Spatial variability of in situ weathered soil

Dasaka, Satyanarayana Murty; Zhang, Li Min

doi:10.1680/geot.8.p.151.3786

Cited by 154 publications

(28 citation statements)

References 15 publications

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“…A simple and useful first approach is to estimate the semi-variogram from available data and best fit it with a theoretical semi-variogram (Baecher & Christian, 2003;Dasaka & Zhang, 2012). This method has been used here to estimate h h , albeit using only the seven CPTs available for the 2D section investigated.…”

Section: Spatial Statistics (Scales Of Fluctuation)mentioning

confidence: 99%

Investigation of the reduction in uncertainty due to soil variability when conditioning a random field using Kriging

Lloret-Cabot

Hicks

Eijnden

2012

Géotechnique Letters

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Spatial variability of soil properties is inherent in soil deposits, whether as a result of natural geological processes or engineering construction. It is therefore important to account for soil variability in geotechnical design in order to represent more realistically a soil's in situ state. This variability may be modelled as a random field, with a given probability density function and scale of fluctuation. A more convenient way to deal with the uncertainty of a soil property due to spatial variability, by constraining the generated random field at the locations of actual field measurements, is presented in this article. Conditioning the random field at known locations is a powerful tool, not only because it more accurately represents the observed variability on site, but also because it uses the available field information more efficiently. In situ cone penetration test (CPT) data from a particular test site are used to determine the input statistics for generating random fields, which are later constrained (conditioned) at the locations of actual CPT measurements using the Kriging interpolation method. The results from the conditional random fields are then analysed, to quantify how the number of field measurements used influences the reduction of uncertainty. It is shown that the spatial uncertainty relative to the original (unconditional) random field reduces with the number of CPTs used in the conditioning.

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Section: Spatial Statistics (Scales Of Fluctuation)mentioning

confidence: 99%

Investigation of the reduction in uncertainty due to soil variability when conditioning a random field using Kriging

Lloret-Cabot

Hicks

Eijnden

2012

Géotechnique Letters

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“…Although the parameter values of engineering geological characteristics can be measured via in situ tests and laboratory experiments directly, the probability of construction work accidents may increase when the measured geological parameters are not very representative of reality . Field‐measured physical‐mechanical parameters have some drawbacks, including the representation of the sampling sites, the disturbance of the test samples, and the scale effect …”

Section: Introductionmentioning

confidence: 99%

“…9,10 Fieldmeasured physical-mechanical parameters have some drawbacks, including the representation of the sampling sites, the disturbance of the test samples, and the scale effect. [11][12][13] A more reasonable way to make up for the shortage of the field-measured parameters is to perform parameter identification using the back-analysis method based on field observations. [14][15][16][17][18][19] The purpose of inverse analysis is to determine the accurate values of physical-mechanical parameters.…”

Section: Introductionmentioning

confidence: 99%

Inverse analysis for geomaterial parameter identification using Pareto multiobjective optimization

Jiang

Sun

Yi³

et al. 2018

Num Anal Meth Geomechanics

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Summary An inverse analysis method that combines the back propagation neural network (BPNN) and vector evaluated genetic algorithm (VEGA) was proposed to identify mechanical geomaterial parameters for a more accurate prediction of deformation. The BPNN is used to replace the time‐consuming numerical calculations, thus enhancing the efficiency of the inverse analysis. The VEGA is used to find the Pareto‐optimal solutions to multiobjective functions. Unlike traditional back‐analysis methods which are based on only 1 type of field measurement and a single objective function, this proposed method can consider multiple field observations simultaneously. The proposed method was applied to the Shapingba foundation pit excavation located in Chongqing city, China. Two types of measurements are considered in the method simultaneously: the displacements in the x‐direction (north orientation) and those in the y‐direction (east orientation). Five deformation modulus parameters for artificial backfill soil, silty clay, siltstone, sandstone, and mudstone were selected as the inversion parameters. Compared with the weighted sum approach, the proposed method was demonstrated as an efficient multi‐objective optimization tool for back calculating undetermined parameters. After performing a forward‐calculation using the optimized parameters obtained by the inverse analysis, the predicted results were well consistent with the practical deformation in magnitude and trend.

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“…There are several techniques that can be employed to estimate σ and δ (in particular δ), such as the method of moments (Uzielli et al 2005;Dasaka and Zhang 2012;Firouzianbandpey et al 2014;Lloret-Cabot et al 2014), the fluctuation function method (Wickremesinghe and Campanella 1993;Cafaro and Cherubini 2002), the maximum likelihood (ML) method (DeGroot and Baecher 1993), and the Bayesian method (Wang et al 2010). Phoon and Fenton (2004) proposed a practical bootstrap technique to obtain a more robust estimate of the autocorrelation function and to gain an appreciation of the underlying variability in the estimate with minimal assumptions.…”

Section: Introductionmentioning

confidence: 99%

Statistical characterization of random field parameters using frequentist and Bayesian approaches

Ching

Phoon

2016

Can. Geotech. J.

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Due to limited information in site investigation, it is not possible to obtain the actual values for the mean, standard deviation, and scale of fluctuation of a soil property of interest. The deviation between the estimated values and the actual values is called the statistical uncertainty. There are at least two schools of thoughts on how to model the statistical uncertainty: frequentist thought and Bayesian thought. The purpose of this paper is to discuss their philosophical difference, to show how to quantify the statistical uncertainty based on these two distinct schools of thoughts, and to compare their performances. For the frequentist school of thought, the confidence interval will be used to quantify the statistical uncertainty, whereas the posterior probability distribution will be used for the Bayesian school of thought. Examples will be presented to compare the performances of these two schools of thoughts in terms of their consistencies. The results show that in general the Bayesian thought performs better in terms of consistency. In particular, the Markov chain Monte Carlo method is recommended when the information amount is very limited.

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Spatial variability of in situ weathered soil

Cited by 154 publications

References 15 publications

Investigation of the reduction in uncertainty due to soil variability when conditioning a random field using Kriging

Investigation of the reduction in uncertainty due to soil variability when conditioning a random field using Kriging

Inverse analysis for geomaterial parameter identification using Pareto multiobjective optimization

Statistical characterization of random field parameters using frequentist and Bayesian approaches

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