Evaluation of dependency of compression index on toughness limit for fine-grained soils

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dc.authoridShimobe, Satoru/0000-0002-7151-8283
dc.authorscopusid55189861600
dc.authorscopusid55614913300
dc.authorscopusid8575708700
dc.contributor.authorShimobe, Satoru
dc.contributor.authorKarakan, Eyyub
dc.contributor.authorSezer, Alper
dc.date.accessioned2024-08-25T18:32:00Z
dc.date.available2024-08-25T18:32:00Z
dc.date.issued2023
dc.departmentEge Üniversitesien_US
dc.description.abstractPrediction of the ultimate settlement is vital for the assessment of the service life of a structure, particularly when it is underlain by fine-grained soils. As known, this value is a function of the compression index (C-c) of soils, which can simply be found by performance of oedometer tests. For this purpose, more than 2000 test results from past studies were compiled to constitute a database. Then, multiple linear regression analyses were employed to predict the C-c parameter by use of toughness limit (TL) and a function of this parameter, namely the soil state index (SSI). It was noticed that SSI was a better predictor of C-c, in comparison with TL. Prediction ability of many equations from literature was questioned, and it was concluded that these equations were good predictors of their own data. Moving to a generalized behavior, data show a more scattered structure, which needs more sophisticated methods using above-mentioned parameters as inputs. In this regard, artificial neural networks were employed to estimate the C-c by use of single input parameters: TL or SSI. Additionally, a combination of Atterberg limits was also instructed as inputs for prediction of C-c. A comparative analysis of the effects of learning algorithm, input data, and number of neurons in hidden layer was given. It was concluded that the TL and SSI are reasonable predictors of compression index.en_US
dc.identifier.doi10.1007/s00521-023-08292-8
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.scopus2-s2.0-85148600918en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08292-8
dc.identifier.urihttps://hdl.handle.net/11454/100099
dc.identifier.wosWOS:000945060200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240825_Gen_US
dc.subjectCompression indexen_US
dc.subjectToughness limiten_US
dc.subjectArtificial neural networksen_US
dc.subjectRegression analysisen_US
dc.subjectGeotechnical Propertiesen_US
dc.subjectHydraulic Conductivityen_US
dc.subjectEmpirical Correlationsen_US
dc.subjectTrial Embankmenten_US
dc.subjectShear-Strengthen_US
dc.subjectSoft Clayen_US
dc.subjectConsolidationen_US
dc.subjectBehavioren_US
dc.subjectPerformanceen_US
dc.subjectSettlementsen_US
dc.titleEvaluation of dependency of compression index on toughness limit for fine-grained soilsen_US
dc.typeArticleen_US

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