An adaptive sampling approach to reduce uncertainty in slope stability analysis

Jing Sen Cai, Tian Chyi Jim Yeh, E. Chuan Yan, Rui Xuan Tang, Jet Chau Wen, Shao Yang Huang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1193-1204
Number of pages12
Issue number6
StatePublished - Jun 1 2018


  • Conditional analysis
  • Reliability
  • Sampling approach
  • Shear strength
  • Slope stability
  • Spatial variability

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology


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