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Environmental Earth Sciences

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01.02.2016 | Original Article

Deriving an intelligent model for soil compression index utilizing multi-gene genetic programming

verfasst von: Danial Mohammadzadeh S, Jafar Bolouri Bazaz, S. H. Vafaee Jani Yazd, Amir H. Alavi

Erschienen in: Environmental Earth Sciences | Ausgabe 3/2016

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Abstract

Multi-gene genetic programming (MGGP) is a new nonlinear system modeling approach that integrates the capabilities of standard GP and classical regression. This paper deals with the prediction of compression index of fine-grained soils using this robust technique. The proposed model relates the soil compression index to its liquid limit, plastic limit and void ratio. Several laboratory test results for fine fine-grained were used to develop the models. Various criteria were considered to check the validity of the model. The parametric and sensitivity analyses were performed and discussed. The MGGP method was found to be very effective for predicting the soil compression index. The prediction coefficients of determination were 0.856 and 0.840 for the training and testing data, respectively. A comparative study was further performed to prove the superiority of the MGGP model to the existing soft computing and traditional empirical equations.

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Titel: Deriving an intelligent model for soil compression index utilizing multi-gene genetic programming
verfasst von: Danial Mohammadzadeh S
Jafar Bolouri Bazaz
S. H. Vafaee Jani Yazd
Amir H. Alavi
Publikationsdatum: 01.02.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Environmental Earth Sciences / Ausgabe 3/2016
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-015-4889-2

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