CAREER: An Integrated Framework for Resilience Analytics: From Physics-based Modeling of Building Components to Dynamics of Community Level Recovery

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This Faculty Early Career Development (CAREER) program will perform fundamental research that advances the field of resilience analytics and enhances the ability of civil infrastructure systems to cope with natural disasters. The research will push the frontier on infrastructure resilience by developing a holistic methodology to define and quantify infrastructure damage and functional integrity (the ability to continue to function after a hazard event) at multiple scales ranging from a single building to an entire community. The research will account for the probabilistic nature of resilience. The research will provide quantitative data that inform design, adaptation and mitigation strategies in hazard prone areas, and as such lead to significant reductions in economic loss and negative societal impacts of natural hazards. Furthermore, the integration of dependencies within and between infrastructure subsystems (e.g. buildings) allows for systematic characterization of the relationship between the type and extent of damage and disruption, the supply chain response, and the estimation of how recovery can be achieved at the community scale. In addition, this proposal provides opportunities for positive societal impacts from the perspectives of education and outreach. Relevant activities include training the next generation of scholars in resilience engineering with strong emphasis and experience on computation, and broadening the participation of underrepresented groups in STEM fields by capitalizing on the PIs strong track record on impactful commitment to inclusiveness and diversity. The specific goal of this research is to advance the boundaries of infrastructure resilience through integration of a combined theoretical and computational study that allows for a holistic, systematic and efficient evaluation of all dimensions of resilience. The activities to achieve this goal include: (i) Adapting statistical physics and discrete modeling techniques to model the type and extent of damage in structural and non-structural elements and subsystems, as well as to predict the functional integrity of the system. The primary idea is to leverage the versatility of statistical physics and discrete modeling techniques in modeling complex failure mechanisms and their capability in defining precise associations between performance and vulnerability. The adaptation to modeling complex structural behavior is complemented with strategies for modeling local and global structural damping mechanisms; (ii) Development of novel learning strategies that rest on smart and optimal sampling and multi-fidelity information fusion allowing for accurate and efficient estimation of the statistics of damage (and loss) in the presence of low-probability extreme events; (iii) Devising a systems dynamics platform for modeling supply chain response that feeds from integration of inter- and intra-building dependencies for estimation of aggregated community level damage and recovery trajectory.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.