Integration of reliability and sensitivity assessment with data assimilation for improved decision support

将可靠性和敏感性评估与数据同化相结合，以改进决策支持

• 批准号: 312913068
• 负责人: Professor Dr. Daniel Straub
• 金额: --
• 依托单位: Professur für Risikoanalyse und Zuverlässigkeit
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/312913068?language=en
• 关键词: Integration; reliability; sensitivity; assessment; data

The management of engineering structures and systems requires adequate predictions of their performance throughout their intended service life. For most applications, effective numerical models for such predictions exist, but the parameters of these models are commonly uncertain or random. This is particularly relevant when the interest is in the reliability of these systems, because rare events are most affected by uncertainty. Because the uncertainty arises from different sources, it has been recognized that it is not always sufficient to provide an estimate of reliability as a single number, in particular if the underlying calculation is based on vague information. In many instances, it can be desirable to separate the influence of different sources of uncertainty on the final prediction. Such a polymorphic treatment of uncertainties leads to computational challenges in the underlying structural reliability analysis, which will be addressed in this project. The goal of this project is to develop an integral framework to adaptively estimate the structural reliability and its sensitivity to changes in design parameters under varying information. This work is based on a recently proposed method for reliability analysis using Sequential Importance Sampling, as well as new approach for Bayesian analysis of rare events. The latter is of interest in many - if not most - engineering applications, where data assimilation (e.g. by monitoring and collection of field data) is an effective way of reducing uncertainty and increasing reliability. The framework shall be combined with lower-dimensional representations of uncertain quantities and adaptive proxy models of the potentially complex engineering model for enhancing the computational efficiency of the estimation. The framework will enable the investigation of effective ways for representing reliability and sensitivities for engineering decision support in view of polymorphic uncertainties, which will be the focus of the second phase of the project.

工程结构和系统的管理需要在其预期使用寿命内对其性能进行充分的预测。对于大多数应用，存在用于这种预测的有效数值模型，但是这些模型的参数通常是不确定的或随机的。当人们对这些系统的可靠性感兴趣时，这一点尤其重要，因为罕见事件受不确定性的影响最大。由于不确定性来自不同的来源，人们认识到，以单一数字提供可靠性估计并不总是足够的，特别是如果基本计算是基于模糊的信息。在许多情况下，可能需要将不同来源的不确定性对最终预测的影响分开。这种多态处理的不确定性导致计算的挑战，在基础结构的可靠性分析，这将在本项目中解决。本计画的目标是发展一个完整的架构，以适应性地评估结构的可靠度及其在变化资讯下对设计参数变化的敏感度。这项工作是基于最近提出的方法，使用序贯重要性抽样的可靠性分析，以及新的方法，贝叶斯分析的罕见事件。后者在许多-如果不是大多数-工程应用中是有意义的，在这些应用中，数据同化（例如通过监测和收集实地数据）是减少不确定性和提高可靠性的有效方法。该框架应结合不确定量的低维表示和潜在复杂工程模型的自适应代理模型，以提高估计的计算效率。该框架将使调查的有效方法，代表可靠性和灵敏度的工程决策支持，鉴于多态性的不确定性，这将是该项目的第二阶段的重点。