A Novel Dynamically Coupled Storm Surge Hazard-Infrastructure Model for Effective Real-Time Risk-Informed Decision Making

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Coastal areas in the US face substantial risk from storm surge. Data from modeling efforts can provide crucial information to decision makers to act against these risks, but available models are limited in scope. Current fragility-based models for flood defense systems are primarily focused on a single mode of failure without consideration of causal relationships and temporal correlations among various failure modes. Failure assessment is treated as a snapshot in time neglecting the time evolution of failure processes. The adoption of surge hydrographs in current methods to independently determine failure probabilities from reliability models neglects the impact of the performance of geo-structures on spatio-temporal surge response. Moreover, we lack an accurate conceptualization of how this informational shortfall impairs decision makers in making critical judgments about storm surge risk and infrastructure investment. This work will provide for the development of the next generation in storm surge and fragility models and will provide for an experimental validation and assessment of the models' effects on decision making, as follows: 1) Development of an adaptive-resolution storm surge model that responds to the changing state of flood protection systems; 2) Derivation of novel time-dependent, multi-dimensional fragility models of geo-structures, fully integrated with the storm surge model; 3) Development and utilization of human-in-the-loop experimentation to validate and test the effects of these models on real-time decision making, and 4) Creation of enhanced educational and research opportunities for students and teachers, along with dissemination of research knowledge to critical stakeholders. The research performed under this project will have a significant impact on the development of the next generation of storm surge models that are fully integrated with time-dependent fragility models to improve forecasting capabilities of flooding scenarios. The research will also improve understanding of how decision makers utilize storm risk assessment information to make critical decisions. Ultimately, this research will lead to more informed decisions about catastrophic risk and infrastructure failure (e.g., evacuation decisions, search and rescue operations, infrastructure investment, and pre-, during, and post-event planning). The educational plan will provide for integrated new curriculum in infrastructure modeling, resilience and risk analysis. Moreover, the educational plan will enhance the self-efficacy of K8 teachers to teach engineering in classrooms and help engineering students to develop pedagogical skills. Results will be disseminated to (and validated with) key federal and regional stakeholders (e.g., Dept. of Homeland Security, FEMA, US Coast Guard) as well as industry partners. The results from this project will provide a significant improvement in storm surge modeling through the development of a novel, dynamically coupled modeling system consisting of hydrodynamic and fragility model components. The stochastic finite difference models combined with machine learning techniques will enable generation of a novel class of multi-dimensional fragility surfaces that will enhance our understanding of various failure processes and characterize time evolution of failure probabilities. The coupled surge/fragility model will adapt the mesh resolution in response to changing conditions of the flood protection systems, resulting in improved forecasting capabilities. The experimental analysis will provide for an assessment of how these modeling capabilities improve real-time decision making.