Continuous Vertical Track Deflection Measurements to Map Subgrade Condition along a Railway Line: Methodology and Case Studies

Roghani, Alireza; Hendry, Michael T.

doi:10.1061/(asce)te.1943-5436.0000892

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…Interference from reflections off of the surrounding railway infrastructure (rails, ties, and signalling) can obscure the air-ballast and ballast-subballast reflections. 26 The ballast thickness is also often variable along a given track section 27 and difficult to determine without destructive investigations (boreholes or trenching). As this study incorporates simulated GPR measurements, undesirable GPR signals can be minimized or removed prior to velocity determination and the ballast thickness is known.…”

Section: Propagation Velocitymentioning

confidence: 99%

Evaluating the sensitivity of low-frequency ground-penetrating radar attributes to estimate ballast fines in the presence of variable track foundations through simulation

Scanlan

Hendry

Martin

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part F:

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The sensitivity of three low-frequency (<1 GHz) ground-penetrating radar attributes commonly used to infer the amount of fines present within railway ballast was evaluated using synthetic datasets. Variations in ballast thickness, saturation, and subballast material type are not often considered during laboratory or small-scale (few kilometres of track) field studies. If ground-penetrating radar were to be applied as a ballast degradation detection tool on a subdivision (hundreds of kilometres) scale, it is critical to assess the impact variations these track foundation conditions will have on the inferred amount of fines present within the ballast. In this analysis, a two-layer (ballast and subballast) track foundation model was incorporated into a series of ground-penetrating radar simulations where the physical dimensions and electromagnetic properties of the model were systematically varied. It was through the electromagnetic properties that the volumetric amount of fines and moisture present within the ballast and the type of subballast material were altered. The ground-penetrating radar response of each model was simulated using a finite-difference time-domain solver for Maxwell’s equations (gprMax). The amount of fines present in the ballast was then inferred through attributes calculated from the synthetic ground-penetrating radar measurements and related to the known model input. This comparison revealed that ambiguities in the ground-penetrating radar attribute amplitudes were common. Specific ground-penetrating radar attribute amplitudes could not be uniquely associated with the known amounts of fines present within the ballast as the other conditions in the track foundation (ballast saturation, ballast thickness, and subballast material) were varied. As such, a quantitative and reliable estimation for the amount of fines present within ballast using ground-penetrating radar measurements over large scales would be difficult without first constraining the variability in the track foundation.

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Section: Propagation Velocitymentioning

confidence: 99%

Evaluating the sensitivity of low-frequency ground-penetrating radar attributes to estimate ballast fines in the presence of variable track foundations through simulation

Scanlan

Hendry

Martin

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part F:

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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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“…The MRail system has been tested over different railway lines in the USA and Canada for evaluating track conditions [18][19][20][21]. Results from the MRail field tests show that the system not only has the potential to identify the local track problems, i.e., muddy ballast, degraded joints, crushed rail head, broken ties, but also provides an opportunity to map the subgrade condition and assess the track performance along the railway line [22][23][24][25].…”

Section: Introductionmentioning

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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Continuous Vertical Track Deflection Measurements to Map Subgrade Condition along a Railway Line: Methodology and Case Studies

Cited by 16 publications

References 8 publications

Evaluating the sensitivity of low-frequency ground-penetrating radar attributes to estimate ballast fines in the presence of variable track foundations through simulation

Evaluating the sensitivity of low-frequency ground-penetrating radar attributes to estimate ballast fines in the presence of variable track foundations through simulation

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

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

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