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

• Computers and Concrete
• /
• v.15 no.5
• /
• pp.847-864
• /
• 2015

In this paper, a reaction-diffusion model of carbonation process in self-compacting concrete (SCC) was realized with a consideration of multi-field couplings. Various effects from environmental conditions, e.g. ambient temperature, relative humidity, carbonation reaction, were incorporated into a numerical simulation proposed by ANSYS. In addition, the carbonation process of SCC was experimentally investigated and compared with a conventionally vibrated concrete (CVC). It is found that SCC has a higher carbonation resistance than CVC with a comparable compressive strength. The numerical solution analysis agrees well with the test results, indicating that the proposed model is appropriate to calculate and predict the carbonation process in SCC. The parameters sensitivity analysis also shows that the carbon dioxide diffusion coefficient and moisture field are essentially crucial to the carbonation process in SCC.

• Computers and Concrete
• /
• v.32 no.2
• /
• pp.119-138
• /
• 2023

Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.

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

• Corrosion Science and Technology
• /
• v.6 no.4
• /
• pp.186-192
• /
• 2007

Carbonation is one of the most important factors causing the corrosion of reinforcement concrete. Nevertheless, experimental studies on the concrete carbonation have not been carried out sufficiently because of the slow process of carbonation process. Therefore, this study adopts an experimental system exploiting a vacuum instrument that has been recently developed to accelerate carbonation instead of existing experimental system to conduct rapid carbonation tests on Portland cement and fly-ash cement concretes. Test results revealed that, compared to water-cement ratio of 40%, the carbonation depth increases from 103% to 138% for an increase of water-cement ratio from 45% to 60%. These results are larger than the carbonation depths obtained by mathematical model, and such difference is increasing with larger water-cement ratios. The results also indicated that larger fly-ash contents lead to sharp increase of the carbonation depth, which is in agreement with previous experimental researches. The adoption of the new accelerated carbonation test system enabled to shorten effectively the time required to produce experimental data compared to the existing carbonation test method. The experimental data obtained in this study together with ongoing acquisition of data using the new carbonation test method are expected to contribute in the understanding of the carbonation process of concrete structures in Korea.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.14 no.1
• /
• pp.141-147
• /
• 2010

The rate of carbonation is usually low in the natural environment due to the low $CO_2$ concentration in the atmosphere. Therefore, investigation of carbonation is usually conducted under accelerated testing conditions so as to speed up the process. This study is to predict carbonation progress by mathematical model, based on the diffusions of $CO_2$ and its reaction with $Ca(OH)_2$ in carbonation progressing region, in the atmosphere. To predict of carbonation progress in the atmosphere, we adopted a diffusion coefficient of $CO_2$ that agreed well the experimental value obtained by the accelerated carbonation test. Consequently the model can predict the rate of carbonation of concrete exposed in the atmosphere regardless of finishing materials.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.4
• /
• pp.220-227
• /
• 2021

PCT (Power Cable Tunnel) and UT (Utility Tunnel), which are non-transport underground infrastructures, are mostly RC (Reinforced Concrete) structures, and their durability decreases due to the deterioration caused by carbonation over time. In particular, since the rate of carbonation varies by use and region, a predictive model based on actual carbonation data is required for individual maintenance. In this study, a carbonation prediction model was developed for non-transport underground infrastructures, such as PCT and UT. A carbonation prediction model was developed using multiple regression analysis and deep neural network techniques based on the actual data obtained from a safety inspection. The structures, region, measurement location, construction method, measurement member, and concrete strength were selected as independent variables to determine the dependent variable carbonation rate coefficient in multiple regression analysis. The adjusted coefficient of determination (Ra2) of the multiple regression model was found to be 0.67. The coefficient of determination (R2) of the model for predicting the carbonation of non-transport underground infrastructures using a deep neural network was 0.82, which was superior to the comparative prediction model. These results are expected to help determine the optimal timing for repair on carbonation and preventive maintenance methodology for PCT and UT.

• Computers and Concrete
• /
• v.15 no.2
• /
• pp.231-257
• /
• 2015

This paper discusses the spatial variability of the carbonation depth caused by the mesoscopic structure of the concrete and the influence of the spatial variability on the thickness of the concrete cover. To conduct the research, a method to generate the random aggregate structure (RAS) based on polygonal particles and a simplified numerical model of the concrete carbonation at meso-scale are firstly developed. Based on the method and model, the effect of the aggregate properties including shape, content and gradation on the spatial variability of the carbonation depth is comprehensively studied. The results show that a larger degree of the spatial variability will be obtained by using (1) the aggregates with a larger aspect ratio; (2) a larger aggregate content; (3) the gradation which has more large particles. The proper sample size and model size used in the analysis are also studied. Finally, a case study is conducted to demonstrate the influence of the spatial variability of the carbonation depth on the proper thickness of the concrete cover. The research in this paper not only provides suggestions on how to decrease the spatial variability, but also proposes the method to consider the effect of the spatial variability in designing the thickness of the concrete cover.

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

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