Development of prediction models for shear strength of SFRCB using a machine learning approach
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- @Article{Sarveghadi:2016:NCA,
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author = "Masoud Sarveghadi and Amir H. Gandomi and
Hamed Bolandi and Amir H. Alavi",
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title = "Development of prediction models for shear strength of
{SFRCB} using a machine learning approach",
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journal = "Neural Computing and Applications",
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year = "2019",
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volume = "31",
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number = "7",
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pages = "2085--2094",
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month = jul,
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keywords = "genetic algorithms, genetic programming, SFRCB,
Multi-expression programming, Shear strength,
Prediction",
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ISSN = "0941-0643",
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DOI = "doi:10.1007/s00521-015-1997-6",
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size = "10 pages",
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abstract = "In this study, new design equations were derived for
the assessment of shear resistance of steel
fibre-reinforced concrete beams (SFRCB) using
multi-expression programming (MEP). The superiority of
MEP over conventional statistical techniques is due to
its ability in modelling of mechanical behaviour
without a need to pre-define the model structure. The
MEP models were developed using a comprehensive
database obtained through an extensive literature
review. New criteria were checked to verify the
validity of the models. A sensitivity analysis was
carried out and discussed. The MEP models provide good
estimations of the shear strength of SFRCB. The
developed models significantly outperform several
equations found in the literature.",
- }
Genetic Programming entries for
Masoud Sarveghadi
A H Gandomi
Hamed Bolandi
A H Alavi
Citations