Estimations of Vertical Rail Bending Moments from Numerical Track Deflection Measurements Using Wavelet Analysis and Radial Basis Function Neural Networks
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 2
Abstract
A method for estimating rail bending moments from relative vertical track deflection data measured by a train-mounted measurement system is presented in this paper. The novelty of the current study is that complete estimations of rail positive and negative bending moments from track deflection measurements are conducted by using a wavelet multiresolution analysis in conjunction with the radial basis function neural network considering the effects of varying track modulus. The simulation results show that the proposed framework can effectively employ vertical track deflections to estimate both maximum positive and negative bending moments in rails, with the average estimation error being 6.22% (i.e., 2.82 kNm). Moreover, the study confirms the capability of the train-mounted vertical track deflection measurement system (commercially known as MRail) in evaluating the rail bending moments over long distances.
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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
The study is funded by IC-IMPACTS (the India-Canada Centre for Innovative Multidisciplinary Partnerships to Accelerate Community Transformation and Sustainability), which was established through the Networks of Centres of Excellence of Canada.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 2 • February 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Apr 13, 2020
Accepted: Sep 23, 2020
Published online: Nov 20, 2020
Published in print: Feb 1, 2021
Discussion open until: Apr 20, 2021
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