NNA Track 2: Collaborative Research: Planning for Infrastructure Resiliency and Adaptation amid Increasing Mass-Movement Risks across the Cryosphere

NNA 轨道 2：协作研究：在整个冰冻圈大规模移动风险不断增加的情况下规划基础设施的弹性和适应

• 批准号: 2022444
• 负责人: Tong Qiu
• 金额: $ 12.49万
• 依托单位: Pennsylvania State Univ University Park
• 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: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2022444&HistoricalAwards=false
• 关键词: NNA; Track; Collaborative; Research; Planning

Navigating the New Arctic (NNA) is one of NSF's 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. The Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by supporting planning activities with clear potential to develop novel, leading edge research ideas and approaches to address NNA goals. It integrates aspects of the natural and built environments to address important societal challenges at this intersection, and engages internationally and with local communities.Changing climate conditions have increased the occurrence of mass-movement hazards in the cryosphere, such as landslides, debris flows, and slope failures resulting from thawing of ice-rich permafrost. Mass-movement hazards across the cryosphere pose a significant risk to people and infrastructure, such as highways and pipelines. This project brings together experts from diverse backgrounds, including engineers, geoscientists, computer scientists, and officials from a variety of academic institutions, public and government agencies, and industry, to discuss key challenges and formulate research priorities in 1) characterizing mass-movement hazards in the cryosphere, 2) mapping such hazards using machine learning, 3) forecasting such hazards using artificial intelligence, and 4) building climate-change-resilient infrastructure through flexible and adaptive approaches to reduce costs. The project outcomes enable planners and policy makers to identify critical infrastructure that is most threatened by mass-movement hazards in the Arctic, sub-Arctic, and mountainous regions, and high- and low-hazard areas when planning future infrastructure near urban and rural sites.This award funds the development and planning activities of a convergent research team to address infrastructure resilience and adaptation to increasing mass-movement risks across the cryosphere as the climate warms. Planning activities center around two annual workshops that will identify gaps in our current understanding and existing methodologies to develop: 1) more automated hazard mapping tools through remote sensing and machine learning, including well-established tools like convolutional neural networks; 2) compilations of datasets and databases and interactions with potential users; 3) more reliable artificial intelligence models including time series machine learning for hazard forecasting; and 4) more flexible and adaptive approaches to reduce costs for infrastructure resiliency and adaptation.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.

航行新北极(NNA)是NSF的十大想法之一。NNA项目解决了快速变化的北极地区汇聚的科学挑战。北极研究需要为国家、更大区域和全球的经济、安全和韧性提供信息。NNA增强了从地方到国际的新的研究伙伴关系，使下一代北极研究人员多样化，加强正式和非正式教育的努力，并在适当的情况下整合共同创造知识。该奖项通过支持具有明显潜力的规划活动来实现这一目标的一部分，以开发新的、前沿的研究想法和方法来解决NNA的目标。它整合了自然和建筑环境的各个方面，以应对这一交叉点的重要社会挑战，并与国际和当地社区接触。气候条件的变化增加了冰冻圈大规模移动危险的发生，如山体滑坡、泥石流和富冰永久冻土融化造成的斜坡坍塌。跨冰冻圈的大规模移动危险对人和基础设施，如高速公路和管道，构成了重大风险。该项目汇集了来自不同背景的专家，包括工程师、地球科学家、计算机科学家以及来自各种学术机构、公共和政府机构以及行业的官员，以讨论关键挑战并制定研究重点，包括1)确定冰冻圈中大规模运动的危险的特征，2)使用机器学习绘制此类危险的地图，3)使用人工智能预测此类危险，以及4)通过灵活和适应性的方法建立适应气候变化的基础设施，以降低成本。项目成果使规划者和政策制定者在规划未来城市和农村附近的基础设施时，能够确定北极、亚北极和山区以及高风险和低风险地区受大规模移动灾害威胁最大的关键基础设施。该奖项为一个汇聚的研究团队的开发和规划活动提供资金，以解决随着气候变暖而跨越冰冻圈的基础设施弹性和适应日益增加的大规模移动风险。规划活动将围绕两个年度研讨会，找出我们当前理解和现有方法中的差距，以开发：1)通过遥感和机器学习，包括卷积神经网络等成熟的工具，更自动化的危险地图工具；2)数据集和数据库的汇编以及与潜在用户的交互；3)更可靠的人工智能模型，包括用于危险预测的时间序列机器学习；以及4)更灵活和适应性的方法，以降低基础设施弹性和适应性的成本。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Machine learning with monotonic constraint for geotechnical engineering applications: an example of slope stability prediction

• DOI: 10.1007/s11440-023-02117-7
• 发表时间: 2023-11
• 期刊: Acta Geotechnica
• 影响因子: 5.7
• 作者: Te Pei;Tong Qiu