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Record W3208982818 · doi:10.32920/ryerson.14652615.v1

Fresh and Mechanical Properties of Self-Consolidating Concrete Incorporating Silica Fume and Metakaolin

2021· preprint· en· W3208982818 on OpenAlexaff
Syed Iftikhar Ahmed

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMetakaolinSilica fumePozzolanMaterials scienceComposite materialSelf-consolidating concreteCompressive strengthCement

Abstract

fetched live from OpenAlex

Self-consolidating concrete (SCC) has been gaining greater interest over the past decades with its excellent offerings of efficiency, beauty, and savings. Due to its high flow ability, resistance to bleeding, and non-segregating properties, SCC holds tremendous potential for use in the construction industry. SCC requires no vibration and can fill capacities, including the ones with even the most congested reinforcements. Since SCC can be obtained by incorporating supplementary cementing materials (SCMs) such as silica fume and metakaolin. It is crucial to develop and test different SCC mixtures with different volumes of SCMs to evaluate fresh and mechanical properties. Although silica fume is used in the production of SCC, the use of metakaoline in SCC is new. In this project, eleven SCC mixtures having different volumes of silica fume and metakaolin are developed. In addition, the influence of the above mentioned pozzolans (silica fume and metakaolin) on the fresh and mechanical properties are analyzed. Recommendations on fresh and mechanical properties of silica fume and metakaoline based SCC mixtures are also provided.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.028
GPT teacher head0.238
Teacher spread0.210 · 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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