Application of durability indexes in probabilistic modelling for chloride ingress of RC members

Authors: R.N. Muigai, P. Moyo, M.G. Alexander, H.D. Beushausen
Paper category: conference
Book title: Concrete Durability and Service Life Planning – ConcreteLife’09
Editor(s): K. Kovler
Print ISBN: 978-2-35158-074-5
e-ISBN: 978-2-35158-085-1
Publisher: RILEM Publications SARL
Publication year: 2009
Pages: 408 – 415
Total Pages: 8
Language: English

Abstract: A significant amount of South African infrastructure is situated in coastal areas. Chlorides from marine exposure initiate corrosion of reinforced concrete structures which, if not intercepted, leads to the eventual deterioration of the entire structure. A reliable prediction method of chloride ingress in concrete is one of the key elements to consider in the design process if the structure is expected to be durable and cost efficient.
In South Africa, durability indexes (DIs) have been adopted as engineering measures of the potential resistance of concrete cover to the transport mechanisms of gaseous diffusion, water absorption and chloride diffusion.
To ensure the ability of the concrete to resist the penetration of aggressive agents during the intended service life this study applied a combination of a service life prediction (SLP) model and accelerated testing results from the chloride conductivity test (CCT), which is one of the durability index tests.
The CCT measures the resistance of concrete to chloride transport under the action of an applied voltage across the specimen. The CCT is useful in obtaining material parameter data required to support the use of SLP models.
The SLP model is based on modified Fick’s second law of diffusion and includes: environmental parameters (surface chloride concentration and critical chloride concentration derived from both field and laboratory tests); a material parameter (diffusion coefficient which is determined from the CCT values) and a geometric parameter (cover depth from field measurements of existing structures).
The natural variability in the concrete material makes it inevitable to use probability theory to formally include the uncertainties in the model parameters and to assess the implications on the predicted service life. Statistical information for each parameter was exploited in this study to provide improved uncertainty estimates of the predicted service life, which was expressed in terms of the probability of failure.

Online publication: 2012-05-16
Publication Type: full_text
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