Random Fields: Analysis and Synthesis (Revised and Expanded New Edition)
By E. Vanmarcke. 2010
324 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/237fbc03150c321aa58cf73904bcf19eabc311dc
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Citations in the corpus
- Bayesian identification of random field model using indirect test data (2016, methodology): “Bayesian identification of random field parameters Random field theory (Vanmarcke, 2010) is used to explicitly model the ISV of φ′ within a statistically homogenous sand layer in this study, bywhichφ′ at different depths aremodeled by a series of spatially correlated normal variables with a mean μ…”
- Learning soil parameters and updating geotechnical reliability estimates under spatial variability – theory and application to shallow foundations (2017, methodology): “The mean m X and variance s2 X of X read m X = mX , (9) s2 X = s2XG(T), (10) where mX and s 2 X are the point-mean and point-variance of X (associated with f̃ X) and G(T) is a variance reduction function, which depends on the correlation structure of X (Rackwitz 2000; Vanmarcke 2010).”
- Slope reliability analysis considering spatially variable shear strength parameters using a non-intrusive stochastic finite element method (2014, methodology): “The autocorrelation function is an important physical quantity for characterizing the spatial correlation of soil properties (Vanmarcke, 2010).”
- System reliability analysis of soil nail wall using random finite element method (2020, background): “Physical and mechanical soil parameters change from one point in the field to another, which necessitates the expression of soil parameters as characterized by random fields (Vanmarcke 2010).”
- A multiple response-surface method for slope reliability analysis considering spatial variability of soil properties (2015, background): “The soil parameters at a particular location are random variables due to the spatial variation, but are more similar to those at adjacent locations than those far away (Vanmarcke, 2010).”