A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems
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- @Article{journals/nca/GandomiA12a,
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author = "Amir Hossein Gandomi and Amir Hossein Alavi",
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title = "A new multi-gene genetic programming approach to
non-linear system modeling. Part {II}: geotechnical and
earthquake engineering problems",
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journal = "Neural Computing and Applications",
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year = "2012",
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number = "1",
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volume = "21",
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pages = "189--201",
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month = feb,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "0941-0643",
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DOI = "doi:10.1007/s00521-011-0735-y",
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size = "13 pages",
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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.",
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notes = "See \cite{journals/nca/GandomiA12}",
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affiliation = "Department of Civil Engineering, The University of
Akron, Akron, OH 44325-3905, USA",
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bibdate = "2012-01-17",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/nca/nca21.html#GandomiA12a",
- }
Genetic Programming entries for
A H Gandomi
A H Alavi
Citations