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Record W2173029846 · doi:10.1139/l09-053

Behaviour of steel reinforced polymer (SRP) strengthened RC members under blast loadThis paper is one of a selection of papers in the Special Issue on Blast Engineering.

2009· article· en· W2173029846 on OpenAlexafffundvenue
Marie Carrière, P. J. Heffernan, R. Gordon Wight, Abass Braimah

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsRoyal Military College of CanadaNatural Resources Canada
FundersMinistère de la Défense Nationale
KeywordsReinforced concreteBeam (structure)Ductility (Earth science)Structural engineeringMaterials scienceBlast waveComposite materialEngineering

Abstract

fetched live from OpenAlex

During the past decade, significant research has been carried out on the strengthening of reinforced concrete (RC) slabs, beams, and columns using externally bonded carbon fibre reinforced polymer (CFRP) sheets. Steel reinforced polymer (SRP) sheets have recently been proposed as an alternative to CFRP to strengthen reinforced concrete beams. This paper reports experimental and numerical results of RC beams and beam-columns transversely wrapped with SRP and tested under blast load. A total of 10 scaled RC members were tested at a variety of blast wave intensities. Detailed observations are reported and validated against numerical models created in AUTODYN for the unstrengthened RC members. The SRP wraps were resilient in the near-field blast range and enhanced the ductility of the concrete likely through enhanced confinement. Member capacity could be increased by the wraps in failure modes dominated by concrete crushing. AUTODYN appears able to reasonably predict the behaviour of the RC members when loaded by blast.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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

Citations35
Published2009
Admission routes3
Has abstractyes

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