MétaCan
Menu
Back to cohort
Record W4200358319 · doi:10.18280/acsm.450508

Study of the Effect of Plastic Waste Fibers Incorporation on the Behavior of Self Compacting Concrete

2021· article· en· W4200358319 on OpenAlexvenueno aff
Laid Baali, Larbi Belagraa, Mohamed Aziz Chikouche, Leila Zeghichi

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2021
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceRheologyUltimate tensile strengthComposite materialPlastic wasteCementContext (archaeology)FiberCompressive strengthMortarComposite numberWaste management

Abstract

fetched live from OpenAlex

The use of waste and by-products has been for many decades for the manufacturing of building materials such as concrete and mortars. That responds simultaneously to reasons of economy of natural resources in aggregates (C&D waste) and the possibility of recovering industrial waste additions as replacement in composites. The present study focuses on the use of plastic waste fibers embedded in the cement matrix of self-compacting concrete SCC to improve its properties, either rheological mainly, the stability at fresh state or mechanical responses such as compressive and tensile strengths at hard state. This incorporation of plastic fiber reinforcement on the structure matrix could result an alternative SCC composite as a ductile material with enhanced properties. In this context, the self-compacting composites by adding a variable percentage of plastic fibers at 0.5, 1, 1.5% is formulated. Hence, the effect of plastic fibers waste on the rheological and mechanical properties of SCC is assessed. The obtained results in the present study let us to conclude the beneficial effect of such inclusion of plastic fibers on this new confected SCC fiber composition with acceptable rheological, physical and mechanical properties compared to those of a normal SCC concrete.

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)0.001
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 score0.018
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.262
Teacher spread0.233 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAnnales de Chimie Science des MatériauxSame topicInnovative concrete reinforcement materialsFrench-language works237,207