Skip to main content
SpringerLink
Log in

Nonlinear genetic-based simulation of soil shear strength parameters

  • Published:
Journal of Earth System Science Aims and scope Submit manuscript

New nonlinear solutions were developed to estimate the soil shear strength parameters utilizing linear genetic programming (LGP). The soil cohesion intercept (c) and angle of shearing resistance (ϕ) were formulated in terms of the basic soil physical properties. The best models were selected after developing and controlling several models with different combinations of influencing parameters. Comprehensive experimental database used for developing the models was established upon a series of unconsolidated, undrained, and unsaturated triaxial tests conducted in this study. Further, sensitivity and parametric analyses were carried out. c and ϕ were found to be mostly influenced by the soil unit weight and liquid limit. In order to benchmark the proposed models, a multiple least squares regression (MLSR) analysis was performed. The validity of the models was proved on portions of laboratory results that were not included in the modelling process. The developed models are able to effectively learn the complex relationship between the soil strength parameters and their contributing factors. The LGP models provide a significantly better prediction performance than the 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

Similar content being viewed by others

References

  • Alavi A H and Gandomi A H 2011a Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing; Comput. Struct., doi: 10.1016/j.compstruc. 2011.08.019.

  • Alavi A H and Gandomi A H 2011b A robust data mining approach for formulation of geotechnical engineering systems; Int. J. Comput. Aided Meth. Eng.-Eng. Computations 28(3) 242–274.

    Article  Google Scholar 

  • Alavi A H, Gandomi A H, Gandomi M and Sadat Hosseini S S 2009 Prediction of maximum dry density and optimum moisture content of stabilized soil using RBF neural networks; The IES J Part A: Civil. Struct. Eng. 2 98–106.

    Article  Google Scholar 

  • Alavi A H, Gandomi A H, Sahab M G and Gandomi M 2010 Multi expression programming: A new approach to formulation of soil classification; Eng. Comput. 26(2) 111–118.

    Article  Google Scholar 

  • Alavi A H, Ameri M, Gandomi A H and Mirzahosseini M R 2011 Formulation of flow number of asphalt mixes using a hybrid computational method; Constr. Build. Mater. 25(3) 1338–1355.

    Article  Google Scholar 

  • Arora K R 1988 Introductory soil engineering; text book. Nem Chand Jane (Prop), Standard Publishers Distributors, Nai Sarak, Delhi.

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

  • ASTM D2850-87 Standard Test Method for Unconsolidated, Undrained Compressive Strength of Cohesive Soils in Triaxial Compression.

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

  • Aytek A and Alp M 2008 An application of artificial intelligence for rainfall-runoff modeling; J. Earth Syst. Sci. 117(2) 145–155.

    Article  Google Scholar 

  • Banzhaf W, Nordin P, Keller R and 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 

  • Barends F B J, Lindenberg J L, De Quelerij L, Verruijt A and Luger H J 1999 Geotechnical Engineering for Transportation Infrastructure: Theory and Practice, Planning and Design, Construction and Maintenance; Balkema Publishers, Netherlands.

    Google Scholar 

  • Baykasoglu A, Gullub H, Canakcı H and Ozbakır L 2008 Prediction of compressive and tensile strength of limestone via genetic programming; Expert. Syst. Appl. 35(1–2) 111–123.

    Article  Google Scholar 

  • Bowles J E 1992 Engineering properties of soils and their measurement; McGraw-Hill, New York.

    Google Scholar 

  • Brameier M and Banzhaf W 2001 A comparison of linear genetic programming and neural networks in medical data mining; IEEE Trans. Evol. Com. 5(1) 17–26.

    Article  Google Scholar 

  • Brameier M and Banzhaf W 2007 Linear Genetic Programming; Springer Science + Business Media, New York.

    Google Scholar 

  • Cevik A and Cabalar A F 2009 Modelling damping ratio and shear modulus of sand–mica mixtures using genetic programming; Expert. Syst. Appl. 36(4) 7749–7757.

    Article  Google Scholar 

  • Conrads M, Dolezal O, Francone F D and Nordin P 2004 Discipulus – fast genetic programming based on AIM learning technology; Register Machine Learning Technologies Inc, Littleton, CO.

  • Deschaine L M 2000 Using Genetic Programming to Develop a C/C+ + Simulation Model of a Waste Incinerator Science Applications; International Corp, Draft Technical Report.

  • El-Maksoud M A F 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, Rousse, Bulgaria.

  • Feldt R and Nordin P 2000 Using Factorial Experiments to Evaluate the Effect of Genetic Programming Parameters; In: Proceedings of EuroGP 2000 LNCS 1802 pp. 271–282.

  • Francone F D 2001 Discipulus ProTM Software Owner’s Manual; Register Machine Learning Technologies Inc: Littleton, CO.

  • Francone F D 2004 Discipulus LiteTM Software Owner’s Manual, Littleton, CO, USA, Machine Learning Technologies Inc.

  • Frank I E and Todeschini R 1994 The data analysis handbook; Elsevier, Amsterdam, The Netherland.

    Google Scholar 

  • Gandomi A H and Alavi A H 2011 Multi-stage genetic programming: A new strategy to nonlinear system modeling; Inf. Sci. 181(23) 5227–5239.

    Article  Google Scholar 

  • Gandomi A H, Alavi A H and Sadathosseini S S 2008 A discussion on genetic programming for retrieving missing information in wave records along the west coast of India; Appl. Ocean Res. 30 338–339.

    Article  Google Scholar 

  • Gandomi A H, Alavi A H and Sahab M G 2010 New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming; Mater. Struct. 43(7) 963–983.

    Article  Google Scholar 

  • Gandomi A H, Alavi A H, Mirzahosseini R and Moqaddas Nezhad F 2011a Nonlinear genetic-based models for prediction of flow number of asphalt mixtures; J. Mater. Civil Eng. ASCE 23(3) 1–18.

    Article  Google Scholar 

  • Gandomi A H, Alavi A H and Yun G J 2011b Nonlinear modeling of shear strength of sfrcb beams using linear genetic programming; Struct. Eng. Mech. 38(1) 1–25.

    Google Scholar 

  • Guven A 2009 Linear Genetic Programming for time-series modelling of daily flow rate; J. Earth Syst. Sci. 118(2) 137–146.

    Article  Google Scholar 

  • Guven A, Gunal M and Cevik A 2006 Prediction of pressure fluctuations on stilling basins; Can. J. Civ. Eng. 33(11) 1379–1388.

    Article  Google Scholar 

  • Guven A, Aytek A, Yuce M I and Aksoy H 2007 Genetic programming-based empirical model for daily reference evapotranspiration estimation; CLEAN–Soil Air Water J. 36(10–11) 905–912.

    Google Scholar 

  • Guven A, Azamathullab H M and Zakaria N A 2009 Linear Genetic Programming for prediction of circular pile scour; Ocean Eng. 36(12–13) 985–991.

    Article  Google Scholar 

  • Heshmati A A R, Alavi A H, Keramati M and Gandomi A H 2009 A radial basis function Neural Network Approach for compressive strength prediction of stabilized soil; Geotech. Spec. Publ. ASCE 191 147–153.

    Google Scholar 

  • Javadi A A and Rezania M 2009 Applications of artificial intelligence and data mining techniques in soil modelling; Geomech. Eng. 1(1) 53–74.

    Google Scholar 

  • Kasabov N K 1998 Foundations of neural networks fuzzy systems and knowledge engineering; Cambridge, MIT Press.

    Google Scholar 

  • Kayadelen C, Günaydın O, Fener M, Demir A and Özvan A 2009 A modeling of the angle of shearing resistance of soils using soft computing systems; Expert. Syst. Appl. 36 11,814–11,826.

    Article  Google Scholar 

  • Korayem A Y, Ismail K M and Sehari S Q 1996 Prediction of soil shear strength and penetration resistance using some soil properties; Mis. J. Agr. Res. 13(4) 119–140.

    Google Scholar 

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

    Google Scholar 

  • Kraslawski A, Pedrycz W and Nyström L 1999 Fuzzy Neural Network as instance generator for case-based reasoning system: An example of selection of heat exchange equipment in mixing; Neu. Comput. Appl. 8(2) 106–113.

    Article  Google Scholar 

  • Maravall A and Gomez V 2004 Eviews Software; Version 5. Quantitative Micro Software, LLC, Irvine CA.

    Google Scholar 

  • Mesbahi E 2000 Application of artificial neural networks in modelling and control of diesel engines; PhD Thesis, University of Newcastle, UK.

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

    Article  Google Scholar 

  • Mousavi S M, Alavi A H, Gandomi A H and Mollahasani A 2011 Formulation of soil angle of shearing resistance using a hybrid GP and OLS Mehod; Eng. Comput. in press. doi: 10.1007/s00366-011-0242-x

  • Murthy S 2008 Geotechnical Engineering: Principles and Practices of Soil Mechanics; 2nd edn, Taylor & Francis, CRC Press, UK.

    Google Scholar 

  • Oltean M and Grosan C 2003 A comparison of several linear genetic programming techniques; Complex Syst. 14(4) 1–29.

    Google Scholar 

  • Panwar J S and Seimens J C 1972 Shear strength and energy of soil failure related to density and moisture; T. ASAE 15 423–427.

    Google Scholar 

  • Poli R, Langdon W B, McPhee N F and Koza J R 2007 Genetic programming: An introductory tutorial and a survey of techniques and applications; Technical report [CES-475], University of Essex, UK.

  • Ryan T P 1997 Modern Regression Methods; Wiley, New York.

    Google Scholar 

  • Shahin M A, Jaksa M B and Maier H R 2009 Recent advances and future challenges for artificial neural systems in geotechnical engineering applications; Adv. Artif. Neur. Syst., Article ID 308239.

  • Shahin M A, Maier H R and Jaksa M B 2001 Artificial neural network applications in geotechnical engineering; Aus. Geomech. 36(1) 49–62.

    Google Scholar 

  • Smith G N 1986 Probability and statistics in civil engineering; Collins, London.

    Google Scholar 

  • Swingler K 1996 Applying neural networks: A practical guide; Academic Press, New York.

    Google Scholar 

  • Terzaghi K, Peck R B and Mesri G 1996 Soil mechanics in engineering practice, 3rd edn, Wiley & Sons, Inc, New York.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography and Urban Planning, Faculty of Humanities and Social Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran

    SEYYED MOHAMMAD MOUSAVI

  2. Young Researchers Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    AMIR HOSSEIN ALAVI

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

    AMIR HOSSEIN GANDOMI

  4. Young Researchers Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    ALI MOLLAHASANI

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. AMIR HOSSEIN GANDOMI
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. ALI MOLLAHASANI
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to AMIR HOSSEIN ALAVI.

Rights and permissions

Reprints and permissions

About this article

Cite this article

MOUSAVI, S.M., ALAVI, A.H., GANDOMI, A.H. et al. Nonlinear genetic-based simulation of soil shear strength parameters. J Earth Syst Sci 120, 1001–1022 (2011). https://doi.org/10.1007/s12040-011-0119-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12040-011-0119-9

Keywords

Search

Navigation