Empirical modeling of plate load test moduli of soil via gene expression programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{Mollahasani:2011:CG,

• author = "Ali Mollahasani and Amir Hossein Alavi and Amir Hossein Gandomi",
• title = "Empirical modeling of plate load test moduli of soil via gene expression programming",
• journal = "Computers and Geotechnics",
• year = "2011",
• volume = "38",
• number = "2",
• pages = "281--286",
• month = mar,
• keywords = "genetic algorithms, genetic programming, Gene expression programming, Soil deformation moduli, Soil physical properties, Nonlinear modelling",
• ISSN = "0266-352X",
• URL = "http://www.sciencedirect.com/science/article/pii/S0266352X1000162X",
• DOI = "doi:10.1016/j.compgeo.2010.11.008",
• size = "6 pages",
• abstract = "New empirical models were developed to predict the soil deformation moduli using gene expression programming (GEP). The principal soil deformation parameters formulated were secant (Es) and reloading (Er) moduli. The proposed models relate Es and Er obtained from plate load-settlement curves to the basic soil physical properties. The best GEP models were selected after developing and controlling several models with different combinations of the influencing parameters. The experimental database used for developing the models was established upon a series of plate load tests conducted on different soil types at depths of 1-24m. To verify the applicability of the derived models, they were employed to estimate the soil moduli of a part of test results that were not included in the analysis. The external validation of the models was further verified using several statistical criteria recommended by researchers. A sensitivity analysis was carried out to determine the contributions of the parameters affecting Es and Er. The proposed models give precise estimates of the soil deformation moduli. The Es prediction model provides considerably better results in comparison with the model developed for Er. The simplified formulation for Es significantly outperforms the empirical equations found in the literature. The derived models can reliably be employed for pre-design purposes.",

}

Genetic Programming entries for Ali Mollahasani A H Alavi A H Gandomi

Citations