Estimation of the undrained shear strength of sensitive clays using optimized inference intelligence system

2022 ◽  
Author(s):  
Quoc Anh Tran ◽  
Lanh Si Ho ◽  
Hiep Van Le ◽  
Indra Prakash ◽  
Binh Thai Pham
Keyword(s):  
Shear Strength ◽  
Undrained Shear Strength ◽  
Intelligence System ◽  
Undrained Shear

scholarly journals Evaluation of Undrained Shear Strength of Miocene Clay from the Weight Sounding Test (WST)

2017 ◽  
Vol 62 (2) ◽  
pp. 367-384
Author(s):  
Sebastian Olesiak
Keyword(s):  
Shear Strength ◽  
Field Testing ◽  
Field Investigation ◽  
Undrained Shear Strength ◽  
Cohesive Soils ◽  
Strength Parameters ◽  
Additional Measurement ◽  
Carpathian Foredeep ◽  
Innovative Solution ◽  
Undrained Shear

Abstract Soil strength parameters needed for the calculation of bearing capacity and stability are increasingly determined from field testing. This paper presents a method to determine the undrained shear strength cuWST of the soil, based on the Weight Sounding Test (WST). The innovative solution which allows for a significant reduction of equipment needed for geotechnical field investigation is presented. The proposed method is based on an additional measurement of the torque during testing. It then becomes possible to estimate the undrained shear strength, cuWST of the soil, using the correlation given in this paper. The research results presented in this paper were carried out on selected cohesive soils, Miocene clays from the Carpathian Foredeep.

scholarly journals Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks

2018 ◽  
Vol 8 (8) ◽  
pp. 1395 ◽  
Author(s):  
Zbigniew Lechowicz ◽  
Masaharu Fukue ◽  
Simon Rabarijoely ◽  
Maria Sulewska
Keyword(s):  
Neural Networks ◽  
Shear Strength ◽  
Artificial Neural Networks ◽  
Relative Error ◽  
Undrained Shear Strength ◽  
Organic Soils ◽  
Maximum Relative Error ◽  
Empirical Methods ◽  
Pressure Reading ◽  
Undrained Shear

The undrained shear strength of organic soils can be evaluated based on measurements obtained from the dilatometer test using single- and multi-factor empirical correlations presented in the literature. However, the empirical methods may sometimes show relatively high values of maximum relative error. Therefore, a method for evaluating the undrained shear strength of organic soils using artificial neural networks based on data obtained from a dilatometer test and organic soil properties is presented in this study. The presented neural network, with an architecture of 5-4-1, predicts the normalized undrained shear strength based on five independent variables: the normalized net value of a corrected first pressure reading (po − uo)/σ′v, the normalized net value of a corrected second pressure reading (p1 − uo)/σ′v, the organic content Iom, the void ratio e, and the stress history indictor (oc or nc). The neural model presented in this study provided a more reliable prediction of the undrained shear strength in comparison to the empirical methods, with a maximum relative error of ±10%.

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