Materiales de Construcción, Vol 70, No 337 (2020)

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


https://doi.org/10.3989/mc.2020.02019

P. Akpinar
Civil Engineering Department, Faculty of Civil and Environmental Engineering, Near East University, TRNC, Turkey
orcid https://orcid.org/0000-0002-6885-8105

I. D. Uwanuakwa
Civil Engineering Department, Faculty of Civil and Environmental Engineering, Near East University, TRNC, Turkey
orcid https://orcid.org/0000-0001-5546-0408

Abstract


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.

Keywords


Concrete; Fly ash; Durability; Carbonation; Modelization

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