Cement take estimation using neural networks and statistical analysis in Bakhtiari and Karun 4 dam sites, in south west of Iran

  • Ebrahim Rahimi
  • Ebrahim Sharifi Teshnizi
  • Ahmad Rastegarnia
  • Ehsan Motamed Al-shariati
Original Paper
  • 14 Downloads

Abstract

Water seepage from dam foundations causes reservoir water loss and raises the risk of dam instability. One method of remediation for controlling instability and leakage of these rock foundations is grouting. Since a considerable portion of the costs for dam construction is allocated to grouting, as a result, study of the influencing factors of cement take in grouting jobs is of paramount importance for each site. The most dominant parameters which play a decisive role in the efficiency of grouting are rock mass strength and permeability, grouting pressure, water-to-cement ratio, and properties of jointed rock mass such as joint aperture, roughness, and spacing. In this paper, the relationship between cement take and Q-system, aperture and spacing of joints, Lugeon number, depth of grouting, and grouting parameters such as grouting pressure and water-to-cement ratio is investigated via statistical analysis and artificial neural networks for two large concrete dam sites, Bakhtiari and Karun 4, located in southwest Iran. Karun 4 has been constructed while Bakhtiari is still under construction with respective heights of 230 and 325 m. The mentioned parameters, the relationships of which are found in relation to cement take, are determined based on engineering geology reports for all the 5-m segments of the trial grouting boreholes. Bivariate statistical analyses showed that the highest correlation (R = 0.64) exists between cement take and Q-system by a logarithmic relationship. In addition, statistical investigations based on multivariate analyses between cement take and all the mentioned variables show a poor correlation (R = 0.48) which encouraged the authors to use neural networks for finding a relationship between cement take and the influencing variables. This resulted in a higher correlation (R = 0.77, RMSE = 9.2) with respect to the statistical method.

Keywords

Artificial neural network Regression Joint characteristics Properties of cement grout Q-system Lugeon 

Notes

Acknowledgements

The authors would like to thank the Iran Water and Power Resources Development Company (IWPRDC) and Mahab Qods Consulting Engineering Company (MQCEC) for field test data of the trial grouting boreholes. The authors also wish to thank Mr. Gholam Reza Lashkaripour for suggestions that improved this paper.

References

  1. Barton N (2002) Some new Q-value correlations to assist in site characterization and tunnel design. Int J Rock Mech Min Sci, Vol 39:185–216CrossRefGoogle Scholar
  2. Barton N (2006) Rock quality, seismic velocity, attenuation and anisotropy. Taylor & Francis, LondonCrossRefGoogle Scholar
  3. Barton N, Lien R, Lunde J (1974) Engineering classification of rock masses for the design of tunnel support. Rock Mech Rock Eng 6(4):189–236CrossRefGoogle Scholar
  4. Chang TC, Chao RJ (2006) Application of back-propagation networks in debris flow prediction. Eng Geol 85:270–280CrossRefGoogle Scholar
  5. Chen DF, Feng XT, Xu DP, Jiang Q, Yang CX, Yao PP (2016) Use of an improved ANN model to predict collapse depth of thin and extremely thin layered rock strata during tunnelling. Tunn Undergr Sp Tech 51:372–386CrossRefGoogle Scholar
  6. BJVC (2008) Bakhtiari Joint Venture Consultants. Engineering geology and rock mechanics report (StageIIstudies). Tehran, Iran: Water and Power Resources Development Co (IWPC) AuthorityGoogle Scholar
  7. Clarck C, Budavari S (1981) Correation of rock mass classification parameters obtained from Borecore and in-situ observation. Eng Geol 17:19–53CrossRefGoogle Scholar
  8. Deere DU (1989) Rock quality designation (RQD) after 20 years. US Army corps engineers. Contract report GL-89-1. Waterways Experimental Station, VicksburgGoogle Scholar
  9. Ewert FK (1985) Rock grouting with emphasis on dam sites, springer. Verlarg, Berlin-New York-Tokyo, 428ppGoogle Scholar
  10. Flood I, Kartam N (1994) Neural networks in civil engineering. I: Principles and understanding. J Comput Civil Eng, ASCE 8(2):131–148CrossRefGoogle Scholar
  11. Ghafoori M, Rastegarnia A, Lashkaripour GR (2018) Estimation of static parameters based on dynamical and physical properties in limestone rocks. J Afr Earth Sci 137:22–31.  https://doi.org/10.1016/j.jafrearsci.2017.09.008 CrossRefGoogle Scholar
  12. Ghodss Consulting Engineering Company (GCEC) (2006) drilling operations and administration instructions damGoogle Scholar
  13. Gomez H, Kavzoglu T (2008) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River basin, Venezuela. Eng Geol 78:11–27CrossRefGoogle Scholar
  14. Hatheway AW (2009) The complete ISRM suggested methods for rock characterization, testing and monitoring; 1974–2006. Environ Eng Geosci 15(1):47–48CrossRefGoogle Scholar
  15. Haykin S (1994) Neural networks: a comprehensive foundation. MacMillan Publishing Company, New York, 696 ppGoogle Scholar
  16. Houlsby A C (1990) Construction and design of cement grouting: a guide to grouting in rock foundations (Vol. 67). John Wiley & SonsGoogle Scholar
  17. Iran Water and Power Resources Development Company (IWPRDC) (2009) Engineering geology report phase I feasibility phase Bakhtiari reservoir damGoogle Scholar
  18. Karagüzel R, Kilic R (2000) The effect of the alteration degree of ophiolitic melange on permeability and grouting. Eng Geol 57(1):1–12CrossRefGoogle Scholar
  19. Kutzner C, (1985) Consideration on rock permeability and grouting criteria, 15th international congress on large dams, Lausanne, Q.58, R.17Google Scholar
  20. Kutzner C (1996) Grouting of rock and soil. Balkema, Rotterdam, 271pGoogle Scholar
  21. Lashkaripour GR, Rastegarnia A, Ghafoori M (2018) Assessment of brittleness and empirical correlations between physical and mechanical parameters of the Asmari limestone in Khersan 2 dam site, in southwest of Iran. J Afr Earth Sci 138:124–132.  https://doi.org/10.1016/j.jafrearsci.2017.11.003 CrossRefGoogle Scholar
  22. Lee S, Ryu JH, Won JS, Park HJ (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71:289–302CrossRefGoogle Scholar
  23. Maji VB, Sitharam TG (2008) Prediction of elastic modulus of jointed rock mass using artificial neural networks. Geotech Geol Eng 26:443–452CrossRefGoogle Scholar
  24. Moosavi M, Yazdanpanah MJ, Doostmohammadi R (2006) Modeling the cyclic swelling pressure of mudrock using artificial neural networks. Eng Geol 87:178–194CrossRefGoogle Scholar
  25. Moselhi O, Hegazy T, Fazio P (1992) Potential applications of neural networks in construction. Can J Civ Eng 19:521–529CrossRefGoogle Scholar
  26. Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97:171–191CrossRefGoogle Scholar
  27. Nonveiller E (1989) Grouting theory and practice, development of geotechnical engineering, Elsevier: 250 ppGoogle Scholar
  28. Ocak I, Seker SE (2012) Estimation of elastic modulus of intact rocks by artificial neural network. Rock Mech Rock Eng 45(6):1047–1054CrossRefGoogle Scholar
  29. Öge İF, Çırak M (2017) Relating rock mass properties with Lugeon value using multiple regression and nonlinear tools in an underground mine site. Bull Eng Geol Environ, 1–14.  https://doi.org/10.1007/s10064-017-1179-0
  30. Priest SD, Hudson JA (1976) Discontinuity spacing in rock. Int J Rock Mech Min Sci 13:135–148CrossRefGoogle Scholar
  31. Rastegarnia A, Lashkaripour GR, Ghafoori M (2017) Prediction of grout take using rock mass properties. Bull Eng Geol Environ 76(4):1643–1654.  https://doi.org/10.1007/s10064-016-0956-5 CrossRefGoogle Scholar
  32. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. Parallel Distrib Process 1:318–362Google Scholar
  33. Sadeghiyeh SM, Hashemi M, Ajalloeian R (2013) Comparison of permeability and groutability of Ostur dam site rock mass for grout curtain design. Rock Mech Rock Eng 46(2):341–357CrossRefGoogle Scholar
  34. Sohrabi-Bidar A, Rastegarnia A, Zolfaghari A (2016) Estimation of the grout take using empirical relationships (case study: Bakhtiari dam site). Bull Eng Geol Environ 75(2):425–438CrossRefGoogle Scholar
  35. Trippi RR, Turban E (1996) Neural networks in finance and investing, Irwin Professional PublishingGoogle Scholar
  36. Wong HY, Farmer IW (1973) Hydrofracture mechanisms in rock during pressure grouting. Rock Mech Rock Eng 5(1):21–41CrossRefGoogle Scholar
  37. Yang CP (2004) Estimating cement take and grout efficiency on foundation improvement for li – Yu – tan dam. Eng Geol 75:1–14CrossRefGoogle Scholar
  38. Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141–158CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ebrahim Rahimi
    • 1
  • Ebrahim Sharifi Teshnizi
    • 2
  • Ahmad Rastegarnia
    • 2
  • Ehsan Motamed Al-shariati
    • 2
  1. 1.School of Earth SciencesDamghan UniversityDamghanIran
  2. 2.Department of Geology, Faculty of ScienceFerdowsi University of MashhadMashhadIran

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