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Record W4200447178 · doi:10.1680/jcoma.21.00049

Long-term alkali–silica mitigation of high-alkali concrete with cement replacements

2021· article· en· W4200447178 on OpenAlexaffabout
R.D. Hooton, Benoît Fournier

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Construction Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversité LavalUniversity of Toronto
Fundersnot available
KeywordsAlkali–silica reactionCementitiousMortarPortland cementCementAlkali–aggregate reactionFly ashMaterials scienceAlkali metalAggregate (composite)Bar (unit)DurabilityComposite materialGeologyChemistry

Abstract

fetched live from OpenAlex

In this study, the impact of high-alkali Portland cements on the prescribed level of supplementary cementitious materials (SCMs) required in the Canadian standard for alkali–silica reaction mitigation was evaluated. On the basis of the results, for concretes containing aggregates exhibiting moderate reactivity, the maximum allowable cement alkali limit was increased from 1.00 to 1.15%. For all the levels of aggregate reactivity, cement alkali contents could be allowed up to 1.25% provided the recommended level of mitigation by SCMs was increased. In the initial laboratory study, mortar bars and concrete prisms were cast and monitored using two different reactive aggregates and recommended levels of fly ash and slag. For the concrete prism tests, the alkali contents of cements were increased to 1.25%, as per the standard, or were increased by 0.25%. Instrumented outdoor exposure concrete blocks, along with additional concrete prisms stored at different temperatures, were cast from numerous mixtures prepared with cement alkali equivalents ranging up to 1.22%. This paper reports on the long-term performance of prisms and concrete blocks after 12 and 27 years. The performance of the outdoor blocks is also compared with the predicted performance based on the results of accelerated mortar bar and concrete prism test.

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 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 score0.010
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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 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

Citations5
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Construction MaterialsSame topicConcrete and Cement Materials ResearchFrench-language works237,207