Project Details


Collaborative Research: RIPS Type 2: Resilience Simulation of Water Power and Road Networks Collaborative Research: RIPS Type 2: Resilience Simulation of Water, Power & Road Networks Overview: The increasing frequency, scale, and damages associated with recent catastrophic events has called for a shift in focus from evading losses through risk analysis to improving threat preparation, planning, absorption, recovery, and adaptation through resilience. However, neither underlying theory nor analytic tools have kept pace with resilience rhetoric. As a consequence, current approaches to engineering resilience analysis often conflate resilience and robustness or collapse into a deeper commitment to the risk analytic paradigm proven problematic in the first place. This proposal seeks to discover a generalizable understanding of resilience that is applicable in multiple disciplinary contexts. We adopt a unique investigative perspective by coupling social and technical analysis with human subjects research to discover the adaptive actions, ideas and decisions that contribute to resilience in three socio-technical infrastructure systems: electric power, water, and roadways. Both physical and knowledge network models will be constructed. Finally, we propose to synthesize this in a new computer-based Resilient Infrastructure Simulation Environment (RISE) to allow individuals, groups (including students) and experts to test different network design configurations and crisis response approaches. By observing simulated failures and best performances, we expect a generalizable understanding of resilience may emerge that yields a measureable understanding of the sensing, anticipating, adapting, and learning processes that are essential to resilient organizations. Intellectual Merit: There is widespread agreement that resilience is not only a function of the collection of assets within a system, but the result of adaptive social processes within the system. However, existing approaches to resilience research focus primarily on system objects, rather than social responses. Examination of the literature in multiple fields reveals that certain processes are understood to be essential to resilience of all human systems: sensing, anticipating, adapting and learning. Therefore, we focus our research on integration of physical models representing network objects with examination of the knowledge systems and social interactions revealed by human subjects making decisions in a simulated crisis environment. To ensure a diversity of contexts, we propose to model electric power, water, roadway and knowledge networks for Phoenix AZ and Indianapolis IN. Social and physical networks will be fused using a simplified systems interface that is faithful to feedback loops and interdependencies that make the systems complex without becoming too complicated. We hypothesize that each of the physical networks can be mathematically represented in analogous network models, that system resilience will depend on effective action and multiple temporal and geospatial scales, and that practice with the simulated systems will result in skills that improve understanding of resilience in multiple? even unfamiliar? decision contexts. Broader Impacts: This proposal will create new knowledge that is directly supportive of national policy needs. It will advance the training of a globally competitive workforce, both by providing support for STEM graduate students and by developing new infrastructure resilience curriculum. University, industry and government partnerships will help shape the study, recruit participants, and identify organizations that will benefit directly from the findings and result in a long-term plan for supporting further work beyond the life of the award. Additionally, the project will span the disciplinary boundaries necessary to respond to a diverse array of stressors that may undermine the performance of US and global infrastructure systems.
Effective start/end date9/15/148/31/18


  • National Science Foundation (NSF): $1,949,788.00


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