Engineering with Computers

, Volume 26, Issue 2, pp 111–118 | Cite as

Multi expression programming: a new approach to formulation of soil classification

  • Amir Hossein Alavi
  • Amir Hossein Gandomi
  • Mohammad Ghasem Sahab
  • Mostafa Gandomi
Original Article

Abstract

This paper presents an alternative approach to formulation of soil classification by means of a promising variant of genetic programming (GP), namely multi expression programming (MEP). Properties of soil, namely plastic limit, liquid limit, color of soil, percentages of gravel, sand, and fine-grained particles are used as input variables to predict the classification of soils. The models are developed using a reliable database obtained from the previously published literature. The results demonstrate that the MEP-based formulas are able to predict the target values to high degree of accuracy. The MEP-based formulation results are found to be more accurate compared with numerical and analytical results obtained by other researchers.

Keywords

Multi expression programming Soil classification Formulation 

References

  1. 1.
    Mostashari M, Muazardalan M, Karimian N, Hosseini HM, Rezai H (2008) Phosphorus fractions of selected calcareous soils of Qazvin province and their relationships with soil characteristics. Am Eurasian J Agric Environ Sci 3(4):547–553Google Scholar
  2. 2.
    Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, CambridgeMATHGoogle Scholar
  3. 3.
    Banzhaf W, Nordin P, Keller R, Francone F (1998) Genetic programming–an introduction: on the automatic evolution of computer programs and its application. Morgan Kaufmann, HeidelbergGoogle Scholar
  4. 4.
    Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, USAMATHGoogle Scholar
  5. 5.
    Oltean M, Dumitrescu D (2002) Multi expression programming. Technical report, UBB-01-2002. Babeş-Bolyai University, Cluj-Napoca, RomaniaGoogle Scholar
  6. 6.
    Oltean M, Grosşan C (2003) A comparison of several linear genetic programming techniques. Complex Syst 14(4):1–29MathSciNetGoogle Scholar
  7. 7.
    Baykasoglu A, Gullub H, Canakcı H, Ozbakır L (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35(1–2):111–123CrossRefGoogle Scholar
  8. 8.
    Alavi AH, Gandomi AH, Gandomi M, Sahab MG (2008) A data mining approach to compressive strength of CFRP confined concrete cylinders (submitted)Google Scholar
  9. 9.
    Alavi AH, Gandomi AH (2009) Energy-based numerical correlations for soil liquefaction assessment. Comput Geotech (in press)Google Scholar
  10. 10.
    Schwefel HP, Wegener I, Weinert K (2002) Advances in computational intelligence–theory and practice. Springer, BerlinGoogle Scholar
  11. 11.
    Levine ER, Kimes DS, Sillito VG (1996) Classifying soil structure using neural networks. Ecol Modell 92(1):101–108CrossRefGoogle Scholar
  12. 12.
    Suresh D (2000) Application of neural networks to civil engineering problems. M.E. Thesis, Bharathiar University, Coimbatore, IndiaGoogle Scholar
  13. 13.
    Rajasekaran S, Amalraj R (2002) Predictions of design parameters in civil engineering problems using SLNN with a single hidden RBF neuron. Comput Struct 80(31):2495–2505CrossRefGoogle Scholar
  14. 14.
    Zhang J, Morris AJ (1988) A sequential learning approach for single hidden layer neural networks. Neural Netw 11(1):65–80CrossRefGoogle Scholar
  15. 15.
    Alavi AH, Heshmati AAR, Gandomi AH, Askarinejad A, Mirjalili M (2008) Utilisation of computational intelligence techniques for stabilised soil. In: Papadrakakis M, Topping BHV (eds) Proceedings of the sixth international conference on engineering computational technology, Civil-Comp Press, Scotland, paper no. 175Google Scholar
  16. 16.
    Alavi AH, Heshmati AAR, Salehzadeh H, Gandomi AH, Askarinejad A (2008) Soft computing based approaches for high performance concrete. In: Papadrakakis M, Topping BHV (eds) Proceedings of the sixth international conference on engineering computational technology, Civil-Comp Press, Scotland, paper no. 86Google Scholar
  17. 17.
    Gandomi AH, Sahab MG, Alavi AH, Heshmati AAR, Gandomi M, Arjmandi P (2008) Application of a coupled simulated annealing-genetic programming algorithm to the prediction of bolted joints behavior. Am Eurasian J Sci Res 3(2):153–162Google Scholar
  18. 18.
    Gandomi AH, Alavi AH, Kazemi S, Alinia MM (2009) Behavior appraisal of steel semi-rigid joints using linear genetic programming. J Constr Steel Res 65:1738–1750CrossRefGoogle Scholar
  19. 19.
    Cevik A, Cabalar AF (2008) Modelling damping ratio and shear modulus of sand–mica mixtures using genetic programming. Expert Syst Appl 36(4):7749–7757CrossRefGoogle Scholar
  20. 20.
    Oltean M, Grosşan C (2003) Evolving evolutionary algorithms using multi expression programming. In: Banzhaf W et al (eds) The seventh European conference on artificial life, vol 2801, 14–17 September, LNAI, Springer-Verlag, Dortmund, pp 651–658Google Scholar
  21. 21.
    Aho A, Sethi R, Ullman J (1986) Compilers: principles, techniques and tools. Addison-Wesley, ReadingGoogle Scholar
  22. 22.
    IS: 2132 (1986) (Reaffirmed 1997) Indian standard methods of test for soils, code of practice for thin walled tube sampling of soils. Bureau of Indian Standards, New DelhiGoogle Scholar
  23. 23.
    IS: 2720 (1985) (Reaffirmed 1995) Indian standard methods of test for soils. Part 5. Determination of liquid and plastic limit. Bureau of Indian Standards, New DelhiGoogle Scholar
  24. 24.
    IS: 2720 (1985) (Reaffirmed 1995) Indian standard methods of test for soils. Part 4. Grain size analysis. Bureau of Indian Standards, New DelhiGoogle Scholar
  25. 25.
    IS: 1498 (1970) (Reaffirmed 2002) Indian standard methods of test for soils. Bureau of Indian Standards, New DelhiGoogle Scholar
  26. 26.
    Oltean M (2004) Multi expression programming source code. http://www.mep.cs.ubbcluj.ro

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Amir Hossein Alavi
    • 1
  • Amir Hossein Gandomi
    • 2
  • Mohammad Ghasem Sahab
    • 3
  • Mostafa Gandomi
    • 4
  1. 1.College of Civil EngineeringIran University of Science & TechnologyTehranIran
  2. 2.The Highest Prestige Scientific and Professional National Foundation, National Elites FoundationTehranIran
  3. 3.College of Civil EngineeringTafresh UniversityTafreshIran
  4. 4.School of Civil Engineering, College of EngineeringUniversity of TehranTehranIran

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