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

EAGER：SAI：通过以人和社会为中心的数字孪生基础设施为沿海社区同步决策支持

基本信息

批准号：
2122054
负责人：
Xinyue Ye
金额：
$ 29.9万
依托单位：
Texas A&M University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122054&HistoricalAwards=false
关键词：
EAGER SAI Synchronizing Decision Support

项目摘要

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.

加强美国基础设施（SAI）是NSF的一项计划，旨在促进以人为本的基础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改善生活质量奠定了坚实的基础。强大、可靠和有效的基础设施刺激私营部门创新，促进经济增长，创造就业机会，提高公共部门服务提供的效率，加强社区建设，促进机会平等，保护自然环境，增强国家安全，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专注于人类推理和决策，治理以及社会和文化过程的知识如何使建设和维护有效的基础设施，改善生活和社会，并建立在技术和工程进步的基础上。该SAI项目侧重于提高沿海社区复原力的战略，技术，机制和政策。该项目的中心是使用数字孪生-物理对象和系统的虚拟副本，这些虚拟副本可以在真实的时间内更新以匹配现实世界的条件。数字孪生模型可以为沿海社区的弹性决策提供所需的见解。通过构建加尔维斯顿岛和德克萨斯州其他沿海社区的数字孪生模型，开发了一个初步的案例研究。该研究采用了一种整体和综合的方法来评估，建模和测试弹性场景。它汇集了多个学科，包括地理学，城市规划，景观建筑学，计算机科学，建筑科学和海洋科学。一个参与性和社区参与平台被用来收集地面实况数据，并在多个尺度上进一步深入了解沿海基础设施机制。居民和利益相关者将深入了解：（1）比较不同规划工作的利弊;（2）现有和未来规划工作可能对利益相关者的个人目标和目的产生的联合影响;以及3）基于数字孪生的信息建模中使用的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、策略和建议的基础设施更改来测试增量和基于位置的规划方法。通过软件和硬件的集成，这一数字孪生模型成为寻求解决沿海基础设施挑战的平台。潜在的回报很高，因为更明智的决定和更好的机构间协调能力可能会降低维持或更换沿海复原力保护系统的成本。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，成为进一步研究数据驱动决策的催化剂。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分析平台中，以进行实时，动态的场景测试，从而为决策提供支持。这个基于数字孪生的决策支持系统允许（1）收集、编辑和共享物理、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）促进以人为本和社会为中心的基础设施规划方法和短期灾害和长期气候变化背景下的综合社会环境系统动力学建模。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，以及参与式增强的基础设施管理考虑因素。它在当地情况的数字孪生模型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了解不同规划工作之间的关系，包括灾害管理，新建筑，维修，恢复和改造活动，定期维护，系统性能和基础设施增加。数字平台收集和模拟在不同政策或灾害响应场景下高度动态和大量的独立行动，反应和交互代理（如人，车辆，结构/基础设施和机构）。再加上沉浸式技术，该平台使人们能够更好地了解建筑和自然环境的变化，通过可视化规划和基础设施的改变和增加如何改变弹性水平（积极或消极）。当地知识与专家对多种洪水情景类型和基础设施变化情景的评估相结合，以测试对城市变化的不同恢复力水平。通过揭示基本的设计和规划原则与行动的影响，该研究改善了美国的基础设施的灾难恢复能力，以支持科学为基础的措施，为可访问的，负担得起的，通用的地理空间设计干预。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Developing Human-Centered Urban Digital Twins for Community Infrastructure Resilience: A Research Agenda

DOI：
10.1177/08854122221137861
发表时间：
2022-11
期刊：
Journal of Planning Literature
影响因子：
4.5
作者：
Xinyue Ye;Jiaxin Du;Yu Han;Galen Newman;D. Retchless;Lei Zou;Youngjib Ham;Zhenhang Cai
通讯作者：
Xinyue Ye;Jiaxin Du;Yu Han;Galen Newman;D. Retchless;Lei Zou;Youngjib Ham;Zhenhang Cai

Analyze the usage of urban greenways through social media images and computer vision