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Record W4417210578 · doi:10.1520/jte20240192

Exploring the Influence of Asphaltenes on Rheology and Aging Characteristics of Asphalt Binders

2025· article· en· W4417210578 on OpenAlexafffundabout
Nirob Ahmed, Mohamed Saleh, Taher Baghaee Moghaddam, Leila Hashemian

Bibliographic record

VenueJournal of Testing and Evaluation · 2025
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates
KeywordsRutAsphalteneAsphaltCreepRheologyDynamic shear rheometerShear modulusModulus

Abstract

fetched live from OpenAlex

ABSTRACT Asphalt pavement, crucial for road infrastructure in Northern America, faces challenges like rutting, reduced elasticity, and aging in binders, necessitating sustainable solutions. This study explores the potential of asphaltenes, a by-product of Alberta oil sands, as an additive to enhance key properties of asphalt binders, such as rutting resistance, elasticity, and aging characteristics, through multiple stress creep recovery (MSCR) and frequency sweep (FS) tests on two distinct binder types. Binders, both neat and modified with an optimum asphaltenes concentration of 12 % (by weight of binder), underwent MSCR and FS tests. The MSCR test results revealed that asphaltenes-modified binders had reduced nonrecoverable creep compliance (Jnr) that met requirements for extremely heavy traffic conditions (>30 million equivalent single axle loads) with Jnr values being lower than 0.5 kPa−1. Stress sensitivity was notably reduced, which emphasized the stabilizing effect of asphaltenes. The FS test results showed notable enhancements in stiffness, with 497–546 % increased complex shear modulus, up to seven times higher rutting parameter values at 0.1 rad/s following asphaltenes modification. Additionally, the aging resistance of the modified binders improved, with the complex shear modulus aging index for short-term aging reduced by 20–23 % and for long-term aging by 44–48 % compared with neat binders. These improvements underscore the promising use of asphaltenes in sustainable asphalt binder modification.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.090
GPT teacher head0.305
Teacher spread0.215 · 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 designObservational
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

Citations0
Published2025
Admission routes3
Has abstractyes

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