Probabilistic decision-support framework for community resilience: Incorporating multi-hazards, infrastructure interdependencies, and resilience goals in a Bayesian network

Sabarethinam Kameshwar, Daniel T. Cox, Andre Barbosa, Karim Farokhnia, Hyoungsu Park, Mohammad S. Alam, & John W. van de Lindt

Submitted December 14, 2018, SCEC Contribution #8974

A probabilistic decision support framework is developed in this study for community resilience planning under multiple hazards using performance goals based guidelines such as the Oregon Resilience Plan (ORP) and the National Institute of Standards and Technology (NIST) Community Resilience Planning Guide (CRPG). In this study, resilience of the community infrastructure systems is defined as the joint probability of achieving robustness and rapidity based performance goals and employs Bayesian networks for resilience quantification. The framework assesses the effects of decision support options such as selection of hazards, resilience goals, and mitigation (ex-ante) and response (ex-post) strategies to identify measures that can improve infrastructure performance to meet community defined resilience goals. Herein, this framework is applied for resilience assessment of building, transportation, water, and electric power infrastructure systems in Seaside, Oregon, when subjected to combined earthquake shaking and tsunami hazards corresponding to different return periods. For this purpose, combined seismic and tsunami damage, restoration, and economic losses are assessed for all the infrastructure systems using existing models such as HAZUS. Uncertainties are explicitly considered and propagated through the model using Monte Carlo simulation (MCS). The MCS results are then used to inform the Bayesian network which evaluates the joint resilience of the infrastructure systems in Seaside considering water system’s dependence on electric power and transportation systems. The results highlight the impact of considering different performance goals, introduction of ex-ante and ex-post measures, and interdependencies between various infrastructure systems on infrastructure resilience. Furthermore, the results enable decision-makers to identify the infrastructure systems that dominate the combined resilience and risk in Seaside.

Key Words
Bayesian networks, Community goals, Community resilience, Decision support, Infrastructure Performance goal, Resilience goals

Citation
Kameshwar, S., Cox, D. T., Barbosa, A., Farokhnia, K., Park, H., Alam, M. S., & van de Lindt, J. W. (2018). Probabilistic decision-support framework for community resilience: Incorporating multi-hazards, infrastructure interdependencies, and resilience goals in a Bayesian network. Reliability Engineering and System Safety, (submitted).