A New Evolutionary Approach to Geotechnical and Geo-Environmental Modelling

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

@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