EAGER: SAI: Synchronizing Decision-Support via Human- and Social-centered Digital Twin Infrastructures for Coastal Communities

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Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.Coastal flooding and storms present a growing global challenge. This SAI project focuses on strategies, technologies, mechanisms, and policies for increasing coastal community resilience. The project centers on the use of digital twins – virtual copies of physical objects and systems that update in real time to match real-world conditions. Digital twins can provide the insights needed to inform resilient decision making in coastal communities. An initial case study is developed through the construction of a digital twin of Galveston Island and portions of other coastal Texan communities. The research adopts a holistic and integrated approach for evaluating, modeling, and testing resilience scenarios. It brings together multiple disciplines including geography, urban planning, landscape architecture, computer science, construction science, and marine science. A participatory and community engagement platform is used to collect ground truth data and gain further in-depth understanding of coastal infrastructure mechanisms at multiple scales. Residents and stakeholders will gain insights into: (1) comparing the pros and cons of different planning efforts; (2) the joint impacts that existing and future planning efforts may have on stakeholders’ individual goals and objectives; and 3) the assets and capacities involved with current dynamic sensors used in digital twin-based information modeling. Decision-makers can leverage the capabilities of this platform to test incremental and place-based planning approaches with real-time priorities, policies, and suggested infrastructure changes. Through software and hardware integration, this digital twin serves as a platform for pursuing solutions to coastal infrastructure challenges. The potential reward is high, as more informed decisions and better affordances for inter-agency coordination may lower the costs of maintaining or replacing the coastal resilience protective system. The digital twin-based decision-support framework serves as a catalyst for further research in data-driven decision making by connecting different datasets and by providing training and collaborative research opportunities for local project participants as well as graduate and undergraduate students.This SAI project supports the resilient design, planning, and development of sustainable infrastructure in coastal communities. It integrates physical, cyber, and social infrastructure data into an analytics platform for real-time, dynamic scenario testing for decision support. This digital twin-based decision support system allows (1) collection, compiling and sharing data on physical, cyber, and social infrastructure; (2) engagement of communities to disseminate information and facilitate citizen science; and (3) promoting a human- and social-centered approach for infrastructure planning and integrated social-environment system dynamics modeling in the context of short-term disasters and long-term climate change. The digital, data-driven decision-making framework integrates a variety of data sources, digital modeling and analytics platforms, and participatory-enhanced infrastructure management considerations. It creates a visualized common operating procedure within a digital twin of local circumstances that local residents and decision-makers can use to better reason about the relationships among different planning efforts, including disaster management, new construction, repair, rehabilitation and retrofitting activities, regular maintenance, system performance, and infrastructure additions. The digital platform collects and simulates highly dynamic and massive volumes of independently-acting, reacting, and interacting agents (such as people, vehicles, structures/infrastructure, and institutions) under different policy or hazard response scenarios. Coupled with immersive technologies, the platform allows people to better understand built and natural environment changes by visualizing how planning and infrastructure alteration and addition can alter resilience levels (positively or negatively). Local knowledge is combined with expert evaluation across multiple flood scenario types and infrastructure change scenarios to test different resilience levels to urban change. By revealing fundamental design and planning principles with implications for action, the research improves U.S. infrastructure for disaster resilience, in support of science-based measures for accessible, affordable, and universal geospatial design interventions.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.