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.

摘要

核安全壳建筑物 (NCB) 或桥梁等大型混凝土结构的老化涉及随时间的连续应变演变，这可能会影响其耐久性、安全性和环境安全。然后，结构完整性的评估需要对长期应变水平进行准确评估。在对大型混凝土结构的滞后力学行为进行概率分析时，预测结果可能存在较大的不确定性，无法为结构维修相关决策提供明确的指标。在这种情况下，贝叶斯更新能够通过将先前的知识状态与结构响应的嘈杂监测数据相结合来减少不确定性。它需要先验概率分布的定义，它在收集监控数据之前汇总了所有可用信息。在以往关于贝叶斯方法应用于延迟应变分析的工作中，先验分布通常通过专家判断来定义，这是一个非常主观的过程，可能对贝叶斯更新结果产生重大影响。此外，先前引用的工作涉及与观察噪声相关的强有力的假设，这些假设通常被认为是完全已知的。目前的贡献旨在通过对 1:3 比例 NCB 的一个说明性案例研究，评估由专家判断定义的先验分布对贝叶斯更新结果的影响。目前的工作还提出了适用于数据观测噪声未知的情况的贝叶斯框架。还研究了监测数据量对贝叶斯更新提供的不确定性减少的影响。结果强调，无论可用数据的数量如何，分析师的建模选择在长期应变预测的框架中都至关重要。此外，结果还表明，贝叶斯更新非常适合提供显着的不确定性减少，即使在处理有限量监测数据的结构的情况下也是如此。