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Record W2999340775 · doi:10.1080/21650373.2019.1709999

Effect of fly ash/silica fume ratio and curing condition on mechanical properties of fiber-reinforced geopolymer

2020· article· en· W2999340775 on OpenAlexaff
Piti Sukontasukkul, Prinya Chindaprasirt, Phattharachai Pongsopha, Tanakorn Phoo-ngernkham, Weerachart Tangchirapat, Nemkumar Banthia

Bibliographic record

VenueJournal of Sustainable Cement-Based Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSilica fumeFly ashCuring (chemistry)Materials scienceComposite materialGeopolymerFlexural strengthMolar ratioCompressive strengthChemistry

Abstract

fetched live from OpenAlex

In this study, the effects of fly ash/silica fume (F/S) ratio and curing conditions on mechanical performance of fly ash based plain and steel fiber reinforced geopolymer (SFRG) was investigated. Plain geopolymer was prepared using NaOH solution of 14 molarity, Na2SiO3/NaOH of 2.5 and paste/fine aggregate of 0.36. For SFRG, the volume fractions of 0.5% and 1% were used. The F/S ratio was varied from 100/0 to 70/30. The specimens were cured under normal and accelerated conditions. Four experiments were carried out: flow, setting time, compression, and flexural performance tests. The results indicated that the addition of fibers enhanced the mechanical properties. The effect of F/S ratio depended strongly on the curing condition. Under ambient curing, the optimum F/S ratio of 90/10 was observed. For accelerated curing, the addition of silica fume provided negative effect on the mechanical properties. The optimum F/S ratio was observed in mix without silica fume.

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 categoriesInsufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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.

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

Citations72
Published2020
Admission routes1
Has abstractyes

Explore more

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