MétaCan
Menu
Back to cohort

Experimental Investigation of Large-Scale Eccentrically Loaded GFRP-Reinforced High-Strength Concrete Columns

2021· article· en· W4200148648 on OpenAlexaff
Mu’taz Almomani, Karam Mahmoud, Ehab El-Salakawy

Bibliographic record

VenueJournal of Composites for Construction · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaterials scienceFibre-reinforced plasticBucklingComposite materialReinforcementEccentricity (behavior)BendingStructural engineeringSpiral (railway)Axial symmetryAspect ratio (aeronautics)

Abstract

fetched live from OpenAlex

Ten large-scale high-strength concrete (HSC) circular columns were constructed and tested to failure. Nine columns were internally reinforced with glass fiber-reinforced polymer (GFRP) bars and spirals, whereas one was reinforced with steel bars and spirals to serve as a reference. All columns had a diameter of 350 mm. The variables tested were reinforcement type, spiral pitch, slenderness ratio, eccentricity-to-diameter ratio (e/D), and type of loading (axial or four-point bending). Experimental results showed that both reinforcement types (steel or GFRP) and the spiral pitch did not have a significant effect on the behavior of GFRP-reinforced HSC columns up to the peak load. In addition, a decrease in the axial capacity of the columns as the e/D ratio increased was observed. This was consistent for specimens of both slenderness ratios of 14 and 20. Columns with a higher slenderness ratio showed a lower axial capacity for all specimens tested under the same e/D ratio. Furthermore, slender columns with higher e/D ratio underwent much larger deformations; both axially and laterally. For columns of both slenderness ratios, axial load–bending moment interaction diagrams were produced using the experimental results and were compared to the predictions of available codes and guidelines.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.054
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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 teacher head, 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

Citations16
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Composites for ConstructionSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207