Reliability Analysis of Slope Stability Considering Uncertainty in Water Table Level
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6, Issue 3
Abstract
Geotechnical analysis and design are usually based on traditional methods and empirical relationships. However, various challenges, such as the inability to model all parameters involved and the uncertainty of model input, make these methods ineffective. Therefore, probabilistic methods recently began to be used to estimate the probability of slope failure. In this study, slope stability is presented as an optimization problem and the simulated annealing (SA) method is used to identify the slip surface with the minimum safety factor. The SA algorithm is then integrated with the Monte Carlo sampling method to calculate the probability of slope failure. Next, the groundwater level is added to the problem formulation as a random variable to model the uncertainty for cases where the groundwater table is not accurately determined. The results of the analysis show that for slopes with low safety factors, the uncertainty of the groundwater table worsens the condition and increases the failure probability. Moreover, the parametric study shows that the insufficient understanding of groundwater-level distribution and the assumption of uniform distribution further increases the failure probability. The results of this study are presented in the form of a probability curve against the groundwater table. This presentation has practical applications in real projects.
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Data Availability Statement
All data used in the paper, including the simulation results and sensitivity analysis, are available in the Supplemental Materials.
Acknowledgments
This research work is supported by China State Construction Engineering Corporation Ltd. (Grant No. CSCEC-2017-Z-29). An early version of this paper was presented at the First International Symposium on Risk Analysis and Safety of Complex Structures and Components (IRAS 2019) held at the University of Porto on July 1 and 2, 2019. The authors appreciate the organizing committee of IRAS 2019 for their help and support in the development of the current version. Additionally, the authors appreciate the facilities and support provided by Tongji University to conduct this research.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6 • Issue 3 • September 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Oct 10, 2019
Accepted: Mar 9, 2020
Published online: May 30, 2020
Published in print: Sep 1, 2020
Discussion open until: Oct 30, 2020
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