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Record W4289889706 · doi:10.1520/acem20220003

Reactivity of Unconventional Fly Ashes, SCMs, and Fillers: Effects of Sulfates, Carbonates, and Temperature

2022· article· en· W4289889706 on OpenAlexaff
Ying Wang, Sivakumar Ramanathan, Lisa Burris, R.D. Hooton, Christopher R. Shearer, Prannoy Suraneni

Bibliographic record

VenueAdvances in Civil Engineering Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Toronto
FundersUniversity of MiamiEnvironmental Research and Education Foundation
KeywordsReactivity (psychology)Fly ashCementitiousMaterials scienceIsothermal processCalcium oxideMineralogyChemistryChemical engineeringComposite materialCementMetallurgyThermodynamics

Abstract

fetched live from OpenAlex

ABSTRACT Reactivity information for a range of unconventional fly ashes is unavailable in literature. The objective of this study is to quantify the reactivity of numerous unconventional fly ashes using the R3 test (ASTM C1897-20, Standard Test Methods for Measuring the Reactivity of Supplementary Cementitious Materials by Isothermal Calorimetry and Bound Water Measurements) and the modified R3 test and to determine how sulfates, carbonates, and temperature affect the measured reactivity. A small set of other supplementary cementitious materials and fillers was used to benchmark the fly ash results. Heat release, calcium hydroxide consumption, and bound water were measured for the different materials. For siliceous materials with relatively low calcium oxide (CaO) + aluminum oxide (Al2O3) contents, temperature had a dominant effect on the heat release. On the other hand, for materials with higher CaO + Al2O3 contents, the effects of sulfates and carbonates dominated the effect of temperature. The slow but sustained reactivity of Class F fly ashes highlighted the importance of kinetic corrections or extrapolations to the reactivity measured in the R3 test. However, when testing at 50°C, the heat release curves of all tested materials plateaued at the end of 10 days, indicating that kinetic corrections were not required. Correlations between reactivity and early- and later-age paste properties are discussed.

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)0.001
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.003
GPT teacher head0.203
Teacher spread0.200 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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