FDM)] is introduced to provide insightful understanding on the deformation and breaking of ballast under cyclic loading. Discrete ballast grains were simulated by bonding of many circular elements together at appropriate sizes and locations. Selected cylinders located at corners, surfaces, and sharp edges of the simulated particles were connected by parallel bonds; and when those bonds were broken, they were considered to represent ballast breakage. The subgrade and rubber mat were simulated as a continuum media using FDM. The predicted axial strain εa and volumetric strain εv obtained from the coupled DEM-FDM model are in good agreement with those measured in the laboratory. The model was then used to explore micromechanical aspects of ballast aggregates including the evolution of particle breakage, contact force distributions, and orientation of contacts during cyclic loading. These findings are imperative for a more insightful understanding of the breakage behavior of ballast from the perspective of microstructure characteristics of discrete particle assemblies. Introduction

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“…Track stiffness is known to be an important parameter influencing the performance of railway track [18,19]. Support conditions have been shown to vary from sleeper to sleeper [20][21][22][23][24] and over longer lengths of track [25,26]. Changes in support stiffness will affect vehicle / track interaction and track deflection [27][28][29] and are often associated with deterioration and increased maintenance need [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

“…Lineside measurement techniques can be used to investigate track performance by monitoring the trackbed or sleeper deflections and determining track stiffness. A variety of technologies could be used for this, including high speed video recording of track-mounted targets for digital image correlation, deflectometers anchored at depth, position sensitive devices, lasers and inertial sensors such as geophones or accelerometers [20][21][22][23][24]37]. Track stiffness can be obtained from these types of measurements based on the load-deflection behaviour of the track [38][39][40][41], or by analysing the spectrum of low frequency vibrations without the need to know the load [42,43].…”

Section: Introductionmentioning

confidence: 99%