Publication:
Prediction of mechanical and penetrability properties of cement-stabilized clay exposed to sulfate attack by use of soft computing methods

dc.contributor.authorSezer, Alper
dc.contributor.authorSezer, Gözde Inan
dc.contributor.authorMardani-Aghabaglou, Ali
dc.contributor.authorAltun, Selim
dc.contributor.buuauthorMardani-Aghabaglou, Ali
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentİnşaat Mühendisliği Bölümü
dc.contributor.orcid0000-0003-0326-5015
dc.contributor.researcheridC-7860-2015
dc.contributor.researcheridAAJ-6415-2021
dc.date.accessioned2024-07-04T10:34:40Z
dc.date.available2024-07-04T10:34:40Z
dc.date.issued2020-05-06
dc.description.abstractSimilar to its effects on any type of cementitious composite, it is a well-known fact that sulfate attack has also a negative influence on engineering behavior of cement-stabilized soils. However, the level of degradation in engineering properties of the cement-stabilized soils still needs more scientific attention. In the light of this, a database including a total of 260 unconfined compression and chloride ion penetration tests on cement-stabilized kaolin specimens exposed to sulfate attack was constituted. The data include information about cement type (sulfate resistant-SR; normal portland (N) and pozzolanic-P), and its content (0, 5, 10 and 15%), sulfate type (sodium or magnesium sulfate) as well as its concentration (0.3, 0.5, 1%) and curing period (1, 7, 28 and 90 days). Using this database, linear and nonlinear regression analysis (RA), backpropagation neural networks and adaptive neuro-fuzzy inference techniques were employed to question whether these methods are capable of predicting unconfined compressive strength and chloride ion penetration of cement-stabilized clay exposed to sulfate attack. The results revealed that these methods have a great potential in modeling the strength and penetrability properties of cement-stabilized clays exposed to sulfate attack. While the performance of regression method is at an acceptable level, results show that adaptive neuro-fuzzy inference systems and backpropagation neural networks are superior in modeling.
dc.description.sponsorshipEge Üniversitesi - 2014-BIL-009
dc.identifier.doi10.1007/s00521-020-04972-x
dc.identifier.eissn1433-3058
dc.identifier.endpage16722
dc.identifier.issn0941-0643
dc.identifier.issue21
dc.identifier.startpage16707
dc.identifier.urihttps://doi.org/10.1007/s00521-020-04972-x
dc.identifier.urihttps://link.springer.com/article/10.1007/s00521-020-04972-x
dc.identifier.urihttps://hdl.handle.net/11452/42895
dc.identifier.volume32
dc.identifier.wos000530789300002
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherSpringer
dc.relation.journalNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.relation.tubitak113M202
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectStrength development
dc.subjectBehavior
dc.subjectDensity
dc.subjectCement-stabilized soil
dc.subjectStrength
dc.subjectPenetrability
dc.subjectBpnn
dc.subjectAnfis
dc.subjectSoft computing
dc.subjectScience & technology
dc.subjectTechnology
dc.subjectComputer science, artificial intelligence
dc.titlePrediction of mechanical and penetrability properties of cement-stabilized clay exposed to sulfate attack by use of soft computing methods
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/İnşaat Mühendisliği Bölümü

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