4.7 Article

Highly energetic rockfalls: back analysis of the 2015 event from the Mel de la Niva, Switzerland

期刊

LANDSLIDES
卷 20, 期 8, 页码 1561-1582

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10346-023-02054-2

关键词

Rockfall; Back analysis; Trajectory; Simulation; Model; Photogrammetry

向作者/读者索取更多资源

Process-based rockfall simulation models are developed and validated using valuable rockfall databases. These databases provide information on various site geometries, rock masses, velocities, and energies that the models are designed for. The reconstruction and analysis of rare rockfall events involving large block fragments contribute to the validation of simulation models.
Process-based rockfall simulation models attempt to better emulate rockfall dynamics to different degrees. As no model is perfect, their development is often accompanied and validated by the valuable collection of rockfall databases covering a range of site geometries, rock masses, velocities, and related energies that the models are designed for. Additionally, such rockfall data can serve as a base for assessing the model's sensitivity to different parameters, evaluating their predictability and helping calibrate the model's parameters from back calculation and analyses. As the involved rock volumes/masses increase, the complexity of conducting field-test experiments to build up rockfall databases increases to a point where such experiments become impracticable. To the author's knowledge, none have reconstructed rockfall data in 3D from real events involving block fragments of approximately 500 metric tons. A back analysis of the 2015 Mel de la Niva rockfall event is performed in this paper, contributing to a novel documentation in terms of kinetic energy values, bounce heights, velocities, and 3D lateral deviations of these rare events involving block fragments of approximately 200 m(3). Rockfall simulations are then performed on a per-impact basis to illustrate how the reconstructed data from the site can be used to validate results from simulation models.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据