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Fatigue crack arrest in steel beams using FRP composites

2021· article· en· W3145928063 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEngineering Failure Analysis · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMaterials scienceComposite materialStiffnessFibre-reinforced plasticStructural engineeringCrack closureFracture (geology)Fracture mechanicsEngineering

Abstract

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Previous studies have demonstrated the effectiveness of strengthening with pre-stressed carbon-fiber reinforced polymer (CFRP) composites to increase the lifetime of cracked steel members. In some cases, complete crack arrest has been observed. This study aims to present a method that can estimate the minimum required prestressing that would result in a complete crack arrest in steel I-beams. Analytical and numerical models based on linear elastic fracture mechanics (LEFM) were developed and verified using a set of experimental results. Three steel I-beams with different crack lengths were strengthened with pre-stressed CFRP composites and later tested under a high-cycle fatigue loading regime. It was shown that the pre-stressed CFRP composites could result in a crack closure mechanism, in which the crack surfaces remained closed even under large external loads. Furthermore, it was shown that by considering the stiffness of the CFRP in the analytical formulation, the amount of prestressing required to arrest the fatigue crack growth can be reduced.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.011
GPT teacher head0.225
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