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Upgrading the Seismic Performance of Reinforced Masonry Columns Using CFRP Wraps

2011· article· en· W2108175711 on OpenAlexafffund
Khaled Galal, Nima Farnia, O. A. Pekau

Bibliographic record

VenueJournal of Composites for Construction · 2011
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Masonry Design CentreFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsMasonryMaterials scienceStructural engineeringReinforced concreteColumn (typography)DissipationDuctility (Earth science)Composite materialFibre-reinforced plasticUnreinforced masonry buildingEngineeringCreep

Abstract

fetched live from OpenAlex

Compared with reinforced concrete, relatively fewer experimental studies address the behavior of masonry columns under combined axial load and cyclic flexure. There exist reinforced concrete masonry (RCM) columns that are part of the moment resisting system of masonry structures that are in need of seismic upgrade. Wrapping such susceptible RCM columns with carbon fiber-reinforced polymers (CFRP) is expected to enhance the seismic behavior of reinforced masonry columns considerably. This paper focuses on assessing the seismic performance of RCM columns wrapped with CFRP. In this experimental study, six 1.4-m reinforced masonry columns were constructed and tested when subjected to constant axial force and cyclic lateral excitations. The columns had a cross-section of 390 mm×390 mm and were constructed using bull-nosed concrete units. The first column had no CFRP wraps and was used as a control specimen whereas the other five columns were wrapped using different layers of CFRP sheets or different wrapping schemes. From the tests, it was observed that wrapping the masonry columns with CFRP wraps enhanced the seismic performance of the columns by offering more ductile behavior, increasing both strength and energy dissipation capacity.

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 score0.278
Threshold uncertainty score0.487

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 head0.021
GPT teacher head0.217
Teacher spread0.196 · 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

Citations7
Published2011
Admission routes2
Has abstractyes

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