Spatially variable soils affecting geotechnical strip foundation design

Abstract

Natural soil variability is a well-known issue in geotechnical design, although not frequently managed in practice. When subsoil must be characterized in terms of mechanical properties for infrastructure design, random finite element method (RFEM) can be effectively adopted for shallow foundation design to gain a twofold purpose: (1) understanding how much the bearing capacity is affected by the spatial variability structure of soils, and (2) optimisation of the foundation dimension (i.e. width B ). The present study focuses on calculating the bearing capacity of shallow foundations by RFEM in terms of undrained and drained conditions. The spatial variability structure of soil is characterized by the autocorrelation function and the scale of fluctuation ( δ ). The latter has been derived by geostatistical tools such as the ordinary Kriging (OK) approach based on 182 cone penetration tests (CPTs) performed in the alluvial plain in Bologna Province, Italy. Results show that the increase of the B / δ ratio not only reduces the bearing capacity uncertainty but also increases its mean value under drained conditions. Conversely, under the undrained condition, the autocorrelation function strongly affects the mean values of bearing capacity. Therefore, the authors advise caution when selecting the autocorrelation function model for describing the soil spatial variability structure and point out that undrained conditions are more affected by soil variability compared to the drained ones. • RFEM is able to consider the soil variability structure in strip foundation design. • Bearing capacity mean value is affected by soil spatial variability. • Bearing capacity is affected by autocorrelation function model. • Coefficient of variation COV reduces as the foundation size increases. • Both drained and undrained soil conditions show similar COV reduction.