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Record W2729104253 · doi:10.1139/cgj-2016-0534

Development of an excavatability test for backfill materials: numerical and experimental studies

2017· article· en· W2729104253 on OpenAlexvenueno aff
Caroline Morin, Thierry Sedran, F De Larrard, Hélène Dumontet, Michel Hardy, Sylvain Murgier, Christophe Dano

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsPunchingGeotechnical engineeringCementitiousStructural engineeringTriaxial shear testFinite element methodTest methodDisplacement (psychology)EngineeringRepeatabilityLaboratory testGeologyMaterials scienceMechanical engineeringCementShear (geology)Composite materialMathematics

Abstract

fetched live from OpenAlex

This paper describes research done on a new testing device including its laboratory test procedure; namely, a laboratory punching test for cementitious backfill materials that evaluates their excavatability with a pick. Three-dimensional and two-dimensional numerical studies using a finite element method were carried out according to calibrated parameters of the material obtained by triaxial tests. This made it possible to study the behavior of the backfill material under action induced by a tool (a pick) as well as to optimize the geometry of the punching test specimen. This paper provides detailed information and results for the experimental study of this laboratory test. Attention was particularly focused on energy relating to displacement as well as types of ruptures. The behavior of these types of materials was found to be greatly influenced by gravel in the material. Repeatability of the test was also studied. Finally, a method for this punching test of backfill materials is also provided.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.271
Teacher spread0.246 · 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 designBench or experimental
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

Citations4
Published2017
Admission routes1
Has abstractyes

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