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Record W1551254081

Fire endurance of FRP-strengthened reinforced concrete columns

2004· article· en· W1551254081 on OpenAlexfundvenueno aff
Venkatesh Kodur, Luke Bisby, Mark F. Green

Bibliographic record

VenueNPARC · 2004
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
FundersQueen's University
KeywordsFibre-reinforced plasticForensic engineeringStructural engineeringEngineeringEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

In buildings, fire represents one of the most severe environmental conditions and should thus be properly accounted for in the design of structural members. In recent years, there has been an increase in the use of fibre-reinforced polymer (FRP) materials for reinforcement and strengthening of concrete structures in buildings, and this has raised concerns regarding the behaviour of such FRP systems in fire. There is currently very little information available on the fire endurance of FRP-reinforced or strengthenedconcrete systems. This paper presents results from full-scale fire resistance experiments on two FRP-strengthened (wrapped) reinforced concrete (RC) columns. A comparison is made between the fire performances of FRP-strengthened RC columns and a conventional unstrengthened RC column tested previously. Data obtained during the experiments is used to show that the fire behaviour of FRP-wrapped concrete columns, using an appropriate fire protection system, is as good as that of unstrengthened RC columns. Thecritical factors that influence the fire endurance of FRP-reinforced concrete columns, namely the fire protection system, FRP wrapping, loading, and type of aggregate in the concrete, are discussed. It is demonstrated that satisfactory fire resistance ratings for FRP-wrapped concrete columns can be obtained by properly incorporating appropriate fire protection measures into the overall structural system.

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 score0.017
Threshold uncertainty score0.846

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 communication0.0000.000
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 head0.205
Teacher spread0.197 · 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

Citations4
Published2004
Admission routes2
Has abstractyes

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