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Record W2023666161 · doi:10.1108/02644400410519811

Thin tunnel liners modelled with particle flow code

2004· article· en· W2023666161 on OpenAlexaff
Dwayne D. Tannant, Caigen Wang

Bibliographic record

VenueEngineering Computations · 2004
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeotechnical engineeringFracture (geology)Particle flowTension (geology)Flow (mathematics)Discrete element methodSmoothed-particle hydrodynamicsMaterials scienceStructural engineeringGeologyEngineeringMechanicsComposite materialUltimate tensile strength

Abstract

fetched live from OpenAlex

Simple discrete element models using PFC 2D models with bonded assemblies of particles were used to numerically simulate direct tension and block punching tests on thin spray‐on tunnel liner materials to gain insight about the liner support mechanisms. PFC 2D input parameters were calibrated such that the rupture load and elongation at rupture were similar to the laboratory test data. The calibrated model of the liner material was then used to simulate a liner around a highly stressed tunnel in rock where stresses caused extensive fracturing near the top of the tunnel. The effect of the liner was analysed by modelling the tunnel with and without the liner and showed that the liner had minimal impact on fracture propagation in the rock because of the liner's highly deformable nature. However, the liner was able to retain the fractured rock in place.

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 score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.194
Teacher spread0.184 · 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

Citations20
Published2004
Admission routes1
Has abstractyes

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