Spatial variability is one of the major sources of uncertainty in geotechnical applications. This variability is characterized customarily by the scale of fluctuation. Scale of fluctuation ...

Scale of Fluctuation for Spatially Varying Soils: Estimation Methods and Values

The paper examines three-dimensional (3D) analyses of the load bearing capacity of square and strip footings on a spatially variable cohesive–frictional (c–φ) soil. Embedded, rigid and smooth footings are considered. Cohesion and friction angle are modelled using random fields. The vertical scale of fluctuation (SOF) is based on earlier studies of natural soils. For the square footing problem, the horizontal SOF is the subject of a parametric study. For the strip footing, only one value of the horizontal SOF is assumed, and the parametric study concerns the modelled length of the foundation. To generate the fields of strength parameters, the Fourier series method is employed. For both problems analyzed, the influence of the parameters on the probabilistic characteristics of the bearing capacity of the soil is investigated. For the square footing problem, both the mean value of bearing capacity and its coefficient of variation increase with an increasing horizontal fluctuation scale, but they stabilize at higher values of SOF. For the strip footing problem, it appears that 2D modelling is a very conservative approach, and for the precise assessment of the probability of foundation failure, the footing should be modelled in 3D.

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3D bearing capacity probabilistic analyses of footings on spatially variable c–φ soil

Identification of sample path smoothness in soil spatial variability

Worst case scale of fluctuation in basal heave analysis involving spatially variable clays

Efficient Markov Chain Monte Carlo for combined Subset Simulation and nonlinear finite element analysis

According to the reliability-based design approach suggested by Eurocode 7, the random finite element method has been employed for calculating the reliability index for the bearing capacity design of strip foundation. Such study has been carried out on a well-defined soil from a stochastic point of view, that is the grey-blue clay from Taranto area. The RFEM formulation used has been implemented by Griffiths and Fenton but here the authors have focused on a description of the anisotropic random field for c and φ design variables. Results clearly show that the introduction of anisotropy in random fields makes RFEM predictions effective for design purpose because it is less conservative and more realistic than the isotropy assumption.

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Joanna Pieczyńska

Papers

Application of Random Finite Element Method to Bearing Capacity Design of Strip Footing