Abstract
Railroad crossties (sleepers) are a key component of the track structure and play an important role in the support of the track structure and the distribution of train loading through the track. The condition of the crossties has been traditionally monitored by visual inspection, which has, in recent years, been supplemented with automated crosstie inspection systems. These provide more accurate information about the condition of each individual crosstie and as such are important in planning and optimizing tie replacement. By using inspection data taken from the same track in different years, it is possible to determine the degradation of ties, and corresponding life of the ties, and to develop improved tie life models that take into account local conditions. The aim of this paper is to provide a method to predict and model tie life based on support condition, as defined by the condition of adjacent crossties. Tie life is modeled using mathematical techniques, such as piecewise reconstruction which allows for the determination of the probability of tie failure as a function of loss of support. The analysis approach, using Dijkstra’s algorithm and Markov chains, is based on the use of tie condition data from two different inspections performed over a span of 3 years. The dataset, which consists of approximately 100,000 ties, included tie inspection data for inspections carried out on the same track during the period 2016 to 2019. Ties are grouped based on their support condition, specifically the loss of support associated with the presence of poor or failed ties on either side of the study tie. The results present average tie life as a function of different support conditions and confirm the fact that loss of support contributes measurably to premature tie failure. This reduction in tie life is presented as a function of tie support condition and compared to earlier studies.
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21 July 2021
A Correction to this paper has been published: https://doi.org/10.1007/s40515-021-00182-7
Notes
Railroad cross-ties are often referred to as sleepers.
The Georgetown Rail “Aurora” tie inspection system.
This was done using the R open source environment. Graphs were generated using the igraph package.
For combined groups, the loss of support is calculated using a weighted average of the individual group loss of support values.
The previous study referred to here is the one presented in Reference (Zarembski et al. 2020) at the 2020 annual AREMA (American Railway Engineering and Maintenance of way Association) conference.
From group F as shown in Fig. 4.
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Acknowledgements
The authors would like to acknowledge the US Department of Transportation University Transportation Center program (RailTeam UTC) for their sponsorship of this research. The authors would also like to acknowledge Georgetown Rail (GREX) for providing the data used in this analysis and for their sponsorship of the preliminary research that laid the groundwork for this research.
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The original version of this article unfortunately contained a mistake in table 2. The second column of the last row exceeds the gutter.
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Soufiane, K., Zarembski, A.M. & Palese, J.W. Effect of Adjacent Support Condition on Premature Wood Crosstie Failure. Transp. Infrastruct. Geotech. 9, 302–320 (2022). https://doi.org/10.1007/s40515-021-00168-5
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DOI: https://doi.org/10.1007/s40515-021-00168-5