Stochastic Evaluation of Railway Track Buckling Using Monte-Carlo Simulations
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 4
Abstract
The necessity of railway track buckling assessment stems from the critical need to mitigate the risks associated with track buckling, which can lead to considerable track system damage and pose significant risks to operational safety and efficiency. Given the challenges in accurately determining the track parameters that influence buckling, the inherent uncertainties in these parameters introduce additional complexity in the evaluation of railway track buckling. Consequently, a stochastic approach to buckling analysis becomes necessary for a more robust and realistic management of buckling risks. This paper introduces a stochastic methodology for evaluating track buckling, leveraging Monte-Carlo simulations and parallel computing to process track parameters as random variables across a huge number of simulations utilizing a dynamic three-dimensional (3D) track model. By conducting about 67,000 simulations, buckling probabilities are calculated based on the frequency of buckling occurrences, offering a probabilistic perspective on track stability management. The findings highlight the effectiveness of the stochastic evaluation method in promoting a risk-based approach to maintaining track stability, improving the precision and reliability of maintenance strategies of railway engineering.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research is financially supported by Australian Research Council (Project No. DE210100273).
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10 • Issue 4 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Mar 6, 2024
Accepted: May 28, 2024
Published online: Aug 24, 2024
Published in print: Dec 1, 2024
Discussion open until: Jan 24, 2025
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