📖
Improving the Resilience of Guided Transport Systems for Natural Risks
Michaël Gonzva, Pierre-Etienne Gautier, Bruno Barroca, Pierre-André Zitt, Youssef Diab

Improving the Resilience of Guided Transport Systems for Natural Risks

Science & Mathematics Published 2016 Verification Pending

Overview & Synopsis

This paper provides the construction of a qualitative and systemic methodology to assess the resilience of rail transport systems against natural hazards. To illustrate the capabilities of the methodology, it is applied for the flood hazard. The resilience of rail transport systems is analysed through the failure mechanisms to which they are subjected under flood conditions. These mechanisms lead to numerous, complex failure scenarios and modelling these scenarios enables the identification the components in the system that are successively damaged as a result of a disruption. The first part presents the construction of the methodology. The second part proposes a perspective of the methodology by shifting from a qualitative to a quantitative approach by using a probabilistic framework based on Bayesian networks. The choice of Bayesian networks , the construction of the quantitative model and its capabilities are demonstrated.

Advertisement
SPONSOR CONTENT
Indexed from repository: HAL Open Science (hal.science)
Loading document from verified source...
Keyboard Shortcuts: ← / → for pages • + / - for zoom • 0 to fit width Powered by Mozilla PDF.js v3.11

Document Information

Document Title Improving the Resilience of Guided Transport Systems for Natural Risks
Author / Organization Michaël Gonzva, Pierre-Etienne Gautier, Bruno Barroca, Pierre-André Zitt, Youssef Diab
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2016
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-110001
Original Source Link Visit Source Page on hal.science »