Bayesian updating for predictions of delayed strains of large concrete structures: influence of prior distribution

Abstract

The aging of large concrete structures such as Nuclear Containment Buildings (NCB) or bridges involves a continuous strain evolution in time, which may affect their durability, safety and the safety of their environment. Then, the evaluation of structural integrity requires an accurate assessment of the long-term strain level. When considering probabilistic analysis of the delayed mechanical behavior of large concrete structures, the prediction results may present large uncertainties, which do not provide clear indicators aiming at supporting decisions related to structures’ maintenance. In this context, Bayesian updating enables to reduce uncertainties, by combining a prior state of knowledge with noisy monitoring data of the structure response. It requires the definition of a prior probability distribution, which summarizes all available information before collecting monitoring data. In former work concerning Bayesian approaches applied to the analysis of delayed strains, the prior distribution is usually defined through expert judgement, which constitutes a quite subjective process which may have a significant influence on Bayesian updating results. Moreover, the previously cited work involved strong hypotheses related to observation noise, which is usually assumed to be perfectly known. The present contribution aims at evaluating the influence of prior distributions defined by expert judgement on Bayesian updating results, through an illustrative case study of a well instrumented 1:3 scale NCB. The present work proposes also a Bayesian framework suitable for cases where the observation noise of data is unknown. The influence of the amount of monitoring data on the uncertainty reduction provided by Bayesian updating is also studied. Results underline that the modeling choices of the analyst are of paramount importance in the framework of long-term strains predictions, regardless the quantity of available data. Furthermore, results also suggest that Bayesian updating is well suitable for providing significant uncertainty reduction, even in the case of structures which dispose of a limited amount of monitoring data.