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Record W3005849288 · doi:10.18552/2019/idscmt5046

Shear strengthening of thick concrete slabs accounting for loading during strengthening

2019· article· en· W3005849288 on OpenAlexafffund
Frédéric Bédard, M. Fiset, Josée Bastien, Dennis Mitchell

Bibliographic record

VenueSustainable construction materials and technologies · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsMcGill UniversityUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsShear (geology)Materials scienceStructural engineeringGeotechnical engineeringComposite materialGeologyEngineering

Abstract

fetched live from OpenAlex

In order to investigate the effect of the service shear load at the time of strengthening a thick slab using bonded transverse reinforcing bars, an experimental study has been carried out. Five (5) beams representing slices of a thick slab were tested to induce different shear load levels in the beams at the time of strengthening then loaded up to shear failure. Tests were conducted on a slab without shear reinforcement and the others on slabs strengthened at two different load levels at the time of strengthening. The added shear reinforcement was distributed according to two different longitudinal spacings. The results show that, even in the presence of usual service loads, the shear strengthening of thick slabs with bonded bars offers a considerable increase in shear capacity compared to a thick slab without shear reinforcement.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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