Skip to main content

Advertisement

Springer Nature Link
Log in

Constitutive modeling of Leighton Buzzard Sands using genetic programming

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

Abstract

This paper investigates the results of laboratory experiments and numerical simulations conducted to examine the behavior of mixtures composed of coarse (i.e. Leighton Buzzard Sand fraction B) and fine (i.e. Leighton Buzzard Sand fraction E) sand particles. Emphasis was placed on assessing the role of fines content in mixture and strain level on the deviatoric stress and pore water pressure generation using experimental (i.e. Triaxial testing) and numerical approaches (i.e. genetic programming, GP). The experimental database used for GP modeling is based on a laboratory study of the properties of saturated coarse and fine sand mixtures with various mix ratios under a 100 kPa effective stresses in a 100 mm diameter conventional triaxial testing apparatus. Experimental results show that coarse–fine sand mixtures exhibit clay-like behavior due to particle–particle effects with the increase in fines content. It is shown that GP modeling of coarse–fine sand mixtures is observed to be quite satisfactory. The results have implications in the design of compressible particulate systems and in the development of prediction tools for the field performance coarse–fine sands.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Georgiannou VN, Burland JB, Hight DW (1990) The undrained behaviour of clayey sands in triaxial compression and extension. Géotechnique 40(3):431–449

    Article  Google Scholar 

  2. Salgado R, Bandini P, Karim A (2000) Shear strength and stiffness of silty sand. J Geotech Geoenviron Eng ASCE 126(5):451–462

    Article  Google Scholar 

  3. Naeini SA, Baziar MH (2004) Effect of fines content on steady-state strength of mixed and layered samples of a sand. Soil Dyn Earthq Eng 24:181–187

    Article  Google Scholar 

  4. Terzaghi K (1925) Erdbaumechanik auf bodenphysikalischer grundlage. Deuticke, Leipzig/Vienna

    MATH  Google Scholar 

  5. Gilboy G (1928) The compressibility of sand-mica mixtures. Proc ASCE 2:555–568

    Google Scholar 

  6. McCarthy DF, Leonard RJ (1963) Compaction and compression characteristics of micaceous fine sands and silts. Highway Research Record 22. Transportation Research Board, Washington, DC, pp 23–37

    Google Scholar 

  7. Olson RE, Mesri G (1970) Mechanisms controlling the compressibility of clay. J Soil Mech Found Div, ASCE, 96, No. SM6, Proceedings paper 7649, November, 1863–1878

  8. Mundegar AK (1997) An investigation into the effects of platy mica particles on the behaviour of sand. M.Sc. thesis, Imperial College, London

  9. Hight DW, Georgiannou VN, Martin PL, Mundegar AK (1998) Flow slides in micaceous sand. Problematic soils. In: Yanagisawa E, Moroto N, Mitachi T (eds) Balkema, Rotterdam, Sendai, Japan, pp 945–958

  10. Theron M (2004) The effect of particle shape on the behaviour of gold tailings. Ph.D. thesis, University of Southampton, UK

  11. Lade PV, Liggio CD Jr, Yamamuro JA (1998) Effects of nonplastic fines on minimum and maximum void ratios of sand. Geotech Test J ASTM 21(4):336–347

    Google Scholar 

  12. Monkul MM, Ozden G (2004) Intergranular and interfine void ratio concepts. In: 10th Turkish congress on soil mechanics and foundation engineering (ZM10), TNCSMFE, Istanbul, pp 3–12 (in Turkish)

  13. Sidarta DE (2000) Neural network-based constitutive modeling of granular material, Ph.D. thesis, University of Illinois at Urbana-Champaign

  14. Yagawa C, Okuda H (1996) Neural networks in computational mechanics. Arch Comp Meth Eng 4:435–512

    Article  Google Scholar 

  15. Waszczyszyn Z (1998) Some new results in applications of backpropagation neural networks in structural and civil engineering. Advances in engineering computational technology. Civil-Comp Press, Edinburgh, pp 173–187

    Google Scholar 

  16. Kortesis S, Panagiotopoulos PD (1993) Neural networks for computing in structural analysis: methods and prospects of applications. Int J Numer Methods Eng 36:2305–2318

    Article  MATH  Google Scholar 

  17. Waszczyszyn Z (2000) Neural networks in plasticity: some new results and prospects of applications. In: European congress on computational methods in applied sciences and engineering, ECCOMAS

  18. Wu X (1991) Neural network-based material modeling. Ph.D. thesis. Department of Civil Engineering, University of Illinois at Urbana-Champaign

  19. Basheer IA (1998) Neuromechanistic-based modeling and simulation of constitutive behaviour of fine-grained soils, Ph.D. thesis, Kansas State University

  20. Ghaboussi J, Garret JH Jr, Wu X (1990) Material modeling with neural networks. In: Proceedings of the ınternational conference on numerical methods in engineering: theory and applications, Swansea, UK, pp 701–717

  21. Ghaboussi J, Garret JH Jr, Wu X (1991) Knowledge-based-modeling of material behavior with neural networks. J Eng Mech ASCE 117(1):132–153

    Article  Google Scholar 

  22. Penumadu D, Jin-Nan L, Chameau J-L, Sandarajah A (1994) Anisotropic rate dependent behavior of clays using neural networks. In: Proceedings of XIIIICSMFE, ICSMFE, New Delhi, India, 4, pp 1445–1448

  23. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  24. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning reading. Addison-Wesley, MA

    Google Scholar 

  25. Haupt RL, Haupt SE (2004) Practical genetic algorithms, 2nd edn. Wiley-Interscience, New York

    MATH  Google Scholar 

  26. Chambers L (2001) The practical handbook of genetic algorithms applications, 2nd edn. Chapman & Hall/CRC, New York

    Google Scholar 

  27. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  28. GeneXpro Tools (2009) Discover the excellent of modelling with GeneXpro Tools 4.0, the best and most intutive modeling software in the market. For more information http://www.gepsoft.com/. Accessed 27 Oct 2009

  29. Ferreira C (2001) Gene expression programming in problem solving. In: Invited tutorial of the 6th online world conference on soft computing in ındustrial applications, September 10–24

  30. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    MATH  Google Scholar 

  31. Ferreira C (2002) Gene expression programming: mathematical modelling by an artificial ıntelligence. Angra do Heroismo, Portugal

    Google Scholar 

  32. Poli R, Langdon WB, Mcphee NF (2008) A field guide to genetic programming. Lulu.com, freely available from the internet. ISBN 978-1-4092-0073-4

Download references

Acknowledgments

The authors would like to thank Prof. C.R.I. Clayton of the University of Southampton. The first writer held a UK Overseas Research Students Awards Scheme (ORSAS) and a Ph.D. Scholarship from the University of Southampton. This study was also supported by Gaziantep University Scientific Research Projects Unit.

Author information

Authors and Affiliations

  1. Geotechnical Engineering Division, Department of Civil Engineering, University of Gaziantep, Gaziantep, Turkey

    Ali Firat Cabalar

  2. Department of Civil Engineering, University of Gaziantep, Gaziantep, Turkey

    Abdulkadir Cevik

  3. Department of Mechanical Engineering, University of Gaziantep, Gaziantep, Turkey

    Ibrahim Halil Guzelbey

Authors
  1. Ali Firat Cabalar
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Abdulkadir Cevik
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ibrahim Halil Guzelbey
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ali Firat Cabalar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cabalar, A.F., Cevik, A. & Guzelbey, I.H. Constitutive modeling of Leighton Buzzard Sands using genetic programming. Neural Comput & Applic 19, 657–665 (2010). https://doi.org/10.1007/s00521-009-0317-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-009-0317-4

Keywords