The role of initial coherence and path materials in the dynamics of three rock avalanche case histories View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Jordan Aaron, Scott McDougall, Jeffrey R. Moore, Jeffrey A. Coe, Oldrich Hungr

ABSTRACT

Rock avalanches are flow-like landslides that can travel at extremely rapid velocities and impact surprisingly large areas. The mechanisms that lead to the unexpected mobility of these flows are unknown and debated. Mechanisms proposed in the literature can be broadly classified into those that rely on intrinsic characteristics of the rock avalanche material, and those that rely on extrinsic factors such as path material. In this work a calibration-based numerical model is used to back-analyze three rock avalanche case histories. The results of these back-analyses are then used to infer factors that govern rock avalanche motion Our study has revealed two key insights that must be considered when analyzing rock avalanches. Results from two of the case histories demonstrate the importance of accounting for the initially coherent phase of rock avalanche motion. Additionally, the back-analyzed basal resistance parameters, as well as the best-fit rheology, are different for each case history. This suggests that the governing mechanisms controlling rock avalanche motion are unlikely to be intrinsic. The back-analyzed strength parameters correspond well to those that would be expected by considering the path material that the rock avalanches overran. Our results show that accurate simulation of rock avalanche motion must account for the initially coherent phase of movement, and that the mechanisms governing rock avalanche motion are unlikely to be intrinsic to the failed material. Interaction of rock avalanche debris with path materials is the likely mechanism that governs the motion of many rock avalanches. More... »

PAGES

5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40677-017-0070-4

DOI

http://dx.doi.org/10.1186/s40677-017-0070-4

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https://app.dimensions.ai/details/publication/pub.1083720131


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