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The probabilistic analysis of geotechnical structures presenting spatial variability in the soil properties is generally performed using Monte Carlo simulation (MCS) methodology. This methodology is not suitable for the computation of a small failure probability because it becomes very time-expensive in such a case due to the large number of simulations required to calculate the failure probability. For this reason, one needs to keep to a minimum the number of calls to the deterministic model when performing the probabilistic analyses. In order to overcome the shortcoming related to the excessive number of calls of the deterministic model when performing Monte Carlo simulations, Echard et al. (2011) proposed an Active learning reliability method (called AK-MCS) combining Kriging and Monte Carlo Simulation. The method was shown to be very efficient as the obtained probability of failure is very accurate needing only a small number of calls to the deterministic model. The probabilistic analyses presented in this paper were performed using AK-MCS methodology by Echard et al. (2011). The present study involves a probabilistic analysis at the ultimate limit state of a strip footing resting on a spatially varying soil using AK-MCS approach. The objective is the computation of the probability Pf of exceeding the ultimate bearing capacity of the footing under a prescribed footing load. The soil cohesion and angle of internal friction were considered as two non-isotropic non-Gaussian random fields. The deterministic model was based on numerical simulations using the finite difference code FLAC 3D . Some probabilistic results are presented and discussed.

BEARING CAPACITY OF STRIP FOOTINGS ON SPATIALLY RANDOM SOILS USING KRIGING AND MONTE CARLO SIMULATION