Skip to main content

Advertisement

SpringerLink
Log in

Formulation of soil angle of shearing resistance using a hybrid GP and OLS method

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

In the present study, a prediction model was derived for the effective angle of shearing resistance (ϕ′) of soils using a novel hybrid method coupling genetic programming (GP) and orthogonal least squares algorithm (OLS). The proposed nonlinear model relates ϕ′ to the basic soil physical properties. A comprehensive experimental database of consolidated-drained triaxial tests was used to develop the model. Traditional GP and least square regression analyses were performed to benchmark the GP/OLS model against classical approaches. Validity of the model was verified using a part of laboratory data that were not involved in the calibration process. The statistical measures of correlation coefficient, root mean squared error, and mean absolute percent error were used to evaluate the performance of the models. Sensitivity and parametric analyses were conducted and discussed. The GP/OLS-based formula precisely estimates the ϕ′ values for a number of soil samples. The proposed model provides a better prediction performance than the traditional GP and regression models.

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
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Mollahasani A, Alavi AH, Gandomi AH, Rashed A (2011) Nonlinear neural-based modeling of soil cohesion intercept. KSCE J Civil Eng 15(5):831–840

    Article  Google Scholar 

  2. Arora KR (1988) Introductory soil engineering. text book, 322, pub.: Nem Chand Jane (Prop.), Standard Publishers Distributors, 1705-NaiSarak, Delhi

  3. Murthy S (2008) Geotechnical engineering: principles and practices of soil mechanics, 2008, 2nd edn. CRC Press/Taylor & Francis, UK

    Google Scholar 

  4. ASTM WK3821 New test method for consolidated drained triaxial compression test for soils

  5. ASTM D 6528 Consolidated undrained direct simple shear testing of cohesive soils

  6. El-Maksoud MAF (2006) Laboratory determining of soil strength parameters in calcareous soils and their effect on chiseling draft prediction. In: Proceedings of energy efficiency and agricultural engineering international conference, vol 9, Rousse, Bulgaria

  7. Bowles JE (1992) Engineering properties of soils and their measurement, 4th edn. McGraw-Hill, New York

    Google Scholar 

  8. Korayem AY, Ismail KM, Sehari SQ (1996) Prediction of soil shear strength and penetration resistance using some soil properties. Missouri J Agric Res 13(4):119–140

    Google Scholar 

  9. Panwar JS, Seimens JC (1972) Shear strength and energy of soil failure related to density and moisture. T ASAE 15:423–427

    Google Scholar 

  10. Terzaghi K, Peck RB, Mesri G (1996) Soil mechanics in engineering practice, 2nd edn. Wiley, New York

    Google Scholar 

  11. Shahin MA, Maier HR, Jaksa MB (2001) Artificial neural network applications in geotechnical engineering. Aust Geomech 36(1):49–62

    Google Scholar 

  12. Alavi AH, Gandomi AH, Mollahasani A, Heshmati AAR (2010) Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. J Plant Nutr Soil Sci 173(3):368–379

    Article  Google Scholar 

  13. Heshmati AAR, Alavi AH, Keramati M, Gandomi AH (2009) A radial basis function neural network approach for compressive strength prediction of stabilized soil. Geotech Spec Pub ASCE 191:147–153

    Google Scholar 

  14. Alavi AH, Ameri M, Gandomi AH, Mirzahosseini MR (2011) Formulation of flow number of asphalt mixes using a hybrid computational method. Constr Build Mater 25(3):1338–1355

    Article  Google Scholar 

  15. Koza J (1992) Genetic programming, on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  16. Banzhaf W, Nordin P, Keller R, Francone F (1998) Genetic programming—an introduction: on the automatic evolution of computer programs and its application. dpunkt/Morgan Kaufmann, Heidelberg/San Francisco

    Google Scholar 

  17. Johari A, Habibagahi G, Ghahramani (2006) A prediction of soil–water characteristic curve using genetic programming. J Geotech Geoenviron ASCE 132(5):661–665

    Article  Google Scholar 

  18. Gandomi AH, Alavi AH, Sahab MG, Arjmandi P (2010) Formulation of elastic modulus of concrete using linear genetic programming. J Mech Sci Tech 24(6):1011–1017

    Article  Google Scholar 

  19. Gandomi AH, Alavi AH, Sahab MG (2009) New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming. Mater Struct 43(7):963–983

    Article  Google Scholar 

  20. Alavi AH, Gandomi AH, Sahab MG, Gandomi M (2010) Multi expression programming: a new approach to formulation of soil classification. Eng Comput 26(2):111–118

    Article  Google Scholar 

  21. Kayadelen C, Günaydın O, Fener M, Demir A, Özvan A (2009) A modeling of the angle of shearing resistance of soils using soft computing systems. Expert Syst Appl 36:11814–11826

    Article  Google Scholar 

  22. Billings S, Korenberg M, Chen S (1988) Identification of nonlinear output-affine systems using an orthogonal least-squares algorithm. Int J Syst Sci 19(8):1559–1568

    Article  MathSciNet  MATH  Google Scholar 

  23. Chen S, Billings S, Luo W (1989) Orthogonal least squares methods and their application to non-linear system identification. Int J Control 50(5):1873–1896

    Article  MathSciNet  MATH  Google Scholar 

  24. Gandomi AH, Alavi AH, Mousavi M, Tabatabaei SM (2011) A hybrid computational approach to derive new ground-motion attenuation models. Eng Appl Artif Int 24(4):717–732

    Article  Google Scholar 

  25. Gandomi AH, Alavi AH, Arjmandi P, Aghaeifar A, Seyednoor M (2010) Genetic programming and orthogonal least squares: a hybrid approach to modeling the compressive strength of CFRP-confined concrete cylinders. J Mech Mater Struct 5(5):735–753

    Article  Google Scholar 

  26. Madar J, Abonyi J, Szeifert F (2005) Genetic programming for the identification of nonlinear input-output models. Ind Eng Chem Res 44(9):3178–3186

    Article  Google Scholar 

  27. Pearson R (2003) Selecting nonlinear model structures for computer control. J Process Control 13(1):1–26

    Article  Google Scholar 

  28. Reeves CR (1997) Genetic algorithm for the operations research. INFORMS J Comput 9:231–250

    Article  MATH  Google Scholar 

  29. Madár J, Abonyi J, Szeifert F (2004) Genetic programming for system identification. In: Proceedings of Intelligent Systems Design and Applications (ISDA 2004) Conference, Budapest

  30. Cao H, Yu J, Kang L, Chen Y (1999) The kinetic evolutionary modelling of complex systems of chemical reactions. Comput Chem Eng 23(1):143–151

    Google Scholar 

  31. Barends FBJ, Lindenberg JL, DeQuelerij L, Verruijt A, Luger HJ (1999) Geotechnical engineering for transportation infrastructure: theory and practice, planning and design, construction and maintenance. AA Balkema Publishers, Rotterdam

    Google Scholar 

  32. ASTM D 1587 Standard practice for thin-walled tube sampling of soils for geotechnical purposes

  33. Kayadelen C (2008) Estimation of effective stress parameter of unsaturated soils by using artificial neural networks. Int J Numer Anal Meth Geomech 32:1087–1106

    Article  Google Scholar 

  34. Dunlop P, Smith S (2003) Estimating key characteristics of the concrete delivery and placement process using linear regression analysis. Civil Eng Environ Syst 20:273–290

    Article  Google Scholar 

  35. Swingler K (1996) Applying neural networks a practical guide. Academic Press, New York

    Google Scholar 

  36. Rafiq MY, Bugmann G, Easterbrook DJ (2001) Neural network design for engineering applications. Comput Struct 79(17):1541–1552

    Article  Google Scholar 

  37. Madár J, Abonyi J, Szeifert F (2005b) Genetic programming for the identification of nonlinear input-output models, white paper. Retrieved September 05, 2009, from http://www.fmt.vein.hu/softcomp/gp/ie049626e.pdf

  38. Silva S (2007) GPLAB, a genetic programming toolbox for MATLAB, ITQB/UNL[M]. http://gplab.sourceforge.net

  39. Ryan TP (1997) Modern regression methods. Wiley, New York

    MATH  Google Scholar 

  40. Maravall A, Gomez V (2004) EViews Software, Ver. 5. Quantitative Micro Software, LLC, Irvine

    Google Scholar 

  41. Smith GN (1986) Probability and statistics in civil engineering. Collins, London

    Google Scholar 

  42. Mollahasani A, Alavi AH, Gandomi AH (2011) Empirical modeling of plate load test moduli of soil via gene expression programming. Comput Geotech 38(2):281–286

    Article  Google Scholar 

  43. Frank IE, Todeschini R (1994) The data analysis handbook. Elsevier, Amsterdam

    Google Scholar 

  44. Golbraikh A, Tropsha A (2002) Beware of q2. J Mol Graph Model 20:269–276

    Article  Google Scholar 

  45. Roy PP, Roy K (2008) On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci 27:302–313

    Article  Google Scholar 

  46. Francone F (2001) Discipulus owner’s manual, Version 4.0. Register Machine Learning Technologies, Littleton

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Sharif University of Technology, Tehran, Iran

    Seyyed Mohammad Mousavi

  2. School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Amir Hossein Alavi

  3. Department of Civil Engineering, University of Akron, Akron, OH, 44325-3905, USA

    Amir Hossein Gandomi

  4. Department of Civil, Environmental and Material Engineering (DICAM), University of Bologna, Bologna, Italy

    Ali Mollahasani

  5. Department of Civil Engineering, Islamic Azad University, Shahrood Branch, Shahrood, Iran

    Milad Arab Esmaeili

Authors
  1. Seyyed Mohammad Mousavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Hossein Alavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Mollahasani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amir Hossein Gandomi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Milad Arab Esmaeili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Milad Arab Esmaeili.

Appendix A

Appendix A

See Table 6.

Table 6 Geotechnical properties of soils used for the model development
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mousavi, S.M., Alavi, A.H., Mollahasani, A. et al. Formulation of soil angle of shearing resistance using a hybrid GP and OLS method. Engineering with Computers 29, 37–53 (2013). https://doi.org/10.1007/s00366-011-0242-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-011-0242-x

Keywords

Search

Navigation