Chloride-induced corrosion significantly contributes to the degradation of reinforced concrete structures, making accurate predictions of chloride migration and its effects on material durability critical. This paper explores two modeling approaches to estimate the effective diffusion coefficient for chloride transport. The first approach follows Gehlen's interpretable diffusion model, which is based on established physical principles and incorporates time and temperature dependencies in predicting chloride migration. The second approach is a neural network-based method, where the neural network approximates the effective diffusion coefficient. In a subsequent step, the calibrated models are used to predict the penetration depth of the critical chloride content, taking into account the uncertainty in the critical chloride content. The models are calibrated using experimental data measured by a wire sensor installed in a concrete test bridge. The calibration results are compared to effective diffusion coefficients derived from drilling dust samples. A comparison of both approaches reveals the advantages of the physics-based model in terms of transparency and interpretability, while the neural network model demonstrates flexibility and adaptability in data-driven predictions. This study emphasizes the importance of combining traditional and machine learning-based methods to improve the accuracy of chloride migration predictions in reinforced concrete.

翻译：氯离子诱导的腐蚀是钢筋混凝土结构劣化的主要因素之一，因此准确预测氯离子迁移及其对材料耐久性的影响至关重要。本文探讨了两种用于估算氯离子传输有效扩散系数的建模方法。第一种方法遵循Gehlen的可解释扩散模型，该模型基于已建立的物理原理，并在预测氯离子迁移时考虑了时间和温度依赖性。第二种方法采用基于神经网络的方案，通过神经网络近似有效扩散系数。在后续步骤中，校准后的模型被用于预测临界氯离子含量的渗透深度，同时考虑了临界氯离子含量的不确定性。模型校准使用了安装在混凝土试验桥中的线传感器所测得的实验数据。校准结果与通过钻取粉尘样本得出的有效扩散系数进行了比较。两种方法的对比表明，基于物理的模型在透明度和可解释性方面具有优势，而神经网络模型则在数据驱动的预测中展现出灵活性和适应性。本研究强调了结合传统方法与基于机器学习的技术对于提高钢筋混凝土中氯离子迁移预测精度的重要性。