Efficient system reliability analysis of soil slopes using multivariate adaptive regression splines-based Monte Carlo simulation

Lei Lei Liu, Yung Ming Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

33 Citations (Scopus)


This paper presents a system reliability analysis approach for layered soil slopes based on multivariate adaptive regression splines (MARS) and Monte Carlo simulation (MCS). The proposed approach is achieved in a two-phase process. First, MARS is constructed based on a group of training samples that are generated by Latin hypercube sampling (LHS). MARS is validated by a specific number of testing samples which are randomly generated per the underlying distributions. Second, the established MARS is integrated with MCS to estimate the system failure probability of slopes. Two types of multi-layered soil slopes (cohesive slope and c–φ slope) are examined to assess the capability and validity of the proposed approach. Each type of slope includes two examples with different statistics and system failure probability levels. The proposed approach can provide an accurate estimation of the system failure probability of a soil slope. In addition, the proposed approach is more accurate than the quadratic response surface method (QRSM) and the second-order stochastic response surface method (SRSM) for slopes with highly nonlinear limit state functions (LSFs). The results show that the proposed MARS-based MCS is a favorable and useful tool for the system reliability analysis of soil slopes.
Original languageEnglish
Pages (from-to)41-54
Number of pages14
JournalComputers and Geotechnics
Publication statusPublished - 1 Oct 2016


  • Monte Carlo simulation
  • Multivariate adaptive regression splines
  • Response surface method
  • Slope stability
  • System reliability analysis

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Computer Science Applications

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