As data from monitored structures become increasingly available, the demand grows for it to be used efficiently to add value to structural operation and management. One way in which this can be achieved is to use structural response measurements to assess the usefulness of models employed to describe deterioration processes acting on a structure, as well the mechanical behavior of the latter. This is what this work aims to achieve by first, framing Structural Health Monitoring as a Bayesian model updating problem, in which the quantities of inferential interest characterize the deterioration process and/or structural state. Then, using the posterior estimates of these quantities, a decision-theoretic definition is proposed to assess the structural and/or deterioration models based on (a) their ability to explain the data and (b) their performance on downstream decision support-based tasks. The proposed framework is demonstrated on strain response data obtained from a test specimen which was subjected to three-point bending while simultaneously exposed to accelerated corrosion leading to thickness loss. Results indicate that the level of \textit{a priori} domain knowledge on the deterioration form is critical.

翻译：随着监测结构所获取的数据日益丰富，如何有效利用这些数据为结构运营与管理增值的需求与日俱增。实现这一目标的途径之一是借助结构响应测量，评估用于描述结构退化过程及其力学行为的模型的有效性。本研究旨在实现该目标：首先，将结构健康监测构建为贝叶斯模型更新问题，其中推断关注的核心变量刻画了退化过程及/或结构状态；继而，基于这些变量的后验估计值，提出一种决策理论定义，从（a）模型解释数据的能力与（b）模型在下游决策支持任务中的表现两个维度对结构及/或退化模型进行评价。所提框架通过受三点弯曲试验同时暴露于加速腐蚀导致厚度损失的试件应变响应数据进行了验证。结果表明，关于退化形式的先验领域知识水平具有关键影响。