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Record W2323164395 · doi:10.1139/l2012-062

Shear strength of concrete beams cast with self-compacting concrete containing different fillers and coarse aggregates

2012· article· en· W2323164395 on OpenAlexvenueno aff
Mohamed A. Safan

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceDolomiteSilica fumeCrackingFly ashComposite materialShear (geology)ReinforcementShear strength (soil)InterlockingCompressive strengthCementGeotechnical engineeringStructural engineeringGeologyMetallurgyEngineering

Abstract

fetched live from OpenAlex

An experimental investigation was conducted to evaluate the shear strength provided by different self-compacting concrete mixes proportioned using different fillers and coarse aggregates. A total of 28 simple beams without shear reinforcement were tested in flexure. The test parameters included the use of gravel versus crushed dolomite as coarse aggregates, the amount of longitudinal reinforcement, and the composition and percentage of fillers. Dolomite stone powder with either silica fume or fly ash was used as fillers replacing cement aiming at reducing the cost of the mix and obtaining better performance. The test results indicated that the overall structural performance in terms of cracking pattern and shear strength was comparable in all mixes. While the dolomite beams sustained higher ultimate shear loads, the normalized shear strength of most of the gravel beams were higher compared to their dolomite counterparts due to the capability of gravel beams to develop interlocking mechanisms and post-cracking shear resistance compensating the influence of the lower compressive strength.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.185
Teacher spread0.176 · 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 designSimulation or modeling
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

Citations12
Published2012
Admission routes1
Has abstractyes

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