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

降雨引发的山体滑坡对环境、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覆盖的斜坡上，表现为浅层破坏；然而，尽管证实了斜坡上植被系统的稳定功能，但从未研究过不同根系对破坏过程和滑坡后果的力学和水力影响。这个学院早期职业发展(CALEAR)项目的主要研究目标是促进对RTL的了解，以更好地预测其潜在的损害，考虑到气候变化对植被的影响和降雨模式的变化。这将通过改进预测工具来实现，这些工具为岩土工程师和利益相关者提供信息，以便他们就滑坡风险做出负责任的决定。这一努力还将提供新的指导方针，以提高生物工程解决方案的效率，以预防山体滑坡和社区备灾。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斜坡稳定性和后果分析的方式。多代人综合教育计划还将通过提高公众科学素养、促进STEM以及通过在区域博物馆开展有指导的活动和展览来提高对山体滑坡危害和气候变化的认识，服务于当地社区，包括低收入的农村少数群体。未来的岩土工程师还将接受关于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。本研究项目的具体研究目标包括：(I)开发基于物理的数值策略，用于RTL的流体力学分析，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先进的土壤本构模型的影响；(Ii)提供新的物理理解，了解不同的根系与初始土壤特性如何影响RTL的整体失稳过程；(Iii)通过高效的基于机械的机器学习替代模型，考虑来自降水、植被特征和土壤参数的不确定性，评估RTL的失效概率和超出距离的概率，该不确定性对于定量风险评估至关重要。新的发展将通过对照现有的实验室数据和基准进行系统验证来评估。由此产生的知识将不仅在RTL领域具有变革性，而且还可以进一步应用于其他极其相关的相关危险，如斜坡上的野火后果、尾矿坝坍塌、内部侵蚀和堤坝冲刷。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。

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.