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Chemical, Morphological, and Fundamental Properties of Rejuvenated Asphalt Binders

2020· article· en· W3108555801 on OpenAlexaff
Rayhan Bin Ahmed, Kamal Hossain, Ramez Hajj

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAsphaltMaterials scienceComposite materialMicrostructureStyrene-butadieneDurabilityStyrene

Abstract

fetched live from OpenAlex

This study investigates the chemical characteristics of rejuvenators and the effect of different rejuvenators on the morphology and fundamental behavior of rejuvenated asphalt binders. Gas chromatography-mass spectroscopy (GC-MS) and atomic force microscopy (AFM) were conducted to determine the chemical composition of rejuvenators and asphalt surfaces’ morphology. Also, surface free energy (SFE) measurements were performed to quantify the cohesive bond energy of rejuvenated binders. The thin film oven test (TFOT)-aged performance graded (PG) 58-28 binder was rejuvenated with waste cooking oil (UT), chemically modified waste cooking oil (TR), and Hydrolene H90T (HL) at concentrations of 3%, 6%, and 9% by weight of the total binder. To understand the compatibility, styrene-butadiene-styrene (SBS) was also blended with rejuvenated binders. The experimental study found the variability of free fatty acid compositions in rejuvenators, which is hypothesized to affect pavement performance. Results showed that rejuvenation alters the surface microstructure of binders, which provides insights into the overall performance of the binder. Also, rejuvenation improves the moisture damage resistance of binders significantly. This experimental study found a good correlation between the chemical, morphological, and fundamental behavior of the rejuvenated binders, which is expected to help quantify the performance of rejuvenated asphalt mixes.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.443

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 head0.030
GPT teacher head0.213
Teacher spread0.183 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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