Quantitative Assessment of System Response during Disruptions: An Application to Water Distribution Systems

Abstract

The resilience of water distribution systems (WDSs) has gained increasing attention in recent years. Various performance loss and recovery behaviors have been observed for WDSs subject to disruptions. However, a model for their characterization, which could provide further insight for resilience assessment and enhancement, is still lacking. Here, the authors develop a recovery function to model WDS performance over time following a disruption. This function is useful to compare system responses under different disruption and recovery scenarios and supports the identification of areas for improvement within various aspects of the resilience of a WDS. The proposed model was applied to two benchmark networks. Different scenarios were analyzed in which one node at a time was disrupted and two recovery strategies were implemented. It was found that the developed model supports the implementation of tailored strategies to improve WDS resilience according to the location of the disruption, therefore enhancing the efficient allocation of resources.

Bridging the Cyber–Physical Divide: A Novel Approach for Quantifying and Visualising the Cyber Risk of Physical Assets

“…As a result, the need to safeguard physical assets against potential cyber-attacks has received increasing attention [17][18][19][20][21]. However, common domain-specific approaches to quantifying associated risks, such as cyber-threat modelling [22] or physical asset performance analysis [23][24][25], fall short of providing a system-wide assessment of the actual cyber-risk of critical infrastructure. This is because they primarily focus either on the cyber or physical domains of CPSs, without bridging the gap between them, or focus on enterprise networks rather than the operational technology networks that directly control infrastructure operations [8,17,26].…”

“…While there are existing approaches to assessing the system-wide risk of failure of physical assets (e.g., that the asset will not meet desired performance requirements, such as the inability of a water supply system to deliver water of suitable quality and pressure to customers), these are either attribute-based or performance-based, involving an assessment of factors such as the critical failure modes of the asset, its expected performance drop during failure, and the likelihood of this occurring based on historical data [23]. However, the risk or vulnerability of failure of specific physical assets due to their connection to cyber systems is often ignored, with generalised network-wide impacts considered instead [8,17].…”

Resilience indices from a family of recovery functions

“…This implies that different networks will need to be constructed for different systems, as demonstrated by Cassottana et al. [ 4 ]. The results are thus not generalizable.…”