A hybrid computational approach to formulate soil deformation moduli obtained from PLT

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Mousavi2011324,

• author = "Seyyed Mohammad Mousavi and Amir Hossein Alavi and Ali Mollahasani and Amir Hossein Gandomi",
• title = "A hybrid computational approach to formulate soil deformation moduli obtained from PLT",
• journal = "Engineering Geology",
• volume = "123",
• number = "4",
• pages = "324--332",
• year = "2011",
• ISSN = "0013-7952",
• DOI = "doi:10.1016/j.enggeo.2011.09.005",
• URL = "http://www.sciencedirect.com/science/article/pii/S0013795211002183",
• keywords = "genetic algorithms, genetic programming, Soil deformation moduli, Soil physical properties, Simulated annealing, Nonlinear modelling",
• abstract = "In this study, new empirical equations were developed to predict the soil deformation moduli using a hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed models relate secant (Es), unloading (Eu) and reloading (Er) moduli obtained from plate load-settlement curves to the basic soil physical properties. Several models with different combinations of the influencing parameters were developed and checked to select the best GP/SA models. The database used for developing the models was established upon a series of plate load tests (PLT) conducted on different soil types at various depths. The validity of the models was tested using parts of the test results that were not included in the analysis. The validation of the models was further verified using several statistical criteria. A traditional GP analysis was performed to benchmark the GP/SA models. The contributions of the parameters affecting Es, Eu and Er were analysed through a sensitivity analysis. The proposed models are able to estimate the soil deformation moduli with an acceptable degree of accuracy. The Es prediction model has a remarkably better performance than the models developed for predicting Eu and Er. The simplified formulations for Es, Eu and Er provide significantly better results than the GP-based models and empirical models found in the literature.",

}

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

Citations