Rock Mechanics and Rock Engineering

, Volume 45, Issue 6, pp 1047–1054

Estimation of Elastic Modulus of Intact Rocks by Artificial Neural Network

Original Paper

DOI: 10.1007/s00603-012-0236-z

Cite this article as:
Ocak, I. & Seker, S.E. Rock Mech Rock Eng (2012) 45: 1047. doi:10.1007/s00603-012-0236-z


The modulus of elasticity of intact rock (Ei) is an important rock property that is used as an input parameter in the design stage of engineering projects such as dams, slopes, foundations, tunnel constructions and mining excavations. However, it is sometimes difficult to determine the modulus of elasticity in laboratory tests because high-quality cores are required. For this reason, various methods for predicting Ei have been popular research topics in recently published literature. In this study, the relationships between the uniaxial compressive strength, unit weight (γ) and Ei for different types of rocks were analyzed, employing an artificial neural network and 195 data obtained from laboratory tests carried out on cores obtained from drilling holes within the area of three metro lines in Istanbul, Turkey. Software was developed in Java language using Weka class libraries for the study. To determine the prediction capacity of the proposed technique, the root-mean-square error and the root relative squared error indices were calculated as 0.191 and 92.587, respectively. Both coefficients indicate that the prediction capacity of the study is high for practical use.


Uniaxial compressive strength Unit weight Estimation of modulus of elasticity ANN Twin tunnel 

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Mining Engineering Department, Engineering FacultyIstanbul UniversityIstanbulTurkey
  2. 2.Computer Engineering Department, Engineering FacultyIstanbul UniversityIstanbulTurkey

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