Using Artificial Intelligence Techniques to Predict Punching Shear Capacity of Lightweight Concrete Slabs
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- @Article{ebid:2022:Materials,
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author = "Ahmed Ebid and Ahmed Deifalla",
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title = "Using Artificial Intelligence Techniques to Predict
Punching Shear Capacity of Lightweight Concrete Slabs",
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journal = "Materials",
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year = "2022",
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volume = "15",
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number = "8",
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pages = "Article No. 2732",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1996-1944",
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URL = "https://www.mdpi.com/1996-1944/15/8/2732",
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DOI = "doi:10.3390/ma15082732",
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abstract = "Although lightweight concrete is implemented in many
mega projects to reduce the cost and improve the
project’s economic aspect, research studies focus
on investigating conventional normal-weight concrete.
In addition, the punching shear failure of concrete
slabs is dangerous and calls for precise and consistent
prediction models. Thus, this current study
investigates the prediction of the punching shear
strength of lightweight concrete slabs. First, an
extensive experimental database for lightweight
concrete slabs tested under punching shear loading is
gathered. Then, effective parameters are determined by
applying the principles of statistical methods, namely,
concrete density, columns dimensions, slab effective
depth, concrete strength, flexure reinforcement ratio,
and steel yield stress. Next, the manuscript presented
three artificial intelligence models, which are genetic
programming (GP), artificial neural network (ANN) and
evolutionary polynomial regression (EPR). In addition,
it provided guidance for future design code
development, where the importance of each variable on
the strength was identified. Moreover, it provided an
expression showing the complicated inter-relation
between affective variables. The novelty lies in
developing three proposed models for the punching
capacity of lightweight concrete slabs using three
different (AI) techniques capable of accurately
predicting the strength compared to the experimental
database",
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notes = "also known as \cite{ma15082732}",
- }
Genetic Programming entries for
Ahmed M Ebid
Ahmed Farouk Mohamed Hassan Deifalla
Citations