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.
Highlights
Carbonation problem in concrete structures results in the formation of calcium carbonate due to the neutralization reaction between calcium hydroxide and carbonic acid, which yielded by the ingress of CO2 gas from the atmosphere into the concrete microstructure. [1, 2]
Information as input parameters, which is not considered in similar works in the related literature before, this study provides the grounds for exploring the possibilities of significantly improved prediction models for carbonation depth in concrete, which might be employed for successful investigations on the progress of carbonation problem with varying conditions, providing further understanding on the fundamentals of this critical durability issue in a systematical and accelerated way
The observed increase in carbonation depth might be related to the increased availability of portlandite (CH) in concrete mixes manufactured with cement having high CaO contents, that can be available for carbonation Further experimental studies focusing on these mechanisms could be beneficial, since this observation might be potentially critical for understanding the critical threshold of CaO contents of cements that are to be considered in a certain concrete mix design, so that a certain tolerated carbonation depth is not exceeded
Summary
Carbonation problem in concrete structures results in the formation of calcium carbonate due to the neutralization reaction between calcium hydroxide and carbonic acid, which yielded by the ingress of CO2 gas from the atmosphere into the concrete microstructure. [1, 2]. A non-destructive model capable of considering different concrete-related parameters individually and predicting the extent (i.e. the depth) of carbonation in concrete that could be exposed to varying environmental conditions within a specified exposure period is required. Such a model would provide extended understanding of carbonation process, and could contribute to the improvement of other holistic studies aiming to determine the general performance of reinforced concrete structures during their service lives
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