Estimation of Soil Parameters Based on Monitored Movement of Subsoil under Consolidation

Arai, K.; Ohta, Hideki; Kojima, K.

doi:10.3208/sandf1972.24.4_95

Cited by 36 publications

(10 citation statements)

References 5 publications

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“…Arai et al (1984Arai et al ( , 1987 identified two-dimensional consolidation parameters using the least squares method combined with the finite element method. Gioda and Sakurai (1987) showed an example of applying the Bayesian approach to the deformation prediction of a soft ground.…”

Section: Introductionmentioning

confidence: 99%

Inverse Analysis of Soft Grounds Considering Nonlinearity and Anisotropy

Nishimura

Nishiyama

Murakami

2005

SOILS AND FOUNDATIONS

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The inverse analysis method, used to accurately predict the two-dimensional deformation behavior of soft grounds, is discussed in this study. To ensure safety when constructing on a soft ground, in-situ observations are usually made. An inverse analysis is then effective for identifying the in-situ parameters of the ground and for predicting future deformation based on the parameters. The settlement, the lateral displacement, and the pore water pressure are measured during the construction of the structure. It is generally difficult to predict the lateral displacement. In this research, therefore, the cross anisotropy (transverse isotropy) of the ground is introduced to overcome the difficulty of predicting the lateral displacement. Furthermore, a simplified hyperbolic model is introduced to simulate the nonlinear shear behavior. The model is convenient for use with the inverse analysis, since it does not require many parameters. The measured pore water pressure is seldom used in the inverse analysis, because information on the pore water pressure is not required, from a mathematical standpoint, in order to identify the consolidation parameters. The effectiveness of applying the measured pore water pressure for the prediction of future consolidation behavior, is clarified in this study. As a result, the hyperbolic nonlinear model and the assumed anisotropy were found to be useful in predicting the future deformation behavior of clay. Furthermore, the pore water pressure measurementwas proved to be effective for the predictions in this study.

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Section: Introductionmentioning

confidence: 99%

Inverse Analysis of Soft Grounds Considering Nonlinearity and Anisotropy

Nishimura

Nishiyama

Murakami

2005

SOILS AND FOUNDATIONS

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“…Some examples of the application of LSE in geomechanical applications include the work of Gioda and Maier [44], Cividini et al [45], Cividini et al [46], Arai et al [47], Arai et al [48], Arai et al [49], Gioda and Sakurai [50], Shoji et al [51], Shoji et al [52], Anandarajah and Agarwal [53], Murakami et al [54], Beck and Woodbury [55], and Xiang et al [56].…”

Section: The Least Square Estimation (Lse)mentioning

confidence: 99%

Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change

Yun

Reddi

2011

Advances in Civil Engineering

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A nonparametric, data-driven methodology of monitoring for geotechnical structures subject to long-term environmental change is discussed. Avoiding physical assumptions or excessive simplification of the monitored structures, the nonparametric monitoring methodology presented in this paper provides reliable performance-related information particularly when the collection of sensor data is limited. For the validation of the nonparametric methodology, a field case study was performed using a full-scale retaining wall, which had been monitored for three years using three tilt gauges. Using the very limited sensor data, it is demonstrated that important performance-related information, such as drainage performance and sensor damage, could be disentangled from significant daily, seasonal and multiyear environmental variations. Extensive literature review on recent developments of parametric and nonparametric data processing techniques for geotechnical applications is also presented.

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“…Assuming that the relationship between measured variables and parameters [eqn (4)] is in general non-linear, the form of a minimization algorithm will be defined in terms of an iterative procedure:…”

Section: Minimization Algorithmmentioning

confidence: 99%

Estimation of parameters in geotechnical back analysis — I. Maximum likelihood approach

1996

International Journal of Rock Mechanics and Mining Sciences &am

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Estimation of Soil Parameters Based on Monitored Movement of Subsoil under Consolidation

Cited by 36 publications

References 5 publications

Inverse Analysis of Soft Grounds Considering Nonlinearity and Anisotropy

Inverse Analysis of Soft Grounds Considering Nonlinearity and Anisotropy

Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change

Estimation of parameters in geotechnical back analysis — I. Maximum likelihood approach

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