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Residual Mechanical Response of Recycled Aggregate Concrete after Exposure to Elevated Temperatures

2012· article· en· W2015874429 on OpenAlexafffund
Salah R. Sarhat, Edward G. Sherwood

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAggregate (composite)Ultimate tensile strengthComposite materialProperties of concreteCompressive strengthResidualGeotechnical engineeringGeology

Abstract

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A considerable number of investigations have been conducted on the mechanical and material properties of concrete made with recycled concrete aggregate (RCA), as these properties can often be different than those of conventional concrete. However, relatively little attention has been directed at studying the performance of concrete made with RCA at elevated temperatures. This is despite the fact that coarse aggregates play an important role in the behavior of concrete under fire exposure. To address the lack of knowledge, an experimental program was conducted in which six different concrete mixes were prepared with different combinations of coarse aggregates made from recycled concrete aggregate, river gravel, and crushed limestone aggregates. A total of 204 concrete cylinders (100×200 mm) were cast and heated under four different temperatures: 20°C (ambient temperature), 250, 500, and 750°C. The residual compressive and tensile strengths, moduli of elasticity, and damage and failure patterns of the concretes were observed and analyzed. The results indicate that concrete with aggregate both fully and partially replaced with RCA exhibits good performance under elevated temperatures and it can be considered comparable to conventional concrete. No concrete disintegration was observed when RCA concrete was heated up to 750°C. The results of tests of residual mechanical properties show some variation among concretes made with different replacement percentages of RCA.

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)0.000
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 score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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

Citations137
Published2012
Admission routes2
Has abstractyes

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