An adaptive sampling approach to reduce uncertainty in slope stability analysis

An adaptive sampling approach is proposed, which can sample spatially varying shear strength parameters efficiently to reduce uncertainty in the slope stability analysis. This approach employs a limit equilibrium model and stochastic conditional methodology to determine the likely sampling locations...

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Bibliographic Details
Published in:Landslides 2018-06, Vol.15 (6), p.1193-1204
Main Authors: Cai, Jing-Sen, Yeh, Tian-Chyi Jim, Yan, E-Chuan, Tang, Rui-Xuan, Wen, Jet-Chau, Huang, Shao-Yang
Format: Article
Language:English
Subjects:
Adaptive sampling
Agriculture
Analysis
Boreholes
Civil Engineering
Computer applications
Computer simulation
Earth and Environmental Science
Earth Sciences
Geography
Monte Carlo simulation
Natural Hazards
Original Paper
Parameter uncertainty
Sampling
Shear strength
Simulation
Slope stability
Stability analysis
Statistical methods
Uncertainty
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Summary:An adaptive sampling approach is proposed, which can sample spatially varying shear strength parameters efficiently to reduce uncertainty in the slope stability analysis. This approach employs a limit equilibrium model and stochastic conditional methodology to determine the likely sampling locations. Karhunen-Loève expansion is used to conduct the conditional Monte Carlo simulation. A first-order analysis is also proposed to ease the computational burden associated with Monte Carlo simulation. These approaches are then tested using borehole data from a field site. Results indicate that the proposed adaptive sampling approach is an effective and efficient sampling scheme for reducing uncertainty in slope stability analysis.
ISSN:1612-510X
1612-5118
DOI:10.1007/s10346-017-0936-2