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Record W2057764693 · doi:10.1520/jte20120111

Curling Control in Concrete Slabs Using Fiber Reinforcement

2014· article· en· W2057764693 on OpenAlexaff
Nemkumar Banthia, Vivek Bindiganavile, Fadhlan Azhari, Cristina Zanotti

Bibliographic record

VenueJournal of Testing and Evaluation · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsCurlingMaterials scienceComposite materialCrackingSlabDurabilityCellulose fiberVolume fractionGeotechnical engineeringFiberStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Curling of concrete remains a major concern in flatwork and its mitigation is critical for crack control, durability, and mechanical performance, not to mention the aesthetics. This report describes a study on concrete slabs that were subjected to a controlled environment of sustained heat and humidity. The resultant curling was evaluated by means of strain gauges placed along the length and breadth of the slab. In addition to the reference plain concrete mix, three fiber reinforced mixes were cast with cellulose microfibers incorporated at a volume fraction up to 0.3 %. The results show that plain concrete will crack and thereby ease the amount of curl whereas the addition of fibers at low dosage rates (<=0.2 %) leads to crack control and consequently, a visible increase in curling. However, at 0.3 % volume fraction, cellulose fibers were able to arrest cracking and also reduce the curl with respect to the reference plain mix.

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 score0.004

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 studies0.0000.000
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 head0.048
GPT teacher head0.288
Teacher spread0.240 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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