Probabilistic Analytical Model for Settlement Risk Assessment of High-Speed Railway Subgrade
Publication: Journal of Performance of Constructed Facilities
Volume 30, Issue 3
Abstract
It is crucial for the design and operation of a high-speed railway to estimate and control the accumulative settlement of the subgrade induced by cyclic train loading. In this study, an analytical model, considering the effect of the initial stress state, is proposed to predict the accumulative settlement of high-speed railway subgrade. Dynamic load triaxial tests are conducted to determine the parameters involved in the computational model. Full-scale model experiments are carried out to verify the effectiveness of the proposed computational model in predicting the accumulative settlement of high-speed railway subgrade. A probabilistic analytical model is developed for a reliability-based settlement risk assessment of the subgrade by considering the uncertainties and randomness of the relevant parameters. The coefficient of variation (COV) of the dynamic stress on the subgrade surface caused by train loading is derived from field data measured on the Wuhan-Guangzhou High-Speed Railway, China. A Monte Carlo simulation is employed to analyze the statistical properties of the accumulative settlement. The effects of the dominant parameters on the reliability index, including the mean value and the COV of the stochastic parameters as well as water level variation, are calculated through a sensitivity analysis.
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Acknowledgments
The work described in this paper was jointly supported by the National Science Foundation of China (Grant Nos. 51225804, U1234204, 51178418, and 51222803) and the research fund of the China Railway Corporation (Grant No. 2014G006).
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Published In
Journal of Performance of Constructed Facilities
Volume 30 • Issue 3 • June 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Oct 6, 2014
Accepted: Apr 17, 2015
Published online: Jun 22, 2015
Discussion open until: Nov 22, 2015
Published in print: Jun 1, 2016
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