Collaborative Research: A Data-centric Uncertainty-informed Framework for Resilience Analytics of Critical Infrastructure Under Extreme Climate Events

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The United States' critical infrastructure and the communities that rely on their services are increasingly prone to climatic risks, with widespread impacts that are often followed by lengthy and costly restoration efforts. There is a fundamental need for scalable and accurate prediction models of natural hazard risks at local and regional scales to better assess and manage the resilience of our nation's infrastructure. The outcome of this research is expected to help policy makers and infrastructure operators characterize infrastructure resilience under various uncertain future scenarios and identify the optimal adaptation or mitigation strategies that result in maximum resilience gain in the system. In addition, this project possesses great potential for other positive societal impacts by educating the next generation of scholars in hazard modeling through a truly interdisciplinary, research-integrated educational program, a commitment to increased diversity in workforce training and broad dissemination of the results to scientific communities and stakeholders.This research project aims to advance the theory and practice of resilience engineering through establishing a pluralistic, data-centric and uncertainty-informed framework to efficiently characterize the multi-dimensional infrastructure resilience under stochastic hazards as well as plausible infrastructure evolution (due to adaptation or mitigation strategies) and climate change scenarios. This will be done through implementing the three key objectives of: (1) creating an accurate and multi-paradigm hurricane risk model, (2) establishing an accurate predictive framework for resilience analytics of critical infrastructure, based on a multi-dimensional Bayesian algorithm, and (3) leveraging recent advancements in stochastic analysis - based on Polynomial Chaos surrogates - to both fully characterize the uncertainties associated with the multi-dimensional resilience model, and implement computationally efficient scenario-based sensitivity analysis. Successful implementation of this project will yield a significant breakthrough in resilience modeling by enabling a scalable, accurate, and multi-dimensional assessment of infrastructure and community resilience; with rigorously and efficiently accounting for uncertainties.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.