Reliability analysis and updating of complex infrastructure systems by Bayesian network

• Applicant: Professor Dr. Daniel Straub
• Subject Area: Applied Mechanics, Statics and Dynamics
• Term: from 2015 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 276986762

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.

• DFG Programme: Research Grants
• International Connection: South Korea
• Cooperation Partner: Professor Junho Song, Ph.D.