• Proceedings of the Korea Concrete Institute Conference
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• 1999.10a
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• pp.767-770
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• 1999

The carbonation process is affected both by the concrete material properties such as W/C ratio, types of cement and aggregated, admixture characteristics and the environmental factors such as CO2 concentration, temperature, humidity. Based on results of preliminary research on carbonation, this study is to propose a carbonation prediction model by taking into account of prediction model by taking into account of CO2 concentration and W/C ratio among major factors affecting the carbonation process.

• Geomechanics and Engineering
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• v.18 no.4
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• pp.373-389
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• 2019

It is important to study the problem of durability for tunnel structures. As a main influence on the durability of tunnel structures, carbonation-induced corrosion is studied. For the complicated environment of tunnel structures, based on the data samples from real engineering examples, the intelligent method (genetic programming) is used to construct the service life prediction model of tunnel structures. Based on the model, the prediction of service life for tunnel structures in carbonation environments is studied. Using the data samples from some tunnel engineering examples in China under carbonation environment, the proposed method is verified. In addition, the performance of the proposed prediction model is compared with that of the artificial neural network method. Finally, the effect of two main controlling parameters, the population size and sample size, on the performance of the prediction model by genetic programming is analyzed in detail.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.5
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• pp.81-88
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• 2007

Among deteriorations of concrete due to environmental exposure, carbonation problems of concrete structures have increased in urban and underground structures. But conventional carbonation-prediction equations that were proposed by foreign references, can not be applied directly to the prediction of carbonation for domestic concrete structures. The purpose of this study is to propose a prediction equation of carbonation depth by considering domestic exposure conditions of concrete structures. For the derivation of the equation, conventional carbonation-prediction equations are analyzed. Through considering the relationship between results of prediction equation and those of various domestic field survey data, the so-called correction factors for different domestic exposure condition of concrete structures are derived. Finally, a carbonation-prediction equation of concrete structures under domestic exposure conditions is proposed with consideration for concrete strength in core and correction factors.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2010.05a
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• pp.229-230
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• 2010

The Carbonation, one of the main deterioration factors of concrete, reduces capacity of members with providing rebar corrosion environment. Consequently it suggested standards of all countries of world, carbonation depth prediction equation of respective researchers and time to rebar corrosion initiation. As a result of carbonation depth prediction equation calculation, difference of time to rebar corrosion initiation is 149 years and difference of carbonation depth prediction equation is 162 years when water cement ratio is 50%. So a study on rebar corrosion with carbonation depth will need existing reliable data and verifications by experiment.

• 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.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.261-268
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• 2022

In this study, Long-term test specimens were tested in the outdoor exposure environment and the carbonation properies of concrete were analyzed. The test specimens were manufactured in 40 %, 50 % and 60 % according to the w/c ratio. Carbonation was measured at 3 years and 15 years of age. Based on the results, long-term carbonation prediction models(KICT model) were derived. As a result, carbonation increased according to the w/c. Based on the w/c 40 %, w/c 50 % increased about 1.8 times and w/c 60 % increased about 3.7 times. Comparison of carbonation according to age was that the carbonation at 15^th year was about 3 times higher that of 3^rd year. As results of comparing the KICT models and other carbonation prediction models, the carbonation prediction showed different values.

• Journal of the Korea Concrete Institute
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• v.22 no.2
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• pp.209-217
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• 2010

Recently, some mathematical models for the prediction on progress of carbonation of concrete were reported. These models take account for $CO_2$ diffusion and chemical reaction between $Ca(OH)_2$ and $CO_2$. Based on the assumption that $CO_2$ diffuses in the carbonation zone and reacts with $Ca(OH)_2$ at the outer face of carbonation zone and non-carbonation zone. In this study, a mathematical model to predict the progress of carbonation of concrete has been established based on the reducing concentration of $Ca(OH)_2$ in the carbonation progress zone, where $Ca(OH)_2$ reacts with $CO_2$ and $Ca(OH)_2$ and $CaCO_3$ coexist. Also, the prediction model of carbonation progress rate of concrete using the air permeability coefficient regarding to $CO_2$ diffusion is developed. As a result of this study, an expression, the model equation is obtained for the prediction of carbonation based on the time and interaction velocity between $CO_2$ and Ca(OH)$_2$ dependent air permeability coefficient. The prediction by the model satisfied the experimental data of the accelerated carbonation for painted concrete. Consequently, the model can predict the rate of carbonation and the potential service life of concrete structure exposed to atmosphere.

• 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.

• Journal of the Korea Concrete Institute
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• v.12 no.5
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• pp.69-80
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• 2000

This paper presents the prediction of remaining service life of the concrete due to steel corrosion caused by the following three cases; carbonation, using sea sand and using deicing salts. The assessment of initiation period was generalized considering the existing perdiction models in the literature, corrosion experiment and field assessment. To evaluate the prediction equation of rust growth, the corrosion accelerating experiments was performed. The polarization resistance was measured by potentiostat and the conversion coefficient of polarzation resistance to corrosion rate was determined by the measurement of real mass loss. Chloride content, carbonation, cover depth, relative humidity, water-cement ratio(W/C), and the use of deicing salts were taken into account and the resulting prediction equation of rust growth was proposed on the basis of these properties. The proposed equation is to predict the rust growth during any specified period of time and be effective in particular for predicting service life of concrete in the case of using sea sand.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.1
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• pp.149-158
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• 2002

The carbonation process is affected by both the concrete material properties such as W/C ratio, types of cement and aggregates, admixture characteristics and the environmental factors such as $CO_2$ concentration, temperature, humidity. Based on results of preliminary study on carbonation, this study is to develop a carbonation prediction model by taking account of $CO_2$ concentration, temperature, humidity ad W/C ratio among major factor affecting the carbonation process. And to constitute a model formula which correspond to the mix design of the nuclear power plant, test coefficient that correspond to the design of the nuclear power plant is obtained based on the results of accelerated carbonation test. Also a field coefficient which is obtained based on results of the field examination is included to improve the conformity of the actual structures of nuclear power plant.