Skip to main content
SpringerLink
Log in

Consolidation Grouting Quality Assessment using Artificial Neural Network (ANN)

  • Original Paper
  • Published:
Indian Geotechnical Journal Aims and scope Submit manuscript

Abstract

Nowadays, grouting plays a vital role in dam foundation. The purpose of the grouting with pressure is to fill the joints, discontinuities, void distance and cavities in the rock masses to consolidate and caulk the rock masses for reducing seepage and uplift pressure in the dam foundation and related structures. Cheraghvays dam is located at 17 km away from the west of the Saqqez in Kurdistan province of Iran. In the Cheraghvays dam, the grout holes were arranged at 2 m intervals and with 10.5 m final depth. There are three grout sections (0–2.5, 2.5–5.5, 5.5–10.5 m) inside each grout hole and the grout process was conducted from the bottom to the top. Finally, the controlling holes are used to perform Lugeon test and to check the grouting quality of the dam foundation. Checking the operations in all areas of the foundation causes cost and time consuming. In this paper, experimental variogram and their mathematical model are calculated by geostatistics methods. To assess consolidation grouting quality, secondary permeability was predicted by artificial neural network (ANN) and the linear regression method. For this aim, datasets of 68 blocks which include first permeability, cement take and secondary permeability have been collected. The obtained results have indicated that ANN can predict secondary permeability in Cheraghvays dam foundation better than the linear regression method. So, ANN can be used in consolidation grouting quality assessment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Abu-kiefa M (1998) General regression neural networks for driven piles in cohesionless soils. J Geotech Geoenviron Eng, ASCE 124(12):1177–1185

    Article  Google Scholar 

  2. Ali M, Chwathe A (2000) Using artificial intelligence to predict permeability from petrographic data. Comput Geosci 26:915–925

    Article  Google Scholar 

  3. Amini H et al (2011) Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Comput Appl. doi:10.1007/s00521-011-0631-5

    Google Scholar 

  4. Armstrong M (1950) Basic linear geostatistics. Springer, Berlin

    Google Scholar 

  5. Atici U (2011) Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Expert system with applications 38:9609–9618

    Article  Google Scholar 

  6. Baker W (1982) Grouting in geotechnical engineering. Geo-Institute of American Society of Civil Engineers, Reston

    Google Scholar 

  7. Basheer I (2000) Selection of methodology for modeling hysteresis behavior of soils using neural networks. J Comput-Aided Civ Infrastruct Eng 5(6):445–463

    Article  Google Scholar 

  8. Basheer I, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. Microbiol Methods 43:3–31

    Article  Google Scholar 

  9. Cambefort H (1967) Injection des sols: principles ET methods. Eyrolles editions, Paris

    Google Scholar 

  10. Canakci H, Baykasoglu A, Gullu H (2009) Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming. Neural Comput Appl 18:1031–1041

    Article  Google Scholar 

  11. Chan W, Chow Y, Liu L (1995) Neural network: an alternative to pile driving formulas. J Comput Geotech 17:135–156

    Article  Google Scholar 

  12. Ewert F (1985) Rock grouting with emphasis on dam sites. Springer, Berlin

    Book  Google Scholar 

  13. Foyo A, Sanchez M (2005) Permeability tests for rock masses, a proposal for a new expression for the equivalent Lugeon unit (ELU). Dam Eng 13(1–3):199–218

    Google Scholar 

  14. Goh A (1994) Seismic liquefaction potential assessed by neural networks. J Geotech Eng 120(9):1467–1480

    Article  Google Scholar 

  15. Goh A (1995) Back-propagation neural networks for modeling complex systems. J Artif Intell Eng 9:143–151

    Article  Google Scholar 

  16. Goh A (1995) Empirical design in geotechnics using neural networks. Geotechnique 45(4):709–714

    Article  MathSciNet  Google Scholar 

  17. Goh A (1995) Modeling soil correlations using neural networks. ASCE, J Comput Civ Eng, pp 275–278

    Google Scholar 

  18. Goh A (1996) Pile driving records reanalyzed using neural networks. J Geotech Eng, ASCE 122(6):492–495

    Article  Google Scholar 

  19. Gothall R, Stille H (2009) Fracture dilation during grouting. Tunnel Undergr Space Technol 24(2):126–135

    Article  Google Scholar 

  20. Hirota Y, Takebayasi S, Shibata I (1990) Prediction of grout take in dam foundation grouting—a case of Granite. J Jpn Soc Civ Eng 13(42):195–202

    Google Scholar 

  21. Houlsby A (1990) Construction and design of cement grouting—a guide to grouting in rock. Wiley, New York

    Google Scholar 

  22. Huang Y, Wanstedt S (1998) The introduction of neural network system and its application in rock engineering. Eng Geol 49:253–260

    Article  Google Scholar 

  23. JSIDRE (1994) The fundamental knowledge on grouting japanese society of irrigation, drainage and reclamation engineering. Tokyo, Japan

    Google Scholar 

  24. Khandelwal M, Roy M, Singh P (2004) Application of artificial neural network in mining industry. Ind Min Eng J 43:19–23

    Google Scholar 

  25. Kriging D (1981) Lognormal-de Wijsian geostatistics for ore evaluation. In: S.A.I.M.M. Monograph Series 1, 2nd edn. South African Institute of mining and metallurgy, Johannesburg

  26. Kulatilake P, Qiong W, Hudaverdi T (2010) Mean particle size prediction in rock blast fragmentation using neural networks. Eng Geol 48:298–311

    Article  Google Scholar 

  27. Kutzner C (1985) Consideration on rock permeability and grouting criteria. In: 15th International Congress on Large Dams, Lausanne, Q.58, R.17

  28. Lee I, Lee J (1996) Prediction of pile bearing capacity using artificial neural networks. Comput Geotech 18(3):189–200

    Article  Google Scholar 

  29. Majidi A, Beiki M (2010) Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. Int J Rock Mech Min Sci 47:246–253

    Article  Google Scholar 

  30. Mert E, Yilmaz S, Inal M (2011) An assessment of total RMR classification system using unified simulation model based on artificial neural networks. Neural Comput Appl 20:603–610

    Article  Google Scholar 

  31. Monjezi M, Amini H, Yazdian Vajiani A (2011) Optimization of open pit blast parameters using genetic algorithm. Int J Rock Mech Min Sci 48:864–869

    Article  Google Scholar 

  32. Monjezi M, Amini H, Yazdian Varjani A (2012) Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci. doi:10.1007/s12517-010-0185-3

    Google Scholar 

  33. Nawari NO, Liang R, Nusairat J (1999) Artificial intelligence techniques for the design and analysis of deep foundations. Elect J Geotech Eng (EJGE) 4. http://www.ejge.com

  34. Neaupane K, Adhikari N (2006) Prediction of tunneling-induced ground movement with the multi-layer perceptron. Int J Tunnel Undergr Space Technol 21:151–159

    Article  Google Scholar 

  35. Nikbakhtan B, Osanloo M (2009) Effect of grout pressure and grout flow on soil physical and mechanical properties in jet grouting operations. Int J Rock Mech Min Sci 46:498–505

    Article  Google Scholar 

  36. Nonveiller E (1989) Grouting in theory and practice. Elsevier, Amsterdam

    Google Scholar 

  37. Olea R (1994) Fundamentals of semivariogram estimation, modeling, and usage. In: Yarus J, Chambers RL (eds) Stochastic modeling and geostatistics principles, methods and case studies. AAPG, Tulsa, pp 27–35

    Google Scholar 

  38. Pan G, Haris D (2000) Information synthesis for mineral exploration. Math Geol 8:851–856

    Google Scholar 

  39. Park H, West T, Woo I (2005) Probabilistic analysis of rock slope stability and random properties of discontinuity parameters, Interstate Highway 40, Western North Carolina, USA. Eng Geol 79:230–250

    Article  Google Scholar 

  40. Ranjith P, Khandelwal M (2011) Artificial neural network for prediction of air flow in a single rock joint. Neural Comput Appl. doi:10.1007/s00521-011-0595-5

    Google Scholar 

  41. Schaap M, Leij F, Van Genuchten M (1998) Neural network analysis for hierarchical prediction of soil hydraulic properties. J Soil Sci Soc Am 62(4):847–855

    Article  Google Scholar 

  42. Shibata I (1989) The determination of a rational injection pressure related to in situ stress in dam foundation grouting. J Jpn Soc Civ Eng 16(436):121–130

    Google Scholar 

  43. Singh V, Singh D, Singh T (2001) Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int J Rock Mech Min Sci 38(2):269–284

    Article  Google Scholar 

  44. Teh C, Wong K, Goh A, Jaritngam S (1997) Prediction of pile capacity using neural networks. J Comput Civil Eng, ASCE 11(2):129–138

    Article  Google Scholar 

  45. Tiryaki B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks and regression trees. Eng Geol 99:51–60

    Article  Google Scholar 

  46. Weaver K (1991) Dam foundation grouting. Geo-Institute of American Society of Civil Engineers, Reston

    Google Scholar 

  47. Wyhthoff B (1993) Back propagation neural networks: a tutorial. Chemometr Intell Lab Syst J 18:115–155

    Article  Google Scholar 

  48. Yamaguchi Y, Matsumoto N (1989) Permeability and Lugeon values of dam foundation. J Jpn Soc Civ Eng 12(412):51–60

    Google Scholar 

  49. Yang C (2004) Estimating cement take and grout efficiency on foundation improvement for Li-Yu-Tan dam. Eng Geol 75:1–14

    Article  Google Scholar 

  50. Yilmaz I, Yuksek A (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41(5):781–795

    Article  Google Scholar 

  51. Zadhesh J, Jalali S, Ramazanzadeh A (2013) Estimation of joint trace length probability distribution function in igneous, sedimentary and metamorphic rocks. Arab J Geosci. doi:10.1007/s12517-013-0861-1

    Google Scholar 

Download references

Acknowledgments

The authors express their great appreciation to the Cheraghvays Dam field engineers; Amir Hafez-Quran and Ali Mushipanahi for technical support and collecting the necessary data.

Author information

Authors and Affiliations

  1. Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran

    Jamal Zadhesh, Faraydoun Sharifi, Hasel Amini & Hossein Mirzaei Nasirabad

  2. Faculty Engineering, University of Tarbiatmodares, Tehran, Iran

    Fuad Rastegar

Authors
  1. Jamal Zadhesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fuad Rastegar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Faraydoun Sharifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hasel Amini
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hossein Mirzaei Nasirabad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jamal Zadhesh.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zadhesh, J., Rastegar, F., Sharifi, F. et al. Consolidation Grouting Quality Assessment using Artificial Neural Network (ANN). Indian Geotech J 45, 136–144 (2015). https://doi.org/10.1007/s40098-014-0116-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40098-014-0116-4

Keywords

Search

Navigation