  Due to the increasing frequency of natural and man-made disasters worldwide,
the scientific community has paid considerable attention to the concept of
resilience engineering in recent years. Authorities and decision-makers, on the
other hand, have been focusing their efforts to develop strategies that can
help increase community resilience to different types of extreme events. Since
it is often impossible to prevent every risk, the focus is on adapting and
managing risks in ways that minimize impacts to communities (e.g., humans and
other systems). Several resilience strategies have been proposed in the
literature to reduce disaster risk and improve community resilience. Generally,
resilience assessment is challenging due to uncertainty and unavailability of
data necessary for the estimation process. This paper proposes a Fuzzy Logic
method for quantifying community resilience. The methodology is based on the
PEOPLES framework, an indicator-based hierarchical framework that defines all
aspects of the community. A fuzzy-based approach is implemented to quantify the
PEOPLES indicators using descriptive knowledge instead of hard data, accounting
also for the uncertainties involved in the analysis. To demonstrate the
applicability of the methodology, data regarding the functionality of the city
San Francisco before and after the Loma Prieta earthquake are used to obtain a
resilience index of the Physical Infrastructure dimension of the PEOPLES
framework. The results show that the methodology can provide good estimates of
community resilience despite the uncertainty of the indicators. Hence, it
serves as a decision-support tool to help decision-makers and stakeholders
assess and improve the resilience of their communities.
