Investigation of the parameters influencing progress of concrete carbonation depth by using artificial neural networks

Carbonation is a deleterious concrete durability problem which may alter concrete microstructure and yield initiation of corrosion in reinforcing steel bars. Previous studies focused on the use of Artificial Neural Networks (ANN) for the prediction of concrete carbonation depth and to minimize the n...

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Bibliographic Details
Published inMateriales de construcción (Madrid) Vol. 70; no. 337; pp. 209 - e209
Main Authors Akpinar, P., Uwanuakwa, I. D.
Format Journal Article
LanguageEnglish
Published Madrid Consejo Superior de Investigaciones Científicas 01.01.2020
Subjects
Artificial neural networks
Carbonation
Cement
Civil engineering
Concrete
Curing
durability
fly ash
Humidity
Laboratories
Laboratory tests
Mathematical models
modelization
Neural networks
Parameters
Parametric analysis
Reinforced concrete
Reinforcing steels
Research methodology
Variables
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Summary:Carbonation is a deleterious concrete durability problem which may alter concrete microstructure and yield initiation of corrosion in reinforcing steel bars. Previous studies focused on the use of Artificial Neural Networks (ANN) for the prediction of concrete carbonation depth and to minimize the need for destructive and elaborated civil engineering laboratory tests. This study aims to provide improved accuracy of simulation and prediction of carbonation with an ANN architecture including eighteen input parameters employing alternative Scaled Conjugate Gradient (SCG) function. After ensuring a promising value of the coefficient of correlation as high as 0.98, the influence of proposed input parameters on the progress of carbonation depth was studied. The results of this parametric analysis were observed to successfully comply with the conventional civil engineering experience. Hence, the employed ANN model can be used as an efficient tool to study in detail and to provide insights into the carbonation problem in concrete.
ISSN:0465-2746
1988-3226
DOI:10.3989/mc.2020.02019