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%.

scholarly journals Evaluation of the Undrained Shear Strength in Preconsolidated Cohesive Soils Based on the Seismic Dilatometer Test

2019 ◽  
Vol 9 (8) ◽  
pp. 1660
Author(s):  
Paweł Galas ◽  
Zbigniew Lechowicz ◽  
Maria Jolanta Sulewska
Keyword(s):  
Shear Strength ◽  
Relative Error ◽  
Shear Wave Velocity ◽  
Empirical Relationship ◽  
Undrained Shear Strength ◽  
Cohesive Soils ◽  
Maximum Relative Error ◽  
Empirical Relationships ◽  
Pressure Reading ◽  
Undrained Shear

The undrained shear strength in cohesive soils can be evaluated based on measurements obtained from the standard dilatometer test (DMT) using single- and multi-factor empirical relationships. However, the empirical relationships presented in the literature may sometimes show relatively high values of the maximum relative error. The add-on seismic module to the seismic dilatometer test (SDMT) extends parameters measurable in a standard dilatometer test by the shear wave velocity Vs as an independent variable. Therefore, a method for evaluating the undrained shear strength in cohesive soils based on data obtained from the seismic dilatometer test is presented in this study. In the method proposed, the two-factor empirical relationship for evaluating the normalized undrained shear strength τfu/σ’v is used based on independent variables: The normalized difference between the corrected second pressure reading and the corrected first pressure reading (p1 − po)/σ’v and the normalized shear wave velocity Vs/100. The proposed two-factor empirical relationship provides a more reliable evaluation of the undrained shear strength in the tested Pleistocene and Pliocene clays in comparison to the empirical relationships presented in the literature, with a maximum relative error max RE at about ±20% and the mean relative error RE at about 8%.

scholarly journals The use of dilatometer test for the determination of undrained shear strength in organic soils

2008 ◽  
Vol 40 (1) ◽  
pp. 97-105
Author(s):  
Simon Rabarijoely
Keyword(s):  
Shear Strength ◽  
Undrained Shear Strength ◽  
Organic Soils ◽  
Engineering Practice ◽  
Empirical Correlations ◽  
Empirical Formulas ◽  
Geotechnical Design ◽  
Design Calculations ◽  
Undrained Shear

The use of dilatometer test for the determination of undrained shear strength in organic soils The use of dilatometer test for the determination of undrained shear strength in organic soils. In engineering practice the empirical correlations or charts are often use to determine soil properties for design calculations. The DMT tests results are analysed on the basis of the empirical formulas proposed by Marchetti (1980). In this paper the new chart to determine the τfu of organic mud was proposed. The chart presents the relationships between dilatometer readings (p0 - u0), (p1 - u0), σ'v0 and τfu. The chart will be helpful in geotechnical design of embankments constructed on organic subsoil.

scholarly journals ANN-based Fatigue Strength of Concrete Under Compression

2021 ◽  
Author(s):  
Miguel Abambres ◽  
Lantsoght E
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Fatigue Strength ◽  
Relative Error ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Concrete Compressive Strength ◽  
Design Code ◽  
Number Of Cycles

<p>When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.</p>

scholarly journals ANN-based Fatigue Strength of Concrete Under Compression

2020 ◽  
Author(s):  
Abambres M ◽  
Lantsoght E
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Fatigue Strength ◽  
Relative Error ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Concrete Compressive Strength ◽  
Design Code ◽  
Number Of Cycles

<p>When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.</p>

scholarly journals ANN-based Fatigue Strength of Concrete Under Compression

2020 ◽  
Author(s):  
Abambres M ◽  
Lantsoght E
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Fatigue Strength ◽  
Relative Error ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Concrete Compressive Strength ◽  
Design Code ◽  
Number Of Cycles

<p>When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.</p>

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