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Record W2063026527 · doi:10.1520/cca12329

Cement and Superplasticizer Combinations: Compatibility and Robustness

2004· article· en· W2063026527 on OpenAlexaff
P. C. Nkinamubanzi, P-C Aïtcin

Bibliographic record

VenueCement Concrete and Aggregates · 2004
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCompatibility (geochemistry)SuperplasticizerCementMaterials scienceRobustness (evolution)Composite materialChemistry

Abstract

fetched live from OpenAlex

Abstract This study highlights the notion of robustness of combinations of cements and superplasticizers. Tests done with various cements and different families of superplasticizers showed that although a combination of a cement and a superplasticizer could be compatible, it is not necessarily robust. Sometimes a little variation in the dosage of the admixture could lead to detrimental side effects, such as segregation, excessive set retardation, or excess air content in the concrete. Results showed that the chemical composition of the cement is critical to ensure good compatibility and adequate robustness of various combinations of cements and superplasticizers. The chemical nature of the superplasticizer also plays a role in the behavior of such combinations. The C3A content, the soluble (alkali) sulphate content, and the fineness of the cement, which influence the adsorption rate of the superplasticizers on the cement particles, are among the key factors that control the compatibility and the robustness of cement-superplasticizer combinations, especially for polysulfonated admixtures. Based on the results of this study, a rough prediction of the compatibility and robustness of cements and superplasticizers could be made by analyzing the chemical composition of the cement and the chemical nature of the superplasticizer.

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 score0.241
Threshold uncertainty score0.667

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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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

Citations40
Published2004
Admission routes1
Has abstractyes

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