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.

降雨引发的滑坡（RTL）对环境、社会和经济产生了巨大的影响，特别是在低收入社区。这些事件通常发生在植被边坡，表现为浅层故障，然而，尽管证明边坡上的植被系统的稳定功能，不同的根系统的破坏过程和滑坡后果的机械和水力效应从来没有被调查。该教师早期职业发展（CAREER）项目的总体研究目标是促进对RTL的理解，以更好地预测其潜在的损害，考虑到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，这些工具将通知岩土工程师和利益相关者对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，以提高滑坡预防和社区准备的生物工程解决方案的效率。一项推广计划将传播技术信息，以最大限度地扩大其对社会的影响，并推动和改变目前处理边坡稳定性和后果分析的方式。综合多代教育计划也将服务于当地社区，包括低收入的农村少数民族，提高公众科学素养，促进STEM，并通过地区博物馆的指导活动和展览提高对山体滑坡危害和气候变化的认识。未来的岩土工程师还将接受教育，了解如何使用最先进的数字和机器学习工具来解决未来的挑战，了解气候变化意识以及有效的沟通。该研究项目的具体研究目标包括：（i）为RTL的流体力学分析开发一种基于物理的数值策略，该策略能够将失效与失效后的运动学统一起来，同时有效地结合植被和高级土壤本构模型的影响;（二）提供了一个新的物理理解，不同的根系如何与初始土壤特性相结合，影响整体的不稳定性RTL的过程;（iii）评估RTL的运行距离的故障概率和重复概率，同时考虑到降水，植被特征和土壤参数的不确定性，这些参数对于通过有效的基于机械的机器学习代理模型进行定量风险评估至关重要。将根据现有的实验室数据和基准对新的发展进行系统的验证。由此产生的知识将不仅在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危害，如斜坡野火后果，尾矿坝失效以及堤坝内部侵蚀和冲刷。该奖项反映了NSF的法定使命，并被认为值得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来支持。