A New Evolutionary Approach to Geotechnical and Geo-Environmental Modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Hussain:2015:hbgpa,
  author =       "Mohammed S. Hussain and Alireza Ahangar-asr and 
                 Youliang Chen and Akbar A. Javadi",
  title =        "A New Evolutionary Approach to Geotechnical and
                 Geo-Environmental Modelling",
  booktitle =    "Handbook of Genetic Programming Applications",
  publisher =    "Springer",
  year =         "2015",
  editor =       "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
  chapter =      "19",
  pages =        "483--499",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-20882-4",
  DOI =          "doi:10.1007/978-3-319-20883-1_19",
  abstract =     "In many cases, models based on certain laws of physics
                 can be developed to describe the behaviour of physical
                 systems. However, in case of more complex phenomena
                 with less known or understood contributing parameters
                 or variables the physics-based modelling techniques may
                 not be applicable. Evolutionary Polynomial Regression
                 (EPR) offers a new way of rendering models, in the form
                 of easily interpretable polynomial equations,
                 explicitly expressing the relationship between
                 contributing parameters of a system of complex nature,
                 and the behaviour of the system. EPR is a recently
                 developed hybrid regression method that provides
                 symbolic expressions for models and works with formulae
                 based on pseudo-polynomial expressions. In this chapter
                 the application of EPR to two important geotechnical
                 and geo-environmental engineering systems is presented.
                 These systems include thermo-mechanical behaviour of
                 unsaturated soils and optimisation of performance of an
                 aquifer system subjected to seawater intrusion.
                 Comparisons are made between the EPR model predictions
                 and the actual measured or synthetic data. The results
                 show that the proposed methodology is able to develop
                 highly accurate models with excellent capability of
                 reflecting the real and expected physical effects of
                 the contributing parameters on the performance of the
                 systems. Merits and advantages of the suggested
                 methodology are highlighted.",
}

Genetic Programming entries for Mohammed S Hussain Alireza Ahangar-Asr You-Liang Chen Akbar A Javadi

Citations