Numerical scenario simulation may serve as an efficient and powerful tool for hazard assessment, but it often suffers from the lack of a definite failure surface before the occurrence of failure. In the present study, an idealized curved surface (ICS) is proposed for mimicking the sliding surface in the numerical simulation. Different from the conventional Sloping Local Base Level (SLBL) or Scoops3D method, this idealized surface consists of two curvatures, which are defined in the down-slope and cross-slope directions, respectively. Applying this idealized surface to 45 historical landslides of sliding type in southern Taiwan, two specific relations of geometry (length, curvature radius, and depth) in the down-slope and cross-slope directions are figured out. These specific relations simplify the complexity of constructing the idealized curved surface for areas prone to landslides for the sake of hazard assessments. That is, once the area with landslide susceptibility is identified and the associated released volume is given, the idealized sliding surface can be uniquely determined with the help of these geometric relations. The proposed method is integrated with a two-phase grain-fluid model and numerically validated against a historical large-scale landslide for investigating its feasibility. Although the idealized failure surface may deviate from that determined by the post-event investigation, it is interesting to note that the major discrepancy is mainly found at the first stage. The differences (both flow thickness and paths) reduce over time, and only minor discrepancies can be identified at the deposition stage. These findings reveal the weak co-relation between the geometry of the failure surface and the flow paths. It also indicates the feasibility of the ICS for predicting the flow paths by scenario investigation, especially when the exact sliding surface of a landslide is not available.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.