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Record W2068735012 · doi:10.3141/2290-18

Reducing Set Retardation in High-Volume Fly Ash Mixtures with the Use of Limestone

2012· article· en· W2068735012 on OpenAlexaff
Lisa R. Gurney, Dale P. Bentz, Taijiro Sato, Jason Weiss

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2012
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsNational Research Council Canada
FundersNational Institute of Standards and Technology
KeywordsFly ashFinenessMaterials scienceCementInertMineralogyVolume (thermodynamics)Composite materialChemistryThermodynamics

Abstract

fetched live from OpenAlex

High-volume fly ash (HVFA) concretes are attractive not only because they reduce cement content and the associated greenhouse gases, but also because they avoid landfilling excessive quantities of fly ash. These sustainability benefits are often tempered by practical constructability limitations that may exist for HVFA concretes: retardation and diminution of the early-age reaction, delay in setting (and finishing operations), and lower early-age strength. This paper explores the alleviation of these deficiencies in HVFA mixtures by the incorporation of fine limestone powders into ternary blends. Isothermal calorimetry and Vicat needle penetration measurements are employed to assess reaction rates and setting times, respectively. A systematic variation of the content and fineness of the limestone powder in mixtures containing either a Class C or a Class F fly ash indicates that setting times are linearly correlated with the surface area supplied by the limestone. Comparison of a limestone system to a system containing an inert titanium dioxide of similar particle size indicates that the acceleration and amplification effects of the limestone can be attributed to both physical (nucleation) and chemical (additional calcium ions) processes. The results indicate that ternary blends with 40% of the cement by volume replaced by 30% to 35% fly ash and 5% to 10% limestone at a constant water volume fraction can be achieved without significant delay in setting.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.090
GPT teacher head0.333
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations59
Published2012
Admission routes1
Has abstractyes

Explore more

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