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

加强美国基础设施（SAI）是美国国家科学基金会的一个项目，旨在促进以人为本的基础研究和潜在的变革性研究，以加强美国的基础设施。有效的基础设施为社会经济活力和广泛的生活质量改善提供了坚实的基础。强大、可靠和有效的基础设施刺激私营部门的创新，发展经济，创造就业机会，使公共部门提供的服务更有效率，加强社区，促进机会平等，保护自然环境，加强国家安全，并推动美国的领导地位。为了实现这些目标，需要来自科学和工程学科的专业知识。SAI侧重于人类推理和决策、治理以及社会和文化过程的知识如何使有效基础设施的建设和维护成为可能，从而改善生活和社会，并以技术和工程的进步为基础。沿海洪水和风暴是一个日益严峻的全球性挑战。本SAI项目侧重于提高沿海社区抗灾能力的战略、技术、机制和政策。该项目的核心是使用数字双胞胎——物理对象和系统的虚拟副本，实时更新以匹配现实世界的条件。数字双胞胎可以为沿海社区的弹性决策提供所需的见解。最初的案例研究是通过加尔维斯顿岛和其他沿海德克萨斯社区部分地区的数字孪生体的建设进行的。该研究采用了一种整体和集成的方法来评估、建模和测试弹性场景。它汇集了多个学科，包括地理、城市规划、景观建筑、计算机科学、建筑科学和海洋科学。利用参与式社区参与平台收集地面真实数据，进一步深入了解多尺度的沿海基础设施机制。居民和持份者将会深入了解：(1)比较不同规划工作的利弊；(2)现有的和未来的规划工作可能对利益相关者的个人目标产生的共同影响；3)基于数字孪生的信息建模中当前动态传感器所涉及的资产和能力。决策者可以利用该平台的功能，通过实时优先级、政策和建议的基础设施变更来测试增量和基于地点的规划方法。通过软件和硬件集成，这个数字孪生体可以作为寻求解决沿海基础设施挑战的平台。潜在的回报是很高的，因为更明智的决策和更好的机构间协调能力可能会降低维护或更换沿海恢复力保护系统的成本。基于数字孪生的决策支持框架通过连接不同的数据集，并为当地项目参与者以及研究生和本科生提供培训和合作研究机会，作为数据驱动决策的进一步研究的催化剂。该SAI项目支持沿海社区可持续基础设施的弹性设计、规划和开发。它将物理、网络和社会基础设施数据集成到一个分析平台中，用于实时、动态的场景测试，以支持决策。这个基于数字孪生的决策支持系统允许(1)收集、编译和共享物理、网络和社会基础设施的数据；(2)社区参与传播信息和促进公民科学；(3)在短期灾害和长期气候变化的背景下，推广以人为中心和以社会为中心的基础设施规划和综合社会环境系统动力学建模方法。数字化、数据驱动的决策框架集成了各种数据源、数字建模和分析平台以及参与性增强的基础设施管理考虑因素。它在当地环境的数字双胞胎中创建了一个可视化的通用操作程序，当地居民和决策者可以使用它来更好地理解不同规划工作之间的关系，包括灾害管理、新建筑、维修、修复和改造活动、定期维护、系统性能和基础设施添加。数字平台在不同的政策或灾害响应场景下收集和模拟高度动态和大量的独立行动、反应和相互作用的主体（如人、车辆、结构/基础设施和机构）。再加上沉浸式技术，该平台通过可视化规划和基础设施的改建和增加如何改变弹性水平（积极或消极），让人们更好地了解建筑和自然环境的变化。将当地知识与多种洪水情景类型和基础设施变化情景的专家评估相结合，以测试对城市变化的不同恢复能力水平。通过揭示具有行动意义的基本设计和规划原则，该研究改善了美国基础设施的抗灾能力，支持以科学为基础的可获取、可负担和通用的地理空间设计干预措施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.