Operationalizing resilience: A deductive fault-driven resilience index for enabling adaptation

Abstract

The impact of climate change and the dynamic nature of environmental conditions underscore the critical need to enhance resilience of systems and process safety considerations. The efficacy of such efforts primarily depends on how resilience is measured. Among the myriad efforts to quantify resilience, composite indicators have emerged as promising tools. However, these indicators typically employ statistical methods to derive weights for aggregation and rely on statistical homogeneity among indicators which can limit their scope and fidelity. In this study, we propose an alternative novel resilience index derived from a system’s structure and the essential conditions for safe operation during and after disruptions. The proposed measure reflects the systems’ ability to resist and respond to failures by addressing possibilities of impact propagation to other infrastructure systems. Moreover, it eliminates the need for weights and allows for compensability among its leading indicators. Using a case study based on the on-site wastewater treatment and disposal systems (OSTDS) in South Florida that faces increasing risks due to rising sea levels, we investigate the validity of the proposed index and perform a comparative analysis with statistically-driven measures. Furthermore, we demonstrate the adaptation of the proposed index for decision making within a generalized optimization framework.