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Record W2019732194 · doi:10.1680/adcr.13.00083

Characterising cement carbonation curing using non-contact electrical resistivity measurement

2014· article· en· W2019732194 on OpenAlexafffund
Abu Zakir Morshed, Alain Azar, Yixin Shao

Bibliographic record

VenueAdvances in Cement Research · 2014
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarbonationCuring (chemistry)Electrical resistivity and conductivityMaterials scienceCementComposite materialDurabilityHardening (computing)CarbonatationPortland cement

Abstract

fetched live from OpenAlex

Carbonation curing of cement paste and concrete promotes early strength gain, improves durability and recycles carbon dioxide in a beneficial manner. To examine the carbonation curing mechanism, fresh cement pastes and concretes subject to carbonation are characterised using a non-contact electrical resistivity measurement method. Resistivity measurements reveal that characteristic features of the hydration accelerated by carbonation are different from conventional hydration. The typical induction period is absent in the carbonation curing owing to the rapid precipitation of calcium carbonate. The resistivity measurement method produces a superimposed result of early hardening and multiple instances of progressive hardening by carbonation curing. The electrical resistivity is 3–6 times higher in the carbonated cement paste and about 8–16 times higher in the carbonated concretes in comparison with the hydration references. The carbonated lightweight concrete shows lower bulk resistivity than the carbonated normal-weight concrete, possibly owing to the release of internal water from the lightweight aggregates during carbonation. This is evidence of internal curing by lightweight aggregates.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.354
Teacher spread0.287 · 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

Citations8
Published2014
Admission routes2
Has abstractyes

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