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Concrete Contribution to Shear Strength of Beams Reinforced with Basalt Fiber-Reinforced Bars

2015· article· en· W2196772633 on OpenAlexaff
Farid Abed

Bibliographic record

VenueJournal of Composites for Construction · 2015
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceAramidFibre-reinforced plasticBasalt fiberReinforcementReinforced concreteShear (geology)Composite materialStructural engineeringTransverse planeFiberEngineering

Abstract

fetched live from OpenAlex

This paper reports on the shear behavior of concrete beams reinforced with basalt fiber-reinforced polymer (BFRP) bars. In this study, 10 reinforced concrete beams with no transverse reinforcement, including 2 beams reinforced with longitudinal steel bars, were constructed and tested until failure. The test variables were the longitudinal reinforcement ratio and the span-to-depth ratio of the beams. The test results were compared with predictions of different available codes and design guidelines. Shear design equations of the CAN/CSA-806-12 code and the JSCE-97 guidelines provided accurate predictions. However, the predictions of CAN/CSA-806-12 were not conservative in some cases, whereas those of the ACI-440.1R-15 and the CAN/CSA-S6-10 were conservative. The test results were combined with the experimental results of a large database, including 75 beams and one-way slabs reinforced with carbon, glass, aramid, and BFRP bars. The behavior of beams reinforced with BFRP bars was quite similar to that of beams reinforced with glass and aramid fiber-reinforced polymer (FRP) bars. The test results demonstrate the potential of using BFRP bars as alternative reinforcement in concrete members.

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: 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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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 head0.225
Teacher spread0.216 · 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

Citations122
Published2015
Admission routes1
Has abstractyes

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