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Record W2382433503

Effect of Rubber Aggregate on Reduction of Compressive Strength of Concrete

2013· article· en· W2382433503 on OpenAlexaff
Guangcheng Long

Bibliographic record

VenueJournal of Building Materials · 2013
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCompressive strengthMaterials scienceAggregate (composite)Natural rubberComposite materialVolume fractionStrength reductionVolume (thermodynamics)Structural engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

Serials experiments were designed to study the effect of rubber aggregate(waste tyre)on compressive strength of normally vibrated concrete and self-compacting concrete.The experiential relationship between the reduction rate of compressive strength of concrete and the volume fraction of rubber aggregate was established.Results indicate that addition of rubber aggregate remarkably reduces the compressive strength of concrete.The more the volume fraction of rubber aggregate in concrete is,the higher the reduction rate of compressive strength of concrete is.The reduction rate of compressive strength of normally vibrated concrete is slightly higher than that of self-compacting concrete with the same volume fraction of rubber aggregate.The effect of rubber aggregate on reduction of compressive strength of concrete is about the same as that of air pore with the same volume fraction of rubber aggregate.The reduction rate of compressive strength of concrete is 4.5%by addition of rubber aggregate of 1%volume fraction,which can be used as reference to design mix proportion.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2013
Admission routes1
Has abstractyes

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