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Record W2113900958 · doi:10.1139/t07-028

Numerical modelling of the hydrogeological and geomechanical behaviour of a large slope movement: the Triesenberg landslide (Liechtenstein)

2007· article· en· W2113900958 on OpenAlexvenueno aff
Bertrand François, Laurent Tacher, Ch. Bonnard, Lyesse Laloui, V. Triguero

Bibliographic record

VenueCanadian Geotechnical Journal · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsHydrogeologyGeologyLandslideGeotechnical engineeringFinite element methodPore water pressureEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Using advanced hydrogeological and geomechanical finite element modelling, it has been possible to model the mechanical behaviour of a large slope movement, the Triesenberg landslide. This slope is located along the Rhine valley in the Principality of Liechtenstein and covers an area of around 5 km2, which includes two villages. Pore-water pressure fields calculated by the hydrogeological model were used as input for the geomechanical model. The results obtained through numerical simulation agree fairly well with field measurements of peak velocity, spatial and temporal distribution of velocity, and total displacements. Such results were obtained using a modified Cam-Clay elastoplastic constitutive model for which the required material parameters were obtained through careful geotechnical tests. These finite element models were carried out in two and three dimensions to gradually improve the understanding of the physical phenomena governing the hydrogeological conditions and the movements of the slope.Key words: landslides, slope movement, hydromechanical coupling, elastoplasticity, numerical modelling.

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)0.000
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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.010
GPT teacher head0.204
Teacher spread0.194 · 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

Citations41
Published2007
Admission routes1
Has abstractyes

Explore more

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