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Record W1930729377 · doi:10.4224/20377587

Fire performance of FRP systems for infrastructure: a state-of-the-art report

2005· article· en· W1930729377 on OpenAlexvenueno aff
La Bisby, Brea Williams, Vrk Kodur, Mf Green, Ershad U. Chowdhury

Bibliographic record

VenueNPARC · 2005
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
Fundersnot available
KeywordsFibre-reinforced plasticForensic engineeringCorrosionReinforced concreteCivil engineeringFire resistanceEngineeringStructural engineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Widespread deterioration of infrastructure resulting from corrosion of reinforcing steel in concrete has led recently to the use of fibre reinforced polymers (FRPs) in a number of infrastructure applications. A significant research effort over the past fifteen years has shown that FRP materials can be effectively used to reinforce and/or strengthen deteriorated or under strength reinforced concrete structures. However, the performance of FRP materials in fire remains a serious concern, which needs to be addressed before these materials can be used with confidence in applications where fire endurance is a design criterion (i.e. buildings, parking garages, etc.). This report presents a review of the literature with respect to the fire and high temperature behaviour of FRP composites as is relevant to the design and construction of FRP-strengthened or reinforced concrete structures.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations25
Published2005
Admission routes1
Has abstractyes

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