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Record W2186255505

Numerical Analysis Of Progressive Failure In Natural Rock Slopes

2002· article· en· W2186255505 on OpenAlexaff
Erik Eberhardt, Peter Kaiser, Doug Stead

Bibliographic record

VenueISRM International Symposium - EUROCK 2002 · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsSimon Fraser UniversityLaurentian University
Fundersnot available
KeywordsRockslideGeologyRock mass classificationSlip (aerodynamics)Context (archaeology)Geotechnical engineeringNatural (archaeology)Failure mechanismFinite element methodDeglaciationLandslideEngineeringGeomorphologyStructural engineeringPaleontologyGlacial period
DOInot available

Abstract

fetched live from OpenAlex

The 1991 Randa rockslide in the Swiss Alps involved several complex mechanisms relating to geological, mechanical and hydrological processes for which no clear triggering mechanism can be ascertained. This paper investigates the concept of progressive failure and the numerical modelling of rock mass strength degradation in natural rock slopes using the Randa rockslide as a working example. Results from continuum (i.e. finite-element) modelling are presented to illustrate an interpretation suggesting that initiation of a progressive rock mass degradation process, ultimately leading to failure, was initiated following deglaciation of the valley. Discontinuum (i.e. distinct-element) modelling is then applied to investigate the underlying mechanisms contributing to the episodic nature of the rockslide. Finally, the use of hybrid methods that combine both continuum and discontinuum techniques to model fracture propagation are discussed in the context of modelling progressive slip surface development linking initiation and degradation to eventual catastrophic failure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.218
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2002
Admission routes1
Has abstractyes

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Same venueISRM International Symposium - EUROCK 2002Same topicLandslides and related hazardsFrench-language works237,207