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%.
Highlights
Undrained shear strength is one of the basic parameters that characterizing the mechanical properties of cohesive soils
Research performed in a torsional shear hollow cylinder apparatus (TSHCA), both on undisturbed and reconstituted cohesive soils, has shown that principal stress rotation has a significant impact on the value of undrained shear strength
ResultsThe TSHC tests allowed the values of undrained shear strength to be obtained at selected angles
Summary
Undrained shear strength is one of the basic parameters that characterizing the mechanical properties of cohesive soils. The value of this parameter depends on soil type, consistency, stress state, history, and depends on factors such as loading type and loading mode [1,2]. Research performed in a torsional shear hollow cylinder apparatus (TSHCA), both on undisturbed and reconstituted cohesive soils, has shown that principal stress rotation has a significant impact on the value of undrained shear strength. Test investigations that determine the impact of principal stress rotation on the undrained shear strength of subsoil are costly and time-consuming. Other methods to evaluate the change in undrained shear strength are used
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