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Record W3216108498 · doi:10.32920/ryerson.14661405.v1

Strain rate effects on ultra high performance fiber reinforced concrete (UHP-FRC) with various fiber contents

2021· preprint· en· W3216108498 on OpenAlexaff
Sayed-Mohammad Banitabaei-Koupaei

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDuctility (Earth science)Materials scienceFlexural strengthStrain rateDurabilityCompression (physics)Fiber-reinforced concreteFiberComposite materialTension (geology)Structural engineeringEngineering

Abstract

fetched live from OpenAlex

Ultra High Performance Fiber Reinforced Concrete (UHP-FRC) was introduced in the mid-1990s and has made striking advances in recent years. Ductal® is a UHP-FRC technology that offers a unique combination of characteristics including but, not limited to ductility, strength and durability, while providing highly moldable products with quality surfaces. Compressive strengths, and equivalent flexural strengths reach up to 200 and 40 MPa, respectively. UHP-FRC also shows an outstanding performance under dynamic loading in structures subjected to extreme loading conditions such as impact, earthquake and blast. Moreover, UHP-FRC indicates an optimized combination of properties for a specific application. Three series of tests including compression, indirect tension, and flexure were conducted under various strain rates from quasi-static to dynamic loading with low strain rates. The objective of this project is to enhance knowledge of strain rate effects on UHP-FRC with various fiber contents and to report Dynamic Increase Factor (DIF).

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.016
GPT teacher head0.245
Teacher spread0.229 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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