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Record W4388523363 · doi:10.1680/jcoma.23.00062

Cold weather concreting: provisions of codes and research advances

2023· article· en· W4388523363 on OpenAlexaff
A. M. Yasien, M. T. Bassuoni

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Construction Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of ManitobaResearch Manitoba
Fundersnot available
KeywordsCold weatherHardening (computing)Environmental scienceCold climateCementitiousExtreme weatherCivil engineeringForensic engineeringEngineeringMaterials scienceCementMeteorologyGeologyClimate changeComposite material

Abstract

fetched live from OpenAlex

In cold regions, concrete practitioners face challenges in achieving the target performance criteria of concrete produced at low temperatures. When concrete temperature drops to −2.8°C, the hydration development of cementitious binders nominally ceases due to the freezing of mixing water, which results in hydraulic and osmotic pressures that exceed the tensile capacity of concrete, especially at early age (immature stage). Subsequently, the hardening and strength gain rates of concrete are adversely affected, resulting in insufficient microstructural development and irreversible deterioration, which makes concrete applications challenging under cold weather. Therefore, multiple investigations have been conducted to develop efficient approaches to overcome the challenges of placing concrete at low temperatures. The current paper synthesises code provisions in North America and Europe and state-of-the-art knowledge on cold weather concreting, in terms of mixture components as well as new inventions and methods of concrete curing and protection at low temperatures. Hence, it should provide informative guidance for the construction industry in cold regions to improve cold weather concreting practices.

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)0.001
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 score0.031
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.272
Teacher spread0.253 · 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
Published2023
Admission routes1
Has abstractyes

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