MétaCan
Menu
Back to cohort
Record W4225121851 · doi:10.11159/icsect22.128

Early-Age Properties of Slag-Fly Ash Blended Geopolymer Concrete Reinforced with Glass Fibers – A Preliminary Study

2022· article· en· W4225121851 on OpenAlexvenueno aff
Mohammad Zuaiter, Hilal El-Hassan, Bilal El-Ariss, Tamer El‐Maaddawy

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersUnited Arab Emirates University
KeywordsFly ashGeopolymer cementMaterials scienceGeopolymerSlag (welding)Composite materialGround granulated blast-furnace slagGlass fiber

Abstract

fetched live from OpenAlex

This research investigates the effect of additional water content, glass fibers addition, glass fiber length, and the handling time on the workability, 1-and 7-day compressive strength, and 7-day splitting tensile strength of slag-fly ash blended geopolymer concrete. The additional water content ranged from 0 to 100 kg/m 3 . Two types of glass fibers were used with lengths of 24 and 43 mm and incorporated by up to 2 and 1.5%, by volume, respectively. Also, the handling time, representing the time from mixing to casting, was varied. The experimental results showed that the additional water content led to a significant increase in the slump and decrease in the 1-and 7-day compressive strengths of plain geopolymer concrete. A value of 75 kg/m 3 was required to attain a slump of 150 mm and 7-day compressive strength of 35 MPa. Furthermore, the addition of 24-mm long glass fibers reduced the slump and increased the compressive and splitting tensile strength by up to 23 and 40%, respectively, compared to the plain control mix. Longer glass fibers (43 mm) resulted in further slump loss and increase in the splitting tensile strength, while the compressive strength was unaffected. Extending the handling time led to lower workability and limited impact on the mechanical properties.

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 score0.036
Threshold uncertainty score0.862

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 head0.006
GPT teacher head0.178
Teacher spread0.171 · 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

Citations26
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicConcrete and Cement Materials ResearchFrench-language works237,207