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Record W4311392821 · doi:10.18280/acsm.460506

Mechanical Properties of Geopolymer Concrete Containing Low-Alkaline Activator

2022· article· en· W4311392821 on OpenAlexvenueno aff
Eri Setia Romadhon, Antonius Antonius, Sumirin

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsGeopolymerCompressive strengthMaterials scienceUltimate tensile strengthActivator (genetics)Fly ashFlexural strengthComposite materialPortland cementCementChemistry

Abstract

fetched live from OpenAlex

This paper investigates the mechanical properties of the geopolymer concrete using a moderately low alkali activator. The main objective is to ascertain the compressive strength, split tensile strength, elastic modulus, shear strength, flexural strength and bond strength of the said concrete. The experimental program was carried out by reviewing the variables, namely, the amount of alkaline activator which was set at 4%, and the ratio of alkali activator to fly ash (AA/FA) which was varied from 0.35, 0.4, 0.5 to 0.6. Experimental results show that the geopolymer concrete with 4% alkaline activator could still produce concrete compressive strength above 19 MPa for AA/FA ratio of 0.6 and with treatment at room temperature (33℃). On this basis, the authors derived the empirical equations for geopolymer concrete containing low alkaline activator. These equations were compared with the mechanical property model of geopolymer concrete and that of concrete containing Portland cement. The comparison shows that our model has almost the same trend as the other models.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.042
GPT teacher head0.263
Teacher spread0.221 · 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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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Same venueAnnales de Chimie Science des MatériauxSame topicConcrete and Cement Materials ResearchFrench-language works237,207