Service Life Design for Concrete Engineering in Marine Environments of Northern China Based on a Modified Theoretical Model of Chloride Diffusion and Large Datasets of Ocean Parameters

Taotao Feng , Hongfa Yu , Yongshan Tan , Haiyan Ma , Mei Xu , Chengjun Yue

Engineering ›› 2022, Vol. 17 ›› Issue (10) : 123 -139.

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Engineering ›› 2022, Vol. 17 ›› Issue (10) : 123 -139. DOI: 10.1016/j.eng.2021.03.030
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Service Life Design for Concrete Engineering in Marine Environments of Northern China Based on a Modified Theoretical Model of Chloride Diffusion and Large Datasets of Ocean Parameters

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Abstract

In this study, through experimental research and an investigation on large datasets of the durability parameters in ocean engineering, the values, ranges, and types of distribution of the durability parameters employed for the durability design in ocean engineering in northern China were confirmed. Based on a modified theoretical model of chloride diffusion and the reliability theory, the service lives of concrete structures exposed to the splash, tidal, and underwater zones were calculated. Mixed concrete proportions meeting the requirement of a service life of 100 or 120 years were designed, and a cover thickness requirement was proposed. In addition, the effects of the different time-varying relationships of the boundary condition (Cs) and diffusion coefficient (Df) on the service life were compared; the results showed that the time-varying relationships used in this study (i.e., Cs continuously increased and then remained stable, and Df continuously decreased and then remained stable) were beneficial for the durability design of concrete structures in marine environment.

Keywords

Large datasets / Modified theoretical model / Reliability theory / Service life / Boundary condition / Diffusion coefficient

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Taotao Feng, Hongfa Yu, Yongshan Tan, Haiyan Ma, Mei Xu, Chengjun Yue. Service Life Design for Concrete Engineering in Marine Environments of Northern China Based on a Modified Theoretical Model of Chloride Diffusion and Large Datasets of Ocean Parameters. Engineering, 2022, 17(10): 123-139 DOI:10.1016/j.eng.2021.03.030

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