Evaluation of effective cross-area of reinforced concrete wall considering chloride diffusion using ANN

• Published in: Nuclear engineering and technology Vol. 56; no. 10; pp. 4254 - 4262
• Main Authors: Yang, Hyeon-Keun, Park, Jun-Hee
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2024; Elsevier; 한국원자력학회
• Subjects: Chloride diffusion factor; Fragility evaluation; Reinforced concrete; Reinforcing bar corrosion; Shear strength; 원자력공학; Shear strength; Chloride diffusion factor; Reinforcing bar corrosion; Fragility evaluation; Reinforced concrete
• ISSN: 1738-5733; 2234-358X
• DOI: 10.1016/j.net.2024.05.031

Reinforced concrete structures are subject to exposure to chloride ions in the air, leading to chloride penetration, and carbonation attacks resulting from exposure to carbon dioxide. This chemical degradation process induces corrosion of reinforcing bars within concrete, significantly impacting durability. Structures situated in coastal areas, such as nuclear power plants, are particularly susceptible to rapid chloride penetration due to the high chloride concentration in the air. This study utilizes existing experimental data to forecast the chloride diffusion coefficient employing artificial neural network (ANN technology). The total number of experimental data was 535 gathered from 18 papers. Through analysis of the chloride coefficient and predicted degradation depth, the effective cross-sectional area of concrete is examined, and the deterioration of wall performance is forecasted.