Model-based reinforcement corrosion prediction: Continuous calibration with Bayesian optimization and corrosion wire sensor data

Abstract

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.

Figures (17)

• Figure 1: The 'Concerto' test bridge in 2005
• Figure 2: Resistance jumps over time period
• Figure 3: Temperature vs. time with best-fit cosine curve
• Figure 4: Measured chloride content from the drilling dust analysis or from the regression analysis as a function of depth
• Figure 5: Termination Criteria