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Cellular Automata Simulations of Random Pitting Process on Steel Reinforcement Surface

Ying Wang*, Haoran Shi, Shibo Ren

Jiangsu Key Laboratory of Engineering Mechanics, Southeast University, Nanjing, 211189, China

* Corresponding Author: Ying Wang. Email: email

Computer Modeling in Engineering & Sciences 2021, 128(3), 967-983. https://doi.org/10.32604/cmes.2021.015792

Abstract

The corrosion of reinforcement in the concrete will cause the effective cross-sectional area of reinforcement to be weakened and the performance of reinforcement to change and lead to the degradation of the bond behavior between reinforcement and concrete, which can seriously affect the mechanical properties of the structural elements. Therefore, it is of great practical significance to accurately simulate the corrosion morphology and the corrosion products of reinforcement. This paper improves the previous cellular automata models and establishes a new cellular automata model framework for simulating the random pitting corrosion process of reinforcement in concrete. This model defines the detailed local evolution laws of material transformation, penetration and diffusion processes during the corrosion. Meanwhile, based on the spatial inhomogeneity of corrosion, three parameters are introduced into the model: The dissolution probability parameter p, the local corrosion space parameter λ and the local corrosion probability parameter ɛ, which establishes a parameterized model of corrosion probability. The research results show that the common steel reinforcement corrosion morphology can be obtained by adjusting the parameters. The volume expansion rate of the corrosion products is about 2, which is consistent with the relevant experimental research results. The cellular automata model in this paper can simulate the common steel reinforcement corrosion morphology and corrosion products in engineering.

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Wang, Y., Shi, H., Ren, S. (2021). Cellular Automata Simulations of Random Pitting Process on Steel Reinforcement Surface. CMES-Computer Modeling in Engineering & Sciences, 128(3), 967–983.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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