1 Ocak 2020 Dergi makalesi Açık Erişim

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

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        "affiliation": "Ege Univ, Engn Fac, Dept Civil Engn, Izmir, Turkey", 
        "name": "Sezer, Alper"
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      {
        "affiliation": "Ege Univ, Engn Fac, Dept Civil Engn, Izmir, Turkey", 
        "name": "Sezer, Gozde Inan"
      }, 
      {
        "affiliation": "Bursa Uludag Univ, Engn Fac, Dept Civil Engn, Bursa, Turkey", 
        "name": "Mardani-Aghabaglou, Ali"
      }, 
      {
        "affiliation": "Ege Univ, Engn Fac, Dept Civil Engn, Izmir, Turkey", 
        "name": "Altun, Selim"
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    "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.", 
    "doi": "10.1007/s00521-020-04972-x", 
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    "journal": {
      "issue": "21", 
      "pages": "16707-16722", 
      "title": "NEURAL COMPUTING & APPLICATIONS", 
      "volume": "32"
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    "title": "Prediction of mechanical and penetrability properties of cement-stabilized clay exposed to sulfate attack by use of soft computing methods"
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