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Record W3184954923 · doi:10.1139/cjce-2020-0007

Effect of different waste plastic fractions on the thermal kinetics and microstructural behaviour of bitumen used for bituminous mix

2021· article· en· W3184954923 on OpenAlexvenueno aff
Sandip Karmakar, Tapas Kumar Roy

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltMaterials scienceRaw materialComposite materialThermal stabilityMixing (physics)PolypropylenePolymerPlastic wasteWaste managementDispersion (optics)Chemical engineeringChemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The price of raw polymers used for making most of the waste plastics has confined the implementation of polymer modified bitumen (PMB) only to the major road projects. In view of the same, an attempt to make a PMB by such wastes was considered as a “state-of-art” in this investigation. Therefore, the different proportions of waste plastic fractions were blended with the pristine bitumen and the resulting blend characterized by thermal kinetics analysis, microstructural analysis, and Marshall mix design, respectively. The major findings have predicted the highest thermal stability of the blend was achieved by mixing plastic bags, plastic milk pouches, and plastic cups together in proportion of 2:0.25:1 to virgin bitumen with no thermal decomposition. Further, uniform dispersion of “bee-like structure” in that blend has reflected its homogeneity. Besides, such modified bitumen has elevated the Marshall quotient of the bituminous mix by 16%, which can preferentially be used in the rural roads safely, confirmed by Marshall mix design.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.402

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)0.0000.000

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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