An adaptive sampling approach to reduce uncertainty in slope stability analysis

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

Research output: Contribution to journalArticlepeer-review

2 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)1-12
Number of pages12
StateAccepted/In press - Dec 29 2017


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

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology

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