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Record W2563050122 · doi:10.1139/cgj-2016-0523

Stability analysis of a deep buried elliptical tunnel in cohesive–frictional (<i>c–</i>ϕ) soils with a nonassociated flow rule

2016· article· en· W2563050122 on OpenAlexvenueno aff
Feng Yang, Xinlei Sun, Xiangcou Zheng, Junsheng Yang

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsDimensionless quantityCohesion (chemistry)Geotechnical engineeringFriction angleDilatantLimit analysisUpper and lower boundsMathematicsSoil waterDilation (metric space)GeometryYield surfaceGeologyFinite element methodMechanicsPhysicsMathematical analysisThermodynamics

Abstract

fetched live from OpenAlex

The stabilities and associated collapse mechanisms of deep buried unlined elliptical tunnels in cohesive–frictional (c–[Formula: see text]) soils with the action of soil weight are investigated by the “upper-bound finite element method with rigid translatory moving elements” (UBFEM–RTME). The soil masses are assumed to obey the Mohr–Coulomb yield criterion and a nonassociated flow rule. Upper-bound stability coefficients (γcrD/c, where γcr is critical unit weight; D is tunnel height; c is cohesion) are deduced for different values of friction angles ([Formula: see text]), dilatancy coefficients (ψ/[Formula: see text], where ψ is dilation angle), and dimensionless spans (B/D, where B is span). The obtained collapse mechanisms do not extend to the ground surface and are primarily composed of a series of mutually movable rigid blocks. The γcrD/c values increase while the collapse zones decrease with an increasing [Formula: see text] and ψ/[Formula: see text] and a decreasing B/D.

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 score0.004
Threshold uncertainty score0.008

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 science0.0000.000
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 head0.006
GPT teacher head0.178
Teacher spread0.172 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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