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Chloride ingress and carbonation are the main factors leading to corrosion initiation of reinforced concrete structures. Lifetime assessment and maintenance optimisation of these deteriorating structures has been based for several practical applications on analytical predictive models. The literature review shows that there are numerous analytical models of chloride ingress and carbonation. It was also found that comprehensive lifetime assessment requires that the selected models be able to propagate uncertainties in a correct manner. Thus, this article proposes a methodology for probabilistic sensitivity analysis of analytical models for chloride ingress and carbonation. Given that different parameter notations were found for each model, a homogenisation of notations and generic forms are first proposed. The sensitivity analysis is based on the following indicators: elasticity coefficient, Pearson coefficient, bias of the mean and standard deviation of the response of the model. The results highlighted which parameters are influential for each model by considering three concrete types. Overall results indicated a higher effect of the non-linearity of the models on the sensitivity indicators. It was also found that the level of influence of each parameter depends also on the concrete type and the duration of the exposure to chlorides or carbon dioxide. [ABSTRACT FROM AUTHOR]

This paper addresses the topic of long-term characterisation and probabilistic modelling of chloride ingress into reinforced concrete (RC) structures. Since the corrosion initiation stage may cover various decades, normal tests which simulate chloride penetration into concrete in laboratory conditions as the same as natural conditions, will require significant experimental times. Hence, long-term lifetime assessment of RC structures under chloride attack remains still a challenge. In practice, this problem is solved through the use of accelerated tests which speed up the chloride ingress rate and provide valuable mid- and long-term information on the chloride penetration process. Nevertheless, this information cannot be directly used for parameter statistical characterisation if the equivalent times required in natural conditions to reach the same chloride concentrations in the accelerated tests are unknown. Consequently, this study proposes a novel iterative approach based on Bayesian network updating to estimate chloride ingress model parameters from the data obtained under accelerated laboratory conditions. The Bayesian network structure and iterative approach are first tested with numerical evidences. Thereafter, the complete proposed methodology is verified with results from real experimental measurements. The results indicate that combining data from normal and accelerated tests significantly reduces the statistical characterisation error of model parameters. [ABSTRACT FROM AUTHOR]

Probabilistic modelling of deterioration processes is an important task to plan and quantify maintenance operations of structures. Relevant material and environmental model parameters could be determined from inspection data; but in practice, the number of measures required for uncertainty quantification is conditioned by time-consuming and expensive tests. The main objective of this study was to propose a method based on Bayesian networks for improving the identification of uncertainties related to material and environmental parameters of deterioration models when there is limited available information. The outputs of the study are inspection configurations (in space and time) that could provide an optimal balance between accuracy and cost. The proposed methodology was applied to the identification of random variables for a chloride ingress model. It was found that there is an optimal discretisation for identifying each model parameter and that the combination of these configurations minimises identification errors. An illustration to the assessment of the probability of corrosion initiation showed that the approach is useful even if inspection data are limited. [ABSTRACT FROM AUTHOR]

Reinforced concrete (RC) structures placed in chloride-contaminated environments are subjected to deterioration processes that affect their performance, serviceability and safety. Chloride ingress leads to corrosion initiation and its interaction with service loading could reduce its operational life. Chloride ingress and corrosion propagation are highly influenced by weather conditions in the surrounding environment including climate change. Therefore, both structural design and maintenance should be adapted to these new environmental conditions. This study focuses on the assessment of the costs and benefits of climate adaptation strategies for existing RC structures subjected to chloride ingress and climate change. We studied RC structures built at different periods under different construction standards in France. The cost-effectiveness of adaptation measures was measured in terms of the benefit-to-cost ratio (BCR) and the probability that BCR exceeds unity – i.e. Pr(BCR>1). The results of the paper could provide practical advice to policy-makers to improve the management of existing RC structures under a changing climate by discussing the influence of the following factors on the mean BCR and Pr(BCR>1): specific exposure conditions, climate change scenarios, risk reduction due to the implementation of adaptation strategies, type of structural component, years of construction and adaptation, discount rates and damage costs. [ABSTRACT FROM PUBLISHER]

Carbonation affects the performance, serviceability and safety of reinforced concrete (RC) structures when they are placed in environments with important CO2concentrations. Since the kinetics of carbonation depends on parameters that could be affected by climate change (temperature, atmospheric CO2pressure and relative humidity (RH)), this study aims at quantifying the effect of climate change on the durability of RC structures subjected to carbonation risks. This work couples a carbonation finite element model with a comprehensive reliability approach to consider the uncertainties inherent to the deterioration process. The proposed methodology is applied to the probabilistic assessment of carbonation effects for several cities in France under various climate change scenarios. It was found that climate change and local RH have a significant impact on corrosion initiation risks. [ABSTRACT FROM AUTHOR]

An introduction to the special issue of the journal is presented in which the editor discusses various reports within the issue on topics including Climate Adaptation Engineering, climate change consequences, and types of hazards for various infrastructure.