Energy-based numerical models for assessment of soil liquefaction

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  author =       "Amir Hossein Alavi and Amir Hossein Gandomi",
  title =        "Energy-based numerical models for assessment of soil
  journal =      "Geoscience Frontiers",
  volume =       "3",
  number =       "4",
  pages =        "541--555",
  year =         "2012",
  ISSN =         "1674-9871",
  DOI =          "doi:10.1016/j.gsf.2011.12.008",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Soil
                 liquefaction, Capacity energy, Multi expression
                 programming, Sand, Formulation",
  abstract =     "This study presents promising variants of genetic
                 programming (GP), namely linear genetic programming
                 (LGP) and multi expression programming (MEP) to
                 evaluate the liquefaction resistance of sandy soils.
                 Generalised LGP and MEP-based relationships were
                 developed between the strain energy density required to
                 trigger liquefaction (capacity energy) and the factors
                 affecting the liquefaction characteristics of sands.
                 The correlations were established based on well
                 established and widely dispersed experimental results
                 obtained from the literature. To verify the
                 applicability of the derived models, they were employed
                 to estimate the capacity energy values of parts of the
                 test results that were not included in the analysis.
                 The external validation of the models was verified
                 using statistical criteria recommended by researchers.
                 Sensitivity and parametric analyses were performed for
                 further verification of the correlations. The results
                 indicate that the proposed correlations are effectively
                 capable of capturing the liquefaction resistance of a
                 number of sandy soils. The developed correlations
                 provide a significantly better prediction performance
                 than the models found in the literature. Furthermore,
                 the best LGP and MEP models perform superior than the
                 optimal traditional GP model. The verification phases
                 confirm the efficiency of the derived correlations for
                 their general application to the assessment of the
                 strain energy at the onset of liquefaction.",

Genetic Programming entries for A H Alavi A H Gandomi