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

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, <italic>τ_fu</italic>/<italic>σ’_v</italic>, based on soil type, over-consolidation ratio (OCR), plasticity index, <italic>I_P</italic>, 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%. [ABSTRACT FROM AUTHOR]