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Record W1964879097 · doi:10.1680/macr.14.00116

Enhancing cracking resistance of ultra-high-performance concrete slabs using steel fibres

2015· article· en· W1964879097 on OpenAlexaff
Doo‐Yeol Yoo, Hyun-Oh Shin, Jin-Young Lee, Young‐Soo Yoon

Bibliographic record

VenueMagazine of Concrete Research · 2015
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsMcGill University
FundersKorea Agency for Infrastructure Technology Advancement
KeywordsShrinkageCrackingMaterials scienceComposite materialUltimate tensile strengthTensile strain

Abstract

fetched live from OpenAlex

This study investigates the effect of steel fibres on early-age shrinkage cracking behaviour of ultra-high-performance concrete (UHPC) slabs. For this, four large UHPC slabs, 2600 mm long and 80 mm deep, are fabricated. Two different UHPC mixtures with and without steel fibres (Vf = 0% and 2%) are considered. Test results show that all UHPC slabs without steel fibres exhibit shrinkage cracks at a very early age (before 1 d), whereas no shrinkage cracks are found in the UHPC slabs with steel fibres. The two principal reasons for these observations are the higher shrinkage strain and lower tensile strength of UHPC without steel fibres compared with its counterpart with steel fibres incl. Restrained shrinkage strain is found to be much lower (approximately 75% or more) than the free shrinkage strain, and exhibits a completely different behaviour to that of strain under free conditions.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.060
GPT teacher head0.317
Teacher spread0.256 · 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

Citations51
Published2015
Admission routes1
Has abstractyes

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