Societal Risk and Resilience Analysis: Dynamic Bayesian Network Formulation of a Capability Approach

Armin Tabandeh, Paolo Gardoni, Colleen Murphy, Natalie Myers

Research output: Contribution to journalArticle

Abstract

The operation of modern societies relies on the functionality of complex infrastructure such as those for potable water, electric power, and transportation. Difficulty in accessing life-supporting resources due to the loss of the functionality of infrastructure in the aftermath of natural or anthropogenic hazards can result in widespread societal disruptions. To promote societal risk and resilience analysis, this paper makes the following novel contributions: (1) probabilistic models are developed to predict the broad societal impact of disruptive events over time in terms of their impact on the well-being of individuals; (2) a mathematical formulation for societal resilience analysis is developed that integrates the immediate impact on and the recovery of individuals' well-being; (3) the developed probabilistic models are implemented with Dynamic Bayesian Networks; and (4) a formulation is proposed to evaluate the quantified risks. To estimate the immediate impact on individuals' well-being and model the subsequent recovery, the information from the recovery modeling of infrastructure and variations in the socioeconomic characteristics were incorporated into a time-dependent reliability analysis. The probabilistic modeling of the immediate impact and recovery of well-being were used to quantify societal resilience. To facilitate the probabilistic modeling, the time-dependent reliability analysis was implemented with a Dynamic Bayesian Network. Finally, the quantified risk and resilience were evaluated to provide insights about the severity levels of disruptive events. The proposed approach is explained, through a real case study, to quantify the cascading impact of infrastructure disruptions.

Original languageEnglish (US)
Article number04018046
JournalASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume5
Issue number1
DOIs
StatePublished - Mar 1 2019

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Bayesian networks
Dynamic analysis
Recovery
Reliability analysis
Potable water
Hazards
Statistical Models

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Safety, Risk, Reliability and Quality

Cite this

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abstract = "The operation of modern societies relies on the functionality of complex infrastructure such as those for potable water, electric power, and transportation. Difficulty in accessing life-supporting resources due to the loss of the functionality of infrastructure in the aftermath of natural or anthropogenic hazards can result in widespread societal disruptions. To promote societal risk and resilience analysis, this paper makes the following novel contributions: (1) probabilistic models are developed to predict the broad societal impact of disruptive events over time in terms of their impact on the well-being of individuals; (2) a mathematical formulation for societal resilience analysis is developed that integrates the immediate impact on and the recovery of individuals' well-being; (3) the developed probabilistic models are implemented with Dynamic Bayesian Networks; and (4) a formulation is proposed to evaluate the quantified risks. To estimate the immediate impact on individuals' well-being and model the subsequent recovery, the information from the recovery modeling of infrastructure and variations in the socioeconomic characteristics were incorporated into a time-dependent reliability analysis. The probabilistic modeling of the immediate impact and recovery of well-being were used to quantify societal resilience. To facilitate the probabilistic modeling, the time-dependent reliability analysis was implemented with a Dynamic Bayesian Network. Finally, the quantified risk and resilience were evaluated to provide insights about the severity levels of disruptive events. The proposed approach is explained, through a real case study, to quantify the cascading impact of infrastructure disruptions.",
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