Sezer, AlperSezer, Gözde InanMardani-Aghabaglou, AliAltun, Selim2024-07-042024-07-042020-05-060941-0643https://doi.org/10.1007/s00521-020-04972-xhttps://link.springer.com/article/10.1007/s00521-020-04972-xhttps://hdl.handle.net/11452/42895Similar 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.eninfo:eu-repo/semantics/closedAccessStrength developmentBehaviorDensityCement-stabilized soilStrengthPenetrabilityBpnnAnfisSoft computingScience & technologyTechnologyComputer science, artificial intelligenceArticle0005307893000021670716722322110.1007/s00521-020-04972-x1433-3058