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

  • Amir Hossein Alavi
  • Amir Hossein Gandomi
  • Ali Mollahasani


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.


Mean Square Error Genetic Programming Parent Program Mean Absolute Error Soil Stabilization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Amir Hossein Alavi
    • 1
  • Amir Hossein Gandomi
    • 2
  • Ali Mollahasani
    • 3
  1. 1.School of Civil EngineeringIran University of Science and TechnologyTehranIran
  2. 2.Intelligent Structural Engineering and Health Monitoring Laboratory, Department of Civil EngineeringUniversity of AkronAkronUSA
  3. 3.Department of Civil, Environmental and Material EngineeringUniversity of BolognaBolognaItaly

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