Risk and Failure Resilience Quantification of Interdependent Transportation Systems
- Pingfeng Wang | Wichita State University
- Janet Twomey | Wichita State University
Start date: 10/01/14
End date: 06/30/17
- Midwest Transportation Center
- Wichita State University
About the research
Complex Interdependencies between critical infrastructure systems such as transportation infrastructures exacerbate the consequences of initial failure events through cascading failure effects and propagating damages. To address an increasing demand to develop highly resilient transportation infrastructure systems, the objective of this research project is to create a Bayesian network (BN) based probabilistic platform for analysis and design, that enables not only interdependency between components and subsystems being ultimately considered, but also resilience realization through system design and resilience restoration by optimized failure mitigation/recovery before or after major adverse events.
This research is motivated by the emerging need for developing high-reliability low-cost critical interdependent transportation infrastructure systems, in which not only reliable functions for each subsystems but also the reliable dependencies across subsystems are required to maintain desired functionality of the system in facing system failures due to major nature disastrous events or graduate aging effects of systems. This research project is dedicated to explore the gap between quantitative and qualitative assessment of engineering resilience in the domain of complex transportation infrastructure systems.
A conceptual framework is first proposed for modeling engineering resilience, and then a Bayesian network is employed as a quantitative tool for the assessment and analysis of engineering resilience for complex transportation systems. A case study within the scope of the transportation system for an aircraft manufacturing supply chain is employed to demonstrate the developed research and tools. The developed resilience quantification and analysis approach using Bayesian Networks would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.