Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks
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 erro...
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MDPI AG
2018-08-01
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Online Access: | http://www.mdpi.com/2076-3417/8/8/1395 |
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author | Zbigniew Lechowicz Masaharu Fukue Simon Rabarijoely Maria Jolanta Sulewska |
author_facet | Zbigniew Lechowicz Masaharu Fukue Simon Rabarijoely Maria Jolanta Sulewska |
author_sort | Zbigniew Lechowicz |
collection | DOAJ |
description | 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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spelling | doaj.art-c14ad3ab9c9e4230800f798c03fbf5c22022-12-21T18:11:51ZengMDPI AGApplied Sciences2076-34172018-08-0188139510.3390/app8081395app8081395Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural NetworksZbigniew Lechowicz0Masaharu Fukue1Simon Rabarijoely2Maria Jolanta Sulewska3Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences–SGGW, Nowoursynowska 159 St., 02-776 Warsaw, PolandTokai University, 3-20-1, Orido Shimizu-ku, Shizuoka 424-8610, JapanFaculty of Civil and Environmental Engineering, Warsaw University of Life Sciences–SGGW, Nowoursynowska 159 St., 02-776 Warsaw, PolandFaculty of Civil and Environmental Engineering, Bialystok University of Technology, Wiejska 45E St., 15-351 Bialystok, PolandThe 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%.http://www.mdpi.com/2076-3417/8/8/1395organic soilsundrained shear strengthdilatometer testartificial neural networks |
spellingShingle | Zbigniew Lechowicz Masaharu Fukue Simon Rabarijoely Maria Jolanta Sulewska Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks Applied Sciences organic soils undrained shear strength dilatometer test artificial neural networks |
title | Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks |
title_full | Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks |
title_fullStr | Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks |
title_full_unstemmed | Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks |
title_short | Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks |
title_sort | evaluation of the undrained shear strength of organic soils from a dilatometer test using artificial neural networks |
topic | organic soils undrained shear strength dilatometer test artificial neural networks |
url | http://www.mdpi.com/2076-3417/8/8/1395 |
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