Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61817
Title: Efficient system reliability analysis of soil slopes using multivariate adaptive regression splines-based Monte Carlo simulation
Authors: Liu, LL
Cheng, YM 
Keywords: Monte Carlo simulation
Multivariate adaptive regression splines
Response surface method
Slope stability
System reliability analysis
Issue Date: 2016
Publisher: Elsevier
Source: Computers and geotechnics, 2016, v. 79, p. 41-54 How to cite?
Journal: Computers and geotechnics 
Abstract: System effects should be considered in the probabilistic analysis of a layered soil slope due to the potential existence of multiple failure modes. 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.
URI: http://hdl.handle.net/10397/61817
ISSN: 0266-352X
DOI: 10.1016/j.compgeo.2016.05.001
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