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Record W2095485699 · doi:10.1139/l2012-069

Bond characteristics of steel fibre reinforced self-compacting concrete

2012· article· en· W2095485699 on OpenAlexvenueno aff
Farhad Aslani, Shami Nejadi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialCrackingToughnessUltimate tensile strengthBrittlenessDuctility (Earth science)Slip (aerodynamics)Fiber-reinforced concreteStructural engineeringReinforced concreteCreepEngineering

Abstract

fetched live from OpenAlex

Steel fibre reinforced self-compacting concrete (SFRSCC) is a relatively new composite material that combines the benefits of the self-compacting concrete (SCC) technology with the advantages derived from the fibre addition to a brittle cementitious matrix. Steel fibres improve many of the properties of SCC elements including tensile strength, ductility, toughness, energy absorption capacity, fracture toughness and cracking. Although the available research regarding the influence of steel fibres on the properties of SFRSCC is limited, this paper investigates the bond characteristics between steel fibre and SCC. Based on the available experimental results, the current analytical steel fibre pullout model is modified by considering the different SCC properties and different fibre types (smooth, hooked) and fibre inclination. To take into account the effect of fibre inclination in the pullout model, apparent shear strengths (τ (app) ) and slip coefficient (β) are incorporated to express the variation of pullout peak load and the augmentation of peak slip as the inclined angle increases. These variables are expressed as functions of the inclined angle (ϕ).

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)
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 score0.250
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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

Citations42
Published2012
Admission routes1
Has abstractyes

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