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Record W4392366855 · doi:10.1139/cjce-2023-0419

Assessment of performance characteristics of pavement base course incorporating reclaimed asphalt pavement and asphaltenes

2024· article· en· W4392366855 on OpenAlexafffundvenueabout
Farshad Kamran, Nusrat Nazim Jhora, Taher Baghaee Moghaddam, Leila Hashemian

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates
KeywordsBase courseAsphalt pavementCourse (navigation)AsphaltWearing courseEnvironmental scienceGeotechnical engineeringCivil engineeringAsphaltenePavement engineeringEngineeringForensic engineeringMaterials science

Abstract

fetched live from OpenAlex

Asphalt pavement is demolished and milled at the end of its service life, resulting in an enormous amount of waste material known as reclaimed asphalt pavement (RAP). Using cold recycling techniques, it is possible to reuse RAP for base course construction. However, using a high amount of RAP could have negative effects on the mechanical properties and durability of the base course. In this study, to improve the performance of asphalt emulsion recycled base course prepared with RAP, asphaltenes derived from Alberta oil-sands bitumen was added to the mixture. The performance properties of the selected mixtures were investigated at high, intermediate, and low temperatures. Testing results and statistical analysis revealed that asphaltenes enhances the high and intermediate temperature performance of the modified mixes significantly. The low-temperature creep compliance of asphaltenes-modified mixtures was slightly lower than the unmodified mixtures, however, the low-temperature properties of the modified mixes were not significantly affected.

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 score0.125
Threshold uncertainty score0.890

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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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

Citations2
Published2024
Admission routes4
Has abstractyes

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