CPT-Based Probabilistic Characterization of Undrained Shear Strength of Clay

Mi Tian,Xiaotao Sheng

doi:10.1155/2020/9617698

Abstract

Applying random field theory involves two important issues: the statistical homogeneity (or stationarity) and determination of random field parameters and correlation function. However, the profiles of soil properties are typically assumed to be statistically homogeneous or stationary without rigorous statistical verification. It is also a challenging task to simultaneously determine random field parameters and the correlation function due to a limited amount of direct test data and various uncertainties (e.g., transformation uncertainties) arising during site investigation. This paper presents Bayesian approaches for probabilistic characterization of undrained shear strength using cone penetration test (CPT) data and prior information. Homogeneous soil units are first identified using CPT data and subsequently assessed for weak stationarity by the modified Bartlett test to reject the null hypothesis of stationarity. Then, Bayesian approaches are developed to determine the random field parameters and simultaneously select the most probable correlation function among a pool of candidate correlation functions within the identified statistically homogeneous layers. The proposed approaches are illustrated using CPT data at a clay site in Shanghai, China. It is shown that Bayesian approaches provide a rational tool for proper determination of random field model for probabilistic characterization of undrained shear strength with consideration of transformation uncertainty.

Highlights

Soil materials are nature materials, and their properties inherently vary from one location to another due to their natural geologic process, which is known as “inherent spatial variability (ISV).” ISV of design soil properties can be probabilistically characterized using random field theory in probability-based geotechnical analyses and designs
In the context of random field theory, a design soil property within a statistically homogeneous layer is described by a series of random variables. ese random variables are with the same mean, μ, and standard deviation, σ; and the autocorrelation among these random variables depends on scale of fluctuation, λ, and the correlation function (e.g., [2,3,4,5,6])
Summary and Conclusions is paper presented Bayesian approaches for probabilistic characterization of ISV of undrained shear strength within a statistically homogeneous clay layer based on cone penetration test (CPT) data and prior knowledge

Summary

Introduction

Soil materials are nature materials, and their properties inherently vary from one location to another due to their natural geologic process, which is known as “inherent spatial variability (ISV).” ISV of design soil properties (e.g., undrained shear strength, Su) can be probabilistically characterized using random field theory in probability-based geotechnical analyses and designs. For a statistically homogeneous soil layer, the random field model of design soil properties can be uniquely defined by a given set of random field parameters (i.e., μ, σ, and λ) and correlation function. Note that applying random field model to describe ISV of design soil properties involves two important issues: the statistical homogeneity (or stationarity) and determination of random field parameters and correlation function. Statistical homogeneity (or stationarity) means that the mean and standard deviation of design soil properties are assumed to be spatially constant, and the correlation function only depends on their separation distance between two different locations (e.g., [2, 7, 8]). Statistical homogeneity is an important prerequisite for statistical analyses (e.g., determining random field parameters) on a set of observation data (e.g., [2, 7, 9, 10]). If the stationarity of observation data is not properly verified, the random field

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