Corrosion behavior of steel fiber reinforced concrete under ambipolar stray current interference

Liu, Hu; Li, Kaibin; Li, Yongqiang; Liu, Suyun; Dong, Zhijun; Cui, Hongzhi; Liu, Wei

doi:10.1016/j.conbuildmat.2023.134302

Cited by 1 publication

(1 citation statement)

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“…Based on the fluctuation of oxygen concentration, they proposed the concept of quasi steady state and systematically compared the corrosion behavior of different pore saturations. Liu et al [14] used various methods such as corrosion morphology observation, weight loss experiments, and electrochemical testing to deeply explore the influence of stray currents with bipolar characteristics on the corrosion behavior of steel fibers in simulated pore solutions and concrete environments. They found that the short-term corrosion effect of stray currents on steel fibers is relatively limited.…”

Section: Introductionmentioning

confidence: 99%

Erosion depth prediction of chloride ions under stray current using FEM based CNNs

Li,

Zhang,

Liu

et al. 2024

Meas. Sci. Technol.

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Stray currents can accelerate the transport of corrosive ions, especially Cl-, in concrete materials, which is very detrimental to structural safety. Effectively predicting the erosion depth of Cl- is crucial for evaluating structural safety. This article is based on a finite element model and verifies the erosion depth of Cl- under different voltages, Cl- concentrations, and corrosion time through experimental data. A polynomial was used to fit the quantitative relationship between erosion depth, Cl- concentrations, and corrosion time under single voltage condition. However, this formula only applies to a single voltage and has too many parameters. Therefore, this article also established a CNNs regression model to predict the depth of Cl-, and the results showed the multiple regression ability of CNNs. It has been proven that CNNs can accurately predict the erosion depth, which helps to accurately evaluate structural safety. After comparing experimental values, CNNs, ResNet, and ResNet-Attention, it was found that residual networks and attention mechanisms did not significantly improve the prediction accuracy of deep networks, which may be related to insufficient data volume. After expanding the dataset, ResNet performed the best overall, and ResNet-Attention had better testing performance, which is related to the powerful feature extraction ability of the attention mechanism.

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Section: Introductionmentioning

confidence: 99%