Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming
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- @Article{amin:2022:Materials,
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author = "Muhammad Nasir Amin and Muhammad Raheel and
Mudassir Iqbal and Kaffayatullah Khan and
Muhammad Ghulam Qadir and Fazal E. Jalal and Anas Abdulalim Alabdullah and
Ali Ajwad and Majdi Adel Al-Faiad and
Abdullah Mohammad Abu-Arab",
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title = "Prediction of Rapid Chloride Penetration Resistance to
Assess the Influence of Affecting Variables on
Metakaolin-Based Concrete Using Gene Expression
Programming",
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journal = "Materials",
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year = "2022",
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volume = "15",
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number = "19",
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pages = "Article No. 6959",
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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/1996-1944/15/19/6959",
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DOI = "doi:10.3390/ma15196959",
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abstract = "The useful life of a concrete structure is highly
dependent upon its durability, which enables it to
withstand the harsh environmental conditions.
Resistance of a concrete specimen to rapid chloride ion
penetration (RCP) is one of the tests to indirectly
measure its durability. The central aim of this study
was to investigate the influence of different
variables, such as, age, amount of binder, fine
aggregate, coarse aggregate, water to binder ratio,
metakaolin content and the compressive strength of
concrete on the RCP resistance using a genetic
programming approach. The number of chromosomes (Nc),
genes (Ng) and, the head size (Hs) of the gene
expression programming (GEP) model were varied to study
their influence on the predicted RCP values. The
performance of all the GEP models was assessed using a
variety of performance indices, i.e., R2, RMSE and
comparison of regression slopes. The optimal GEP model
(Model T3) was obtained when the Nc = 100, Hs = 8 and
Ng = 3. This model exhibits an R2 of 0.89 and 0.92 in
the training and testing phases, respectively. The
regression slope analysis revealed that the predicted
values are in good agreement with the experimental
values, as evident from their higher R2 values.
Similarly, parametric analysis was also conducted for
the best performing Model T3. The analysis showed that
the amount of binder, compressive strength and age of
the sample enhanced the RCP resistance of the concrete
specimens. Among the different input variables, the RCP
resistance sharply increased during initial stages of
curing (28-d), thus validating the model results.",
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notes = "also known as \cite{ma15196959}",
- }
Genetic Programming entries for
Muhammad Nasir Amin
Muhammad Raheel
Mudassir Iqbal
Kaffayatullah Khan
Muhammad Ghulam Qadir
Fazal E Jalal
Anas Abdulalim Alabdullah
Ali Ajwad
Majdi Adel Al-Faiad
Abdullah Mohammad Abu-Arab
Citations