High-Cycle Computational Model for Accumulative Deformation of Granular Material under Repeated Traffic Loading
Publication: Journal of Engineering Mechanics
Volume 149, Issue 10
Abstract
The ballast, essentially unbound granular material, contributes more than 50% to the total settlement of the ballasted railway track, greatly affecting the safety and comfort of train operation. To reduce the deformation-related distress in the trackbed, predicting the development of irreversible deformation in the granular material is a crucial issue. Therefore, to better calculate plastic deformation in the ballasted trackbed, this study extends the existing cyclic densification model to a more realistic loading condition by incorporating the impact of principal stress rotation (PSR). We first investigated the evolution characteristics of PSR-induced irreversible deformation in the granular material via discrete element method (DEM) simulations, on the basis of which appropriate reduction of the shakedown threshold and modification of the contraction/dilation function for the frictional sliding mechanism were then introduced to realize the consideration of the effect of PSR in the original cyclic densification model. Subsequently, after determining the modification coefficients through model calibration taking the DEM result as the benchmark, the proposed model was employed in a full-scale physical model and on-site track test, respectively, to predict long-term accumulative settlement in the ballasted trackbed subjected to actual train loading. Results indicated that the proper reduction or modification of relevant parameters reflected the effects of PSR on accumulating permanent deformations at the granular trackbed well. Furthermore, the modified cyclic densification model gave more reasonable predictions of plastic strain in the granular trackbed under actual train loading than the original model. Overall, our findings apply to the prediction of long-term postconstruction accumulative settlements in practical engineering, having broad application prospects.
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Data Availability Statement
All data, models, and code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 52125803 and 51988101), the Key Research and Development Program of Zhejiang Province (Grant No. 2019C03111), and the National Key Research and Development Program (Grant No. 2018YFE0207100).
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Received: Mar 9, 2023
Accepted: Jun 19, 2023
Published online: Aug 12, 2023
Published in print: Oct 1, 2023
Discussion open until: Jan 12, 2024
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