Nonlinear modelling of soil deformation modulus through LGP-based interpretation of pressuremeter test results

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@Article{Rashed2011,

• author = "Azadeh Rashed and Jafar Bolouri Bazaz and Amir Hossein Alavi",
• title = "Nonlinear modelling of soil deformation modulus through {LGP-based} interpretation of pressuremeter test results",
• journal = "Engineering Applications of Artificial Intelligence",
• year = "2012",
• volume = "25",
• number = "7",
• pages = "1437--1449",
• note = "Advanced issues in Artificial Intelligence and Pattern Recognition for Intelligent Surveillance System in Smart Home Environment",
• ISSN = "0952-1976",
• DOI = "doi:10.1016/j.engappai.2011.11.008",
• URL = "http://www.sciencedirect.com/science/article/pii/S0952197611002193",
• URL = "https://profdoc.um.ac.ir/articles/a/1035742.pdf",
• size = "13 pages",
• keywords = "genetic algorithms, genetic programming, Soil deformation modulus, Pressure meter test, Soil physical properties",
• abstract = "Soil deformation modulus is an essential parameter for the analysis of behaviour of substructures. Despite its importance, little attention is paid to developing empirical models for predicting the deformation moduli obtained from the field tests. To cope with this issue, this paper presents the development of a new prediction model for the pressuremeter soil deformation modulus using a linear genetic programming (LGP) methodology. The LGP model relates the soil secant modulus obtained from the pressuremeter tests to the soil index properties. The best model was 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 several pressuremeter tests conducted on different soil types at depths of 3-40 m. To verify the applicability of the derived model, it was employed to estimate the soil moduli of portions of test results that were not included in the analysis. Further, the generalisation capability of the model was verified via several statistical criteria. The sensitivity of the soil deformation modulus to the influencing variables was examined and discussed. Moisture content and soil dry unit weight were found to be efficient representatives of the initial state and consolidation history of the soil for determining its deformation modulus. The results indicate that the LGP approach accurately characterises the soil deformation modulus leading to a very good prediction performance. The correlation coefficients between the experimental and predicted soil modulus values are equal to 0.908 and 0.901 for the calibration and testing data sets, respectively.",

}

Genetic Programming entries for Azadeh Rashed Jafar Bolouri Bazaz A H Alavi

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