Correlations among some clay parameters — the multivariate distribution

2014 ◽  
Vol 51 (6) ◽  
pp. 686-704 ◽  
Author(s):  
Jianye Ching ◽  
Kok-Kwang Phoon

This paper constructs a 10-dimensional multivariate probability distribution covering 10 clay parameters. The parameters are the liquid limit, plasticity index (PI), liquidity index, effective vertical stress, undrained shear strength, sensitivity, and three piezocone test parameters. A CLAY/10/7490 database is compiled in a companion paper for this purpose. The database consists of 7490 data points from 251 studies. The number of data points associated with each study varies from 1 to 419 with an average 30 data points per study. The clay properties cover a wide range of overconsolidation ratios (but mostly 1∼10), a wide range of sensitivity (St) (sites with St = 1∼tens or hundreds are fairly typical), and a wide range of PI (but mostly 8∼100). The constructed multivariate probability distribution can be used as a prior distribution to derive the joint distribution of design parameters based on limited but site-specific field data. Note that the entire joint distribution of the 10 clay parameters is derived, not marginal distributions or simply means and coefficients of variation. These multiple design parameters can be updated from multiple field measurements, which is more useful than updating one design parameter using one field measurement that is typical in current practice. This paper also demonstrates that it is practical to build multivariate probability models by combining available bivariate models, which are prevalent in the geotechnical engineering correlation literature. The proposed approach circumvents the need to collect multivariate data, which are rarely found in typical site investigation programs.

2020 ◽  
Vol 273 ◽  
pp. 105675 ◽  
Author(s):  
Dongming Zhang ◽  
Yelu Zhou ◽  
Kok-Kwang Phoon ◽  
Hongwei Huang

2014 ◽  
Vol 51 (6) ◽  
pp. 663-685 ◽  
Author(s):  
Jianye Ching ◽  
Kok-Kwang Phoon

This study compiles a large database of 10 clay parameters (labeled as CLAY/10/7490) from 251 studies, covering clay data from 30 regions or countries worldwide. Hence, the range of data covered by this “global” database is broader than that underlying the calibration of existing transformation models in the literature. These transformation models relate test measurements (e.g., cone tip resistance) to appropriate design parameters (e.g., undrained shear strength). The correlation behaviours exhibited by the database among the 10 clay parameters are consistent with those exhibited by existing transformation models in the literature. The biases and transformation uncertainties of these transformation models with respect to the global database are calibrated. It is found that more recent transformation models are less biased and that the transformation uncertainties are typically fairly large. Such large transformation uncertainties are further reduced by incorporating secondary input parameters, such as plasticity index or sensitivity. In a companion paper written by the same authors, a 10-dimensional multivariate probability distribution coupling these clay parameters is constructed from CLAY/10/7490 and a useful application involving updating the entire bivariate probability distribution of two design parameters from three separate measurements is presented.


2021 ◽  
Author(s):  
Givanildo Nascimento-Jr ◽  
Cristopher Freitas ◽  
Osvaldo Rosso ◽  
André Aquino

2019 ◽  
Vol 56 (8) ◽  
pp. 1080-1097 ◽  
Author(s):  
Jianye Ching ◽  
Kok-Kwang Phoon ◽  
Kuang-Hao Li ◽  
Meng-Chia Weng

A multivariate probability distribution model for nine parameters of intact rocks, including unit weight (γ), porosity (n), L-type Schmidt hammer hardness (RL), Shore scleroscope hardness (Sh), Brazilian tensile strength (σbt), point load strength index (Is50), uniaxial compressive strength (σc), Young’s modulus (E), and P-wave velocity (Vp), is constructed based on the ROCK/9/4069 database that was compiled by the authors. It is shown that the multivariate probability distribution captures the correlation behaviors in the database among the nine parameters. This multivariate distribution model serves as a prior distribution model in the Bayesian analysis and can be updated into the posterior distribution of the design intact rock parameter when multivariate site-specific information is available. In this paper, the parameters for the posterior distribution of the design intact rock parameter are summarized into user-friendly tables so that engineers do not need to conduct the actual Bayesian analysis. Caution should be taken in extrapolating the results of this paper to cases that are not covered by ROCK/9/4069, because the resulting posterior distribution can be misleading.


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