Prediction of California bearing ratio (CBR) of fine grained soils by AI methods

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dc.contributor.authorTaskiran, T.
dc.date.accessioned2024-04-24T16:10:43Z
dc.date.available2024-04-24T16:10:43Z
dc.date.issued2010
dc.departmentDicle Üniversitesien_US
dc.description.abstractAdvances in field of artificial intelligence (AI) offers opportunities of utilizing new algorithms and models that enable researchers to solve the most complex systems. As in other engineering fields, AI methods have widely been used in geotechnical engineering. Unlikely, there seems quite insufficient number of research related to the use of AI methods for the estimation of California bearing ratio (CBR). There were actually some attempts to develop prediction models for CBR, but most of these models were essentially statistical correlations. Nevertheless, many of these statistical correlation equations generally produce unsatisfactory CBR values. However, this paper is likely one of the very first research which aims to investigate the applicability of AI methods for prediction of CBR. In this context, artificial neural network (ANN) and gene expression programming (GEP) were applied for the prediction of CBR of fine grained soils from Southeast Anatolia Region/Turkey. Using CBR test data of fine grained soils, some proper models are successfully developed. The results have shown that the both ANN and GEP are found to be able to learn the relation between CBR and basic soil properties. Additionally, sensitivity analysis is performed and it is found that maximum dry unit weight (gamma(d)) is the most effective parameter on CBR among the others such as plasticity index (PI), optimum moisture content (W-opt), sand content (S), clay + silt content (C + S), liquid limit (LL) and gravel content (G) respectively. (C) 2010 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshiplaboratory branch of 9th Regional Directorate of Highways Diyarbakiren_US
dc.description.sponsorshipI would like to thank to M. Ali Sabaz who is the head of laboratory branch of 9th Regional Directorate of Highways Diyarbakir, especially due to the valuable contribution and supports throughout this article work.en_US
dc.identifier.doi10.1016/j.advengsoft.2010.01.003
dc.identifier.endpage892en_US
dc.identifier.issn0965-9978
dc.identifier.issn1873-5339
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-77951207679
dc.identifier.scopusqualityQ1
dc.identifier.startpage886en_US
dc.identifier.urihttps://doi.org/10.1016/j.advengsoft.2010.01.003
dc.identifier.urihttps://hdl.handle.net/11468/15058
dc.identifier.volume41en_US
dc.identifier.wosWOS:000278221800007
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofAdvances in Engineering Software
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networken_US
dc.subjectGene Expression Programmingen_US
dc.subjectCalifornia Bearing Ratioen_US
dc.titlePrediction of California bearing ratio (CBR) of fine grained soils by AI methodsen_US
dc.titlePrediction of California bearing ratio (CBR) of fine grained soils by AI methods
dc.typeArticleen_US

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