Abstract
Currently, the available decision support systems (DSS) rely on risk/vulnerability measures while interdependencies and their resilience in response to extreme environmental hazards are overlooked. Interdependencies among Critical Infrastructure (CI) can cause cascading failures and hence amplify negative consequences due to these failures. This can also affect CI’s service restoration rate and consequently reducing their resilience in coping with these hazardous environmental events. As infrastructures are becoming more interdependent at some sectors, there is an increasing need for better management of these interactions and interdependencies. Conventional infrastructure management techniques aim to provide a high degree of reliability in design process and risk analysis is the commonly used technique in assessing the response to disastrous threats. However, there are limitations to risk assessment in which not all risks can be quantified due to the existence of emerging and unobserved threats and highly improbable events with high degree of uncertainty are dealt with poorly. To manage the infrastructure interdependencies and their interactions in response to disastrous events, in addition to holistic risk/vulnerability mitigation approach; there is a need for resilience-informed management system. This will establish the key components of existing CI network and will assess the sensitivity of these components to disastrous events and their capacity in coping with such events. Resilience informed management systems put the main emphasis on systems’ recoverability, resourcefulness and rapidity, in addition to its robustness. Unlike the available DSSs, RV-DSS provides a measure of network resilience in response to hazardous events, in addition to vulnerability measure. This measure provides a quantitative metric of the robustness, recoverability, rapidity and resourcefulness of the infrastructure network in response to environmental hazards. This paper presents the application of RV-DSS on a transport network case study, quantifying failure propagation due to inherent infrastructure interdependencies and streamlining strategic planning (e.g., winter preparedness in road and rail networks; use of smart technologies in structural health monitoring) by focusing on risk zones of infrastructure networks, and improve the resilience of infrastructure systems in response to low probability/high impact hazardous events.