New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Babanajad:2017:AES,
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author = "Saeed K. Babanajad and Amir H. Gandomi and
Amir H. Alavi",
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title = "New prediction models for concrete ultimate strength
under true-triaxial stress states: An evolutionary
approach",
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journal = "Advances in Engineering Software",
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year = "2017",
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keywords = "genetic algorithms, genetic programming, Gene
expression programming, Artificial intelligence,
Triaxial, Machine learning, Computer-aided, Strength
model",
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ISSN = "0965-9978",
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URL = "http://www.sciencedirect.com/science/article/pii/S096599781630566X",
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DOI = "doi:10.1016/j.advengsoft.2017.03.011",
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abstract = "The complexity associated with the in-homogeneous
nature of concrete suggests the necessity of conducting
more in-depth behavioral analysis of this material in
terms of different loading configurations. Distinctive
feature of Gene Expression Programming (GEP) has been
employed to derive computer-aided prediction models for
the multiaxial strength of concrete under true-triaxial
loading. The proposed models correlate the concrete
true-triaxial strength (sigma 1) to mix design
parameters and principal stresses (sigma 2, sigma 3),
needless of conducting any time-consuming laboratory
experiments. A comprehensive true-triaxial database is
obtained from the literature to build the proposed
models, subsequently implemented for the verification
purposes. External validations as well as sensitivity
analysis are further carried out using several
statistical criteria recommended by researchers. More,
they demonstrate superior performance to the other
existing empirical and analytical models. The proposed
design equations can readily be used for pre-design
purposes or may be used as a fast check on
deterministic solutions.",
- }
Genetic Programming entries for
Saeed K Babanajad
A H Gandomi
A H Alavi
Citations