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（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覆盖的斜坡上，表现为浅层破坏；然而，尽管植被系统在边坡上具有稳定作用，但不同根系对破坏过程和滑坡后果的力学和水力效应从未被研究过。这个教师早期职业发展（Career）项目的总体研究目标是促进对RTL的理解，以更好地预测它们的潜在危害，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，这些工具可以告知岩土工程师和利益相关者对滑坡风险做出负责任的决定。这项工作还将为提高滑坡预防和社区准备的生物工程解决方案的效率提供新的指导方针。一项外联计划将传播技术信息，以最大限度地发挥其对社会的影响，并推进和改变目前处理边坡稳定性和后果分析的方式。一项综合的多代教育计划还将服务于当地社区，包括低收入的农村少数民族，通过在地区博物馆开展指导活动和展览，提高公众科学素养，推广STEM，提高对滑坡灾害和气候变化的认识。未来的岩土工程师还将学习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，提高对气候变化的认识，并进行有效的沟通。本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，该策略能够统一破坏与破坏后运动学，同时有效地结合植被和先进土壤本构模型的影响；（ii）对不同根系与初始土壤特征的结合如何影响RTL的整体不稳定过程提供新的物理认识；（iii）考虑到降水、植被特征和土壤参数的不确定性，通过高效的基于机械的机器学习代理模型评估RTL的失效概率和跳跃距离的超出概率，这些参数对定量风险评估至关重要。将根据现有实验室数据和基准进行系统验证，评估新进展。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危害，如野火对斜坡的影响、尾矿坝的破坏以及堤坝的内部侵蚀和冲刷。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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.