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Anchorage System to Prestress FRP Laminates for Flexural Strengthening of Steel-Concrete Composite Girders

2012· article· en· W2104048824 on OpenAlexafffund
Raafat El‐Hacha, Mohamed Y. E. Aly

Bibliographic record

VenueJournal of Composites for Construction · 2012
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsFibre-reinforced plasticMaterials scienceGirderComposite materialFlexural strengthComposite numberStructural engineeringUltimate tensile strengthComposite laminatesEngineering

Abstract

fetched live from OpenAlex

Using externally bonded (EB) fiber-reinforced polymer (FRP) laminates for strengthening steel-concrete composite girders has recently received more attention from researchers. By prestressing the EB FRP laminates, the material is used more efficiently because a greater portion of its tensile capacity is employed and it contributes to the load-bearing capacity under both service and ultimate conditions. This is an ideal technique because it combines the advantage of using noncorrosive and lightweight advanced composite materials in the form of bonded FRP laminates with the high efficiency offered by external prestressing. An innovative mechanical anchorage system was developed to prestress the FRP laminates directly by jacking and reacting against the steel girder itself. The efficiency of the system was investigated using two types of FRP laminates for flexural strengthening of large-scale steel-concrete composite girders. The used FRP composite materials included carbon-fiber-reinforced polymer (CFRP) plate and steel-fiber-reinforced polymer (SFRP) sheets. The developed anchorage/prestressing system was easy to use/apply and proved to be a feasible and practical system for prestressing both CFRP plate and SFRP sheet. The prestressing levels in the FRP laminates were sufficiently maintained. The prestressing losses were insignificant.

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 score0.074
Threshold uncertainty score0.979

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 head0.010
GPT teacher head0.235
Teacher spread0.226 · 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

Citations35
Published2012
Admission routes2
Has abstractyes

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