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Record W2057804717 · doi:10.1155/2012/635176

Shear Strengthening of RC Beams Using Sprayed Glass Fiber Reinforced Polymer

2012· article· en· W2057804717 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Civil Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFibre-reinforced plasticMaterials scienceComposite materialGlass fiberShear (geology)Beam (structure)Structural engineeringStrain gaugeEngineering

Abstract

fetched live from OpenAlex

The effectiveness of externally bonded sprayed glass fiber reinforced polymer (Sprayed GFRP) in shear strengthening of RC beams under quasi-static loading is investigated. Different techniques were utilized to enhance the bond between concrete and Sprayed GFRP, involving the use of through bolts and nuts paired with concrete surface preparation through sandblasting and through the use of a pneumatic chisel prior to Sprayed GFRP application. It was found that roughening the concrete surface using a pneumatic chisel and using through bolts and nuts were the most effective techniques. Also, Sprayed GFRP applied on 3 sides (U-shaped) was found to be more effective than 2-sided Sprayed GFRP in shear strengthening. Sprayed GFRP increased the shear load-carrying capacity and energy absorption capacities of RC beams. It was found that the load-carrying capacity of strengthened RC beams was related to an effective strain of applied Sprayed GFRP. This strain was related to Sprayed GFRP configuration and the technique used to enhance the concrete-FRP bond. Finally, an equation was proposed to calculate the contribution of Sprayed GFRP in the shear strength of an RC beam.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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