Seismic resilience assessment of urban communities using Bayesian network

Abstract

The importance of the disaster resilience of urban communities becomes substantial as people and capital are integrated and affect each other both directly and indirectly. This study presents three main contributions to quantify the seismic resilience performance of an urban community and support the decision-making process. First, a Bayesian network (BN)-based regional seismic resilience assessment framework is developed to estimate the seismic losses of an urban community considering the structural deterioration efficiently and effectively. Second, a resilience assessment framework is developed, which estimates the reliability and recoverability indices of every district in the urban community. A concept of resilience limit-state is introduced with a graphical representation tool. Third, a retrofit strategy is proposed from the disaster resilience perspective, which facilitates finding the optimal scheme considering the multidimensional aspects of the urban community. An urban community consisting of 10 districts mimicking the downtown Vancouver area is introduced as a numerical example to demonstrate the efficiency and applicability of the findings.