Rock Mechanics and Rock Engineering

, Volume 45, Issue 6, pp 1047–1054 | Cite as

Estimation of Elastic Modulus of Intact Rocks by Artificial Neural Network

Original Paper

Abstract

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.

Keywords

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

References

  1. Deere DU, Miller RP (1966) Engineering classification and index properties of intact rock. Department of Civil Engineering, University of Illinois, Illinois, USA, pp 90–101Google Scholar
  2. Ercelebi S, Copur H, Ocak I (2011) Surface settlement predictions for Istanbul Metro tunnels excavated by EPB-TBM. Environ Earth Sci 62(2):357–365CrossRefGoogle Scholar
  3. Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first international conference on neural networks. San Diego, USA, pp 11–14Google Scholar
  4. ISRM (1981) International society for rock mechanics suggested methods: rock characterization, testing and monitoring. In: Brown ET (ed). Pergamon, LondonGoogle Scholar
  5. Lashkaripour GR, Nakhaei M (2001) A statistical investigation on mudrocks characteristics. In: Proceedings of the ISRM regional symposium of rock mechanics. Espoo, Finland, pp 131–136Google Scholar
  6. Lunetta R, Congalton R, Fenstermaker L, Jensen J, McGwyer K, Tinney L (1991) Remote sensing and geographic information system data integration: error sources and research issues. Photogramm Eng Remote Sens 57:677–687Google Scholar
  7. Mitchell TM (1997) Machine learning. McGraw Hill, New YorkGoogle Scholar
  8. Ocak I (2008a) Estimating the modulus of elasticity of the rock material from compressive strength and unit weight. J S Afr Inst Min Metall 108(10):621–626Google Scholar
  9. Ocak I (2008b) Control of surface settlements with umbrella arch method in second stage excavations of Istanbul Metro. Tunn Undergr Sp Tech 23(6):674–681CrossRefGoogle Scholar
  10. Ocak I (2008c) Comparison of machine utilization time and performance for roadheader and impact hammer in Kadikoy–Kartal Metro Tunnels (Istanbul). In: 8th international scientific conference, modern management of mine producing, geology and environmental protection, vol 1. Varna, Bulgaria, pp 269–276Google Scholar
  11. Ocak I (2009a) Empirical estimation of intact rock elastic modulus. In: The 21st international mining congress of Turkey. Antalya, Turkey, pp 165–172Google Scholar
  12. Ocak I (2009b) Environmental problems caused by Istanbul subway excavation and suggestions for remediation. Environ Geol 58(7):1557–1566CrossRefGoogle Scholar
  13. Ocak I (2009c) Environmental effects of tunnel excavation in soft and shallow ground with EPBM: the case of Istanbul. Environ Earth Sci 59(2):347–352CrossRefGoogle Scholar
  14. Ocak I (2011) Overview to ongoing metro projects in Istanbul, Turkey. In: 22nd world mining congress and expo. Istanbul, Turkey, pp 161–168Google Scholar
  15. Ocak I, Bilgin N (2010) Comparative studies on the performance of a roadheader, impact hammer and drilling and blasting method in the excavation of metro station tunnels in Istanbul. Tunn Undergr Sp Tech 25(2):181–187CrossRefGoogle Scholar
  16. Palchik V (1999) Influence of porosity and elastic modulus on uniaxial compressive strength in soft brittle porous sandstones. Rock Mech Rock Eng 32(4):303–309CrossRefGoogle Scholar
  17. Palchik V (2011) On the ratios between elastic modulus and uniaxial compressive strength of heterogeneous carbonate rocks. Rock Mech Rock Eng 44(1):121–128CrossRefGoogle Scholar
  18. Qureshi SA, Mirza SM, Arif M (2006) Fitness function evaluation for image reconstruction using binary genetic algorithm for parallel ray transmission tomography, emerging technologies. In: ICET’06 international conference. Islamabad, Pakistan, pp 196–201Google Scholar
  19. Rohde J, Feng H (1990) Ana1ysis of the variability of unconfined compression tests of rock. Rock Mech Rock Eng 23:231–236CrossRefGoogle Scholar
  20. Rosenblatt FX (1961) Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Spartan Books, Washington, DCGoogle Scholar
  21. Sachpazis CI (1990) Correlating schmidt hardness with compressive strength and young’s modulus of carbonate rocks. Bull Int Assoc Eng Geol 42:75–83CrossRefGoogle Scholar
  22. Schalkoff RJ (1997) Artificial neural network. McGraw Hill, New YorkGoogle Scholar
  23. Sonmez H, Gokceoglu C, Kayabas A, Nefeslioglu HA (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43(2):224–235CrossRefGoogle Scholar
  24. Sönmez H, Gökçeoğlu C, Kasapoğlu KE, Tuncay E, Zorlu K (2004a) An empirical equation for estimating elasticity modulus of intack rock. In: Rockmec VIIth regional rock mechanics symposium, Sivas-TurkeyGoogle Scholar
  25. Sönmez H, Tuncay E, Gökçeoğlu C (2004b) Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara agglomerate. Int J Rock Mech Min Sci 41(5):717–729CrossRefGoogle Scholar
  26. Tuğrul A, Zarif IH (1999) Correlation of mineralogical and textural characteristics with engineering properties o selected granitic rock from Turkey. Eng Geol 51(4):303–317CrossRefGoogle Scholar
  27. Wasserman PD, Schwartz T (1988) Neural networks II. What are they and why is everybody so interested in them now? IEEE Expert 3(1):10–15CrossRefGoogle Scholar
  28. Yagiz S (1999) Predicting uniaxial compressive strength, modulus of elasticity and index properties of rocks using the Schmidt hammer. Bull Eng Geol Environ 68(1):55–63CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

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

Personalised recommendations