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Title |
Relation between the Apparent Chloride Diffusion Coefficient under Various Marine Exposure Conditions and Passed Charge by Indoor Rapid Chloride Penetration Tests
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Authors |
권성준(Seung-Jun Kwon) ; 고경택(Kyung-Taek Koh) ; 김경철(Kyong Chul Kim) ; 임광모(Kwang-Mo Lim) ; 윤용식(Yong-Sik Yoon) |
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DOI |
https://doi.org/10.4334/JKCI.2025.37.2.151 |
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Keywords |
겉보기 염화물 확산계수; 통과전하량; 상관관계; 해양 폭로 조건; 촉진 염화물 확산시험 apparent chloride diffusion coefficient; passed charge; the condition of marine environment exposure; rapid chloride penetration test |
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Abstract |
The rapid chloride penetration test (RCPT) is widely used for evaluating and interpreting the chloride durability ofconcrete structures due to its relatively simple procedure and short testing time. However, its inherent characteristic of electricallyaccelerating chloride ion movement often leads to overly conservative durability assessments. In this study, the relationship betweenlong-term outdoor marine exposure tests performed under multi-level exposure conditions and rapid chloride permeability testsconducted indoors was analyzed. Data from previous studies were utilized, involving both long-term marine exposure tests and RCPTassessments on concrete mixtures incorporating ordinary Portland cement (OPC), ground granulated blast furnace slag (GGBFS), andfly ash (FA) with varying water-to-binder ratios. The experimental results were analyzed to assess the effects of concrete age andwater-to-binder ratio on chloride durability. Furthermore, a multiple linear regression analysis was performed, using passed charge andexposure duration (aged days) as input variables and the apparent chloride diffusion coefficient as the output variable, to evaluate therelationship between the two test methods. The analysis revealed determination coefficients (R²) ranging from 0.75 to 0.94 across allconditions, indicating a strong correlation between the two methods. However, relatively lower determination coefficients wereobserved for certain mixtures, and these were attributed to structural limitations of the linear regression model.
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