Bearing-capacity prediction of spatially random c ϕ soils

By Gordon A. Fenton, D. V. Griffiths. 2003

551 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e7dfff7c0bef6a69c9cc683e474a72c47a837f17

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In Civil and Structural Engineering

In Safety, Risk, Reliability and Quality

Citations in the corpus

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;…”
A multiple response-surface method for slope reliability analysis considering spatial variability of soil properties (2015, background): “As reported in the literature (Jaksa, 1995; Fenton and Griffiths, 2003; Kasama et al., 2012; Wu et al., 2012; Ji, 2014; Jiang et al., 2014b,c; Salgado and Kim, 2014), the soil properties in a statistically homogeneous soil layer are considered to be weakly stationary random fields.”
Probabilistic analysis of strip footings resting on a spatially random soil using subset simulation approach (2012, background): “…that most previous studies that considered the soil spatial variability have modelled the uncertain parameters by isotropic random fields (e.g. Fenton and Griffiths 2003, Popescu et al. 2005, Griffiths et al. 2006, and Babu et al. 2006 for the ultimate limit state (ULS) analysis and Fenton…”
Slope reliability analysis considering spatially variable shear strength parameters using a non-intrusive stochastic finite element method (2014, background): “The rationale underlying these assumptions has been explained by Fenton and Griffiths (2003).”
Worst-case spatial correlation length in probabilistic slope stability analysis (2019, methodology): “In the framework of reliability-based design, the worst-case spatial correlation length is important (e.g. Fenton & Griffiths, 2003), because in the absence of high-quality and plentiful field data, it can be used in preliminary studies to ensure a conservative design.”
Probabilistic analysis of simple slopes with cohesive soil strength using RLEM and RFEM (2017, methodology): “Fenton and Griffiths (2003) defined the worst-case spatial correlation length as a critical spatial correlation length that yields the maximum probability of failure for the soil structure.”
A computational study on the uncertainty quantification of failure of clays with a modified Cam-Clay yield criterion (2021, methodology): “investigated with the implementation of the stochastic finite element method [17, 44].”
Effect of spatial variability of cross‐correlated soil properties on bearing capacity of strip footing (2010, background): “Griffiths and Fenton [10, 11], Fenton and Griffiths [12], and Popescu et al.”