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Record W4399114936 · doi:10.1051/e3sconf/202452901016

Experimental Study on the Substitution of Waste Rubber Tyre Ash with Natural Sand in the Cement Concrete

2024· article· en· W4399114936 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicMaterials Engineering and Processing
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsCementNatural rubberSubstitution (logic)Waste managementNatural (archaeology)Environmental scienceGeotechnical engineeringMaterials scienceGeologyMetallurgyComposite materialEngineering

Abstract

fetched live from OpenAlex

The importance of using recycled materials like rubber in construction materials is rising rapidly today. By incorporating used rubber into cement and mortar, we can save landfill space and reduce our dependence on natural resources. Rubber scrap can be mixed in as either fine or coarse aggregate. Add it to Portland cement for a stronger, more durable product (PC). This paper reviews the studies conducted so far on the feasibility of using waste rubber in place of conventional PC-based mortar and concrete’s natural fine aggregate. The strength and water-absorption capacity of materials made from ash from scrap rubber tyres were measured. Test results indicate that waste rubber ash was substitute with natural sand up 10% then strengths of the sample were enhanced after increasing the content of waste rubber tyre ash then strength was decreased. Water absorption capacity of samples was improved as increased the content of waste rubber tyre ash into concrete mix.

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.

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 score0.090
Threshold uncertainty score0.179

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 head0.019
GPT teacher head0.243
Teacher spread0.224 · 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