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Article analysis:

The article discusses the development of probabilistic capacity models for reinforced concrete (RC) columns subject to corrosion. The authors highlight the importance of considering uncertainties in predicting the onset and progression of corrosion, as it can significantly weaken structural elements and increase vulnerability to extreme loads and natural hazards. The paper presents a probabilistic model for chloride-induced corrosion and a time-dependent corrosion rate function to estimate the loss of steel cross-section due to corrosion.

The authors also merge the work by Gardoni et al. and Choe et al. with the probabilistic diffusion model described above to develop probabilistic capacity models for corroding RC columns. They assume that structural capacities do not vary prior to time Tcorr, which is defined as the time at which the chloride concentration in the concrete reaches the critical level at the depth of steel reinforcement.

Overall, the article provides valuable insights into developing probabilistic capacity models for corroding RC columns. However, there are some potential biases and limitations in this study that need consideration.

Firstly, while the authors acknowledge that there are uncertainties in predicting corrosion onset and progression, they do not discuss how these uncertainties may affect their results or how they have addressed them in their analysis. This lack of discussion may lead readers to question whether their findings are robust enough to be applied in practice.

Secondly, while they limit their scope to consider only loss of reinforcement area due to corrosion, other factors such as loss of pullout resistance of reinforcement bars and cracking in cover concrete may also contribute significantly to loss of load-carrying capacity in RC structures subjected to corrosion.

Thirdly, although they present sensitivity analysis results on identifying parameters that most affect reliability, they do not explore counterarguments or alternative approaches that could potentially improve their models' accuracy or applicability.

Finally, while they mention potential applications for their developed models such as predicting service-life of existing and new structures and life-cycle cost analysis for RC structures, they do not provide any evidence or examples of how their models have been applied in practice.

In conclusion, the article provides valuable insights into developing probabilistic capacity models for corroding RC columns. However, there are potential biases and limitations that need consideration, such as uncertainties in predicting corrosion onset and progression, limited scope of considering only loss of reinforcement area due to corrosion, lack of exploration of counterarguments or alternative approaches, and absence of evidence on practical applications.