Evaluation of the Change in Undrained Shear Strength in Cohesive Soils due to Principal Stress Rotation Using an Artificial Neural Network
Source abstract
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%.
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Research context
OpenAlex topics: Geotechnical Engineering and Soil Stabilization · Geotechnical Engineering and Analysis · Geotechnical Engineering and Soil Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences, Nowoursynowska 159 St., 02-776 Warsaw, Poland
Faculty of Civil and Environmental Engineering, Bialystok University of Technology, Wiejska 45E St., 15-351 Bialystok, Poland
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