MétaCan
Menu
Back to cohort
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 OpenAlexaff
Luaay Hussein, Lamya Amleh

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.

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 score0.001
Threshold uncertainty score0.004

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 integrity0.0000.000
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

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

Citations28
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueStructural ConcreteSame topicInnovative concrete reinforcement materialsFrench-language works237,207