Evolutionary-based approaches for settlement prediction of shallow foundations on cohesionless soils

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Habib Shahnazari and Mohamed A. Shahin and 
                 Mohammad A. Tutunchian",
  title =        "Evolutionary-based approaches for settlement
                 prediction of shallow foundations on cohesionless
  journal =      "International Journal of Civil Engineering",
  year =         "2014",
  volume =       "12",
  number =       "1",
  pages =        "55--64",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, shallow
                 foundations, settlement prediction, evolutionary
                 polynomial regression, gene expression programming,
                 cohesionless soils.",
  URL =          "http://ijce.iust.ac.ir/browse.php?a_code=A-10-269-5&slc_lang=en&sid=1",
  URL =          "http://ijce.iust.ac.ir/browse.php?a_code=A-10-269-5&slc_lang=en&sid=1&ftxt=1",
  size =         "10 pages",
  abstract =     "Due to the heterogeneous nature of granular soils and
                 the involvement of many effective parameters in the
                 geotechnical behaviour of soil-foundation systems, the
                 accurate prediction of shallow foundation settlements
                 on cohesionless soils is a complex engineering problem.
                 In this study, three new evolutionary-based techniques,
                 including evolutionary polynomial regression (EPR),
                 classical genetic programming (GP), and gene expression
                 programming (GEP), are used to obtain more accurate
                 predictive settlement models. The models are developed
                 using a large databank of standard penetration test
                 (SPT)-based case histories. The values obtained from
                 the new models are compared with those of the most
                 precise models that have been previously proposed by
                 researchers. The results show that the new EPR and
                 GP-based models are able to predict the foundation
                 settlement on cohesionless soils under the described
                 conditions with R2 values higher than 87percent. The
                 artificial neural networks (ANNs) and genetic
                 programming (GP)-based models obtained from the
                 literature, have R2 values of about 85percent and
                 83percent, respectively which are higher than 80percent
                 for the GEP-based model. A subsequent comprehensive
                 parametric study is further carried out to evaluate the
                 sensitivity of the foundation settlement to the
                 effective input parameters. The comparison results
                 prove that the new EPR and GP-based models are the most
                 accurate models. In this study, the feasibility of the
                 EPR, GP and GEP approaches in finding solutions for
                 highly nonlinear problems such as settlement of shallow
                 foundations on granular soils is also clearly
                 illustrated. The developed models are quite simple and
                 straightforward and can be used reliably for routine
                 design practice.",

Genetic Programming entries for Habib Shahnazari Mohamed Shahin Mohammad Amin Tutunchian