Skip to main content
SpringerLink
Log in

A Genetic Programming-Based Approach for the Performance Characteristics Assessment of Stabilized Soil

  • Chapter
Book cover Variants of Evolutionary Algorithms for Real-World Applications

Abstract

This chapter presents a variant of genetic programming, namely linear genetic programming (LGP), and a hybrid search algorithm coupling LGP and simulated annealing (SA), called LGP/SA, to predict the performance characteristics of stabilized soil. LGP and LGP/SA relate the unconfined compressive strength (UCS), maximum dry density (MDD), and optimum moisture content (OMC) metrics of stabilized soil to the properties of the natural soil as well as the types and quantities of stabilizing additives. Different sets of LGP and LGP/SA-based prediction models have been separately developed. The contributions of the parameters affecting UCS, MDD, and OMC are evaluated through a sensitivity analysis. A subsequent parametric analysis is carried out and the trends of the results are compared with previous studies. A comprehensive set of data obtained from the literature has been used for developing the models. Experimental results confirm that the accuracy of the proposed models is satisfactory. In particular, the LGP-based models are found to be more accurate than the LGP/SA-based models.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akpokodje, E.G.: The stabilization of some arid zone soils with cements and lime. Quarterly Journal of Engineering Geology London 18, 173–180 (1985)

    Article  Google Scholar 

  2. Bell, F.G.: Lime stabilization of clay minerals and soils. Engineering Geology 42(40), 223–237 (1996)

    Article  Google Scholar 

  3. Soil engineering and stabilization. Technical report, USACE (2000)

    Google Scholar 

  4. Ngowi, A.B.: Improving the traditional earth construction: a case study of Botswana. Construction and Building Materials 11(1), 1–7 (1997)

    Article  Google Scholar 

  5. Bryan, A.J.: Criteria for the suitability of soil for cement stabilization. Building and Environment 23(4), 309–319 (1988)

    Article  MathSciNet  Google Scholar 

  6. Osula, D.O.A.: A comparative evaluation of cement and lime modification of laterite. Engineering Geology 42, 71–81 (1996)

    Article  Google Scholar 

  7. Jumikis, A.R.: Geology and soils of the Newark (NJ) metropolitan area. Journal of the Soil Mechanics and Foundations Division ASCE 93 (SM2), 71–95 (1946)

    Google Scholar 

  8. Linveh, M., Ishai, I.: Using indicative properties to predict the density-moisture relationship of soil. Transportation Research Record 60P, 22–28 (1978)

    Google Scholar 

  9. Wang, M.C., Huang, C.C.: Soil compaction and permeability prediction models. Journal of Environmental Engineering ASCE 110, 1063–1083 (1984)

    Article  Google Scholar 

  10. Blotz, L.R., Benson, C.H., Boutwell, G.P.: Estimating optimum water content and maximum dry unit weight for compacted clays. Journal Geotechnical and Geo-Environmental Engineering ASCE 124(9), 907–912 (1998)

    Article  Google Scholar 

  11. Sridharan, A., Nagaraj, H.B.: Plastic limit and compaction characteristics of fine-grained soils. Ground Improvement 9(1), 17–22 (2005)

    Article  Google Scholar 

  12. Sinha, S.K., Wang, M.C.: Artificial neural network prediction models for soil compaction and permeability. Geotechnical and Geological Engineering 26, 47–64 (2008)

    Article  Google Scholar 

  13. Gunaydin, O.: Estimation of soil compaction parameters by using statistical analyses and artificial neural networks. Environmental Geology 10, 1300–1306 (2008)

    Google Scholar 

  14. Horpibulsuk, S.: Analysis and assessment of engineering behavior of cement stabilised clays. PhD thesis, Saga University, Japan (2001)

    Google Scholar 

  15. Tan, T.S., Goh, T.L., Yong, K.Y.: Properties of singapore marine clays improved by cement mixing. Geotechnical Testing Journal 25, 422–433 (2002)

    Google Scholar 

  16. Horpibulsuk, S., Bergado, D.T., Lorenzo, G.A.: Compressibility of cement-admixed clays at high water content. Geotechnique 54(2), 151–154 (2004)

    Article  Google Scholar 

  17. Narendra, B.S., Sivapullaiah, P.V., Suresh, S., Omkar, S.N.: Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: A comparative study. Computers and Geotechnics 33, 196–208 (2006)

    Article  Google Scholar 

  18. Koza, J.R.: Genetic programming: On the programming of computers by means of natural selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  19. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming – An introduction on the automatic evolution of computer programs and its application. Morgan Kaufmann Publishers, Heidelberg, San Francisco (1998)

    Google Scholar 

  20. Bäck, T.: Evolutionary algorithms in theory and practice: Evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, USA (1996)

    MATH  Google Scholar 

  21. Weise, T.: Global optimization algorithms-Theory and application (2007), http://www.it-weise.de

  22. Brameier, M., Banzhaf, W.: Linear genetic programming. Springer Science + Business Media, New York (2007)

    MATH  Google Scholar 

  23. Brameier, M., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. IEEE Transactions on Evolutionary Computation 5(1), 17–26 (2001)

    Article  Google Scholar 

  24. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing mechanics. Journal of Chemical Physics 21(6), 1087–1092 (1953)

    Article  Google Scholar 

  25. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  26. Cerny, V.: Thermo-dynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications 45, 41–52 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  27. Aarts, E.: Simulated annealing and boltzmann machines: A stochastic approach to combinatorial optimization and neural Computing. Wiley, New York (1989)

    MATH  Google Scholar 

  28. Kita, H.: Simulated annealing. Japan Society for Fuzzy Theory and Intelligent Informatics 9(6), 870–875 (1997)

    Google Scholar 

  29. Ingber, L.: Simulated annealing: Practice versus theory. Mathematical and Computer Modeling 18(11), 29–57 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  30. Folino, G., Pizzuti, C., Spezzano, G.: Genetic programming and simulated annealing: A hybrid method to evolve decision trees. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000, vol. 1802, pp. 294–303 (2000)

    Google Scholar 

  31. Poli, R., Langdon, W.B., McPhee, N.F., Koza, J.R.: Genetic programming: An introductory tutorial and a survey of techniques and applications. Technical Report ces-475, UK: University of Essex (2007)

    Google Scholar 

  32. Oltean, M., Dumitrescu, D.: Multi expression programming. Technical Report UBB-01-2002, Babeş-Bolyai University, Cluj-Napoca, Romania (2002)

    Google Scholar 

  33. Oltean, M., Grosan, C.: A comparison of several linear genetic programming techniques. Complex Systems 14(4), 1–29 (2003)

    MathSciNet  Google Scholar 

  34. Francone, F.D.: Discipulus(TM) owner’s manual, version 4.0. Register Machine Learning Technologies (2001)

    Google Scholar 

  35. Francone, F.D., Deschaine, L.M.: Extending the boundaries of design optimization by integrating fast optimization techniques with machine-code-based, linear genetic programming. Information Sciences 161, 99–120 (2004)

    Article  Google Scholar 

  36. Francone, F.D.: Discipulus Lite(TM) owner’s manual, version 4.0. Register Machine Learning Technologies (2004)

    Google Scholar 

  37. Soh, C.K., Yang, Y.: Genetic programming-based approach for structural optimization. Journal of Computing in Civil Engineering 14(1), 31–37 (2000)

    Article  MathSciNet  Google Scholar 

  38. Hong, Y.S., Rao, B.: Evolutionary self-organising modeling of a municipal wastewater treatment plant. Water Research 37(6), 1199–1212 (2003)

    Article  Google Scholar 

  39. Johari, A., Habibagahi, G., Ghahramani, A.: Prediction of soil-water characteristic curve using genetic programming. Journal of Geotechnical and Geoenvironmental Engineering ASCE 132(5), 661–665 (2006)

    Article  Google Scholar 

  40. Javadi, A.A., Rezania, M., Mousavinezhad, M.: Evaluation of liquefaction induced lateral displacements using genetic programming. Computers and Geotechnics 33(4-5), 222–233 (2006)

    Article  Google Scholar 

  41. Baykasoglu, A., Gullub, H., Canakci, H., Ozbakir, L.: Prediction of compressive and tensile strength of limestone via genetic programming. Expert Systems with Applications 35(1-2), 111–123 (2008)

    Article  Google Scholar 

  42. Gandomi, A.H., Alavi, A.H., Kazemi, S., Alinia, M.M.: Behavior appraisal of steel semi-rigid joints using linear genetic programming. Journal of Constructional Steel Research 65(1-2), 1738–1750 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  44. Guven, A., Azamathulla, H.M., Zakaria, N.A.: Linear genetic programming for prediction of circular pile scour. Ocean Engineering 36(12-13), 985–991 (2009)

    Article  Google Scholar 

  45. Gandomi, A.H., Sahab, M.G., Alavi, A.H., Heshmati, A.A.R., Gandomi, M., Arjmandi, P.: Application of a coupled simulated annealing-genetic programming algorithm to the prediction of bolted joints behavior. American-Eurosian Journal of Scientific Research 3(2), 153–162 (2008)

    Google Scholar 

  46. Burroughs, V.S.: Quantitative criteria for the selection and stabilization of soils for rammed earth wall construction. PhD thesis, University of New South Wales, Australia (2001)

    Google Scholar 

  47. Frank, I.E., Todeschini, R.: The data analysis handbook. Elsevier, Amsterdam (1994)

    Google Scholar 

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

    Google Scholar 

  49. Mesbahi, E.: Application of artificial neural networks in modelling and control of diesel engines. PhD thesis, University of Newcastle upon Tyne (2000)

    Google Scholar 

  50. Guven, A.: Linear genetic programming for time-series modelling of daily flow rate. Journal of Earth System Science 118(2), 137–146 (2009)

    Article  Google Scholar 

  51. Conrads, M., Dolezal, O., Francone, F.D., Nordin, P.: Discipulus-fast genetic programming based on aim learning technology. Technical report, Register Machine Learning Technologies Inc., Littleton, CO (2000)

    Google Scholar 

  52. Deschaine, L.M.: Using genetic programming to develop a c/c++ simulation model of a waste incinerator. Technical report, Science Applications International Corp. (2000)

    Google Scholar 

  53. Conrads, M., Dolezal, O., Francone, F.D., Nordin, P.: Discipulus-fast genetic programming based on aim learning technology. Technical report, Register Machine Learning Technologies Inc., Littleton, CO. (2004)

    Google Scholar 

  54. Smith, G.N.: Probability and statistics in civil engineering. Collins, London (1986)

    Google Scholar 

  55. Golbraikh, A., Tropsha, A.: Beware of q2. Journal of Molecular Graphics and Modelling 20(4), 269–276 (2002)

    Article  Google Scholar 

  56. Roy, P.P., Roy, K.: On some aspects of variable selection for partial least squares regression models. QSAR & Combinatorial Science 27, 302–313 (2008)

    Article  Google Scholar 

  57. Cybenko, J.: Approximations by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems 2, 303–314 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  58. Alavi, A.H., Gandomi, A.H., Mollahasani, A., Heshmati, A.A.R., Rashed, A.: Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. Journal of Plant Nutrition and Soil Science 173(3), 368–379 (2010)

    Article  Google Scholar 

  59. Bryan, A.J.: Soil/cement as a walling material. i: Stress/strain properties. Building and Environment 23(4), 321–330 (1988)

    Article  MathSciNet  Google Scholar 

  60. Walker, P.J.: Strength durability and shrinkage characteristics of cement stabilised soil blocks. Cement and Concrete Composites 17, 301–310 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Amir Hossein Alavi

  2. Intelligent Structural Engineering and Health Monitoring Laboratory, Department of Civil Engineering, University of Akron, Akron, OH, USA

    Amir Hossein Gandomi

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

    Ali Mollahasani

Authors
  1. Amir Hossein Alavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Hossein Gandomi
    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

Editor information

Editors and Affiliations

  1. Faculty of ICT, Swinburne University of Technology, 3122, Melbourne, VIC, Australia

    Raymond Chiong

  2. Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology, University of Science and Technology of China (USTC), 230027, Hefei, Anhui, China

    Thomas Weise

  3. School of Computer Science, University of Adelaide, 5005, Adelaide, SA, Australia

    Zbigniew Michalewicz

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Alavi, A.H., Gandomi, A.H., Mollahasani, A. (2012). A Genetic Programming-Based Approach for the Performance Characteristics Assessment of Stabilized Soil. In: Chiong, R., Weise, T., Michalewicz, Z. (eds) Variants of Evolutionary Algorithms for Real-World Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23424-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23424-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23423-1

  • Online ISBN: 978-3-642-23424-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation