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Continuum cavity expansion and discrete micromechanical models for inferring macroscopic snow mechanical properties from cone penetration data: Continuum Versus Discrete CPT Models in Snow

Continuum cavity expansion and discrete micromechanical models for inferring macroscopic snow mechanical properties from cone penetration data: Continuum Versus Discrete CPT Models in Snow
Continuum cavity expansion and discrete micromechanical models for inferring macroscopic snow mechanical properties from cone penetration data: Continuum Versus Discrete CPT Models in Snow
Digital cone penetration measurements can be used to infer snow mechanical properties, for instance, to study snow avalanche formation. The standard interpretation of these measurements is based on statistically inferred micromechanical interactions between snow microstructural elements and a well‐calibrated penetrating cone. We propose an alternative continuum model to derive the modulus of elasticity and yield strength of snow based on the widely used cavity expansion model in soils. We compare results from these approaches based on laboratory cone penetration measurements in snow samples of different densities and structural sizes. Results suggest that the micromechanical model underestimates the snow elastic modulus for dense samples by 2 orders of magnitude. By comparison with the cavity expansion‐based model, some of the discrepancy is attributed to low sensitivity of the micromechanical model to the snow elastic modulus. Reasons and implications of this discrepancy are discussed, and possibilities to enhance both methodologies are proposed.
0094-8276
8377-8386
Ruiz, Siul
d79b3b82-7c0d-47cc-9616-11d29e6a41bd
Capelli, Achille
48704d26-d2b6-45a8-8955-0d60162f4c10
Van Herwijnen, Alec
0c62087b-7da1-41b2-9e57-e38f03b63243
Schneebeli, Martin
d5987171-b0fc-4fec-a622-755461515767
Or, Dani
a0259fc3-35b3-4d5d-9540-867daf06473a
Ruiz, Siul
d79b3b82-7c0d-47cc-9616-11d29e6a41bd
Capelli, Achille
48704d26-d2b6-45a8-8955-0d60162f4c10
Van Herwijnen, Alec
0c62087b-7da1-41b2-9e57-e38f03b63243
Schneebeli, Martin
d5987171-b0fc-4fec-a622-755461515767
Or, Dani
a0259fc3-35b3-4d5d-9540-867daf06473a

Ruiz, Siul, Capelli, Achille, Van Herwijnen, Alec, Schneebeli, Martin and Or, Dani (2017) Continuum cavity expansion and discrete micromechanical models for inferring macroscopic snow mechanical properties from cone penetration data: Continuum Versus Discrete CPT Models in Snow. Geophysical Research Letters, 44 (16), 8377-8386. (doi:10.1002/2017GL074063).

Record type: Article

Abstract

Digital cone penetration measurements can be used to infer snow mechanical properties, for instance, to study snow avalanche formation. The standard interpretation of these measurements is based on statistically inferred micromechanical interactions between snow microstructural elements and a well‐calibrated penetrating cone. We propose an alternative continuum model to derive the modulus of elasticity and yield strength of snow based on the widely used cavity expansion model in soils. We compare results from these approaches based on laboratory cone penetration measurements in snow samples of different densities and structural sizes. Results suggest that the micromechanical model underestimates the snow elastic modulus for dense samples by 2 orders of magnitude. By comparison with the cavity expansion‐based model, some of the discrepancy is attributed to low sensitivity of the micromechanical model to the snow elastic modulus. Reasons and implications of this discrepancy are discussed, and possibilities to enhance both methodologies are proposed.

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More information

Accepted/In Press date: 2 August 2017
e-pub ahead of print date: 19 August 2017
Published date: 28 August 2017

Identifiers

Local EPrints ID: 434288
URI: http://eprints.soton.ac.uk/id/eprint/434288
ISSN: 0094-8276
PURE UUID: 50c5ece1-625b-4a7d-94a1-823e1ffd0845

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Date deposited: 18 Sep 2019 16:30
Last modified: 16 Mar 2024 04:07

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Contributors

Author: Siul Ruiz
Author: Achille Capelli
Author: Alec Van Herwijnen
Author: Martin Schneebeli
Author: Dani Or

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