Concrete carbonation is one of the main causes of steel corrosion in reinforced concrete (RC) structures. Over the past decades, concrete carbonation has been extensively studied. The problem of carbonation prediction is generally treated as deterministic or based on a classical probabilistic approach. However, classical estimation methods do not incorporate prior information. This paper presents a Bayesian approach for the prediction of the carbonation front in RC structures. Bayesian estimation handles the parameters of the distributions as random variables and can effectively combine theoretical predictions and monitoring-based data. A Markov chain Monte Carlo sampling method is used for computing the posterior probability density functions based on monitoring data from both the structure and local environment. The Metropolis-Hastings algorithm is considered for the sampling process. The methodology is illustrated through application to an existing RC bridge in Brazil.
|Titolo:||Bayesian inference for the assessment of the carbonation front in RC structures|
|Autori interni:||BIONDINI, FABIO|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||04.1 Contributo in Atti di convegno|
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|2016_IALCCE_044.pdf||2016_IALCCE_044||1.17 MB||Adobe PDF||PDF editoriale||Accesso riservato|