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Record W4243572681 · doi:10.32920/ryerson.14668683

Strain rate sensitivity of ultra-high performance fiber reinforced concrete

2021· preprint· en· W4243572681 on OpenAlexaff
Aria Aghajani-Namin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceStrain rateHammerComposite materialDurabilityUltimate tensile strengthFiber-reinforced concreteFlexural strengthDynamic testingSensitivity (control systems)Dynamic loadingMetakaolinStructural engineeringFiberCompressive strengthEngineeringMetallurgy

Abstract

fetched live from OpenAlex

Ultra High Performance Fiber Reinforced Concrete (UHP-FRC) is relatively new cementitious material, which has been developed to enhance material performance such as, durability, workability and strength. UHP-FRC has an outstanding dynamic performance with high capacity to absorb damage. Because of its superior performance under dynamic loading, UHP-FRC has been induced in structures where dynamic resistance is required. It is proven that like other concrete materials, UHP-FRC strength increases significantly when subjected to high strain rates. The objective of this study is to develop understanding of strain rate sensitivity of UHP-FRC with 2% steel fiber by volume fraction and plain High Strength Concrete (HSC). Compressive and flexural tensile strength of each concrete composite were investigated to evaluate and compare their strain rate sensitivity through dynamic increase factor (DIF). The specimens were tested under six different strain rates; three in quasi-static and three in dynamic domain. Strain rates in quasi-static domain conducted by MTS test machine and strain rates in dynamic domain conducted using the drop hammer technique. The test results revealed that UHP-FRC exhibits less strain rate sensitivity while HSC show much higher rate sensitivity in comparison to other materials.

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 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)0.001
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 score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.222
Teacher spread0.207 · 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 teacher head, not a consensus.

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

Citations4
Published2021
Admission routes1
Has abstractyes

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