La pratique du « secret » ou comment aller mieux quand les « réponses institutionnelles » ne suffisent plus ? Le cas de l’Hôpital du Valais (Suisse)

Fournier, Claude-Alexandre

doi:10.4000/books.septentrion.12112

Cited by 1 publication

(1 citation statement)

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“…A few days later, on October 19, a section of the cliff collapsed (Fig. 3), generating several rockfalls (Fournier 2015). Two large block fragments traveled more than a kilometer downslope (~ 1.0 km for block 1, and ~ 2.0 km for block 2, Fig.…”

Section: Original Papermentioning

confidence: 99%

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

et al. 2023

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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 m3. 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.

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Section: Original Papermentioning

confidence: 99%