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

  • Hanifi Çanakcı
  • Adil Baykasoğlu
  • Hamza Güllü
Original Article


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.


Artificial neural networks Gene expression programming Basalt Tensile and compressive strength 


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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Hanifi Çanakcı
    • 1
  • Adil Baykasoğlu
    • 2
  • Hamza Güllü
    • 1
  1. 1.Department of Civil EngineeringUniversity of GaziantepGaziantepTurkey
  2. 2.Department of Industrial Engineering, Faculty of EngineeringUniversity of GaziantepGaziantepTurkey

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