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Probabilistic Framework to Quantify the Resilience of Urban Gas Distribution Networks

Baratian, Amin | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53730 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Kashani, Hamed
  7. Abstract:
  8. This study proposes a probabilistic framework to quantify the seismic resilience of urban natural gas distribution networks. Infrastructure resilience is defined as the ability to maintain performance after a hazard or, in case of a loss of performance, return to the minimum desired level in the optimal time. The framework presented in this research is comprised of multiple interconnected probabilistic models. These models work in harmony to characterize the impact of the earthquake on various infrastructures such as the buildings and urban gas distribution network components, as well as the probable consequences of the earthquake. The post-earthquake recovery of the gas distribution network and the associated infrastructures is modeled using discrete-event simulation, which facilitates the estimation of the recovery time and cost of the urban gas distribution networks. An essential feature of this framework is that it considers the uncertainty about several parameters. For instance, the uncertainty about the location and time of the earthquake occurrence, the earthquake intensity, the response of network components and the earthquake-induced damage they may sustain, the time and cost of the recovery process, and the demand for natural gas are characterized in this framework. To showcase the capabilities of the proposed framework, it is used to evaluate the resilience of the gas distribution network in a community. Officials and decision-makers can evaluate various strategies to enhance the resilience of urban natural gas distribution networks using the proposed framework. In this way, they can identify appropriate policies to reduce the vulnerability of the urban natural gas distribution networks before the earthquake and accelerate the recovery process of earthquake-damaged components
  9. Keywords:
  10. Probability Model ; Earthquake ; Network Resiliance ; Earthquake Damage ; Urban Gas Distribution Resilience ; Performance Recovery

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