Influence of Spatial Variability on Rock Slope Reliability Using 1-D Random Fields

2015 ◽  
pp. 1235-1238 ◽  
Author(s):  
Lysandros Pantelidis ◽  
Elias Gravanis ◽  
D. V. Griffiths
Keyword(s):  
Spatial Variability ◽  
Random Fields ◽  
Rock Slope

scholarly journals Influence of Spatial Variability on Slope Reliability Using 2-D Random Fields

2009 ◽  
Vol 135 (10) ◽  
pp. 1367-1378 ◽  
Author(s):  
D. V. Griffiths ◽  
Jinsong Huang ◽  
Gordon A. Fenton
Keyword(s):  
Spatial Variability ◽  
Random Fields

scholarly journals Characterisation of the spatial variability of material properties of Gilsocarbon and NBG-18 using random fields

2018 ◽  
Vol 511 ◽  
pp. 91-108 ◽  
Author(s):  
José David Arregui-Mena ◽  
Philip D. Edmondson ◽  
Lee Margetts ◽  
D.V. Griffiths ◽  
William E. Windes ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Material Properties ◽  
Random Fields

Accounting for spatial variability in partial factor based design verifications

2021 ◽  
Author(s):  
Wouter Botte ◽  
Robby Caspeele
Keyword(s):  
Spatial Variability ◽  
Spatial Correlation ◽  
Correlation Length ◽  
Material Properties ◽  
Random Fields ◽  
Structural Reliability ◽  
Engineering Practice ◽  
Traditional Design ◽  
Partial Factor Method ◽  
Partial Factor

<p>Traditional design and assessment approaches usually assume that e.g. material properties and environmental influences are uniform in space. However, it is well-known that such parameters can show considerable spatial variability. Furthermore, it has been shown that such spatial variability can significantly influence structural reliability. One way to account for spatial variability is by means of random fields. However, the use of such advanced calculations has not found its way to everyday engineering practice. Therefore, a methodology is developed in order to include spatial variability in the partial factor method in a way which is consistent with the current Eurocode format for design. This is done by introducing a separate partial factor which depends on the correlation length and the variability of the parameter under consideration. As such, an easy-to-use graph is generated, which can be applied in practice for the adjustment of partial factors to take into account spatial correlation. Finally, the proposed approach is validated by means of full-probabilistic calculations.</p>

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