MétaCan
Menu
Back to cohort
Record W2075237535 · doi:10.1680/coma.800047

Effects of heat and mixing time on self-compacting concrete

2010· article· en· W2075237535 on OpenAlexaff
Moncef L. Nehdi

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Construction Materials · 2010
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsWestern University
Fundersnot available
KeywordsSlumpCompressive strengthMaterials sciencePortland cementMixing (physics)Composite materialRheologySuperplasticizerCementGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

The coupled effects of ambient temperature and mixing time on the slump loss of self-compacting concrete (SCC) are critical for hot weather concreting. Ordinary Portland cement concrete mixtures were made with a water/cement ratio of 0·38 and incorporating polycarboxylate-, melamine sulfonate-, or naphthalene sulfonate-based superplasticisers. The concrete mixtures were continuously agitated for up to 110 min using a low-shear rate mixer under controlled temperature ranging from 22 to 45°C. The effects of such a prolonged mixing scheme under various temperatures on the slump loss and compressive strength of concrete at ages of 12 h, 1, 3, 7 and 28 days were investigated. The results show that concrete can undergo substantial slump loss when subjected to prolonged mixing at high temperature. Knowledge of the superplasticiser effects at high temperature and prolonged mixing time is critical to achieve adequate rheological properties of concrete in hot weather. The results also indicate that the compressive strength of concrete mixtures is dependent on temperature, mixing time and superplasticiser type. The early-age strength of concrete at high temperature may be about four-fold that at moderate temperature. On the other hand, the long-term compressive strength of concrete subjected to early-age high temperature can decrease by more than 10%.

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 score0.004
Threshold uncertainty score0.532

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)0.0000.000

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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

Explore more

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