MétaCan
Menu
Back to cohort

Viscoelastic properties of fresh cement paste: measuring procedures and influencing parameters

2023· article· en· W4386051034 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRILEM Technical Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsViscoelasticityMaterials scienceRheologyCementitiousCementComposite materialViscosityThixotropy

Abstract

fetched live from OpenAlex

Fresh cement pastes behave as viscoelastic materials below the flow onset. The measurements of viscoelastic properties of fresh cement paste provide valuable insight into the dispersion of solid particles as well as the hydration kinetics at early age and its influence on the structural evolution and solidification behavior at quasi-static conditions. Monitoring the development of viscoelastic properties of fresh cement paste using dynamic oscillatory shear measurements can also elucidate the working mechanisms of chemical admixtures. These properties are efficient indicators to guide mixture proportion design and are necessary to understand the rheology and stability of concrete. In this paper, the most common techniques, including dynamic oscillatory measurements, used to assess the viscoelastic properties of fresh cement paste are presented and discussed. The measurement challenges and their effects on the accuracy of the obtained properties are highlighted. On the other hand, the effects of high-range water-reducer, viscosity-modifying admixture, and supplementary cementitious materials are discussed. Furthermore, the use of viscoelastic measurements to assess yield stress and structural build-up of cement paste is presented.

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.

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 score0.003
Threshold uncertainty score0.415

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 head0.022
GPT teacher head0.205
Teacher spread0.183 · 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