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Record W4309458987 · doi:10.3390/ma15228043

Impact of Polypropylene Fibers on the Mechanical and Durability Characteristics of Rubber Tire Fine Aggregate Concrete

2022· article· en· W4309458987 on OpenAlexaff
Arash Karimipour, Zahra Mohajeri, Ehsan Noroozinejad Farsangi

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceShrinkageComposite materialPolypropyleneDurabilityAggregate (composite)Ultimate tensile strengthFlexural strengthVolume fractionDeformation (meteorology)Natural rubberCompressive strength

Abstract

fetched live from OpenAlex

In this research, the consequence of using rubber tire aggregates (RTA) on the durability and mechanical characteristics of polypropylene fibers (PF) reinforced concrete is evaluated. Fifteen concrete mixtures were produced and tested in the laboratory. RTA was utilized instead of fine natural aggregates (FNA) to the concrete at concentrations of 0%, 5%, 10%, 15%, and 20% by a volumetric fraction; also, the contents of PF in the concrete mixtures were 0%, 1%, and 2% by weight fraction. Finally, the following parameters were tested for all the mixtures: compressive and tensile resistances, fracture, changes in drying shrinkage, bulk electrical resistivity, elastic moduli, and resonance occurrences. The control sample was the one without RTA and PF. According to the results, by adding RTA to the mixtures, the shrinkage deformation amplified, but the PF addition caused a decrease in the shrinkage deformation. Furthermore, adding 0%, 5%, 10%, and 15% RTA, with 2% PF leads to an upsurge in the flexural resistance by 34%, 24%, 16%, and 6%, respectively, relative to the control sample without PF and RTA. Moreover, the fracture energy of mixtures increased by utilizing PF and RTA simultaneously.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0040.000

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 head0.012
GPT teacher head0.228
Teacher spread0.215 · 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.

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

Citations10
Published2022
Admission routes1
Has abstractyes

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