NICE: Networked Infrastructures under Compound Extremes

As climate change continues and technology advances, facilities around the world face ever increasing threats from natural hazards, cyber attacks, etc. The interconnectivity of systems has the potential to exacerbate resultant system failures. When one system fails, any system which depends on it also fails, producing a cascade effect from one network to the next. Such events have been observed in the power grid and other interdependent infrastructure networks amplifying blackouts. Furthermore, the possibility of compound events, like malicious actors launching a cyber-attack during a natural disaster, magnify the risk to already stressed networks.

In order to mitigate this risk, we are developing new computationally tractable theoretical frameworks and methods to help ensure installation-level resilience. We are implementing Multiplex Network Science (MNS) to capture the structural properties of the internal network and Multiscale System Dynamics (MSD) to investigate the infrastructure response to compound extremes. In particular, we are focusing on mapping failure and recovery pathways, adapting to changing conditions, and recovering from disruptions.  Upon completion, we aim to have developed an integrated resilience framework informed by a suite of models, embedded in a proof-of-concept software. This will be capable of continuously monitoring the state-of-resilience of a facility and understanding failure and recovery pathways at multiple scales in order to minimize risk and downtime.

Sample Case: Flows in the installation of fuel transportation networks

Sample Case: Environmental scenarios

Related Literature:
He, Yiyi (Adviser: John Radke, Committee Member: Gonzalez, M.), 2018. A Network Approach to Measuring the Impact of Coastal Flooding on the Fuel Transportation Network under the Changing Climate – A Case Study in the San Francisco Bay Area. Master’s Thesis in Landscape Architecture, University of California, Berkeley.

Universal resilience patterns in complex networks, Jianxi Gao, Baruch Barzel & Albert-László Barabási

HuMNet Lab