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Record W4379055242 · doi:10.5267/j.esm.2023.5.007

Experimental study of artificial lightweight aggregates using coal fly ash and epoxy resin

2023· article· en· W4379055242 on OpenAlexvenueno aff
Ani Firda, Anis Saggaff, Hanafiah Hanafiah, Saloma Saloma

Bibliographic record

VenueEngineering Solid Mechanics · 2023
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsFly ashCompressive strengthEpoxyMaterials scienceAggregate (composite)CoalComposite materialFlexural strengthPolymer concreteGeopolymerPulp and paper industryCementWaste managementEngineering

Abstract

fetched live from OpenAlex

A lack of natural aggregates in the future is unavoidable, which generates issues for building development. For many industries, natural resources constitute a significant source of revenue. As a result, light artificial aggregate is produced to anticipate the decreasing source of natural aggregate. Production of artificial geopolymer aggregates, fly ash from the burning of coal has been proposed. This paper investigates the optimal proportion of epoxy resin and coal fly ash-based synthetic aggregates. The artificial aggregates are produced following specific gravity and compressive strength standards that may be used as a component of lightweight structural concrete (LWC). The production polymer lightweight aggregate (PLA) comes from a combination of coal fly ash and epoxy resin. The results show that PLA 50:50 to PLA 74:26 can be used for 6 hours to make structural concrete with a strength of more than 17 MPa. PLA 80:20 could achieve compressive strength with the range of 7-17 MPa. PLA 84:16 achieves a compressive strength range of 0.35 to 7 MPa and is utilized as a non-structural element. However, the flexural strength values in concrete LWC 70:30 and LWC 80:20 are higher, at 46.1% and 7.63%, respectively.

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 categoriesMeta-epidemiology (narrow)
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 score0.283
Threshold uncertainty score1.000

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 head0.024
GPT teacher head0.261
Teacher spread0.238 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

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