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Record W2566506768 · doi:10.1520/acem20160006

Effect of Pozzolanic Admixtures on the Fresh Properties of Cement-Based Foam

2016· article· en· W2566506768 on OpenAlexaff
Farnaz Batool, Vivek Bindiganavile

Bibliographic record

VenueAdvances in Civil Engineering Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceComposite materialPozzolanFoam concretePozzolanic activityCementCompressive strengthPortland cement

Abstract

fetched live from OpenAlex

Abstract This experimental study was conducted to investigate the influence of pozzolanic admixtures on the fresh properties of cement-based foams. The cement-based foam mixes were tested at three different cast densities namely, 800, 600, and 400 kg/m3. Along with a reference mix, other series were prepared in which fly ash, silica fume, and metakaolin were added to the binder at up to 10 % and 20 % replacement by cement mass. The Marsh cone test and the flow cone test techniques were employed to measure the flowability and spreadability for 21 cement-based foam mixes. The results show that the addition of pozzolanic admixtures increases the flow time and the longest time recorded with metakaolin. A linear relationship of spread with the density of the mixes was found in this study. It was also found that with the addition of fly ash and silica fume, there was an increase in the demand for foam content. On the other hand, adding metakaolin reduced this demand. Based on the experimental results, an equation to predict the spreadability of the mixes with pozzolanic admixtures has been suggested.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.227
Teacher spread0.219 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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