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

Virginia Polytechnic Institute And State University, Blacksburg VA

Investigators

Abstract

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.