Evaluating Shear Strength of Light-Weight and Normal-Weight Concretes through Artificial Intelligence
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- @Article{ebid:2022:Sustainability,
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author = "Ahmed M. Ebid and Ahmed Farouk Deifalla and
Hisham A. Mahdi",
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title = "Evaluating Shear Strength of Light-Weight and
Normal-Weight Concretes through Artificial
Intelligence",
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journal = "Sustainability",
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year = "2022",
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volume = "14",
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number = "21",
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pages = "Article No. 14010",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2071-1050",
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URL = "https://www.mdpi.com/2071-1050/14/21/14010",
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DOI = "doi:10.3390/su142114010",
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abstract = "The strength of concrete elements under shear is a
complex phenomenon, which is induced by several
effective variables and governing mechanisms. Thus,
each parameter’s importance depends on the values
of the effective parameters and the governing
mechanism. In addition, the new concrete types,
including lightweight concrete and fibered concrete,
add to the complexity, which is why machine learning
(ML) techniques are ideal to simulate this behaviour
due to their ability to handle fuzzy, inaccurate, and
even incomplete data. Thus, this study aims to predict
the shear strength of both normal-weight and
light-weight concrete beams using three well-known
machine learning approaches, namely evolutionary
polynomial regression (EPR), artificial neural network
(ANN) and genetic programming (GP). The methodology
started with collecting a dataset of about 1700 shear
test results and dividing it into training and testing
subsets. Then, the three considered (ML) approaches
were trained using the training subset to develop three
predictive models. The prediction accuracy of each
developed model was evaluated using the testing subset.
Finally, the accuracies of the developed models were
compared with the current international design codes
(ACI, EC2 & JSCE) to evaluate the success of this
research in terms of enhancing the prediction accuracy.
The results showed that the prediction accuracies of
the developed models were 68percent, 83percent &
76.5percent for GP, ANN & EPR, respectively, and
56percent, 40percent & 62percent for ACI, EC2 &
JSCE, in that order. Hence, the results indicated that
the accuracy of the worst (ML) model is better than
those of design codes, and the ANN model is the most
accurate one.",
-
notes = "also known as \cite{su142114010}",
- }
Genetic Programming entries for
Ahmed M Ebid
Ahmed Farouk Mohamed Hassan Deifalla
Hisham A Mahdi
Citations