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

Abstract

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 and the National Institute of Standards and Technology Community Resilience Planning Guide. Herein, resilience of community infrastructure systems is defined as the joint probability of achieving robustness and rapidity based performance goals, which is quantified using Bayesian networks. 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. This framework is applied for resilience assessment of building, transportation, water, and electric power infrastructure systems in Seaside, Oregon, under combined earthquake ground shaking and tsunami inundation hazards corresponding to different return periods. Uncertainties in damage, restoration, and economic losses are explicitly considered and propagated in the framework using Monte Carlo simulation (MCS). The MCS results are then used to inform the Bayesian network, which evaluates the joint resilience of infrastructure systems in Seaside. 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.