MétaCan
Menu
Back to cohort
Record W7019154817

Flexural strengthening of a steel beam with prestressed CFRP strips -preliminary investigation

2009· article· en· W7019154817 on OpenAlexfundno aff

Bibliographic record

VenueDORA Empa (Swiss Federal Laboratories for Materials Science and Technology (Empa)) · 2009
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFibre-reinforced plasticWeldingSTRIPSRetrofittingFlexural strengthCrackingBeam (structure)Carbon fiber reinforced polymerAdhesive
DOInot available

Abstract

fetched live from OpenAlex

Conventional methods of repairing and strengthening steel structures involve welding or bolting additional steel plates to the structure. These methods have a number of drawbacks: welding may lead to fatigue cracking and bolted connections can be time consuming and costly. Therefore an alternative is needed to retrofit fatigue-damaged steel bridges and structures. Fiber reinforced polymer (FRP) reinforcements have superior mechanical and physical properties as well as minimum visual impact on the aesthetic appearance, making them quite promising for retrofitting steel structures with minimum disturbance to the functionality of the structure. Several studies have been reported in the literature on FRP strengthened steel structures [1-6] and design guidelines for FRP strengthened steel structures were proposed by Rizkalla et al. [7]. Limited research has been reported on the use of prestressed FRP strips for strengthening steel structures [8, 9]. This study presents preliminary results on the effectiveness of using adhesively bonded prestressed carbon fiber reinforced polymer (CFRP) strips to increase the static flexural strength of a steel member. Transfer length data of prestressed CFRP strips bonded with epoxy adhesive to a steel member is also be presented.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
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
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.220
Teacher spread0.213 · 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 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
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueDORA Empa (Swiss Federal Laboratories for Materials Science and Technology (Empa))Same topicStructural Behavior of Reinforced ConcreteFrench-language works237,207