Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash
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- @Article{kovacevic:2022:Materials,
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author = "Miljan Kovacevic and Silva Lozancic and
Emmanuel Karlo Nyarko and Marijana Hadzima-Nyarko",
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title = "Application of Artificial Intelligence Methods for
Predicting the Compressive Strength of Self-Compacting
Concrete with Class {F} Fly Ash",
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journal = "Materials",
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year = "2022",
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volume = "15",
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number = "12",
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pages = "Article No. 4191",
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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/1996-1944/15/12/4191",
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DOI = "doi:10.3390/ma15124191",
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abstract = "Replacing a specified quantity of cement with Class F
fly ash contributes to sustainable development and
reducing the greenhouse effect. In order to use Class F
fly ash in self-compacting concrete (SCC), a prediction
model that will give a satisfactory accuracy value for
the compressive strength of such concrete is required.
This paper considers a number of machine learning
models created on a dataset of 327 experimentally
tested samples in order to create an optimal predictive
model. The set of input variables for all models
consists of seven input variables, among which six are
constituent components of SCC, and the seventh model
variable represents the age of the sample. Models based
on regression trees (RTs), Gaussian process regression
(GPR), support vector regression (SVR) and artificial
neural networks (ANNs) are considered. The accuracy of
individual models and ensemble models are analysed. The
research shows that the model with the highest accuracy
is an ensemble of ANNs. This accuracy expressed through
the mean absolute error (MAE) and correlation
coefficient (R) criteria is 4.37 MPa and 0.96,
respectively. This paper also compares the accuracy of
individual prediction models and determines their
accuracy. Compared to theindividual ANN model, the more
transparent multi-gene genetic programming (MGPP) model
and the individual regression tree (RT) model have
comparable or better prediction accuracy. The accuracy
of the MGGP and RT models expressed through the MAE and
R criteria is 5.70 MPa and 0.93, and 6.64 MPa and 0.89,
respectively.",
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notes = "also known as \cite{ma15124191}",
- }
Genetic Programming entries for
Miljan Kovacevic
Silva Lozancic
Emmanuel Karlo Nyarko
Marijana Hadzima-Nyarko
Citations