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Record W4404470800 · doi:10.1139/cjce-2024-0438

Sustainable application of recycled plastics in asphalt pavement: case study of a trial in Newtonville, Ontario, Canada

2024· article· en· W4404470800 on OpenAlexafffundvenueabout
Jianmin Ma, Hai Yuan, Hanwalle M.C. Nawarathna, Simon A.M. Hesp

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesChina Scholarship Council
KeywordsAsphaltAsphalt pavementEngineeringRutForensic engineeringCivil engineeringWaste managementEnvironmental scienceGeographyArchaeology

Abstract

fetched live from OpenAlex

Global research on using plastic waste in asphalt roads highlights its benefits: recycling waste, reducing greenhouse gas emissions, and providing an alternative to non-renewable asphalt binders. This study aims to bridge the gap between laboratory results and field performance of recycled plastic-modified asphalt in cold regions, promoting broader adoption. Laboratory analysis of core samples was performed as part of a quality assurance program. High-temperature rutting resistance was evaluated using the Hamburg wheel tracking test, while low-temperature cracking resistance was assessed through semi-circular bending testing. Results show that the impact of recycled plastics and fibers on rutting resistance varies with temperature, with the greatest benefit at the highest temperatures. For low-temperature cracking resistance, polyethylene terephthalate fibers outperform mixed plastics by delaying crack propagation. Low-temperature conditioning can induce thermal shrinkage in the asphalt mixture, slightly moderating the effects of recycled plastics and fibers.

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 score0.059
Threshold uncertainty score0.630

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 communication0.0000.000
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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations6
Published2024
Admission routes4
Has abstractyes

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