دانلود مقاله ISI انگلیسی شماره 135213
ترجمه فارسی عنوان مقاله

مدل زمانی زمانبندی کلراید سطح برای پیش بینی های طول عمر مفید است

عنوان انگلیسی
A time-variant model of surface chloride build-up for improved service life predictions
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
135213 2017 47 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Cement and Concrete Composites, Volume 84, November 2017, Pages 99-110

پیش نمایش مقاله
پیش نمایش مقاله  مدل زمانی زمانبندی کلراید سطح برای پیش بینی های طول عمر مفید است

چکیده انگلیسی

Current service life models for predicting the time to chloride-induced corrosion initiation of steel reinforcement in concrete structures are based on a hypothesized surface chloride concentration, Cs, as a boundary condition. This is either assumed to be constant or varies with time, generally disregarding other factors that influence Cs. For example, Fickian models use a constant Cs and existing time-variant models assume Cs is only a function of time. In this paper, an improved time-variant Cs model is hypothesized using general physical concepts and is then validated by an empirical study. The proposed model, as opposed to the existing time-variant models, not only accounts for the variability of Cs with exposure time but also incorporates the effects of time to exposure and the effects of the concentration of chlorides in the exposure environment. The model assumes that Cs is sigmoidal in shape with an asymptote that is a function of the concentration of chlorides in the environment. The input variables for the proposed model were selected based on best subset sampling analysis on the results of the experimental work to determine the influence of water-to-cement ratio, time to exposure, the concentration of chlorides in the exposure environment, and exposure time on Cs. The accuracy of the proposed model is assessed versus existing time-variant Cs models and the results indicate that the proposed model better predicts the Cs in concrete exposed to chlorides. Moreover, results of service life predictions using the proposed model show that the proposed model yields more accurate results, better protecting owners from financial and engineering risks.