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Record W2070569562 · doi:10.1139/l04-005

Flexural strengthening of precast reinforced concrete bridge girders using bonded carbon fibre reinforced polymer strips or external post-tensioning

2004· article· en· W2070569562 on OpenAlexvenueaboutno aff
E.Y. Sayed-Ahmed, Amr H. Riad, Nigel G. Shrive

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
Fundersnot available
KeywordsPrecast concreteGirderFlexural strengthStructural engineeringBridge (graph theory)Fibre-reinforced plasticMaterials scienceCarbon fiber reinforced polymerReinforced concreteSTRIPSComposite materialEngineering

Abstract

fetched live from OpenAlex

Strengthening of reinforced concrete bridge girders has become a major concern. Many reinforced concrete highway bridges were constructed in Canada using precast reinforced concrete girders, and currently many of them need rehabilitation. Thus, two techniques for flexural strengthening of precast reinforced concrete bridge girders (HC-type) were investigated experimentally. The first technique involved bonding carbon fibre reinforced polymer (CFRP) strips to the soffits of the girders. The second technique was classical external post-tensioning with steel bars. A simple, yet innovative method was developed for applying the external prestressing to these girders. The experimental investigation was performed on full-scale girders from a dismantled bridge in Alberta (Canada). Outcomes of the experimental investigation led to the conclusion that classical methods of flexural strengthening (e.g., external prestressing) should not be completely disregarded. Also, an additional mode of failure needs to be added to currently available prediction models for estimating the gain in flexural strength of reinforced concrete girders retrofitted using bonded CFRP strips.Key words: bridges, CFRP, flexure strengthening, precast girders, post tension, prestressing, rehabilitation.

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 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 score0.230
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.208
Teacher spread0.194 · 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.

Study designSimulation or modeling
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

Citations10
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207