Bayesian updating of relationships between crack width and corrosion level in reinforced concrete based on a large set of experimental data

The evaluation of rebar corrosion in reinforced concrete (RC) structures presents a significant challenge for structural engineers, with on-site visual inspections and crack width measurements playing a key role in the preliminary assessment. A general approach is to use corrosion models to predict the corrosion level of existing structures, and subsequently update these predictions by means of on-site data. In this process, empirical models are often applied that relate the observed damage, such as corrosion-induced cracks, to the corrosion level. However, these models have large uncertainties and are often not applicable to conditions that deviate from the test setups used to derive the empirical relations. This research aims to expand the applicability of these practical, existing empirical models for on-site corrosion assessment through Bayesian updating techniques. To this aim, a Bayesian framework is developed to update crack width-corrosion models. The Bayesian updating methodology is illustrated for a simple regression model, and for a more complex relation that accounts for additional variables. A novel online database (KUL-edCCRC) is used that is published accompanying this paper. The obtained results illustrate that the updated models have better agreement with the experimental results, and it is found that the confidence intervals of the regression parameters decrease for an increasing number of observations, even for a small number of additional datapoints. The results hence prove the efficiency of the approach to integrate information from new observations with prior information to adjust the crack width-corrosion model for specific conditions or cases, resulting in a more relevant model as more information becomes available.