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Record W3216012827 · doi:10.32920/ryerson.14644704.v1

Shear and flexure behavior of hybrid composite beams with high performance concretes

2021· preprint· en· W3216012827 on OpenAlexafffund
Md. Saiful Hasib

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceShear (geology)Composite numberComposite materialFlexural strengthCrackingStructural engineering

Abstract

fetched live from OpenAlex

Shear and flexure performances of composite beams with different engineered cementitious composites (ECC) to self-consolidating concrete (SCC) depth ratio were investigated. Shear reinforced composite ECC/SCC beams showed similar behavior compared to their non-shear reinforced counterparts until the formation of diagonal cracks but exhibited higher ultimate shear resistance and ductility. Compared to the full depth SCC and full depth ECC beams, non-shear reinforced composite ECC/SCC beams showed higher ductility and energy absorption capacity. Composite ECC/SCC beams showed higher number of cracks with lower crack width because of fiber bridging and micro-cracking characteristics of ECC. Code based equations and other design specifications were conservative in predicting shear strength of shear/non-shear reinforced composite ECC/SCC beams. Composite ECC/SCC flexure beams showed satisfactory flexural performance compared to their full depth ECC and SCC counterparts.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.009
GPT teacher head0.209
Teacher spread0.199 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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