Improved subset simulation for the SLS analysis of two neighboring strip footings resting on a spatially random soil

The computation of the failure probability of geotechnical structures with the consideration of the soil spatial variability is generally performed using Monte Carlo Simulation (MCS) methodology. This method is very time-consuming when computing a small failure probability. As an alternative, Subset Simulation (SS) approach was proposed by Au and Beck (2001) to efficiently calculate the small failure probability. In the present paper, a more efficient approach called the improved Subset Simulation (iSS) is employed. In this approach the efficiency of SS is increased by replacing the first step of SS by a conditional simulation in which the realizations are generated outside a hypersphere of a given radius. This approach is illustrated here through the probabilistic analysis at the serviceability limit state (SLS) of two neighboring strip footings that rest on a soil with spatially varying Young's modulus. A comparison between SS and iSS approaches has shown that a considerable reduction in the number of realizations can be achieved when using the iSS approach.