CPT-Based Probabilistic Characterization of Undrained Shear Strength of Clay
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 rigoro...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/9617698 |
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| author | Mi Tian Xiaotao Sheng |
| author_facet | Mi Tian Xiaotao Sheng |
| author_sort | Mi Tian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-390748368e4540cbab602e3bf2092a09 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-390748368e4540cbab602e3bf2092a092025-02-03T01:30:32ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/96176989617698CPT-Based Probabilistic Characterization of Undrained Shear Strength of ClayMi Tian0Xiaotao Sheng1School of Civil Engineering, Architecture and Environment, Hubei University of Technology, 28 Nanli Road, Wuhan 430068, ChinaKey Laboratory of Geotechnical Mechanics and Engineering of Ministry of Water Resources, Yangtze River Scientific Research Institute, 23 Huangpu Street, Wuhan 430010, ChinaApplying 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.http://dx.doi.org/10.1155/2020/9617698 |
| spellingShingle | Mi Tian Xiaotao Sheng CPT-Based Probabilistic Characterization of Undrained Shear Strength of Clay Advances in Civil Engineering |
| title | CPT-Based Probabilistic Characterization of Undrained Shear Strength of Clay |
| title_full | CPT-Based Probabilistic Characterization of Undrained Shear Strength of Clay |
| title_fullStr | CPT-Based Probabilistic Characterization of Undrained Shear Strength of Clay |
| title_full_unstemmed | CPT-Based Probabilistic Characterization of Undrained Shear Strength of Clay |
| title_short | CPT-Based Probabilistic Characterization of Undrained Shear Strength of Clay |
| title_sort | cpt based probabilistic characterization of undrained shear strength of clay |
| url | http://dx.doi.org/10.1155/2020/9617698 |
| work_keys_str_mv | AT mitian cptbasedprobabilisticcharacterizationofundrainedshearstrengthofclay AT xiaotaosheng cptbasedprobabilisticcharacterizationofundrainedshearstrengthofclay |