CAREER: Transformative Understanding of Rainfall-Triggered Landslides with Vegetation Effects from a Climate Change Perspective: Initiation and Consequences

职业：从气候变化的角度对降雨引发的山体滑坡及其植被影响进行变革性的理解：起因和后果

• 批准号: 2340657
• 负责人: Alba Yerro Colom
• 金额: $ 57.1万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340657&HistoricalAwards=false
• 关键词: CAREER; Transformative; Understanding; Rainfall; Triggered

Rainfall-triggered landslides (RTL) have enormous impacts on the environment, society, and economy, especially in low-income communities. These events commonly develop in vegetated slopes and manifest as shallow failures; however, despite the demonstrated stability functions of vegetated systems on slopes, the mechanical and hydraulic effects of different root systems on the failure process and landslide consequences have never been investigated. The overarching research objective of this Faculty Early Career Development (CAREER) project is to advance the understanding of RTL to better predict their potential damage, considering vegetation effects and changes in rainfall patterns due to climate change. This will be achieved by improving predictive tools that inform geotechnical engineers and stakeholders to make responsible decisions about landslide risk. This effort will also provide new guidelines to improve the efficiency of bioengineering solutions for landslide prevention and community preparedness. An outreach plan will disseminate the technical information to maximize its impact on society and advance and transform the way slope stability and consequence analyses are currently approached. An integrated multi-generational education plan will also serve local communities, including low-income rural minorities, by increasing public scientific literacy, promoting STEM, and raising awareness about landslide hazards and climate change through guided activities and expositions in regional museums. Future geotechnical engineers will also be educated on use of state-of-the-art numerical and machine learning tools capable of solving future challenges, on climate change awareness, and on effective communication. The specific research objectives of this research project include: (i) developing a physically-based numerical strategy for the hydromechanical analysis of RTL capable of unifying failure with post-failure kinematics while efficiently incorporating the effects of vegetation and advanced soil constitutive models; (ii) providing a new physical understanding of how different root systems in combination with initial soil characteristics influence the overall instability process of RTL; (iii) assessing the probability of failure and exceedance probability of the runout distance of RTL taking into account uncertainties from precipitation, vegetation characteristics, and soil parameters vital for quantitative risk assessments by means of an efficient mechanistic-based machine learning surrogate model. The new developments will be assessed by systematic validation against available laboratory data and benchmarks. The resulting knowledge will be transformative not only in the field of RTL, but it can be further applied to other extremely relevant related hazards such as wildfire consequences on slopes, tailings dam failures, and internal erosion and scour of levees.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.

降雨引发的山体滑坡 (RTL) 对环境、社会和经济产生巨大影响，特别是在低收入社区。这些事件通常发生在植被斜坡上，并表现为浅层破坏；然而，尽管斜坡上的植被系统已被证明具有稳定功能，但不同根系对破坏过程和滑坡后果的机械和水力影响从未被研究过。该教师早期职业发展 (CAREER) 项目的总体研究目标是增进对 RTL 的了解，以更好地预测其潜在损害，同时考虑植被影响和气候变化导致的降雨模式变化。这将通过改进预测工具来实现，这些工具可以告知岩土工程师和利益相关者就滑坡风险做出负责任的决策。这项工作还将提供新的指导方针，以提高山体滑坡预防和社区防备的生物工程解决方案的效率。外展计划将传播技术信息，以最大限度地发挥其对社会的影响，并推进和改变目前边坡稳定性和后果分析的方式。综合的多代教育计划还将通过提高公众科学素养、促进 STEM 以及通过地区博物馆的指导活动和展览提高对山体滑坡灾害和气候变化的认识，为包括低收入农村少数民族在内的当地社区服务。未来的岩土工程师还将接受有关使用能够解决未来挑战的最先进的数字和机器学习工具、气候变化意识和有效沟通的教育。该研究项目的具体研究目标包括：（i）开发一种基于物理的 RTL 流体力学分析数值策略，能够将失效与失效后运动学统一起来，同时有效地结合植被和先进土壤本构模型的影响； (ii) 提供关于不同根系与初始土壤特征如何影响 RTL 整体不稳定过程的新物理理解； (iii) 评估 RTL 运行距离的失效概率和超出概率，同时考虑降水、植被特征和土壤参数的不确定性，这些不确定性对于定量风险评估至关重要，通过基于机械的有效机器学习替代模型。新的发展将通过针对现有实验室数据和基准的系统验证来评估。由此产生的知识不仅在 RTL 领域具有变革性，而且可以进一步应用于其他极其相关的相关灾害，例如斜坡上的野火后果、尾矿坝溃坝以及堤坝的内部侵蚀和冲刷。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。