A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems

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

@Article{journals/nca/GandomiA12a,

• author = "Amir Hossein Gandomi and Amir Hossein Alavi",
• title = "A new multi-gene genetic programming approach to non-linear system modeling. Part {II}: geotechnical and earthquake engineering problems",
• journal = "Neural Computing and Applications",
• year = "2012",
• number = "1",
• volume = "21",
• pages = "189--201",
• month = feb,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "0941-0643",
• DOI = "doi:10.1007/s00521-011-0735-y",
• size = "13 pages",
• abstract = "Complexity of analysis of geotechnical behaviour is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behaviour, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional methods may lead to very large errors. This paper presents an endeavour to exploit a robust multi-gene genetic programming (MGGP) method for the analysis of geotechnical and earthquake engineering systems. MGGP is a modified genetic programming approach for model structure selection combined with a classical technique for parameter estimation. To justify the abilities of MGGP, it is systematically employed to formulate the complex geotechnical engineering problems. Different classes of the problems analysed include the assessment of (i) undrained lateral load capacity of piles, (ii) undrained side resistance alpha factor for drilled shafts, (iii) settlement around tunnels, and (iv) soil liquefaction. The validity of the derived models is tested for a part of test results beyond the training data domain. Numerical examples show the superb accuracy, efficiency, and great potential of MGGP. Contrary to artificial neural networks and many other soft computing tools, MGGP provides constitutive prediction equations. The MGG-based solutions are particularly valuable for pre-design practices.",
• notes = "See \cite{journals/nca/GandomiA12}",
• affiliation = "Department of Civil Engineering, The University of Akron, Akron, OH 44325-3905, USA",
• bibdate = "2012-01-17",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca21.html#GandomiA12a",

}

Genetic Programming entries for A H Gandomi A H Alavi

Citations