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Record W4389949237 · doi:10.1139/cjce-2023-0306

Exploring the low-temperature performance of MPP-modified asphalt binders and mixtures using wet method

2023· article· en· W4389949237 on OpenAlexafffundvenueabout
Ali Qabur, Hassan Baaj, Mohab El-Hakim

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAsphaltMaterials scienceRheologyComposite materialCrackingAsphalt pavement

Abstract

fetched live from OpenAlex

Thermal cracking significantly impacts the structural integrity of flexible pavements, particularly in colder regions like Canada. Limited studies investigated the impact of plastic modification of asphalt binders and mixtures using the wet method on the low-temperature performance of asphalt materials. The plastic material utilized in this project is multilayer plastic packaging (MPP). This study aims to determine whether MPP can be integrated to enhance the performance of the MPP-modified binder and MPP-modified mixtures, especially considering that MPP accounts for just over 40% of total plastic usage, making it the largest end-use market segment. This research evaluates the impact on rheological and mechanical behaviour when introducing MPP additives to conventional hot mix asphalt. This study used the wet method to test MPP-modified asphalt materials at 2%, 4%, and 8% (by weight of the asphalt binder). Test results demonstrate that the MPP modification percentage should ideally not exceed 2% as blends with 4% MPP or higher exhibited lower performance at low temperatures. The use of a softer binder as a base binder would help increase the MPP modification rates, but this hypothesis needs to be validated experimentally.

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 score0.016
Threshold uncertainty score0.631

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 head0.045
GPT teacher head0.244
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 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
Published2023
Admission routes4
Has abstractyes

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