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Effect of crumb rubber aggregate on the performance of cementitious composites: A review

2020· review· en· W3012996751 on OpenAlexaff
Adeyemi Adesina, O. D. Atoyebi

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2020
Typereview
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCrumb rubberAggregate (composite)Materials scienceComposite materialCementitiousNatural rubberDurabilityComposite numberCement

Abstract

fetched live from OpenAlex

Abstract The increasing use of cementitious composites for different infrastructure constructions has led to an increase in the consumption of raw materials such as aggregate which is a major component in cementitious components. In order to reduce the huge strain posed on the natural sources of aggregate, and continually meet the increasing demand of aggregates to produce cementitious composites; the use of recycled products such as crumb rubber as aggregate in these composites is a viable alternative. However, as the properties of crumb rubber differ from those of conventional aggregate, it is paramount to understand how the incorporation of crumb rubber affects the performance of the cementitious composites. Therefore, this paper presents an overview of how the use of crumb rubber as aggregate affects the performance of cementitious composites in terms of its mechanical, durability, thermal and insulation properties. Conclusions from this paper showed that crumb rubber content in cementitious composites can be optimized to achieve desired properties. Also, the use of crumb rubber as aggregates in cementitious composites reduce significantly the embodied carbon and cost of the composite.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score0.885

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 studies0.0000.001
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 head0.015
GPT teacher head0.230
Teacher spread0.215 · 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 designOther design
Domainnot available
GenreReview

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

Citations25
Published2020
Admission routes1
Has abstractyes

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