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dc.contributor.authorPulat, Hasan Fırat
dc.contributor.authorTayfur, Gökmen
dc.contributor.authorYükselen Aksoy, Yeliz
dc.date.accessioned2017-05-23T08:38:09Z
dc.date.available2017-05-23T08:38:09Z
dc.date.issued2014-10
dc.identifier.citationPulat, H.F., Tayfur, G., and Yükselen Aksoy, Y. (2014). Developing cation exchange capacity and soil index properties relationships using a neuro-fuzzy approach. Bulletin of Engineering Geology and the Environment, 73(4), 1141-1149. doi:10.1007/s10064-014-0644-2en_US
dc.identifier.issn1435-9529
dc.identifier.urihttps://doi.org/10.1007/s10064-014-0644-2
dc.identifier.urihttp://hdl.handle.net/11147/5577
dc.description.abstractArtificial intelligence methods are employed to predict cation exchange capacity (CEC) from five different soil index properties, namely specific surface area (SSA), liquid limit, plasticity index, activity (ACT), and clay fraction (CF). Artificial neural networks (ANNs) analyses were first employed to determine the most related index parameters with cation exchange capacity. For this purpose, 40 datasets were employed to train the network and 10 datasets were used to test it. The ANN analyses were conducted with 15 different input vector combinations using same datasets. As a result of this investigation, the ANN analyses revealed that SSA and ACT are the most effective parameters on the CEC. Next, based upon these most effective input parameters, the fuzzy logic (FL) model was developed for the CEC. In the developed FL model, triangular membership functions were employed for both the input (SSA and ACT) variables and the output variable (CEC). A total of nine Mamdani fuzzy rules were deduced from the datasets, used for the training of the ANN model. Minimization (min) inferencing, maximum (max) composition, and centroid defuzzification methods are employed for the constructed FL model. The developed FL model was then tested against the remaining datasets, which were also used for testing the ANN model. The prediction results are satisfactory with a determination coefficient, R2 = 0.94 and mean absolute error, (MAE) = 7.1.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.isversionof10.1007/s10064-014-0644-2en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligence methoden_US
dc.subjectArtificial neural networken_US
dc.subjectCation exchange capacityen_US
dc.subjectClayey soilsen_US
dc.subjectFuzzy logicen_US
dc.subjectSoil index propertiesen_US
dc.titleDeveloping cation exchange capacity and soil index properties relationships using a neuro-fuzzy approachen_US
dc.typearticleen_US
dc.contributor.authorIDTR2054en_US
dc.contributor.iztechauthorTayfur, Gökmen
dc.relation.journalBulletin of Engineering Geology and the Environmenten_US
dc.contributor.departmentİYTE, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.identifier.volume73en_US
dc.identifier.issue4en_US
dc.identifier.startpage1141en_US
dc.identifier.endpage1149en_US
dc.identifier.wosWOS:000344323700020
dc.identifier.scopusSCOPUS:2-s2.0-84919333652
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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