The characteristics and morphology of precipitated calcium carbonate (PCC) particles produced by carbonation process with various experimental conditions are investigated in this study. The crystal structures of PCC formed by carbonation process are calcite and aragonite. The crystal structure of PCC particles synthesized without adipic acid additive is calcite only, regardless of the reaction temperature. Needle-like shape aragonite phase started to form at reactor temperature of 80°C with the adipic acid additive. Particle size of the single phase calcite PCC synthesized without adipic acid additive is about 1 ~ 3 μm, with homogenous distribution. The aragonite PCC also shows uniform size distribution. The reaction temperature and concentration of adipic acid additive do not show any significant effects on the particle size distribution. Aragonite phase grown to a large aspect ratio of needle-like shape showed relatively improved whiteness. The measured whiteness value of single calcite phase is about 95.95, while that of the mixture of calcite and aragonite is about 99.11.

Carbonation of reinforced concrete is a major factor in the deterioration of reinforced concrete, and prediction of the resistance to carbonation is important in determining the durability life of reinforced concrete structures. In this study, basic research on the prediction of carbonation penetration depth of concrete using Deep Learning algorithm among artificial neural network theory was carried out. The data used in the experiment were analyzed by deep running algorithm by setting W/B, cement and blast furnace slag, fly ash content, relative humidity of the carbonated laboratory, temperature, CO2 concentration, Deep learning algorithms were used to study 60,000 times, and the analysis of the number of hidden layers was compared.

This study aimed to propose a reasonable performance evaluation process of freeze-thaw and carbonation depth on concrete structures, especially considering the time-dependent and environmental effects, to develop a long-term maintenance plan in Korea.