Bayesian Updating of Slope Reliability in Undrained Clay with Vane Shear Test Data

In-situ test data, monitoring data and other site-specific information are a common basis for assessing geotechnical performance. These information enable one to learn probabilistic models of uncertain geotechnical properties and update the reliability estimate of geotechnical structures. This learning process is facilitated by the application of Bayesian analysis, which makes optimal use of site-specific information. The objective of this study is to investigate the application of Bayesian analysis to update the probabilistic description of spatially varying soil properties and the reliability of slope stability with in-situ test data. For proper characterization of the prior information on the undrained shear strength s _u , a non-stationary random field model is proposed to account for the depth-dependent nature of s _u . Bayesian updating for learning the distribution of s _u and updating the slope reliability is performed with the adaptive BUS approach with subset simulation. The approach is applied to a saturated clay slope in spatially variable soil. The spatial distribution of the s _u is updated with vane shear test data. In addition, the effect of the borehole location on the updated slope reliability is investigated, to inform future optimal test program.