Scale of Fluctuation for Spatially Varying Soils: Estimation Methods and Values

Cami, Brigid; Javankhoshdel, Sina; Phoon, Kok‐Kwang; Ching, Jianye

doi:10.1061/ajrua6.0001083

Cited by 94 publications

(37 citation statements)

References 98 publications

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“…As mentioned in the introduction section, an important part of identifying the uncertainty of soil strength parameters in modeling them using a random field is identifying the scale of fluctuation. A typical method of determining the SOF based on CPT results [ 17 , 23 ] includes fitting the assumed theoretical ACF (with the SOF being the fitting parameter) to the so-called experimental ACF. The values of the latter are calculated for subsequent values of the lag distance between observations in CPT profile (multiplication of observation interval Δ τ ) τj = j Δ τ , j εN , as [ 24 ]:

where

Section: Methodsmentioning

confidence: 99%

“…The values of the latter are calculated for subsequent values of the lag distance between observations in CPT profile (multiplication of observation interval Δ τ ) τj = j Δ τ , j εN , as [ 24 ]:

where

is the single observation of the parameter for which the scale is determined, k is the number of observations in the sample, and μ and ν 2 denote the mean value and variance of the observations. According to the method [ 17 , 20 , 23 ], before applying Equation (14), all observations need to be initially detrended and normalized (trends and mean values are determined separately for each CPT).…”

Section: Methodsmentioning

confidence: 99%

“…Another idea is presented in work by Uzielli [ 16 ], where quantile regression is used to obtain a safe design value of ϕ ′ based on CPT measurements. A review of some other approaches can be found in Cami et al [ 23 ]. However, all of these approaches require a large database, including the results of field and laboratory tests performed on soil from nearby locations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Identification of the Uncertainty in Soil Strength Parameters Based on CPTu Measurements and Random Fields

Pieczyńska-Kozłowska

Bagińska

Kawa

2021

Sensors

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The present paper responds to the challenge of modeling uncertainty in soil strength parameters concerning its spatial variability in a situation of limited soil information. Understanding this uncertainty allows for the management of the risk of geotechnical structure failure. In the present work, this uncertainty is identified based on signals from the cone penetration test (CPT) device. Signals are directly transformed using existing interpretation methods (typically used as a source of mean values of parameters for a given range of depths) to obtain depth-varying effective strength parameters of the soil. The process is performed by incorporating data from two case studies from different locations in similar soil materials. First, Keswick clay from Australia, for which the results of both CPT and laboratory tests are available, is examined. Second, to further verify the obtained results, the soil from Poland called Świerzna clay, for which only CPT signals were available, is also tested. As shown, the variability of the transformed signals can be a good source of information for identifying uncertainty in soil strength. It agrees well with literature data and can be used to identify random fields describing soil parameters.

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where

Section: Methodsmentioning

confidence: 99%

where

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Identification of the Uncertainty in Soil Strength Parameters Based on CPTu Measurements and Random Fields

Pieczyńska-Kozłowska

Bagińska

Kawa

2021

Sensors

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show abstract

“…The single exponential correlation function is utilized in this paper because it is the most frequently used model in characterizing the spatial correlation of soil properties. 38 The choice of the form of covariance function and the values of statistical characteristics (e.g., variance and correlation length) represent the problem-specific prior information. The adopted covariance function does not limit the proposed method.…”

Section: Simulation Of Random Fields Based On the Karhunen-loève Expansionmentioning

confidence: 99%

“…The values of vertical and horizontal correlation length are set to 1 m and 20 m, respectively. 38,51 The exponential correlation function is adopted since it is the most commonly used autocorrelation model for soil. 38 The schematic diagram of the numerical model is shown in Figure 2, including the geometries, loading conditions, boundary conditions, and meshes.…”

Section: Numerical Casementioning

confidence: 99%

Bayesian inference of spatially varying parameters in soil constitutive models by using deformation observation data

Tao

Sun

Cai

2021

Num Anal Meth Geomechanics

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Parameters of soil constitutive models are usually identified through laboratory tests. The spatial variability of these parameters is generally not considered due to the limitation of the test scale. This study proposes a data-driven approach to infer the spatially varying parameter of the modified Cam-clay model from limited field observations and subsequently improves soil settlement predictions. The observation data and numerical results of random finite element method are assimilated in an inverse modeling process based on the iterative Ensemble Kalman filtering (iEnKF). Different unknown variables and number of observations are used to study their effects on parameter estimations and settlement predictions. The effectiveness of the proposed approach is illustrated through a synthetic partial-loading test. The results show that the site-specific spatial variability can be estimated reasonably, and predictions of settlement can be improved by using the inferred parameter field. When the variables to be inferred change from all 60 variables to the selected 17 important variables, the average error of the estimated fields increases, but the variance decreases. A reduction in the observation spacing and an increase in the number of observations lead to a slightly smaller error of the mean and considerably reduced uncertainties of soil parameters. Although the inferred results of parameter field show different accuracies, the corresponding calculated settlements are generally similar and satisfactory.

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Spatial Variability of Soils

Zhang,

Xiao,

et al. 2023

Geotechnical Reliability Analysis

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Scale of Fluctuation for Spatially Varying Soils: Estimation Methods and Values

Cited by 94 publications

References 98 publications

The Identification of the Uncertainty in Soil Strength Parameters Based on CPTu Measurements and Random Fields

The Identification of the Uncertainty in Soil Strength Parameters Based on CPTu Measurements and Random Fields

Bayesian inference of spatially varying parameters in soil constitutive models by using deformation observation data

Spatial Variability of Soils

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