Updating Soil Spatial Variability and Reducing Uncertainty in Soil Excavations by Kriging and Ensemble Kalman Filter

Abstract

Field measurements can be used to improve the estimation of the performance of geotechnical projects (e.g., embankment slopes and soil excavation pits). Previous research has utilised inverse analysis (e.g., the ensemble Kalman filter (EnKF)) to reduce the uncertainty of soil parameters, when measurements are related to performance, such as inflow, hydraulic head, and deformation. In addition, there are also direct measurements, such as CPT measurements, where parameters (i.e., tip resistance and sleeve friction) can be directly correlated with, e.g., soil deformation and/or strength parameters, where conditional simulation via constrained random fields can be used to improve the estimation of the spatial distribution of parameters. This paper combines these two (i.e., direct and indirect) methods together in a soil excavation analysis. The results demonstrate that the parameter uncertainty (and thereby the uncertainty in the response) can be significantly reduced when the two methods are combined.