Influence of spatial variability on slope reliability using 2-D random fields.

By D. V. Griffiths, Jinsong Huang, Gordon A. Fenton. 2009

545 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/2f0291848762644d945c8ae3c8c23c37ead53845

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In Civil and Structural Engineering

In Safety, Risk, Reliability and Quality

Citations in the corpus

Probabilistic analysis of simple slopes with cohesive soil strength using RLEM and RFEM (2017, methodology): “…of spatial variation on slope reliability based on the random finite element method (RFEM) which combines the strength reduction method with random fields that are generated using the local average subdivision (LAS) method (Griffiths and Fenton 2004; Griffiths et al. 2009; Hicks and Spencer 2010).”
Multiple response surfaces for slope reliability analysis (2015, methodology): “The finite element method (FEM) was combined with random field to automatically consider the variation of the most probable slip surface [6, 8], which requires much computational effort than slope reliability evaluation on one or a few limited slip surfaces.”
On the reliability of earth slopes in three dimensions (2009, methodology): “…with three-dimensional slope analysis is that the methods have tended to be extensions of two-dimensional limit equilibrium methods of slices (to ‘methods of columns’), where soil variability and boundary conditions are hard to account for in a systematic way (e.g. Griffiths et al. 2009).”
An analytical solution in probabilistic rock slope stability assessment based on random fields (2014, unknown): “Although the geotechnical profession has been quite slow to adopt probabilistic approaches to geotechnical design, especially in traditional areas such as slopes and foundations [22], an increase in their use has been visible in recent years.”
On the estimation of scale of fluctuation in geostatistics (2014, background): “…2013 Taylor & Francis heterogeneity (i.e. inherent variability), in advanced stochastic analyses (Fenton 1999; Fenton and Griffiths 2003; Hicks and Onisiphorou 2005; Fenton and Griffiths 2005; Griffiths, Huang, and Fenton 2009; Hicks and Spencer 2010; Cassidy, Uzielli, and Tian 2013).”
Worst-case spatial correlation length in probabilistic slope stability analysis (2019, methodology): “Preliminary studies indicated that 2000 Monte-Carlo simulations were enough to give statistically reproducible results (e.g. Griffiths et al., 2009) and the probability of failure pf is computed as the proportion of those 2000 RFEM analyses which failed.”