Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP
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- @Article{nafees:2021:Materials,
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author = "Afnan Nafees and Muhammad Faisal Javed and
Sherbaz Khan and Kashif Nazir and Furqan Farooq and
Fahid Aslam and Muhammad Ali Musarat and Nikolai Ivanovich Vatin",
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title = "Predictive Modeling of Mechanical Properties of Silica
{Fume-Based} Green Concrete Using Artificial
Intelligence Approaches: {MLPNN,} {ANFIS,} and {GEP}",
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journal = "Materials",
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year = "2021",
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volume = "14",
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number = "24",
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keywords = "genetic algorithms, genetic programming, gene
expression programming",
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ISSN = "1996-1944",
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URL = "https://www.mdpi.com/1996-1944/14/24/7531",
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DOI = "doi:10.3390/ma14247531",
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abstract = "Silica fume (SF) is a mineral additive that is widely
used in the construction industry when producing
sustainable concrete. The integration of SF in concrete
as a partial replacement for cement has several evident
benefits, including reduced CO2 emissions,
cost-effective concrete, increased durability, and
mechanical qualities. As environmental issues continue
to grow, the development of predictive machine learning
models is critical. Thus, this study aims to create
modelling tools for estimating the compressive and
cracking tensile strengths of silica fume concrete.
Multilayer perceptron neural networks (MLPNN), adaptive
neural fuzzy detection systems (ANFIS), and genetic
programming are all used (GEP). From accessible
literature data, a broad and accurate database of 283
compressive strengths and 149 split tensile strengths
was created. The six most significant input parameters
were cement, fine aggregate, coarse aggregate, water,
superplasticizer, and silica fume. Different
statistical measures were used to evaluate models,
including mean absolute error, root mean square error,
root mean squared log error and the coefficient of
determination. Both machine learning models, MLPNN and
ANFIS, produced acceptable results with high prediction
accuracy. Statistical analysis revealed that the ANFIS
model outperformed the MLPNN model in terms of
compressive and tensile strength prediction. The GEP
models outperformed all other models. The predicted
values for compressive strength and splitting tensile
strength for GEP models were consistent with
experimental values, with an R2 value of 0.97 for
compressive strength and 0.93 for splitting tensile
strength. Furthermore, sensitivity tests revealed that
cement and water are the determining parameters in the
growth of compressive strength but have the least
effect on splitting tensile strength. Cross-validation
was used to avoid overfitting and to confirm the output
of the generalised modelling technique. GEP develops an
empirical expression for each outcome to forecast
future databases features to promote the usage of green
concrete.",
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notes = "also known as \cite{ma14247531}",
- }
Genetic Programming entries for
Afnan Nafees
Muhammad Faisal Javed
Sherbaz Khan
Kashif Nazir
Furqan Farooq
Fahid Aslam
Muhammad Ali Musarat
Nikolai Ivanovich Vatin
Citations