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Record W4405237812 · doi:10.11159/ijci.2024.020

Evaluating the Impact of Specimen Shape and Size on the Strength Characteristics of Recycled Aggregate Concrete

2024· article· en· W4405237812 on OpenAlexvenueno aff
Yahya Salah, Karol Sikora, Kamal Jaafar, Sana Amir

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2024
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsnot available
FundersUniversity of Wollongong
KeywordsAggregate (composite)Materials scienceEnvironmental scienceComposite materialForensic engineeringEngineering

Abstract

fetched live from OpenAlex

The use of green concrete is increasing worldwide.The growing global demand for utilizing recycled materials in concrete mixes to enhance sustainability makes it important to study the strength properties and behaviour of recycled aggregate concrete (RAC).While the impact of specimen shape and size on the compressive and flexural strength of normal concrete is well-documented, their effects on concrete mixes containing recycled aggregate and ceramic waste are not yet defined.This paper studies the effect of specimen shape and size on the strength properties of RAC by conducting compression tests on cubes and cylinders and performing four-point flexural tests on beams.Locally available recycled coarse aggregate, ceramic fine aggregate, and ceramic waste powder were used to develop the recycled aggregate concrete.Compression tests were conducted on standard cubes, standard cylinders, and halfscale cylinders.Flexural tests were performed on beams sized 150 mm150 mm460 mm and 75 mm75 mm230 mm.Based on the experimental results, it was found that the specimen shape and size significantly affect the strength properties of RAC, and the conversion factors differ from those of normal concrete with the same strength.Additionally, a preliminary relationship between the compressive strength and flexural strength of RAC is suggested.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.686

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)0.0010.000

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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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