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Record W7097032592

GLASS FRP JACKETING OF PRESTRESSED CONCRETE BEAMS

2015· article· en· W7097032592 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
Fundersnot available
KeywordsFibre-reinforced plasticPrestressed concreteCorrosionConcrete coverBridge (graph theory)Service lifeBeam (structure)
DOInot available

Abstract

fetched live from OpenAlex

Like many other authorities, the Ministère des Transports du Québec must deal with problems caused by the corrosion of steel reinforcement in bridges. This research project focuses on a particular problem encountered on many AASHTO-type prestressed concrete beams. Those beams, which are otherwise in good condition and of adequate strength, are often damaged by localized but severe concrete delamination. After repeated repair works on bridges built over roadways, the preferred solution is sometimes complete replacement, in order to ensure the safety of people and vehicles. The research project presented here evaluates an alternative jacketing solution using glass FRP to prevent the concrete cover that is delaminated by the corrosion of steel stirrups from falling. The first phase of the project consisted of laboratory tests conducted on beam segments salvaged from a bridge in order to evaluate various parameters of GFRP jacketing. The test results indicate that the jacketing is able to support many times the expected weight of the delaminated concrete, and suffers no significant deterioration during freeze/thaw cycles. The second phase involved installing the approved solution on real bridges in service in Montréal. The cost efficiency and the service life expectation for this bridge repair solution will be evaluated. 1.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.231
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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