Estimation of California bearing ratio by using soft computing systems

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dc.contributor.authorYildirim, B.
dc.contributor.authorGunaydin, O.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2011
dc.departmentNiğde ÖHÜ
dc.description.abstractThis study presents the application of different methods (simple-multiple analysis and artificial neural networks) for the estimation of the California bearing ratio (CBR) from sieve analysis, Atterberg limits, maximum dry unit weight and optimum moisture content of the soils. The resistance of granular soils, which are in the superstructure foundation and subgrade layers are usually tested by CBR (California bearing ratio), which is an old and still extensively used experiment. The data were collected from the public highways of Turkey's different regions. Regression analysis and artificial neural network estimation indicated strong correlations (R-2 = 0.80-0.95) between the sieve analysis, Atterberg limits, maximum dry unit weight (MOD) and optimum moisture content (OMC). It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time. (c) 2010 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.eswa.2010.12.054
dc.identifier.endpage6391
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue5
dc.identifier.scopus2-s2.0-79151483453
dc.identifier.scopusqualityQ1
dc.identifier.startpage6381
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2010.12.054
dc.identifier.urihttps://hdl.handle.net/11480/4731
dc.identifier.volume38
dc.identifier.wosWOS:000287419900199
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONS
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCalifornia bearing ratio (CBR)
dc.subjectSieve analysis
dc.subjectAtterberg limits
dc.subjectMaximum dry unit weight
dc.subjectOptimum moisture content
dc.subjectArtificial neural networks
dc.subjectCorrelation
dc.titleEstimation of California bearing ratio by using soft computing systems
dc.typeReview Article

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