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Record W4405015597 · doi:10.1139/cjce-2024-0294

Effects of antifreeze admixtures on masonry performance at subfreezing temperatures

2024· article· en· W4405015597 on OpenAlexaffvenue
Jawdat Alfarra, Leon D. Wegner, Mohamed Boulfiza

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAntifreezeMasonryEnvironmental scienceForensic engineeringGeotechnical engineeringMaterials scienceStructural engineeringComposite materialWaste managementEngineeringChemistry

Abstract

fetched live from OpenAlex

Concrete brick prisms prepared with Type S masonry mortar that included sodium nitrite and/or nanocellulose as potential antifreeze admixtures were cured for 28 days at −10 °C before measuring compressive and flexural bond strengths. Addition of sodium nitrite allowed masonry prisms cured at −10 °C to reach 88% and 86% of the compressive and flexural bond strengths, respectively, of control specimens cured at room temperature. Nanocellulose was only effective when used in combination with sodium nitrite, improving the compressive and flexural bond strengths by an additional 3% and 27%, respectively. Results are primarily attributed to the ability of the sodium nitrite to lower the freezing point, allowing hydration to continue in subfreezing conditions, as confirmed by results of companion tests on mortar samples. Both additives also reduced the required w/ c ratio and porosity. The findings offer a sustainable alternative to energy-intensive protective heating methods, potentially lengthening the construction season into colder periods.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.003
GPT teacher head0.160
Teacher spread0.157 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes2
Has abstractyes

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