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Record W2795999582 · doi:10.1002/suco.201700078

Size effect of ultra‐high performance fiber reinforced concrete composite beams in shear

2018· article· en· W2795999582 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

VenueStructural Concrete · 2018
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceDuctility (Earth science)Structural engineeringStiffeningFiber-reinforced concreteTension (geology)Composite materialComposite numberShear (geology)FiberFinite element methodReinforced concreteStiffnessCompression (physics)EngineeringCreep

Abstract

fetched live from OpenAlex

In this paper, a finite element model (FEM) based on a concrete damage‐plasticity approach was developed to investigate the size effect of ultra‐high performance fiber reinforced concrete (UHPFRC) and normal‐strength concrete or high‐strength concrete (NSC/HSC) composite beams. The material behavior of UHPFRC was modeled by introducing a suitable tension stiffening model to simulate the behavior of UHPFRC beams in tension. Specimens containing UHPFRC with different fiber volume content that have an overall height between 300 and 1,200 mm, a constant shear span‐effective depth ratio of 3 and no stirrups were investigated. The validity of the proposed model was established through comparisons between the experimental test results and those obtained in other studies. The results revealed that the size effect of UHPFRC members was diminished for fiber volume content higher than 1.5% due to the high ductility of UHPFRC material.

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), Insufficient payload (model declined to judge)
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 score0.016
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.220
Teacher spread0.215 · 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