Nonlinear genetic-based simulation of soil shear strength parameters

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Mousavi:2011:JESS,

• author = "Seyyed Mohammad Mousavi and Amir Hossein Alavi and Amir Hossein Gandomi and Ali Mollahasani",
• title = "Nonlinear genetic-based simulation of soil shear strength parameters",
• journal = "Journal of Earth System Science",
• year = "2011",
• volume = "120",
• number = "6",
• pages = "1001--1022",
• month = dec,
• keywords = "genetic algorithms, genetic programming, linear-based genetic programming, Soil shear strength parameters, soil physical properties, prediction",
• ISSN = "0253-4126",
• language = "English",
• publisher = "Springer",
• URL = "http://link.springer.com/article/10.1007%2Fs12040-011-0119-9",
• DOI = "doi:10.1007/s12040-011-0119-9",
• size = "22 pages",
• abstract = "New nonlinear solutions were developed to estimate the soil shear strength parameters using linear genetic programming (LGP). The soil cohesion intercept (c) and angle of shearing resistance (phi) were formulated in terms of the basic soil physical properties. The best models were selected after developing and controlling several models with different combinations of influencing parameters. Comprehensive experimental database used for developing the models was established upon a series of unconsolidated, undrained, and unsaturated triaxial tests conducted in this study. Further, sensitivity and parametric analyses were carried out. c and phi were found to be mostly influenced by the soil unit weight and liquid limit. In order to benchmark the proposed models, a multiple least squares regression (MLSR) analysis was performed. The validity of the models was proved on portions of laboratory results that were not included in the modelling process. The developed models are able to effectively learn the complex relationship between the soil strength parameters and their contributing factors. The LGP models provide a significantly better prediction performance than the regression models.",

}

Genetic Programming entries for Seyyed Mohammad Mousavi A H Alavi A H Gandomi Ali Mollahasani

Citations