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

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Abstract

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.

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Acknowledgements

The authors appreciate contributions of the Scientific and Technological Research Council of Turkey (TUBITAK) and Ege University Science and Technology Centre—Technology Transfer Office (EBILTEM) under grant numbers 113M202 and 2014-BIL-009, and the support provided by Çimentaş Group, Denizli Çimento A.Ş. and Akçansa for providing cements used in the experimental part of this study.

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Authors and Affiliations

  1. Department of Civil Engineering, Engineering Faculty, Ege University, Bornova, İzmir, Turkey

    Alper Sezer, Gözde İnan Sezer & Selim Altun

  2. Department of Civil Engineering, Engineering Faculty, Bursa Uludag University, Nilüfer, Bursa, Turkey

    Ali Mardani-Aghabaglou

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  1. Alper Sezer
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Correspondence to Ali Mardani-Aghabaglou.

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Sezer, A., Sezer, G.İ., Mardani-Aghabaglou, A. et al. Prediction of mechanical and penetrability properties of cement-stabilized clay exposed to sulfate attack by use of soft computing methods. Neural Comput & Applic 32, 16707–16722 (2020). https://doi.org/10.1007/s00521-020-04972-x

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