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Record W2901101061 · doi:10.1080/01694243.2018.1527671

Effect of the type of superplasticizer on the properties of cementitious systems incorporating slag

2018· article· en· W2901101061 on OpenAlexaff
Ahmed Laichaoui, Ablam Zidol, Ratiba Mitiche-Kettab, Abderrahim Bali, Arezki Tagnit‐Hamou

Bibliographic record

VenueJournal of Adhesion Science and Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSuperplasticizerCementitiousMaterials scienceCementCompressive strengthComposite materialSlag (welding)RheologyPortland cementMortar

Abstract

fetched live from OpenAlex

Superplasticizers have become an integral ingredient in the formulation of concretes. After 40 years, their use and dosage remain uncertain due to variations in their compositions and those of cement. In addition, the substitution of cement by supplementary cementitious materials having different chemical compositions exacerbates the problem without counting the multiplicity of superplasticizers to choose from. The present work consists of a study of the rheological and mechanical properties of cementitious systems containing slag and various types of superplasticizers. The tests were carried out on pastes, mortars and concretes incorporating slag in partial cement replacement and four superplasticizers types, polynaphthalene sulphonate (PNS) and three polycarboxylates (PC). The results of this study demonstrate that the viscosity and the yield stress increase with the rate of incorporation of the slag. The air increases with the polycarboxylates but this effect is less perceptible in the presence of the slag. Polycarboxylates improve workability more than PNS. The use of polycarboxylates reduces the viscosity and the yield stress. The compressive strength of concretes containing slag is low at early age but high at long run. They exhibit good resistances to scaling. The permeability to chloride ions is considerably reduced in the presence of the slag independently of the type of superplasticizer, suggesting good durability of these concretes in potentially aggressive external environments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.263
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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".

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Citations4
Published2018
Admission routes1
Has abstractyes

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