1 Ocak 2020 Dergi makalesi Açık Erişim

Sezer, Alper; Sezer, Gozde Inan; Mardani-Aghabaglou, Ali; Altun, Selim

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  <dc:creator>Sezer, Alper</dc:creator>
  <dc:creator>Sezer, Gozde Inan</dc:creator>
  <dc:creator>Mardani-Aghabaglou, Ali</dc:creator>
  <dc:creator>Altun, Selim</dc:creator>
  <dc:date>2020-01-01</dc:date>
  <dc:description>Similar 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>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/7817</dc:identifier>
  <dc:identifier>oai:zenodo.org:7817</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>NEURAL COMPUTING &amp; APPLICATIONS 32(21) 16707-16722</dc:source>
  <dc:title>Prediction of mechanical and penetrability properties of cement-stabilized clay exposed to sulfate attack by use of soft computing methods</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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