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

Yajun Li,Kang Liu

doi:10.1155/2019/8518792

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.

Highlights

Soil properties are spatially varying due to mineralogical compositions, stress histories, and geological disposal processes [1,2,3]. erefore, in a routine site investigation program where soil samples are tested at some places, soil property values at unsampled locations cannot be interpolated or extrapolated with perfect certainty
Conditional simulation based on both direct and indirect measurements is surprisingly scarce in geotechnical engineering, except the study by Li et al [4], who claimed that their approach can take account of both sources of measurement data, they provided an explicit relationship between the response and the property of interest in their case. e relationship between the soil response and soil property, does not have an explicit form in most cases. erefore, this paper presents a framework for uncertainty reduction in soil deposits with spatial variability by conditioning a random field using both direct soil property measurements and indirect soil response measurements
A numerical soil excavation example was used to demonstrate the improvement of soil property field and the soil deformation estimations during various stages of excavation. e idea is to show the efficiency of the two sources of information when used to condition the random fields of soil properties and their relative contribution to the overall uncertainty reduction in the performance

Summary

Introduction

Soil properties are spatially varying due to mineralogical compositions, stress histories, and geological disposal processes [1,2,3]. erefore, in a routine site investigation program where soil samples are tested at some places, soil property values at unsampled locations cannot be interpolated or extrapolated with perfect certainty (i.e., due to the spatial variability). Conditional random field approaches that aim to reduce the uncertainty are available to generate realisations of random fields, constrained by the direct measurement data at sampling locations. Conditional simulation of random fields of soil properties and of the soil structures on or around these soils can be achieved by using these indirect soil response data and inverse modelling techniques. Conditional simulation based on both direct and indirect measurements is surprisingly scarce in geotechnical engineering, except the study by Li et al [4], who claimed that their approach can take account of both sources of measurement data, they provided an explicit relationship between the response and the property of interest in their case. Erefore, this paper presents a framework for uncertainty reduction in soil deposits with spatial variability by conditioning a random field using both direct soil property measurements and indirect soil response measurements. A numerical soil excavation example was used to demonstrate the improvement of soil property field and the soil deformation estimations during various stages of excavation. e idea is to show the efficiency of the two sources of information when used to condition the random fields of soil properties and their relative contribution to the overall uncertainty reduction in the performance

RF Model

Methodology