• Advances in concrete construction
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• v.7 no.3
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• pp.167-174
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• 2019

Metakaolin (MK), which is increasingly being used to produce high performance concrete, is produced by calcining purified kaolinite between 650 and $700^{\circ}C$ in a rotary kiln. The carbonation resistance of metakaolin blended concrete is lower than that of control concrete. Hence, it is critical to consider carbonation durability for rationally using metakaolin in the concrete industry. This study presents microstructure modeling during the carbonation of metakaolin blended concrete. First, based on a blended hydration mo del, the amount of carbonatable substances and porosity are determined. Second, based on the chemical reactions between carbon dioxide and carbonatable substances, the reduction of concrete porosity due to carbonation is calculated. Furthermore, $CO_2$ diffusivity is evaluated considering the concrete composition and exposed environment. The carbonation depth of concrete is analyzed using a diffusion-based model. The proposed microstructure model takes into account the influences of concrete composition, concrete curing, and exposure condition on carbonation. The proposed model is useful as a predetermination tool for the evaluation of the carbonation service life of metakaolin blended concrete.

• Computers and Concrete
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• v.10 no.3
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• pp.219-229
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• 2012

Carbonation is a widespread degradation of concrete and may be coupled with more severe degradations. An experimental investigation was carried out to study the effect of carbonation on chloride ion diffusion of concrete. The characteristic of concrete after carbonation was measured, such as carbonation depth, strength and pore structure. Results indicated that carbonation depth has a good linear relation with square root of carbonate time, and carbonation can improve compressive strength, but lower flexural strength. Results about pore structure of concrete before and after carbonation have shown that carbonation could cause a redistribution of the pore sizes and increase the proportion of small pores. It also can decrease porosities, most probable pore size and average pore diameters. Chloride ion diffusion of concrete after carbonation was studied through natural diffusion method and steady state migration testing method respectively. It is supposed that the chloride ion concentration of carbonation region is higher than that of the sound region because of the separation of fixed salts, and chloride ion diffusion coefficient was increased due to carbonation action evidently.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.10a
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• pp.655-660
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• 2002

Major deterioration in concrete structures are salt attack and carbonation. Especially severe problems due to carbonation occur in tile concrete structures of city, tunnel, underground structures. Cracks in concrete during service life including early age due to hydration heat and/or shrinkage accelerate the diffusion of concrete so that the deterioration is also accelerated. In this study, carbonation depths of both non-cracked concrete and cracked concrete are evaluated and weight change test and TGA are carried out. Through the tests, a relation between water-cement ratio and carbonation depth is derived and also carbonation increase rate is derived in the function of crack width.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10c
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• pp.203-206
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• 1998

In reinforced concrete carbonation of concrete leads to depassivation of the reinforcement, and hence to initiation of corrosion. As a result of carbonation accelerating experiment with using effect of wet-dry cycle and 15% concentration of CO ₂, the carbonation rate shows very distinct difference according to W/C ratio. OPC-40 estimated no carbonation depth, whereas OPC-60 estimated rapidly the carbonation rate. The comparative analysis of the carbonation rate accelerating depends on different kinds of cement shows fastest FAC-20. Also, highly W/C ratio's concrete shows low half-cell potential value and fast corrosion rate. During period for 14 weeks. corrosion rate was not severe. So, it can be concluded that only carbonation attack on concrete doesn't severly deteriorated except very poor qualitified concrete.

• Corrosion Science and Technology
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• v.11 no.5
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• pp.157-164
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• 2012

In first experiment series, this paper is devoted for examining progress of reinforcement corrosion due to carbonation in concrete and to quantify uncarbonation depth to protect reinforcement from corroding. The tolerance of cover depth should be considered in order to prevent carbonation-induced corrosion. From the relationship between the weight loss of reinforcement and corrosion current density for a given time, therefore, the tolerance of cover depth to prevent carbonation-induced corrosion is computed. It is observed that corrosion occurs when the distance between carbonation front and reinforcement surface (uncarbonated depth) is smaller than 5 mm.As a secondary purpose of this study, it is investigated to examine the interaction between carbonation and chloride penetration and their effects on concrete. This was examined experimentally under various boundary conditions. For concrete under the double condition, the risk of deterioration due to carbonation was not severe. However, it was found that the carbonation of concrete could significantly accelerate chloride penetration. As a result, chloride penetration in combination with carbonation is a serious cause of deterioration of concrete.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.05a
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• pp.30-31
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• 2019

Carbonation of the root concrete reduces the durability of the reinforced concrete, and it is important to check the carbonation resistance of the concrete to ensure the durability of the reinforced concrete structure. In this study, a basic study on the prediction of carbonation progress was conducted by considering the mixing conditions of concrete using deep learning algorithm during the theory of artificial neural network theory. The data used in the experiment used values that converted the carbonation velocity coefficient obtained from the mixing conditions of concrete and the accelerated carbonation experiment into the actual environment. The analysis shows that the error rate of the deep learning model according to the Hidden Layer is the best for the model using five layers, and based on the five Hidden layers, we want to verify the predicted performance of the carbonation speed coefficient of the carbonation test specimen in which the exposure experiment took place in the real environment.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.60-61
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• 2019

Concrete carbonation is one of the factors that reduce the durability of concrete. In modern times, due to industrialization, the carbon dioxide concentration in the atmosphere is increasing, and the impact of carbonation is increasing. So, it is important to understand the carbonation resistance according to the concrete compounding to secure the concrete durability life. In this study, we want to predict the concrete carbonation velocity coefficient, which is an indicator of the carbonation resistance of concrete, through the deep learning algorithm, and to find the activation function suitable for the prediction of carbonation rate coefficient as a process to determine the learning accuracy through the deep learning algorithm. In the scope of this study, using the ReLU function showed better accuracy than using other activation functions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.5
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• pp.27-33
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• 2014

Electrical resistivity of concrete can be measured in a more rapid and simple way for estimating durability of the concrete, however, carbonation causes a result of misleading for durability testing because carbonation leads to a significant reduction in the permeability and porosity of concrete. The purpose of this study is to estimate and quantify the effect of carbonation of concrete on a surface electrical resistivity measurement. Samples of three mixes with difference w/c were prepared and exposed in a carbonation chamber for 330 days. The results show that carbonation leads high electrical resistivity. The increase is substantial and has been shown to proportional to the extent of the carbonation by some of extent. The relationship between electrical resistivity and carbonation depth is taken in the study. Resistivity ratio of carbonated concrete to air concrete decreased significantly from the specific carbonation depth, however, resistivity ratio of carbonated concrete to air concrete had a linear relation with carbonation depth. From the relationship between electrical resistivity and carbonation depth, it is expected that the result should be subsequently used as a calibration curve for estimating carbonated concrete to overcome the interruption effect of carbonation on regular measurements of the electrical resistivity.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Structural Engineering and Mechanics
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• v.63 no.1
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• pp.55-64
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• 2017

The paper concerns concrete carbonation, the phenomena that occurs in every type of climate, especially in urban-industrial areas. In European Standards, including Eurocode (EC) for concrete structures the demanded durability of construction located in the conditions of the carbonation threat is mainly assured by the selection of suitable thickness of reinforcement cover. According to EC0 and EC2, the thickness of the cover in the particular class of exposure depends on the structural class/category and concrete compressive strength class which is determined by cement content and water-cement ratio (thus the quantitative composition) but it is not differentiated for various cements, nor additives (i.e., qualitative composition), nor technological types of concrete. As a consequence the selected thickness of concrete cover is in fact a far estimation - sometimes too exaggerated (too safe or too risky). The paper presents the elaborated "self-terminated carbonation model" that includes abovementioned factors and enables to indicate the maximal possible depth of carbonation. This is possible because presented model is a hyperbolic function of carbonation depth in time (the other models published in the literature use the parabolic function that theoretically assume the infinite increase of carbonation depth value). The paper discusses the presented model in comparison to other models published in the literature, moreover it contains the algorithm of concrete cover design with use of the model as well as an example of calculation of the cover thickness.