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Record W4396624842 · doi:10.1139/cjce-2023-0502

Investigating the influence of polyethylene terephthalate fibres with different lengths on the cracking resistance of high-performance asphalt concrete

2024· article· en· W4396624842 on OpenAlexafffundvenue
Nirob Ahmed, Mohamed Saleh, Taher Baghaee Moghaddam, Leila Hashemian

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsCanadian Natural ResourcesUniversity of Alberta
FundersAlberta Innovates
KeywordsPolyethylene terephthalateCrackingAsphaltMaterials scienceComposite materialRutAsphalt concretePolyethyleneForensic engineeringEngineering

Abstract

fetched live from OpenAlex

High-performance asphalt concrete is an innovative paving material that typically relies on the properties of polymer-modified asphalt binders. However, the application is limited due to their high cost and phase separation issues. A solution is explored in the present study by blending 12% asphaltenes and a straight-run asphalt binder with a true performance grade of 70.2–25.9, resulting in a true performance grade of 82.9–21.8, which elevates the binder’s stiffness. Three lengths of polyethylene terephthalate fibres are used to improve the cracking resistance of these asphaltenes-modified mixes. An indirect tensile cracking test is conducted at 25 and 37 °C, revealing a significant improvement in cracking tolerance and failure energy, particularly at 37 °C. The cracking tolerance index value peaked at 105 for 12 mm polyethylene terephthalate fabricated sample, while control asphaltenes-modified, 6 mm, and 18 mm polyethylene terephthalate fabricated samples achieved 61, 87, and 99, respectively, with failure energy increasing with longer fibres.

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 score0.495
Threshold uncertainty score0.498

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)0.0000.000

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 head0.197
Teacher spread0.187 · 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

Citations7
Published2024
Admission routes3
Has abstractyes

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