Modeling the recovery process: A key dimension of resilience

Critical infrastructure systems are essential for the survival of society. In recent years, resilience has been considered an effective approach to enable these systems to recover from disruptions.

In the first webinar of a series jointly organised by the ETH Risk Centre and the Future Resilient Systems (FRS) programme, Dr Beatrice Cassottana will discuss recovery functions to model the recovery process, which is a key determinant of system resilience and describes the capability of a system to restore its performance after a disruption. Different families of recovery functions are developed that satisfy the necessary conditions to model the performance of critical infrastructure systems over time, during periods of loss and restoration following a disruptive event.

Dr Cassottana will also apply the resilience assessment to the case of a water distribution system leakage to characterize system responses under different disruption and recovery scenarios. This will support the identification of areas for improvement within various aspects of a system’s resilience.

Ultimately, these findings will be able to support governments and system operators to design more resilient infrastructures.

The speaker

Dr Beatrice Cassottana is a postdoctoral researcher in the Control, Detection and Recovery of Resilient Cyber-​Physical System module at the FRS and lead principal investigator of the Disaster REsilience Assessment, Modelling and INnovation - Singapore (DREAMIN’ SG) project.

Her current research focuses on enhancing existing and developing new methods for assessing the resilience of cyber-​physical systems, i.e. the electricity grid and the open communication network, with the ultimate goal of identifying optimal recovery schemes for their recovery from disruptions.

Prior to joining FRS, Beatrice was a doctoral researcher in the Department of Industrial Systems Engineering and Management at the National University of Singapore, where she used system modelling, data analysis, and statistical inference techniques to develop quantitative tools to model the recovery and assess the resilience of infrastructure systems, i.e., power grids and water networks.