Reliability analysis and updating of complex infrastructure systems by Bayesian network

生命线网络等民用基础设施系统是现代社会和经济的重要支柱。为了了解和提高其抗灾能力，必须利用来自多个科学和工程领域的模型和数据来量化系统级风险和可靠性。理想的情况是，在系统的生命周期内，当有新的信息可用时，对这种风险估计进行更新。例如，应在灾害事件发生后立即利用现有数据更新风险估计，以便能够迅速而审慎地作出关于灾害应对和灾后网络运作的决定。最近证明，贝叶斯网络（BN）的方法有可能评估和沟通的复杂系统的状态和可靠性在近实时，由于其能力，进行可靠性分析和更新。然而，要将该方法应用于现实生活中的基础设施系统，必须克服建模和计算的局限性，这是本研究项目的目标，为了实现这一目标，本文提出了以下研究目标：（1）开发生命线系统可靠性的有效表示和计算方法;（2）在分析中加入复杂的依赖关系，提高BN处理复杂依赖关系的计算效率;（3）评估大规模现实系统;（4）在生命线系统中进行应用试验和论证。相应的研究任务如下。首先，精确BN算法量身定制的系统可靠性分析将开发和基于聚类的多尺度系统可靠性分析。此后，将研究新的基于采样的BN算法，以克服精确算法的一些基本限制，但仍然有利于快速计算。这些算法将有助于将复杂的组件故障之间的依赖关系。对于大规模的现实系统，代理模型将开发，可以处理非包容性的模型。最后，所开发的BN模型和方法将应用于自然灾害下复杂基础设施系统的可靠性分析和更新。拟议的BN框架将能够将（实时）数据纳入系统可靠性评估，以随时提供系统状态的最新预测。开发的理论、模型和方法将足够通用，适用于大多数主要的生命线系统，并应带来知识价值，可以启动广泛的相关研究工作。该提案是TUM的丹尼尔斯特劳布教授和首尔国立大学（SNU）的宋俊浩教授联合提案的一部分。TUM将专注于BN的开发，SNU将专注于系统表示。两个小组的互补专业知识以及他们之间的密切合作将确保该项目在这一高度跨学科领域取得成功。

Civil infrastructure systems such as lifeline networks are critical backbones of modern societies and economies. To understand and enhance their hazard resilience, it is essential to quantify the system-level risk and reliability by use of models and data from multiple fields of science and engineering. Ideally, such risk estimates are updated when new information becomes available during the lifetime of the system. As an example, the risk estimates should be updated with available data immediately after a disaster event such that decisions on hazard response and post-disaster network operations can be made promptly but prudently. As recently demonstrated, the Bayesian Network (BN) methodology has the potential for assessing and communicating the state and reliability of complex systems in near-real time, due to its capability to perform reliability analysis and updating. However, to implement the approach for real-life infrastructure systems, modeling and computational limitations must be overcome, which is the goal of this proposed research project.To achieve this goal, the following research objectives are addressed: (1) develop efficient representation and computation of lifeline system reliability; (2) include complex dependence into the analysis and enhance the computational efficiency of the BN in dealing with those; (3) assess large-scale realistic systems; and (4) test and demonstrate the application to lifeline systems. The corresponding research tasks are as follows. First, exact BN algorithms tailored for system reliability analysis will be developed and coupled with clustering-based multi-scale system reliability analysis. Thereafter, novel sampling-based BN algorithms will be investigated, to overcome some fundamental limitations of exact algorithms, but still facilitate fast computation. These algorithms will facilitate incorporating complex dependence between component failures. For large-scale realistic systems, surrogate models will be developed that can handle non-inclusive models. Finally, the developed BN models and methods will be applied to reliability analysis and updating of complex infrastructure systems under natural hazards. The proposed BN framework will be able to include (real-time) data into system reliability assessments to provide updated predictions of the system state at all times. The theories, models and methods developed will be general enough to be applicable to most major lifeline systems, and should bring about intellectual merits that can initiate a broad range of related research efforts.This proposal is part of a joint proposal between Prof. Daniel Straub at TUM and Prof. Junho Song at Seoul National University (SNU). TUM will focus on the development of the BN, SNU will focus on the system representation. The complimentary expertise of the two groups together with close collaboration between them will ensure the success of the project in this highly interdisciplinary area.