2005•Journal of Structural EngineeringRequires access

Predicting the Likelihood and Extent of Reinforced Concrete Corrosion-Induced Cracking

Kim Anh T. Vu, Mark G. Stewart

Open publisher page 169 citations

Abstract

Corrosion-induced cracking is observed to vary spatially over concrete surfaces. A two-dimensional spatial time-dependent reliability model is developed to predict the likelihood and extent of corrosion-induced cracking. The spatial variability of concrete cover, concrete compressive strength, and surface chloride concentration are considered in the spatial time-dependent reliability model. The reliability analysis predicts: (1) probability of the first incidence of cracking, (2) proportion of an area subject to severe cracking, and (3) probability that a given percentage of a concrete surface has cracked. Corrosion-induced crack initiation and propagation models are developed for limit crack widths up to 1 mm. The present paper presents results for a typical reinforced concrete bridge deck. The effect of concrete cover, concrete quality, limit crack width, and environment are considered. It was shown that for poor durability design specifications the likelihood and extent of spalling is high. When combined with a life-cycle cost analysis, this predictive capability enables the extent of future repair costs to be estimated and the optimal durability design specifications or repair/maintenance strategies determined.

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What this paper is about

Corrosion-induced cracking is observed to vary spatially over concrete surfaces. A two-dimensional spatial time-dependent reliability model is developed to predict the likelihood and extent of corrosion-induced cracking. The spatial variability of concrete cover, concrete compressive strength, and surface chloride concentration are considered in the spatial time-dependent reliability model. The reliability analysis predicts: (1) probability of the first incidence of cracking, (2) proportion of an area subject to severe cracking, and (3) probability that a given percentage of a concrete surface has cracked. Corrosion-induced crack initiation and propagation models are developed for limit crack widths up to 1 mm. The present paper presents results for a typical reinforced concrete bridge deck. The effect of concrete cover, concrete quality, limit crack width, and environment are considered. It was shown that for poor durability design specifications the likelihood and extent of spalling is high. When combined with a life-cycle cost analysis, this predictive capability enables the extent of future repair costs to be estimated and the optimal durability design specifications or repair/maintenance strategies determined.

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Available abstract

Corrosion-induced cracking is observed to vary spatially over concrete surfaces. A two-dimensional spatial time-dependent reliability model is developed to predict the likelihood and extent of corrosion-induced cracking. The spatial variability of concrete cover, concrete compressive strength, and surface chloride concentration are considered in the spatial time-dependent reliability model. The reliability analysis predicts: (1) probability of the first incidence of cracking, (2) proportion of an area subject to severe cracking, and (3) probability that a given percentage of a concrete surface has cracked. Corrosion-induced crack initiation and propagation models are developed for limit crack widths up to 1 mm. The present paper presents results for a typical reinforced concrete bridge deck. The effect of concrete cover, concrete quality, limit crack width, and environment are considered. It was shown that for poor durability design specifications the likelihood and extent of spalling is high. When combined with a life-cycle cost analysis, this predictive capability enables the extent of future repair costs to be estimated and the optimal durability design specifications or repair/maintenance strategies determined.

Key concepts: Spall, Cracking, Durability, Corrosion, Concrete cover, Structural engineering, Reliability (semiconductor), Materials science

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