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Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming

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Abstract

In this paper, two soft computing approaches, which are known as artificial neural networks and Gene Expression Programming (GEP) are used in strength prediction of basalts which are collected from Gaziantep region in Turkey. The collected basalts samples are tested in the geotechnical engineering laboratory of the University of Gaziantep. The parameters, “ultrasound pulse velocity”, “water absorption”, “dry density”, “saturated density”, and “bulk density” which are experimentally determined based on the procedures given in ISRM (Rock characterisation testing and monitoring. Pergamon Press, Oxford, 1981) are used to predict “uniaxial compressive strength” and “tensile strength” of Gaziantep basalts. It is found out that neural networks are quite effective in comparison to GEP and classical regression analyses in predicting the strength of the basalts. The results obtained are also useful in characterizing the Gaziantep basalts for practical applications.

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Acknowledgments

Prof. Dr. Adil Baykasoğlu is grateful to Turkish Academy of Sciences (TÜBA) for supporting his scientific studies.

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Authors and Affiliations

  1. Department of Civil Engineering, University of Gaziantep, Gaziantep, Turkey

    Hanifi Çanakcı & Hamza Güllü

  2. Department of Industrial Engineering, Faculty of Engineering, University of Gaziantep, 27310, Gaziantep, Turkey

    Adil Baykasoğlu

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  1. Hanifi Çanakcı
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  2. Adil Baykasoğlu
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  3. Hamza Güllü
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Correspondence to Adil Baykasoğlu.

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Çanakcı, H., Baykasoğlu, A. & Güllü, H. Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming. Neural Comput & Applic 18, 1031–1041 (2009). https://doi.org/10.1007/s00521-008-0208-0

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  • DOI: https://doi.org/10.1007/s00521-008-0208-0

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