In reinforced concrete (RC) structures, concrete carbonation depth is an important criterion for the deterioration of durability of RC structures. Concrete carbonation is influenced by multiple factors such as chloride attack, crack, concrete compressive strength, etc. However, due to its complex mechanism, most previous studies considered only one or two deterioration factors to estimate the concrete carbonation depth. In this study, therefore, inspection data were collected from 8 buildings, and the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm that estimates the concrete carbonation depth of RC structures has been proposed. The proposed ANFIS model provided good estimations on the carbonation depths.

• 조해창(서울시립대학교 건축학부, 박사과정) | Cho, Hae-Chang
• 주현진(서울시립대학교 건축학부, 박사과정) | Ju, Hyun-Jin
• 오재열(서울시립대학교 건축학부, 박사과정) | Oh, Jae-Yuel
• 이경진(한국전력공사 전력연구원, 부장) | Lee, Kyung Jin
• 김강수(서울시립대학교 건축학부, 교수) | Kim, Kang Su