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Record W4253900742 · doi:10.32920/ryerson.14664390.v1

Properties of concrete containing recycled concrete aggregate of preserved quality

2021· preprint· en· W4253900742 on OpenAlexaff
Jonathan Andal

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAggregate (composite)ShrinkageDurabilityQuality (philosophy)Environmentally friendlyCarbonationEnvironmental scienceCivil engineeringEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Today, there is a growing need for the implementation of sustainability in construction. Continuous construction and rehabilitation projects have begun to deplete virgin aggregate sources. The use of recycled concrete aggregate (RCA) in concrete has been regarded as a sustainable and environmentally friendly alternative aggregate source. This thesis focuses on producing RCA of preserved quality through the use of a new protocol aimed at maintaining the original properties of returned-to-plant concrete. The performance of RCA with preserved quality and commercially available RCA when used in concrete was compared. Different concrete properties were evaluated including the fresh, hardened and durability characteristics. Results showed that the RCA with preserved quality performed better in many categories, including strength, drying shrinkage and salt scaling resistance compared to the commercial RCA. The use of 30% preserved-quality RCA as partial replacement of coarse aggregate produced concrete of comparable quality to that produced with virgin aggregate.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.050
GPT teacher head0.258
Teacher spread0.208 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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