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Performance of Eccentrically Loaded Reinforced-Concrete Masonry Columns Strengthened Using FRP Wraps

2019· article· en· W2955774044 on OpenAlexafffund
Hossam El-Sokkary, Khaled Galal

Bibliographic record

VenueJournal of Composites for Construction · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEccentricity (behavior)Materials scienceColumn (typography)Fibre-reinforced plasticMasonryStructural engineeringReinforced concreteComposite materialReinforced concrete columnEngineering

Abstract

fetched live from OpenAlex

This paper presents an experimental study that investigates the behavior of reinforced masonry (RM) columns strengthened using carbon fiber-reinforced polymer (CFRP) wraps under eccentric axial loading. Nine half-scale square RM columns with reinforced concrete (RC) end blocks were tested under three different eccentricity-to-section height (e/h) ratios (0.15, 0.25, and 0.50). For each load eccentricity, the columns were tested unwrapped, wrapped with one layer of CFRP, and wrapped with two layers. The effect of partially grouted masonry cells on column capacity was also evaluated. Existing analytical procedures for RC column sections were applied to the fully grouted RM columns. The experimental tests showed that CFRP wrapping increased the column’s axial capacity by up to 41% at a nominal e/h ratio of 0.25, while an enhancement of 45% was achieved at a nominal e/h ratio of 0.50. The analytical procedures were able to predict the failure mode and axial capacity of the tested columns. The columns’ axial capacities were estimated with a maximum error of 11%, which indicates the validity of the analytical procedures for eccentrically loaded RM columns.

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 score0.247
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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
Published2019
Admission routes2
Has abstractyes

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