scholarly journals Estimation of undrained shear strength of fine grained soils from cone penetration resistance

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Abdul Karim Mohamad Zein
Keyword(s):  
Shear Strength ◽  
Penetration Resistance ◽  
Undrained Shear Strength ◽  
Cone Penetration ◽  
Fine Grained ◽  
Cone Penetration Resistance ◽  
Undrained Shear

scholarly journals Statistical Analysis on Shear Strength Parameter from Index Properties of Fine-grained Soils

2021 ◽  
pp. 15-28
Author(s):  
Tariku Tafari Bakala ◽  
Emer Tucay Quezon ◽  
Mohammed Yasin
Keyword(s):  
Shear Strength ◽  
Statistical Analysis ◽  
Linear Regression ◽  
Statistical Models ◽  
Undrained Shear Strength ◽  
Strength Parameter ◽  
Index Properties ◽  
Fine Grained ◽  
Shear Strength Parameter ◽  
Undrained Shear

Shear strength is the essential engineering property of soil required to analyze and design foundations, retaining walls, bridges, embankment, and related infrastructure. The laboratory equipment and field instruments are not sufficient in developing countries to obtain soil engineering properties, especially strength properties. Thus, Geotechnical engineers usually endeavor to develop statistical models that best fit a particular area and soil type, especially for analysis and design purposes. In this research, a Statistical Analysis on the Shear Strength parameter from the Index Properties of Fine-Grained Soils was studied. For predicting the undrained shear strength parameter, single linear regression (SLR) and multiple linear regressions (MLR) analyses were developed. To develop the intended statistical models for a study, SAS JMP Pro 13, SPSS v22, and Microsoft Excel-2013 software were introduced. The results of a  study indicated that undrained shear strength(Cu) was significantly correlated with liquid limit(LL), plastic limit(PL), bulk density (ρbulk), dry density(ρdry), natural moisture content(NMC), and plasticity index(PI). While it was not significantly correlated with a specific gravity (Gs) and liquidity index (LI) of study area soil. Finally, a strong Model of Cu with a coefficient of determination (R2 = 0.806), good significance level, and less Std. error was obtained from multiple linear regression (MLR) analysis. The developed model can figure undrained shear strength parameter and wide application in the construction industry to minimize the cost, effort, and time for laboratory tests of shear strength parameter of a study area.

scholarly journals Evaluation of Undrained Shear Strength of Fine-Grained Soils in Consideration of Soil Plasticity

2016 ◽  
Vol 36 (3) ◽  
Author(s):  
Kamil KAYABALI ◽  
Özgür AKTÜRK ◽  
Mustafa FENER ◽  
Ayla BULUT ÜSTÜN ◽  
Orhan DİKMEN ◽  
...  
Keyword(s):  
Shear Strength ◽  
Undrained Shear Strength ◽  
Soil Plasticity ◽  
Fine Grained ◽  
Undrained Shear

scholarly journals A Random Forest Model for the Prediction of Spudcan Penetration Resistance in Stiff-Over-Soft Clays

2020 ◽  
Vol 27 (4) ◽  
pp. 130-138
Author(s):  
Pan Gao ◽  
Zhihui Liu ◽  
Ji Zeng ◽  
Yiting Zhan ◽  
Fei Wang
Keyword(s):  
Shear Strength ◽  
Random Forest ◽  
Penetration Resistance ◽  
Undrained Shear Strength ◽  
Soil Parameters ◽  
Good Prediction ◽  
Resistance Profile ◽  
Soft Clays ◽  
Centrifuge Tests ◽  
Undrained Shear

AbstractPunch-through is a major threat to the jack-up unit, especially at well sites with layered stiff-over-soft clays. A model is proposed to predict the spudcan penetration resistance in stiff-over-soft clays, based on the random forest (RF) method. The RF model was trained and tested with numerical simulation results obtained through the Finite Element model, implemented with the Coupled Eulerian Lagrangian (CEL) approach. With the proposed CEL model, the effects of the stiff layer thickness, undrained shear strength ratio, and the undrained shear strength of the soft layer on the bearing characteristics, as well as the soil failure mechanism, were numerically studied. A simplified resistance profile model of penetration in stiff-over-soft clays is proposed, divided into three sections by the peak point and the transition point. The importance of soil parameters to the penetration resistance was analysed. Then, the trained RF model was tested against the test set, showing a good prediction of the numerical cases. Finally, the trained RF was validated against centrifuge tests. The RF model successfully captured the punch-through potential, and was verified using data recorded in the field, showing advantages over the SNAME guideline. It is supposed that the trained RF model should give a good prediction of the spudcan penetration resistance profile, especially if trained with more field data.

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