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Record W4402651422 · doi:10.1051/e3sconf/202456917004

Loss of stability in geosynthetic-reinforced slopes

2024· article· en· W4402651422 on OpenAlexaff
Terrance Ma, Sina Javankhoshdel

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsRocscience (Canada)
Fundersnot available
KeywordsGeotechnical engineeringStability (learning theory)GeologyGeosyntheticsSlope stabilityComputer science

Abstract

fetched live from OpenAlex

Geosynthetics are commonly considered to provide restoring forces against sliding during overall slope stability analyses. Where a slipping surface intersects a geosynthetic layer, the geosynthetic layer produces a reaction force either in the opposite direction of sliding, in the direction of the geosynthetic alignment, or in some intermediate direction. The provision of geosynthetic reinforcements typically increases the factor of safety in limit equilibrium against overall sliding in the design of a mechanically stabilized earth (MSE) wall, and for this reason has become popular among practitioners. However, geosynthetics are typically installed in contiguous layers. These layers are potential interfaces for sliding which need to be checked with respect to slope stability. In other words, it is possible for a slope to become unstable via partial sliding along the interface of a geosynthetic. In this paper, a comprehensive method of analysis is demonstrated via an example which evaluates the stability of a slope reinforced by geosynthetics. All the cases of failure with respect to slope stability are considered via the dual treatment of the geosynthetic elements as weak layers and supporting elements in a limit equilibrium analysis in software.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.057
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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 teacher head, 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

Citations1
Published2024
Admission routes1
Has abstractyes

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