Application of Artificial Neural Networks for the prediction of undrained shear modulus in cohesive soils
The paper presents a study carried out in a Hollow Cylinder Apparatus (HCA) to determine the shear modulus Gu in undrained conditions. Selected results of tests performed for undisturbed cohesive soils are presented. Values of undrained shear modulus Gu have been determined at shear strain of 0.1% a...
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description | The paper presents a study carried out in a Hollow Cylinder Apparatus (HCA) to determine the shear modulus Gu in undrained conditions. Selected results of tests performed for undisturbed cohesive soils are presented. Values of undrained shear modulus Gu 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 Ip 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 Gu using Artificial Neural Networks (ANNs). |
doi_str_mv | 10.1002/cepa.774 |
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Selected results of tests performed for undisturbed cohesive soils are presented. Values of undrained shear modulus Gu 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 Ip of 77.6% and 34.7%, respectively. HCA tests were performed with anisotropic consolidation and shearing in undrained conditions. 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Selected results of tests performed for undisturbed cohesive soils are presented. Values of undrained shear modulus Gu 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 Ip 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 Gu using Artificial Neural Networks (ANNs).</description><subject>Artificial Neural Networks</subject><subject>Cohesive soils</subject><subject>Hollow Cylinder Apparatus</subject><subject>Undrained shear modulus</subject><issn>2509-7075</issn><issn>2509-7075</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kDtPwzAUhS0EElWpxE_wyJJi3zycjFFVHlIFDN0j27lWDGkc2QlV_z0JBYkF3eHc4Ttn-Ai55WzNGYN7jb1cC5FckAWkrIgEE-nln_-arEJ4Z4zFwHkOsCC27PvWajlY11FnaOkHa6y2sqUvOPrvGI7OfwRqnKdDg7T3WFv9Wxi72kvbYU1Dg9LTg6vHdgzUdlS7BoP9RBqcbcMNuTKyDbj6ySXZP2z3m6do9_r4vCl3kRY8iSCLIVUsjQGQ8ywVxXRJhsZkqlZYKCEBQak8R64TQCFzKGSWZoVihcpZvCR351ntXQgeTdV7e5D-VHFWzZKqWVI1SZrQ6IwebYunf7lqs30rZ_4Ly-Fp3w</recordid><startdate>201806</startdate><enddate>201806</enddate><creator>WRZESIŃSKI, Grzegorz</creator><creator>LECHOWICZ, Zbigniew</creator><creator>SULEWSKA, Maria J.</creator><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>201806</creationdate><title>Application of Artificial Neural Networks for the prediction of undrained shear modulus in cohesive soils</title><author>WRZESIŃSKI, Grzegorz ; LECHOWICZ, Zbigniew ; SULEWSKA, Maria J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c714-26325b05322e11657979746eff6bdbe9b7a2e2bb88e1c42e7a829a6569b09b803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Artificial Neural Networks</topic><topic>Cohesive soils</topic><topic>Hollow Cylinder Apparatus</topic><topic>Undrained shear modulus</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>WRZESIŃSKI, Grzegorz</creatorcontrib><creatorcontrib>LECHOWICZ, Zbigniew</creatorcontrib><creatorcontrib>SULEWSKA, Maria J.</creatorcontrib><collection>CrossRef</collection><jtitle>ce/papers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>WRZESIŃSKI, Grzegorz</au><au>LECHOWICZ, Zbigniew</au><au>SULEWSKA, Maria J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of Artificial Neural Networks for the prediction of undrained shear modulus in cohesive soils</atitle><jtitle>ce/papers</jtitle><date>2018-06</date><risdate>2018</risdate><volume>2</volume><issue>2-3</issue><spage>833</spage><epage>838</epage><pages>833-838</pages><issn>2509-7075</issn><eissn>2509-7075</eissn><abstract>The paper presents a study carried out in a Hollow Cylinder Apparatus (HCA) to determine the shear modulus Gu in undrained conditions. Selected results of tests performed for undisturbed cohesive soils are presented. Values of undrained shear modulus Gu 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 Ip 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 Gu using Artificial Neural Networks (ANNs).</abstract><doi>10.1002/cepa.774</doi><tpages>6</tpages></addata></record> |
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subjects | Artificial Neural Networks Cohesive soils Hollow Cylinder Apparatus Undrained shear modulus |
title | Application of Artificial Neural Networks for the prediction of undrained shear modulus in cohesive soils |
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