Model updating for slope stability assessment in spatially varying soil parameters using multi-type observations

With site-specific information, model updating can calibrate the probability distributions of material parameters, and the updated distributions can be further utilized to facilitate a more realistic slope reliability assessment. Many model updating methods have been proposed, while the inherent spatial variability of material properties is scarcely discussed in the literature, mainly due to the curse of dimensionality when considering thousands of random variables. The BUS (Bayesian updating with structural reliability methods) algorithm can well tackle the highdimensional problem by converting it into an equivalent structural reliability problem. The BUS algorithm can integrate monitoring data and field observations to back analyse stability parameters. However, when generating a conditional random field using a large number of in-situ test data, the BUS algorithm becomes less efficient because low acceptance probability often occurs, while the Kriging algorithm is applicable for this situation. This study proposes an effective approach for model updating that combines Kriging-based conditional random field with the BUS algorithm to integrate multi-type observations. To illustrate the effectiveness of the proposed approach, an undrained saturated clay slope with a spatially varying soil parameter is taken as an example. The results indicate that the proposed approach is able to update the probability distribution of spatially varying soil parameters and update the slope reliability using multi-type observations with reasonable calculation efficiency.

Automated summary

This study proposes an effective approach that combines Kriging-based conditional random field with the BUS algorithm to integrate multi-type observations, in order to update the probability distribution of soil parameters and assess the slope reliability.