A Bayesian inference approach for the updating of spatially distributed corrosion model parameters based on heterogeneous measurement data

Abstract

In many countries concrete bridges are reaching the end of their service-life, showing signs of deterioration, e.g. due to corrosion. Hence, the question arises whether their safety level is still acceptable. To improve the estimate of the safety level, information extracted from different tests, e.g. proof-loading, operational modal analysis, etc., where deflections, strains or accelerations are measured, can be used. Unfortunately, in practice inspection results are often not used directly to improve our knowledge of the degree of deterioration and it is difficult to combine information from different types of tests when making inferences. In this contribution, a methodology is developed and demonstrated in order to update estimations of parameters of service life models for concrete girders subjected to chloride-induced corrosion based on heterogeneous measurement data, with focus on strains measured under proof-loading and modal parameters extracted from ambient vibration tests. A Bayesian framework is adopted, where posterior distributions of the parameters describing the corrosion process are generated based on Markov Chain Monte Carlo sampling. These updated distributions reflect the information on the actual deterioration state of the bridge, which can be extracted from limited data. The updating procedure based on strain and modal data is illustrated by application on a simply supported beam and a case study.

Influence of the correlation model on the failure probability of a reinforced concrete structure considering spatial variability

“…These can include visual observations, static strain or deflection measurements, dynamic tests resulting in modal data, destructive measurements, etc. (Vereecken et al, 2021). In this work, the focus was on static strain data.…”

“…The measurement results (i.e. static strain data) are generated based on a finite element model of the beam as described in (Vereecken et al, 2021), and this for the different correlation models. The load applied to the beam consists of a point load of 30 kN at midspan.…”

“…In this work, the posterior distributions are derived based on strain measurements in a similar way as explained in (Vereecken, Botte, Lombaert, & Caspeele, 2021), but now using MAP estimates instead of MCMC (Markov Chain Monte Carlo) sampling, since in the current work the distributions of the parameters of the initiation period and corrosion rate are updated and not the distributions of the variables itself. The relation between the strains along the structure and the corrosion variables is obtained by a finite element model.…”

“…In (Vereecken et al, 2021) a correlation model for the random field is selected among different candidate models without investigating the influence of this assumption. Moreover, the distributions of the variables describing the corrosion process are updated directly instead of the distributions of their parameters (i.e.…”

Performance Assessment of an Energy–Based Approximation Method for the Dynamic Capacity of RC Frames Subjected to Sudden Column Removal Scenarios

“…When designing a structure with respect to robustness, it must also be taken into consideration that progressive collapses caused by extreme events are low-probability high-consequence phenomena [1]. In order to be able to accurately predict the structural response, it is important to study the progressive collapse behavior using a probabilistic approach, as it has been found that there is a significant influence of many uncertainties on the structural behavior, e.g., randomness from material properties, which is neglected by most existing studies [30][31][32][33][34][35][36][37][38][39][40][41][42]. However, in the three widely used approaches in relation to the quantification of the structural robustness, i.e., deterministic-based method, probabilisticbased method, and risk-based method [30,43,44], both the probabilistic-based method and the risk-based method require a reliability analysis.…”