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Record W4402871144 · doi:10.1139/cjce-2023-0244

Laboratory evaluation of asphalt mixtures reinforced with aramid fibres coated with bituminous oils

2024· article· en· W4402871144 on OpenAlexaffvenue
Haya Almutairi, Ali Qabur, Hassan Baaj

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAsphaltComposite materialMaterials scienceAramidForensic engineeringEnvironmental scienceEngineeringFiber

Abstract

fetched live from OpenAlex

Adding additives such as fibres to asphalt mixtures is believed to improve the overall performance of hot mix asphalt (HMA). Unlike previous studies, this research investigates the performance of HMA modified with oil-coated aramid fibers, exploring its potential to enhance rutting resistance, fatigue behavior, and low-temperature cracking. The fibers were utilized at various dosages (110, 138, and 164 g/tonne) and lengths (13, 20, and 25 mm). Comprehensive evaluations were conducted using the Hamburg wheel tracking device, four-point bending fatigue test, and thermal stress restrained specimen test to assess the impact of these modifications on HMA properties. The results found that 25 mm long aramid fibres, at all dosages, improved the rutting resistance by up to 65% compared to the control mixture. However, the fatigue behaviour and low-temperature cracking resistance were not as good as the rutting performance. Therefore, and based on this study, it was concluded that the addition of the newly developed bituminous oil-coated aramid fibres has no positive impact as a reinforcement of the asphalt mixture.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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