Quantifying live bending moments in rail using train-mounted vertical track deflection measurements and track modulus estimations

Nafari, Saeideh Fallah; Gül, Mustafa; Cheng, Jianwei

doi:10.1007/s13349-017-0248-1

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…From the Winkler model, the vertical deflection profile of a rail is only dependent on the track modulus value when the rail size and vertical loads are known. Once a value is assumed for track modulus, the rail vertical deflection profile can be estimated using Equation ((1), and from the rail vertical deflection profile, Yrel can be calculated as the relative vertical deflection between the rail surface and the rail/wheel contact plane at a distance of 1.22 m from the nearest wheel ( Figure 1a) [11,30]. The main shortcoming in this method is that the Winkler model assumes a track modulus is constant along the track while the field data shows that a track modulus stochastically varies along the track [31,32].…”

Section: Winkler Modelmentioning

confidence: 99%

Continuous Evaluation of Track Modulus from a Moving Railcar Using ANN-Based Techniques

Ngoan¹,

Gül²,

Nafari³

2020

Vibration

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Track foundation stiffness (also referred as the track modulus) is one of the main parameters that affect the track performance, and thus, quantifying its magnitudes and variations along the track is widely accepted as a method for evaluating the track condition. In recent decades, the train-mounted vertical track deflection measurement system developed at the University of Nebraska–Lincoln (known as the MRail system) appears as a promising tool to assess track structures over long distances. Numerical methods with different levels of complexity have been proposed to simulate the MRail deflection measurements. These simulations facilitated the investigation and quantification of the relationship between the vertical deflections and the track modulus. In our previous study, finite element models (FEMs) with a stochastically varying track modulus were used for the simulation of the deflection measurements, and the relationships between the statistical properties of the track modulus and deflections were quantified over different track section lengths using curve-fitting approaches. The shortcoming is that decreasing the track section length resulted in a lower accuracy of estimations. In this study, the datasets from the same FEMs are used for the investigations, and the relationship between the measured deflection and track modulus averages and standard deviations are quantified using artificial neural networks (ANNs). Different approaches available for training the ANNs using FEM datasets are discussed. It is shown that the estimation accuracy can be significantly increased by using ANNs, especially when the estimations of track modulus and its variations are required over short track section lengths, ANNs result in more accurate estimations compared to the use of equations from curve-fitting approaches. Results also show that ANNs are effective for the estimations of track modulus even when the noisy datasets are used for training the ANNs.

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Section: Winkler Modelmentioning

confidence: 99%

Continuous Evaluation of Track Modulus from a Moving Railcar Using ANN-Based Techniques

Ngoan¹,

Gül²,

Nafari³

2020

Vibration

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“…Increases in traffic and speeds on existing railways, a short season for maintenance work, and the construction of most Canadian railway infrastructure dating back to early 20th century call for new methods to evaluate the condition of rail infrastructure. New technologies and methods for track evaluation such as Portancemetre, 1 rolling stiffness measurement, 2 falling weight deflectometer (FWD), 3,4 and vertical track deflection (VTD) measurement [5][6][7][8][9][10][11] have been developed to evaluate the track stiffness/modulus. However, in passenger rail transit, the level of vibrations felt by passengers due to dynamic interaction of the vehicle and track cannot be solely assessed by measurements of track stiffness.…”

Section: Introductionmentioning

confidence: 99%

Use of measured accelerations from a passenger rail car to evaluate ride quality and track roughness – A case study

Abdulrazagh

Hendry

Gül

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part F:

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Increasing traffic and speeds on passenger rail lines, and a short season for maintenance work, have motivated the industry to find new methods to assess the condition of existing infrastructure and determine where upgrades are required. In this study, acceleration data from the car body and axle boxes of a revenue car over 92 km of a Canadian passenger rail route in Ontario were collected for two purposes: first, to apply weighted filtering method according to ISO 2631-1997 standard as a technique to determine the locations which highly impact the ride quality and to investigate the effect of type of track features and speed on the ride quality; second, a new analytical method called the envelope of acceleration was applied to use the recorded accelerations to evaluate the alignment and surface roughness along the track. Since the alignment and surface roughness values are always positive and are calculated over a specified length (e.g. 9.5 m, 18.9 m, 38 m) an envelope technique was employed which uses spline interpolations over local maxima of the absolute magnitude of accelerations at every separated n samples corresponding to best fit with track roughness. The regression analysis between the envelope of accelerations and alignment and surface roughness presented a meaningful correlation and showed the applied method is a promising analytical technique to indicate rough sections of the track. The limitations to the application of envelope of acceleration are also discussed.

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“…This means that track modulus is governed by a large set of attributes, i.e., material properties, component dimensions, interconnectivity, etc. Perhaps because of this intricacy the vast majority of existing methods for determining track modulus rely on field measurements (Kerr, 2000;Norman et al, 2004;Lu et al, 2008;Zakeri and Abbasi, 2012;Nafari et al,. 2017;Narayanan et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

A Priori Determination of Track Modulus Based on Elastic Solutions

Bose

Levenberg

2020

KSCE Journal of Civil Engineering

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Quantifying live bending moments in rail using train-mounted vertical track deflection measurements and track modulus estimations

Cited by 4 publications

References 14 publications

Continuous Evaluation of Track Modulus from a Moving Railcar Using ANN-Based Techniques

Continuous Evaluation of Track Modulus from a Moving Railcar Using ANN-Based Techniques

Use of measured accelerations from a passenger rail car to evaluate ride quality and track roughness – A case study

A Priori Determination of Track Modulus Based on Elastic Solutions

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