The corrosion of reinforcement has always been a problem to be solved in the field of architecture. In this paper, the corrosion characteristics of chromium alloy steel under different pH conditions are studied. The impedance characteristics and equivalent circuit are predicted by neural network model. First of all, in simulated pore solution with different pH values, the characteristics of Nyquist impedance spectroscopy of the whole chromium alloy under passivation stage and the damaged passivation film of reinforcing bars under initial corrosion stage have been found. Then, according to the difference of impedance characteristics under different pH values, different equivalent circuits have been established and [Formula: see text] values of different equivalent circuits under different chloride ion concentration have been calculated. By fitting the electrochemical parameters of the equivalent circuit with [Formula: see text] values, the equivalent circuit model which can be predicted by neural network has good consistency with the equivalent circuit which can be predicted by [Formula: see text] values.

中文翻译：

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基于神经网络的模拟混凝土孔隙解中钢筋腐蚀预测与分析

钢筋锈蚀一直是建筑领域亟待解决的问题。本文研究了铬合金钢在不同pH条件下的腐蚀特性。通过神经网络模型预测阻抗特性和等效电路。首先，在不同pH值的模拟孔隙溶液中，发现了钝化阶段全铬合金的奈奎斯特阻抗谱特征和初始腐蚀阶段钢筋钝化膜的破坏情况。然后，根据不同pH值下阻抗特性的差异，建立了不同的等效电路，计算了不同氯离子浓度下不同等效电路的[公式：见正文]值。