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Record W2594676519 · doi:10.11113/jt.v79.9957

MECHANICAL PROPERTIES OF SELF-COMPACTING GEOPOLYMER CONCRETE CONTAINING SPENT GARNET AS REPLACEMENT FOR FINE AGGREGATE

2017· article· en· W2594676519 on OpenAlexaff
Habeeb Lateef Muttashar, Mohd Warid Hussin, Mohd Azreen Mohd Ariffin, Jahangir Mirza, Nor Hasanah, Ali Umara Shettima

Bibliographic record

VenueJurnal Teknologi · 2017
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsHydro-Québec
FundersKementerian Pendidikan Malaysia
KeywordsGround granulated blast-furnace slagAggregate (composite)SlumpGeopolymer cementCompressive strengthFlexural strengthGeotechnical engineeringUltimate tensile strengthMaterials scienceConcrete slump testEnvironmental scienceWaste managementMetallurgyComposite materialGeopolymerGeologyEngineeringCement

Abstract

fetched live from OpenAlex

Millions of tons of spent garnet, a by-product of surface treatment operations, are disposed of in landfills, oceans, rivers, and quarries, among others every year, thus it causes environmental problems. The main objective of this study is to evaluate spent garnet as a sand replacement in concrete prepared with ground granulated blast furnace slag (GGBS)-based self-compacting geopolymer concrete (SCGC). Concrete mixtures containing 0%, 25%, 50%, 75% and 100% spent garnet as a replacement for river sand were prepared with a constant Liquid/Binder (L/B) mass ratio equal to 0.4. Compressive, flexural and splitting tensile strengths as well as workability tests (slump, L-box, U-box and T50) were conducted on concrete containing spent garnet. As per specification and guidelines for self-compacting concrete (EFNARC) standard, the test results showed that the concrete’s workability increased with the increase of spent garnet, while all the other strength values were consistently lower than conventional concrete (SCGC) at all stages of replacement. The results recommended that spent garnet should be used in concrete as a sand replacement up to 25% to reduce environmental problems, costs and the depletion of natural resources.

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)0.001
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 score0.008
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.047
GPT teacher head0.292
Teacher spread0.245 · 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

Citations24
Published2017
Admission routes1
Has abstractyes

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