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Record W2952173280 · doi:10.1139/cgj-2019-0092

Advantages of second-order work as a rational safety factor and stability analysis of a reinforced rock slope

2019· article· en· W2952173280 on OpenAlexvenueno aff
Jie Hu, Zhaohua Li, Félix Darve, Jili Feng

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSafety factorStrength reductionLandslideWork (physics)Kinetic energyFactor of safetyStability (learning theory)Slope stability analysisGeotechnical engineeringSlope stabilityConvergence (economics)Energy (signal processing)Independence (probability theory)MathematicsStructural engineeringGeologyComputer scienceEngineeringPhysicsFinite element methodStatisticsClassical mechanics

Abstract

fetched live from OpenAlex

Landslides can be considered as a static–dynamic transition with the sudden release of kinetic energy. The sharp vanishing of the second-order work is also linked to this phenomenon. In this study, the relation between the second-order work and the kinetic energy is reviewed, and five advantages of the normalized global second-order work (D 2 W n ) as a factor of safety (FOS) are proposed and discussed, comparing this FOS with the one based on the strength reduction method. The D 2 W n is considered in the explicit algorithm of the finite difference method, and its mesh-independence is numerically checked by a series of triaxial compression tests. By simulating the excavations of a reinforced rock slope, the stability analyses are performed using the D 2 W n and the traditional FOS. The D 2 W n is proven completely independent of the convergence criterion and more sensitive to the global failure. Finally, a recently developed energy-absorbing cable is considered for supporting the studied rock slope. Its supporting effect is compared with that of traditional cables.

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 categoriesInsufficient payload (model declined to judge)
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 score0.120
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.196
Teacher spread0.190 · 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.

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
Published2019
Admission routes1
Has abstractyes

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