Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials

  • Varun Kumar Ojha
  • Deepak Amban Mishra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 734)


Uniaxial Compressive Strength (UCS) is the most important parameter that quantifies the rock strength. However, determination of the UCS in laboratory is very expensive and time-consuming. Therefore, common index tests like point load (Is-50), ultrasonic velocity test (Vp), block punch index (BPI) test, rebound hardness (SRH) test, physical properties have been used to predict the UCS. The objective of this work is to develop a predictive model using a neural tree predictor that estimates the UCS with high accuracy and assess the effectiveness of different index tests in predicting the UCS of rock materials. UCS and indices such as BPI, Is-50, SRH, Vp, effective porosity and density were determined for the granite, schist, and sandstone. The constructed model predicted the UCS with a high accuracy and in a quick time (9 s). Additionally, the destructive mechanical rock indices BPI and Is-50 proved to be the best index tests to estimate the UCS.


Uniaxial compressive strength Index tests Rock materials Heterogeneous flexible neural tree Feature analysis 


  1. 1.
    Bieniawski, Z.T.: Engineering Rock Mass Classifications, p. 251. Wiley, New York (1989)Google Scholar
  2. 2.
    ISRM: The complete ISRM suggested methods for rock characterization, testing and monitoring. In: Ulusay, R., Hudson, J.A. (eds.) Suggested Methods Prepared by the Commission of Testing Methods, Kozan Ofset, Ankara, ISRM, 19742006. Compilation Arranged by the ISRM Turkish National Group (2007)Google Scholar
  3. 3.
    Mishra, D.A., Basu, A.: Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system. Eng. Geol. 60, 54–68 (2013)CrossRefGoogle Scholar
  4. 4.
    Meulenkamp, F., Grima, M.A.: Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int. J. Rock Mech. Min. Sci. 36, 29–39 (1999)CrossRefGoogle Scholar
  5. 5.
    Gokceoglu, C., Zorlu, K.: A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Eng. Appl. Artif. Intel. 17(1), 61–72 (2004)CrossRefGoogle Scholar
  6. 6.
    Sonmez, H., Gokceoglu, C., Nefeslioglu, H.A., Kayabasi, A.: 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, 224–235 (2006)CrossRefGoogle Scholar
  7. 7.
    Zorlu, K., Gokceoglu, C., Ocakoglu, F., Nefeslioglu, H.A., Acikalin, S.: Prediction of uniaxial compressive strength of sandstone using petrography-based models. Eng. Geol. 96, 141–158 (2008)CrossRefGoogle Scholar
  8. 8.
    Gokceoglu, C., Zorlu, K., Ceryanc, S., Nefeslioglu, H.A.: A comparative study on indirect determination of degree of weathering of granites from some physical and strength parameters by two soft computing techniques. Mater. Charact. 60, 1317–1327 (2009)CrossRefGoogle Scholar
  9. 9.
    Yilmaz, I., Yuksek, G.: Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN and ANFIS models. Int. J. Rock Mech. Min. Sci. 46, 803–810 (2009)CrossRefGoogle Scholar
  10. 10.
    Dehghan, S., Sattari, G.H., Chehreh, C.S., Aliabadi, M.A.: Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Min. Sci. Tech. 20, 41–46 (2010)Google Scholar
  11. 11.
    Rabbani, E., Sharif, F., Kooliv, M., Salooki, M.A.: Application of neural network technique for prediction of uniaxial compressive strength using reservoir formation properties. Int. J. Rock Mech. Min. Sci. 56, 100–111 (2012)Google Scholar
  12. 12.
    Yesiloglu-Gultekin, N., Gokceoglu, C., Sezer, E.A.: Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances. Int. J. Rock Mech. Min. Sci. 62, 113–122 (2013a)Google Scholar
  13. 13.
    Yesiloglu-Gultekin, N., Sezer, E.A., Gokceoglu, C., Bayhan, H.: An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents. Expert Syst. Appl. 40, 921–928 (2013b)CrossRefGoogle Scholar
  14. 14.
    Armaghani, D.J., Hajihassani, M., Bejarbaneh, B.Y., Marto, A., Mohamad, E.T.: Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Measurement 55, 487–498 (2014)CrossRefGoogle Scholar
  15. 15.
    Armaghani, D.J., Mohamad, E.T., Momeni, E., Narayanasamy, M.S., Amin, M.F.M.: An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Youngs modulus: a study on Main Range granite. Bull. Eng. Geol. Environ. 74, 1301–1319 (2015)CrossRefGoogle Scholar
  16. 16.
    Mishra, D.A., Srigyan, M., Basu, A., Rokade, P.J.: Soft computing methods for estimating the uniaxial compressive strength of intact rock from index tests. Int. J. Rock Mech. Min. Sci. 80, 418–424 (2015)Google Scholar
  17. 17.
    Ojha, V.K., Abraham, A., Snášel, V.: Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming. Appl. Soft. Comput. 52, 909–924 (2017)CrossRefGoogle Scholar
  18. 18.
    Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice Hall, Upper Saddle River (2009)Google Scholar
  19. 19.
    Kohavi, R., Quinlan, J.R.: Data mining tasks and methods: classification: decision-tree discovery. In: Handbook of Data Mining and Knowledge Discovery, pp. 267–276. Oxford University Press (2002)Google Scholar
  20. 20.
    Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series forecasting using flexible neural tree model. Inf. Sci. 174, 219–235 (2005)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Ojha, V.K., Schiano, S., Wu, C.Y., Snášel, V., Abraham, A.: Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree. Neural Comput. Appl., 1–15 (2016)Google Scholar
  22. 22.
    Lam, H.K., Nguyen, H.T.: Computational Intelligence and Its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques. World Scientific, Singapore (2012)CrossRefGoogle Scholar
  23. 23.
    Rezaee, B., Zarandi, M.F.: Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system. Inf. Sci. 180(2), 241–255 (2010)CrossRefGoogle Scholar
  24. 24.
    Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)CrossRefGoogle Scholar
  25. 25.
    Storn, R., Price, K.: Differential evolutiona simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Mishra, D.A., Basu, A.: Use of the block punch test to predict the compressive and tensile strengths of rocks. Int. J. Rock Mech. Min. Sci. 51, 119–127 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ETH ZürichZürichSwitzerland
  2. 2.Indian Institute of Petroleum and EnergyVisakhapatnamIndia
  3. 3.Institute of Geonics of CASOstrava-PorubaCzech Republic

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