Skip to main content

Advertisement

SpringerLink
Log in

Neural computing models for prediction of permeability coefficient of coarse-grained soils

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Shepherd RG (1989) Correlations of permeability and grain size. Ground Water 27(5):633–638

    Article  Google Scholar 

  2. Yilmaz I (2006) Indirect estimation of the swelling percent and a new classification of soils depending on liquid limit and cation exchange capacity. Eng Geol 85:295–301

    Article  Google Scholar 

  3. Alyamani MS, Sen Z (1993) Determination of hydraulic conductivity from complete grain-size distribution curves. Ground Water 31(4):551–555

    Article  Google Scholar 

  4. Hazen A (1892) Some physical properties of sands and gravels. Massachusetts State Board of Health, Annual Report, pp 539–556

  5. Uma KO, Egboka BCE, Onuoha KM (1989) New statistical grain-size method for evaluating the hydraulic conductivity of sandy aquifers. J Hydrol 108:343–366

    Article  Google Scholar 

  6. Krumbein WC, Monk GD (1943) Permeability as a function of the size parameters of unconsolidated sands. Trans Am Inst Min Metall Petroleum Eng 151:153–163

    Google Scholar 

  7. Kozeny J (1953) Das wasser in boden, grundwasserbewegung. Hydraulik 280–445

  8. Rawls WJ, Brakensiek DL (1989) Estimation of soil water retention and hydraulic properties. In: Morel-Seytoux HJ (ed) Unsaturated flow in hydrologic modeling theory and practice. Kluwer, Dordrecht, pp 275–300

    Google Scholar 

  9. Jabro JD (1992) Estimation of saturated hydraulic conductivity of soils from particle size distribution and bulk density data. J Am Soc Agric Eng 35(2):557–560

    Google Scholar 

  10. Sperry JM, Peirce JJ (1995) A model for estimating the hydraulic conductivity of granular material based on grain shape, grain size, and porosity. Ground Water 33(6):892–898

    Article  Google Scholar 

  11. Lebron I, Schaap MG, Suarez DL (1999) Saturated hydraulic conductivity prediction from microscopic pore geometry measurements and neural network analysis. Water Resour Res 35(10):3149–3158

    Article  Google Scholar 

  12. Cronican AE, Gribb MM (2007). Literature review: equations for predicting hydraulic conductivity based on grain-size data. (Supplement to Technical Note entitled: Hydraulic conductivity prediction for sandy soils published in Groundwater 42(3):459–464, 2004). http://coen.boisestate.edu/datedmaterial/images/LITERATURE_REVIEW-final.pdf

  13. ASTM (1990) Sec. 4, Vol. 04.08, soil and rock; dimension stone; geosynthetics. American Society for Testing and Materials, Designation, pp D 421, D 422

  14. ASTM (2006) Standard test method for permeability for granular soils. American Society for Testing and Materials, Designation, pp D 4234–D 4268

  15. Jin Y, Jiang J (1999) Techniques in neural-network based fuzzy system identification and their application to control of complex systems. In: Leondes CT (ed) Fuzzy theory systems, techniques and applications. Academic Press, New York, pp 112–128

    Google Scholar 

  16. Kaynar O, Yilmaz I, Demirkoparan F (2011) Forecasting of natural gas consumption with neural network and neuro fuzzy system. Energy Educ Sci Technol A Energy Sci Res 26:221–238

    Google Scholar 

  17. Singh TN, Kanchan R, Verma AK, Singh S (2003) An intelligent approach for prediction of triaxial properties using unconfined uniaxial strength. Min Eng J 5:12–16

    Google Scholar 

  18. Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput Geosci 35(6):1125–1138

    Article  Google Scholar 

  19. Yilmaz I (2009) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4):821–836

    Article  Google Scholar 

  20. Rumelhart D, McClelland J (1986) Parallel distributed processing: explorations in the microstructure of cognition. Bradford books, MIT Press, Cambridge

    Google Scholar 

  21. Yilmaz I (2010) The effect of the sampling strategies on the landslide susceptibility mapping by Conditional Probability (CP) and Artificial Neural Networks (ANN). Environ Earth Sci 60(3):505–519

    Article  Google Scholar 

  22. Park J, Sandberg IW (1993) Approximation and radial basis function networks. Neural Comput 5:305–316

    Article  Google Scholar 

  23. Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355

    MathSciNet  MATH  Google Scholar 

  24. Moody J, Darken CJ (1989) Fast learning in networks of locally-tunes processing units. Neural Comput 1(2):281–294

    Article  Google Scholar 

  25. Park J, Sandberg IW (1991) Universal approximations using radial-basis-function network. Neural Comput 3(2):246–257

    Article  Google Scholar 

  26. Xu K, Xie M, Tang LC, Ho SL (2003) Application of neural networks in forecasting engine systems reliability. Appl Soft Comput 2:255–268

    Article  Google Scholar 

  27. Chen S, Cowan CFN, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2(2):302–309

    Article  Google Scholar 

  28. Bianchini M, Frasconi P, Gori M (1995) Learning without local minima in radial basis function networks. IEEE Trans Neural Netw 6(3):749–755

    Article  Google Scholar 

  29. Sheta AF, Jong KD (2001) Time-series forecasting using GA-tuned radial basis function. Inf Sci 133:221–228

    Article  MATH  Google Scholar 

  30. Foody GM (2004) Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes. Int J Remote Sens 25(15):3091–3104

    Article  Google Scholar 

  31. Rivas VM, Merelo JJ, Castillo PA, Arenas MG, Castellano JG (2004) Evolving RBF neural networks for time-series forecasting with EvRBF. Inf Sci 165:207–220

    Article  MathSciNet  Google Scholar 

  32. Harpham C, Dawson CW (2006) The effect of different basis functions on a radial basis function network for time series prediction: a comparative study. Neurocomputing 69:2161–2170

    Article  Google Scholar 

  33. Sarimveis H, Doganis P, Alexandridis AA (2006) Classification technique based on radial basis function neural networks. Adv Eng Softw 37:218–221

    Article  Google Scholar 

  34. Zhang R, Huang G, Sundararajan N, Saratchandran P (2007) Improved GAP-RBF network for classification problems. Neurocomputing 70:3011–3018

    Article  Google Scholar 

  35. Yu L, Lai KK, Wang S (2008) Multistage RBF neural network ensemble learning for exchange rates forecasting. Neurocomputing 71:3295–3302

    Article  Google Scholar 

  36. Matlab 7.1 (2005) Software for technical computing and model-based design. The MathWorks Inc.

  37. Alvarez GM, Babuska R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36:339–349

    Article  Google Scholar 

  38. Finol J, Guo YK, Jing XD (2001) A rule based fuzzy model for the prediction of petrophysical rock parameters. L Petr Sci Eng 29:97–113

    Article  Google Scholar 

  39. Yilmaz I, Yüksek AG (2008) An example of artificial neural network application for indirect estimation of rock parameters. Rock Mech Rock Eng 41:781–795

    Article  Google Scholar 

  40. Yilmaz I, Yüksek AG (2009) Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, ANFIS models and their comparison. Int J Rock Mech Min Sci 46(4):803–810

    Article  Google Scholar 

  41. Jang JSR, Chuen-Tsai S (1995) Neuro-fuzzy modeling and control. Proc IEEE 83:378–406

    Article  Google Scholar 

  42. Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Trans Syst Man Cybern 20:404–418

    Article  MATH  Google Scholar 

  43. Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller. II. IEEE Trans Syst Man Cybern 20:419–435

    Article  MATH  Google Scholar 

  44. Jang JR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  45. Loukas YL (2001) Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor for improved QSAR studies. J Med Chem 44:2772–2783

    Article  Google Scholar 

  46. SPSS 10.0.1 (1999) Statistical analysis software (Standard Version). SPSS Inc.

Download references

Acknowledgments

Authors thank anonymous reviewer for very constructive critics and suggestions that led to the improvement of the article.

Author information

Authors and Affiliations

  1. Faculty of Engineering, Department of Geological Engineering, Cumhuriyet University, 58140, Sivas, Turkey

    Işık Yilmaz

  2. Faculty of Mining and Geology, Institute of Geological Engineering, VŠB-Technical University of Ostrava, 17. listopadu 15, 708 33, Ostrava, Czech Republic

    Marian Marschalko & Lucie Fojtova

  3. Faculty of Natural Sciences, Department of Engineering Geology, Comenius University, Mlynská dolina, 842 15, Bratislava, Slovak Republic

    Martin Bednarik

  4. Department of Management Information System, Cumhuriyet University, 58140, Sivas, Turkey

    Oğuz Kaynar

Authors
  1. Işık Yilmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marian Marschalko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin Bednarik
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Oğuz Kaynar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lucie Fojtova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Işık Yilmaz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yilmaz, I., Marschalko, M., Bednarik, M. et al. Neural computing models for prediction of permeability coefficient of coarse-grained soils. Neural Comput & Applic 21, 957–968 (2012). https://doi.org/10.1007/s00521-011-0535-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0535-4

Keywords

Search

Navigation