Abstract
The deterioration and cracking of reinforced concrete (RC) bridges due to the chloride-induced corrosion of steel reinforcement is an inherently time-dependent stochastic phenomenon. In the current practice of bridge management systems, however, the determination of the condition states of deteriorated bridges is highly dependent on the opinion of experienced inspectors. Taking such complexity into account, the current paper presents a new stochastic predictive methodology using a non-homogeneous Markov process, which directly relates the visual inspection data (corrosion rate and crack widths) to the structural vulnerability of deteriorated concrete bridges. This methodology predicts the future condition of corrosion-induced damage (concrete cracking) by linking structural vulnerability analysis and a discrete-time Markov chain model. The application of the proposed methodology is demonstrated through a case-study corrosion-damaged RC bridge pier.This article is part of a discussion meeting issue ‘A cracking approach to inventing new tough materials: fracture stranger than friction’.
Highlights
Chloride-induced corrosion of reinforcing steel, due to aggressive coastal areas or de-icing salt in winter, is the most significant environmental threat affecting the performance of ageing reinforced concrete (RC) structures and bridges worldwide [1]
A discrete-time Markov chain model is presented to predict the condition state (CS) of corroded bridges based on the corrosion-induced crack width, which can be measured on site during the inspection
Using structural vulnerability analysis (SVA), the time-dependent structural performance of deteriorated structures is related to the corrosion-induced crack-based CS probabilities
Summary
Chloride-induced corrosion of reinforcing steel, due to aggressive coastal areas or de-icing salt in winter, is the most significant environmental threat affecting the performance of ageing reinforced concrete (RC) structures and bridges worldwide [1]. There is a significant paucity in the literature to directly quantify and predict the relationship between the observable extent of deterioration (i.e. corrosion rate and corrosioninduced concrete cracking) and the structural performance of ageing bridges through a generic stochastic predictive model. Using a Markov chain-based deterioration model, this paper aims to create a stochastic platform to predict the time-variant performance limit states using SVA of corroded RC bridges, which links the visual inspection data to the numerically simulated residual structural capacity. The proposed methodology employs a state-based discrete-time Markov chain model, where the CSs of the system are defined based on a possible range of visible corrosion-induced crack sizes. Pstate corrosion Markov chain state probabilities (§3d) 1.0 state i time probability of the structure to be in state i at time t linking Markov chain model with SVA (§3e(iii))
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