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Appl. Sci. 2018, 8(5), 781; https://doi.org/10.3390/app8050781

Evaluation of the Change in Undrained Shear Strength in Cohesive Soils due to Principal Stress Rotation Using an Artificial Neural Network

1
Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences, Nowoursynowska 159 St., 02-776 Warsaw, Poland
2
Faculty of Civil and Environmental Engineering, Bialystok University of Technology, Wiejska 45E St., 15-351 Bialystok, Poland
*
Author to whom correspondence should be addressed.
Received: 24 March 2018 / Revised: 28 April 2018 / Accepted: 10 May 2018 / Published: 14 May 2018
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Abstract

This paper presents a method describing the application of artificial neural networks to evaluate the change in undrained shear strength in cohesive soils due to principal stress rotation. For analysis, the results of torsional shear hollow cylinder (TSHC) tests were used. An artificial neural network with an architecture of 7–6–1 was able to predict the real value of normalized undrained shear strength, τfu/σ’v, based on soil type, over-consolidation ratio (OCR), plasticity index, IP, and the angle of principal stress rotation, α, with an average relative error of around ±3%, and a single maximum value of relative error around 6%. View Full-Text
Keywords: artificial neural network analysis; cohesive soil; normalized undrained shear strength; principal stress rotation; torsional shear hollow cylinder test artificial neural network analysis; cohesive soil; normalized undrained shear strength; principal stress rotation; torsional shear hollow cylinder test
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wrzesiński, G.; Sulewska, M.J.; Lechowicz, Z. Evaluation of the Change in Undrained Shear Strength in Cohesive Soils due to Principal Stress Rotation Using an Artificial Neural Network. Appl. Sci. 2018, 8, 781.

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