Skip to main content
SpringerLink
Log in

Estimating compaction parameters of fine- and coarse-grained soils by means of artificial neural networks

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

This paper presents artificial neural network (ANN) prediction models for estimating the compaction parameters of both coarse- and fine-grained soils. A total number of 200 soil mixtures were prepared and compacted at standard Proctor energy. The compaction parameters were predicted by means of ANN models using different input data sets. The ANN prediction models were developed to find out which of the index properties correlate well with compaction parameters. In this respect, the transition fine content ratio (TFR) was defined as a new input parameter in addition to traditional soil index parameters (i.e. liquid limit, plastic limit, passing No. 4 sieve and passing No. 200 sieve). Highly nonlinear nature of the compaction data dictated development of two separate ANN models for maximum dry unit weight (γdmax) and optimum water content (ωopt). It was found that generalization capability and prediction accuracy of ANN models could be further enhanced by sub-clustered data division techniques.

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

Similar content being viewed by others

References

  • Abdel-Rahman AH (2008) Predicting compaction of cohesionless soils using ANN. In: Proceedings of the institution of civil engineers ground improvement, vol 161, pp 3–8

  • Arvelo Guerrero AM (2004) Effects of the soil properties on the maximum dry density obtained from the standard Proctor test. MS thesis, University of Central Florida, Orlando

  • Ashayeri I, Yasrebi S (2009) Free-swell and swelling pressure of unsaturated compacted clays; experiments and neural networks modeling. Geotech Geol Eng 27(1):137–153. doi:10.1007/s10706-008-9219-y

    Article  Google Scholar 

  • Basheer IA (2001) Empirical modeling of the compaction curve of cohesive soils. Can Geotech J 38:29–45

    Article  Google Scholar 

  • Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    Book  Google Scholar 

  • Blotz LR, Benson CH, Boutwell GP (1998) Estimating optimum water content and maximum dry unit weight for compacted clay. J Geotech Geoenviron Eng 124(9):907–912

    Article  Google Scholar 

  • Chan WT, Chow YK, Liu LF (1995) Neural networks: an alternative to pile driving formulas. Comput Geotech 17:135–156

    Article  Google Scholar 

  • Das SK, Samui P, Sabat AK, Sitharam TG (2010) Prediction of swelling pressure of soil using artificial intelligence techniques. Environ Earth Sci 61:393–403

    Article  Google Scholar 

  • Dulyakarn P, Rangsanseri Y (2001) Fuzzy c-means clustering using spatial information with application to remote sensing. In: 22nd Asian conference on remote sensing, 5–9 November, Singapore

  • Ellis GW, Yao C, Zhao R, Penumadu D (1995) Stress-strain modeling of sands using artificial neural networks. J Geotech Eng 121:429–435

    Article  Google Scholar 

  • Gunaydin O (2009) Estimation of soil compaction parameters by using statistical analyses and artificial neural networks. Environ Geol 57:203–215. doi:10.1007/s00254-008-1300-6

    Article  Google Scholar 

  • Gurtug Y, Sridharan A (2002) Prediction of compaction characteristics of fine-grained soils. Geotechnique 52(10):761–763

    Article  Google Scholar 

  • Gurtug Y, Sridharan A (2004) Compaction behavior and prediction of its characteristics of fine grained soils with particular references to compaction energy. Soil Foundations 44(5):27–36

    Article  Google Scholar 

  • Hausmann MR (1990) Engineering principals of ground modification. McGraw-Hill Book Co, New York

  • Hortibulsuk S, Katkan W, Naramitkornburee A (2008) Modified Ohio’s curves: a rapid estimation of compaction curves for coarse- and fine-grained soils. Geotech Test J 32(1):1–12

    Google Scholar 

  • Howell JL, Shackelford CD, Amer NH, Stern RT (1997) Compaction of sand-processed clay soil mixtures. Geotech Test J 20(4):443–458

    Article  Google Scholar 

  • Juang CH, Chen CJ (1999) CPT-based liquefaction evaluation using artificial neural networks. Comput Aided Civil Infrastruct Eng 14(3):221–229

    Article  Google Scholar 

  • Juang CH, Ni SH, Lu PC (1999) Training artificial neural networks with the aid of fuzzy sets. Comput Aided Civil Infrastruct Eng 14(2):407–415

    Article  Google Scholar 

  • Kaunda RB, Chase RB (2010) Neural network modeling applications in active slope stability problems. Environ Earth Sci 60:1545–1558

    Article  Google Scholar 

  • Kiefa MAA (1998) General regression neural networks for driven piles in cohesionless soils. Geotech Geoenviron Eng 124(12):1177–1185

    Article  Google Scholar 

  • Korfiatis GP, Manikopoulos CN (1982) Correlation of maximum dry density and grain size. J Geotech Eng Div 108(GT9):1171–1176

    Google Scholar 

  • Kutlu Y, Kuntalp M, Kunalp D (2008) Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes. Expert Syst Appl. doi:10.1016/j.eswa.2008.09.052

    Google Scholar 

  • Lee IM, Lee JH (1996) Prediction of pile bearing capacity using artificial neural networks. Comput Geotech 18(3):189–200

    Article  Google Scholar 

  • Matlab (2009) The language of technical learning. Matworks Version R2009b

  • Monkul MM, Ozden G (2007) Compressional behavior clayey sand and transition fines content. Eng Geol 89:195–205

    Article  Google Scholar 

  • Nagaraj TS, Lutenegger AJ, Pandian NS, Manoj M (2006) Rapid estimation of compaction parameters for field control. Geotech Test J 29(6):497–506

    Google Scholar 

  • Najjar YM, Basheer IA (1996) Utilizing computational neural networks for evaluating the permeability of compacted clay liners. Geotech Geol Eng 14:193–212

    Google Scholar 

  • Najjar YM, Basheer IA, Naouss WA (1996) On the identification of compaction characteristics by neuronets. Comput Geotech 18(3):167–187

    Article  Google Scholar 

  • Nejad FP, Jaksa MB, Kakhi M, McCabe BA (2009) Prediction of pile settlement using artificial neural networks based on standard penetration test data. Comput Geotech 36:1125–1133

    Article  Google Scholar 

  • Olmez A (2007) Determination of compaction parameters by means of regression approaches. MS thesis, Niğde University

  • Omar M, Shanableh A, Basma A, Barakat S (2003) Compaction characteristics of granular soils in United Arab Emirates. Geotech Geol Eng 21:283–295

    Article  Google Scholar 

  • Pandian NS, Nagaraj TS, Manoj M (1998) Re-examination of compaction characteristics of fine-grained soils. Geotechnique 47(2):363–366

    Google Scholar 

  • Pradeep U, Kurup M, Nitin KD (2002) Neural networks for profiling stress history of clays from PCPT data. J Geotech Geoenviron 128(7):569

    Article  Google Scholar 

  • Shahin MA, Maier HR, Jaksa MB (2002) Predicting settlement of shallow foundations using neural networks. J Geotech Geoenviron Eng 128(9):785

    Article  Google Scholar 

  • Shahin MA, Maier HR, Jaksa MB (2004) Data division for developing neural networks applied to geotechnical engineering. J Comput Civil Eng 18(2):105–114

    Article  Google Scholar 

  • Shahin MA, Maier HR, Jaksa MB (2005) Investigation into the robustness of artificial neural networks for a case study in civil engineering. In: International congress on modeling and simulation, MODSIM 2005, Melbourne, pp 79–83

  • Sinha SK, Wang MC (2008) Artificial neural network prediction models for soil compaction and permeability. Geotech Geol Eng 26:47–64

    Article  Google Scholar 

  • Sivrikaya A (2008) Models of compacted fine-grained soils used as mineral liner for solid waste. Environ Geol 53:1585–1595

    Article  Google Scholar 

  • Sivrikaya A, Togrol E, Kayadelen C (2008) Estimating compaction behavior of fine-grained soils based on compaction energy. Can Geotech J 45:877–887

    Article  Google Scholar 

  • Sridharan A, Nagaraj HB (2005) Plastic limit and compaction characteristics of fine-grained soils. Ground Improv 9(1):17–22

    Article  Google Scholar 

  • Thevanayagam S (1998) Effect of fines on confining stress on undrained shear strength of silty sands. J Geotech Geoenviron Eng 124(6):479–491

    Article  Google Scholar 

  • Thevanayagam S (2007a) Intergrain contact density indices for granular mixes—I: framework. Earthq Eng Eng Vib 6(2):123–134

    Article  Google Scholar 

  • Thevanayagam S (2007b) Intergrain content density indices for granular mixes—II: Liquefaction resistance. Earthq Eng Eng Vib 6(2):135–146

    Article  Google Scholar 

  • Wang MC, Huang CC (1984) Soil compaction and permeability prediction models. J Environ Eng 110(6):1063–1083

    Article  Google Scholar 

  • Wang HB, Xu WY, Xu RC (2005) Slope stability evaluation using back propagation neural networks. Eng Geol 80:302–315

    Article  Google Scholar 

  • Wang K, Wang B, Peng L (2009) CVAP: validation for cluster analyses. Data Sci J 8:88–93

    Article  Google Scholar 

  • Yukselen Y, Erzin Y (2008) Artificial neural networks approach for zeta potential of montmorillonite in the presence of different cations. Environ Geol 54:1059–1066

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Dokuz Eylul University, Kaynaklar Yerleskesi, 35160, Buca, Izmir, Turkey

    Fatih Isik & Gurkan Ozden

Authors
  1. Fatih Isik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gurkan Ozden
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fatih Isik.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Isik, F., Ozden, G. Estimating compaction parameters of fine- and coarse-grained soils by means of artificial neural networks. Environ Earth Sci 69, 2287–2297 (2013). https://doi.org/10.1007/s12665-012-2057-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-012-2057-5

Keywords

Search

Navigation