Interdisciplinary Team to Lead $2.5M NSF CRISP Grant
COS Professor Albert-Laszlo Barabasi (PI) and Co-PIs Kathryn Coronges, Executive Director of the Network Science Institute; Stephen Flynn, Director of the Global Resilience Institute; ECE Professor Edmund Yeh and CEE Professor Auroop Ganguly, Director of the Sustainability and Data Sciences Laboratory (SDS Lab), were awarded a $2.5M NSF CRISP grant for “Interdependent Network-based Quantification of Infrastructure Resilience (INQUIRE)”. This grant is in collaboration with Rob Sampson from Harvard University in addition to advisers Marta Ganzalez, MIT and Lina Sela, University of Texas Austin. Sean Cornelius of the Barabasi Lab and Udit Bhatia of Ganguly’s SDS Lab made significant contributions.
Abstract Source: NSF
Critical infrastructure systems are increasingly reliant on one another for their efficient operation. This research will develop a quantitative, predictive theory of network resilience that takes into account the interactions between built infrastructure networks, and the humans and neighborhoods that use them. This framework has the potential to guide city officials, utility operators, and public agencies in developing new strategies for infrastructure management and urban planning. More generally, these efforts will untangle the roles of network structure and network dynamics that enable interdependent systems to withstand, recover from, and adapt to perturbations. This research will be of interest to a variety of other fields, from ecology to cellular biology.
The project will begin by cataloging three built infrastructures and known interdependencies (both physical and functional) into a “network of networks” representation suitable for modeling. A key part of this research lies in also quantifying the interplay between built infrastructure and social systems. As such, the models will incorporate community-level behavioral effects through urban “ecometrics” — survey-based empirical data that capture how citizens and neighborhoods utilize city services and respond during emergencies. This realistic accounting of infrastructure and its interdependencies will be complemented by realistic estimates of future hazards that it may face. The core of the research will use network-based analytical and computational approaches to identify reduced-dimensional representations of the (high-dimensional) dynamical state of interdependent infrastructure. Examining how these resilience metrics change under stress to networks at the component level (e.g. as induced by inundation following a hurricane) will allow identification of weak points in existing interdependent infrastructure. The converse scenario–in which deliberate alterations to a network might improve resilience or hasten recovery of already-failed systems–will also be explored.