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Application of Artificial Neural Networks for the prediction of undrained shear modulus in cohesive soils
Author(s) -
WRZESIŃSKI Grzegorz,
LECHOWICZ Zbigniew,
SULEWSKA Maria J.
Publication year - 2018
Publication title -
ce/papers
Language(s) - English
Resource type - Journals
ISSN - 2509-7075
DOI - 10.1002/cepa.774
Subject(s) - geotechnical engineering , shear modulus , consolidation (business) , shearing (physics) , soil water , modulus , shear (geology) , plasticity , geology , materials science , composite material , soil science , accounting , business
The paper presents a study carried out in a Hollow Cylinder Apparatus (HCA) to determine the shear modulus G u in undrained conditions. Selected results of tests performed for undisturbed cohesive soils are presented. Values of undrained shear modulus G u have been determined at shear strain of 0.1% and 0.5%. Laboratory tests were performed on slightly overconsolidated clay (Cl) and sandy silty clay (sasiCl) with an overconsolidation ratio OCR of about 3.5 and 2.7, and a plasticity index I p of 77.6% and 34.7%, respectively. HCA tests were performed with anisotropic consolidation and shearing in undrained conditions. Results of laboratory tests have allowed to assess the influence of the principal stress rotation on the values of undrained shear modulus G u using Artificial Neural Networks (ANNs).